For the second part of the assignment, you will compose a synopsis paragraph to annotate each of the references immediately following its citation. A space must follow the citation and the corresponding annotation presented with a hanging indent from the left margin below the citation. Provide a 100 word original summary of the corresponding article. Attempt to capture the essence of each article’s contents as a consolidated resource to be used when writing the Project Paper later in the semester. The process will be repeated for each subsequent citation. 

urgent12 hours

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The Relationship of Perceived
Benefits and Barriers to Reported
Exercise Behaviors in College
Undergraduates

This study examines current exercise habits and perceived benefits and barriers to exercise in a sample
of 147 undergraduate university students. It found a significant relationship between perceived benefits
and barriers to exercise and current exercise habits. Benefits most often associated with regular exercise
habits relate to physical performance and appearance. Barriers most often associated with sporadic or
nonexistent exercise habits relate to physical exertion and time constraints. A greater understanding of
perceived benefits and barriers to exercise may assist health care providers and educators to establish
methods for promoting exercise for the improved physical and mental health of a college-age popula-
tion. Key words: cardiovascular risk factors, exercise in young adults, exercise promotion, health
promotion

Laurie Grubbs, PhD, ARNP
Associate Professor of Nursing
Florida State University
Tallahassee, Florida

Jason Carter, MSN, ARNP
Family Nurse Practitioner
Crown Gastroenterology
Presque Isle, Maine

INTRODUCTION

Regular physical activity during the
childhood years is reinforced through
mandatory physical education classes in
elementary, intermediate, and some sec-
ondary school programs. Unfortunately,
many adolescents do not continue reg-
ular physical activity upon completion
of high school, and, perhaps, not even
through middle school due to the discon-
tinuation of mandatory physical educa-
tion classes in many states. Correlations
between physical activity during adoles-
cence (13 to 18 years) and during young
adulthood (21 to 35 years) are low.1 It
has been suggested that the highest rate
of decline in physical activity occurs in late
adolescence and early adulthood in those
age 18 to 24 years.2

The lack of continuation of regular
physical activity from adolescence to
young adulthood has had a significant im-
pact on morbidity and mortality rates in
the United States. Paffenbarger et al’s3

Fam Community Health 2002;25(2):

76

–84
c© 2002 Aspen Publishers, Inc.

76

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Barriers to Exercise Behaviors in College Undergraduates 77

landmark study of 16,936 college alumni
showed decreased rates of mortality of up
to 49% in participants who maintained
regular physical activity from their college
years to age 70 to 84 years. Due to the
fact that Americans age 18 to 24 years
display the highest rates of decline in reg-
ular physical activity, targeting primary
care interventions to promote exercise in
this population is beneficial in decreasing
morbidity and mortality. Although exten-
sively studied in other populations, fac-
tors influencing exercise adherence in col-
lege undergraduate students have been
poorly defined.

FRAMEWORK

The health promotion model (HPM),
developed by Pender,4 has been used as a
theoretical framework to identify behav-
ioral perspectives that motivate individ-
uals to engage in health-promoting be-
haviors. The HPM can be conceptualized
into three components:

1. individual characteristics and expe-
riences

2. behavior-specific cognitions and af-
fect

3. behavioral outcome
Individual characteristics and experi-

ences, most notable prior related be-
haviors and personal factors (biological,
psychological, sociocultural), provide the
baseline experience from which individu-
als choose to engage in health-promoting
behaviors. Behavior-specific cognitions
and affect, such as perceived benefits and
barriers to action, perceived self-efficacy,
activity-related affect, interpersonal influ-
ences, and situational influences consti-

tute central importance in the HPM, as
they are components that are subject to
modification.4

Perceived benefits to action represent
positive or reinforcing consequences of
a behavior. They may be intrinsic (such
as increased alertness or decreased fa-
tigue) or extrinsic (such as social accep-
tance or monetary awards). The motiva-
tional value of perceived benefits is based
on outcomes of prior personal experi-
ence or outcomes observed in others. In
order for individuals to invest time and
resources in an activity, they must first
perceive a high probability of achieving
a positive outcome from that activity.4

Perceived barriers to action are as-
sociated with the obstacles encountered
with undertaking a specific behavior. Per-
ceived barriers are associated with un-
availability, inconvenience, expense, dif-
ficulty, time, or personal cost. Perceived
barriers may either prevent the initiation
of a new activity or decrease commitment
and adherence to an existing pattern of
activity.4

The influence of individual characteris-
tics and experiences, as well as behavior-
specific cognitions and affect such as
perceived benefits/barriers to action, cul-
minate in the initiation of a behavioral
outcome. In this stage, the individual
makes a commitment to a plan of action
and identifies strategies for carrying out
and reinforcing the behavior. Although
the HPM functions primarily to ex-
plain thought processes behind health-
promoting behaviors, understanding the
motivational factors behind these behav-
iors is ultimately directed toward attain-
ing positive health outcomes for the
individual.

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78 FAMILY & COMMUNITY HEALTH/JULY 2002

METHODS

Design

This article describes a descriptive cor-
relational study of college undergradu-
ate students. Perceived benefits and per-
ceived barriers to regular exercise were
obtained through the use of a written
questionnaire measuring current exercise
beliefs and habits.

Population

The sample consisted of 147 college
undergraduate freshmen, sophomores,
juniors, and seniors aged 18 to 24 years.
Subjects were selected from a conve-
nience sample of those attending under-
graduate classes at a large southeastern
university. The Human Subjects Commit-
tee of the university approved the re-
search protocol.

Instrumentation

The written questionnaire in this study
consisted of four sections. Section 1 con-
tained demographic questions; section 2
assessed current exercise habits; and sec-
tion 3 consisted of the 43-item Exer-
cise Benefits/Barriers Scale (EBBS) de-
veloped by Sechrist et al.5 Section 4
provided an opportunity for subjects to
write in brief comments about the ques-
tionnaire or beliefs about exercise not
addressed by the instrument. These re-
sponses provide qualitative aspects to the
data set.

This instrument was developed to ex-
plore perceived benefits and barriers to
exercise using constructs of Pender’s
HPM.4 Each of the 43 items in the EBBS

featured a 4-point, forced-choice Likert
format to obtain strength of agreement
with the item statements. Choices were
scored at: 4 = strongly agree, 3 = agree,
2 = disagree, and 1 = strongly disagree.
The authors used Cronbach’s alpha tech-
niques to measure internal consistency of
the entire instrument; it achieved a score
of .952. The 29-item scale of perceived
benefits was measured at .953 and the
14-item perceived barriers scale at .866.
Test-retest reliability measures were ob-
tained by the authors of the EBBS, pro-
viding correlation coefficients of .889 for
the entire 43-item instrument, .893 for
the 29-item benefits scale, and .772
for the 14-item barriers scale.5

Data analysis

The research analysis used elements
of descriptive statistical methods, such as
frequency distributions of responses to
different areas of the EBBS. Correlations
between the most frequently cited per-
ceived benefits and barriers to exercise
and current exercise habits are described
using analysis of variance (ANOVA), chi-
square analysis, and independent t test
analysis. The alpha was set at 0.05.

RESULTS

Description of the sample

A total of 147 subjects, mean age
19.9 years, provided responses to the
written questionnaire. The majority of
subjects were female (82%) college un-
dergraduate students enrolled in either
a general anatomy/physiology class of-
fered through the department of biology

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Barriers to Exercise Behaviors in College Undergraduates 79

Table 1. Demographic data summary (N =
147)

Number %

Gender
Male 26 17.7
Female 121 82.3

Marital status
Married 0 0.0
Single 147 100.0

University status
Freshmen 8 5.5
Sophomore 75 51.1
Junior 44 30.0
Senior 20 13.7

Current academic workload
Full time 136 92.5
Part time 11 7.5

Employment status
Full time 10 6.8
Part time 56 38.1
Not employed 81 55.1

Current association with 35 23.8
intercollegiate/
intramural sports

Positive family history of 105 71.4
cardiovascular disease

or an ethics class offered through the de-
partment of philosophy. All subjects re-
ported an academic workload of at least
12 semester hours. See Table 1 for a
summary of study demographics.

Current exercise habits

Six subjects chose not to include cur-
rent exercise habits in their response.
Of the 141 subjects who did, 68.8%
(n = 97) reported current exercise habits
that included involvement of large mus-

cle groups, featured dynamic movement
for periods of 20 minutes or longer (per-
formed 3 days or more per week), and
was of an intensity high enough to raise
heart rate to at least 60% of maximum
(maximum heart rate = 220 − age).
These subjects were classified as “exer-
cisers.” The remaining 31.2% (n = 44)
reported exercise habits that did not meet
established criteria for regular exercise
and were classified as “non-exercisers.”

Perceived benefits to regular
physical exercise

Perceived benefits to regular exercise
were examined by 29 of 43 items of the
EBBS. Overwhelmingly, responses with
the highest mean scores (highest agree-
ment) on statements regarding perceived
benefits of exercise were those related to
physical performance and appearance.
Subjects agreed most strongly with the
statement: “Exercise increases my level
of physical fitness.” The second and third
highest level of agreement was with the
statements: “Exercise improves the way
my body looks” and “My muscle tone is
improved with exercise.” A ranked list of
benefit statement scores and standard de-
viations is featured in Table 2.

Perceived barriers to regular
physical exercise

Perceived barriers to regular exercise
were tabulated by the remaining 14 of
43 items of the EBBS. Although barrier
items were similar Likert-type, forced-
choice statements, scoring was reversed
from 1 = strongly agree to 4 = strongly
disagree. Consistent with responses to

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80 FAMILY & COMMUNITY HEALTH/JULY 2002

Table 2. Top exercise benefit statements

Statement Mean SD

1. Exercise increases my 3.55 .51
level of physical fitness

2. Exercise improves the 3.53 .54
way my body looks

3. My muscle tone is 3.46 .51
improved with exercise

4. Exercise gives me a 3.45 .63
sense of personal
accomplishment

5. Exercise increases 3.44 .51
my muscle strength

SD, standard deviation.

benefit items, subjects reached the high-
est agreement with statements within one
category. Items within the realm of phys-
ical exertion were perceived as the most
substantial barriers to regular exercise. A
ranked list of barrier statement scores and
standard deviations is featured in Table 3.

Table 3. Top exercise barrier statements

Statement Mean SD

1. Exercise tires me 2.49 .69
2. Exercise is hard work 2.58 .79

for me
3. I am fatigued by exercise 2.71 .67
4. Exercising takes too much 2.79 .66

of my time
5. My family members do 3.14 .78

not encourage me to
exercise

Note: Barrier statements are reverse-scored; lower mean
scores indicate stronger agreement with the statement.
SD, standard deviation.

Relationship between EBBS
responses and reported
exercise habits

By the use of mean perceived ben-
efit and barrier scale scores on the
EBBS, significant differences were found
among subjects who exercised regularly
and those who did not. Mean score
benefit scale items were 3.28 (standard
deviation [SD]=0.38) for exercisers
compared with 2.94 (SD=0.36) for non-
exercisers. This variance was significant
at p < .001 using ANOVA. Not surpris- ingly, these results show that subjects who exercised regularly perceived signifi- cantly more benefits to exercise than non- exercising subjects.

Barrier subscale mean scores also var-
ied substantially among those subjects
who exercised and those who did not.
While the mean barrier score for exercis-
ers was 3.18 (SD=0.38), non-exercisers
demonstrated a mean score of 2.80
(SD=0.32), significant at p < .001.

The most significant variances in in-
dividual EBBS item mean scores were
among perceived barriers to regular phys-
ical exercise. Response to the statement,
“Exercise takes too much of my time,”
showed the most significant t value vari-
ance among subjects who exercised and
those who did not (p < .001). In addi- tion, response to the statements, “I am

Not surprisingly, subjects who
exercised regularly perceived
significantly more benefits to
exercise than non-exercising
subjects.

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Barriers to Exercise Behaviors in College Undergraduates 81

too embarrassed to exercise” and “Exer-
cise takes too much time from my fam-
ily responsibilities,” demonstrated signif-
icant variance between exercising and
non-exercising subjects (p < .001).

Among benefit items of the EBBS, re-
sponse to the statement “Exercise in-
creases my stamina” created the most
significant variance ( p < .001) between exercising and non-exercising subjects. In addition, responses to statements, “My muscle tone is improved with exer- cise” and “Exercise increases my muscle strength” varied significantly among sub- jects who exercised and those who did not ( p < .002 and p < .003, respectively).

Ninety-two percent of the male sub-
jects met established criteria to be cate-
gorized as exercisers while only 63% of
female subjects could be categorized as
exercisers. Chi-square analysis confirmed
a significant difference in activity levels of
male versus female ( p < .006), suggest- ing that among the sample group male subjects had a significantly higher rate of participation in regular exercise than fe- males. Results also showed that subjects who participated in intercollegiate or in- tramural sports programs demonstrated significantly higher rates of regular exer- cise than those not involved in such pro- grams ( p < .004).

There was no correlation between em-
ployment and exercise habits suggest-
ing that being employed did not pose a
barrier to exercise although “Exercising
takes too much time” was listed as a fre-
quent barrier. There was no correlation
between family history of cardiac disease
and exercise habits suggesting that even
with a potentially increased risk for car-
diac disease, exercise habits did not in-

crease. Perhaps this population was too
young to place importance on risk fac-
tors that may not manifest themselves un-
til middle age. Prevention apparently was
not seen as an important issue, or sub-
jects felt that their current physical condi-
tion did not warrant a change in exercise
behavior.

Individual responses to the
questionnaire

A number of subjects identified moti-
vating factors behind their exercise habits
or beliefs such as prevention of exces-
sive weight gain, stress relief, or improve-
ment in sense of well-being and self-
esteem. The personal barrier to exercise
that was mentioned most often was time
constraints. Other barriers included in-
juries, medical conditions, and not believ-
ing it necessary to exercise.

DISCUSSION

The rate of participation in regular
physical activity reported among study
subjects was higher than rates reported
in studies of middle-aged and older adults,
which ranged from 10% to 60%, and very
similar to participation rates of adoles-
cent children, estimated at 66% among
those age 10 to 17 years.6–8 The re-
ported physical activity levels of the sub-
jects in this research study are encour-
aging in light of widespread reports of
declining activity levels among all Ameri-
cans. These findings suggest virtually no
drop-off in activity levels from those es-
timated in young adolescents, age 10 to
17 years (66%).

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82 FAMILY & COMMUNITY HEALTH/JULY 2002

Perceived benefits of regular exercise
were strongly associated with physical
performance, appearance, and personal
accomplishment. These findings are in
accordance with previous studies suggest-
ing that among male and female college
undergraduates, health/fitness manage-
ment and appearance/weight manage-
ment were the most important reasons
to exercise.9,10 In contrast, studies fea-
turing middle-aged or older adults cited
perceived benefits such as chronic disease
management, weight control, stress man-
agement, and personal enjoyment as the
most important reasons to exercise.8,11

One would expect a younger population
to be more concerned with performance
and appearance rather than health issues
such as chronic disease prevention.

Significant differences between the
groups regarding barriers were strongly
associated with time constraints, family
responsibilities, and embarrassment. Al-
though the reasons were not explored in
this study, being embarrassed to exercise
may be due to a weight issue, especially
for females. These individuals are likely
the ones who need the exercise most. En-
couraging individual, rather than group,
exercise may prove more successful for
some people. Walking should be encour-
aged as it requires no commuting, no gym
membership, no audience, and no reveal-
ing clothing. Earlier studies do not iden-
tify specific perceived barriers to exercise
among either young adults or college stu-
dents. In middle-aged or older adults, pre-
vious studies6,11 suggest that factors such
as lack of time to exercise, lack of nearby
facilities, and fatigue with and after exer-
cise are the most common cited barriers
to exercise in this population.

The EBBS was found to be useful in
predicting exercise habits of the sam-
ple group. Significant differences in per-
ceived benefit and barrier scores of the
EBBS were demonstrated among sub-
jects who participated in regular exercise
and those who did not. Not surprisingly,
subjects who exercised perceived more
benefits and fewer barriers than non-
exercisers. Agreement with these findings
has been reported in a number of previ-
ous studies.11–14

Perceived barriers to exercise proved
to be the most influential factors on reg-
ular exercise habits among the sample
group. Indeed, the most significant vari-
ances in individual EBBS item scores that
could be attributed to activity level were
among perceived barrier items. More-
over, these findings are consistent with
those reported in a study of 233 female
college students in which perceived bar-
riers to exercise presented the highest
negative correlation with total exercise
minutes per week.15 A similar study8

demonstrated that 30% of the exercise
variability among non-insulin-dependent
diabetics could be attributed to perceived
barriers.

Associations between current exercise
levels and perception of benefits/barriers
to regular exercise were consistent with
theoretical constructs of the HPM. Un-
der HPM constructs, perception of ben-
efits and barriers to health-related behav-
iors are recognized as highly motivational
cues to action. As expected, those who
perceived more benefits and fewer barri-
ers to regular physical exercise reported
higher rates of exercise. Since those who
exercise realize the benefits, helping peo-
ple make the decision to begin exercising

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Barriers to Exercise Behaviors in College Undergraduates 83

may be the most difficult task. Using the
stages of change16 along with the health
promotion model can assist the practi-
tioner in assessing the individual’s stage
of readiness and implementing strategies
to move that person forward to a stage of
readiness. Once he or she is exercising,
it is hoped that the benefits will motivate
him or her to continue.

A higher percentage of male subjects
(92% versus 63.8%) than female subjects
reported regular physical exercise habits.
This is consistent with other research2

that reported that males are more likely
than females to participate in vigorous
physical activity, strengthening activities,
and walking or bicycling. Although the
difference in reported exercise habits did
prove to be significant, the small sample
of male subjects (n = 26) may limit gen-
eralization of these findings. Surprisingly,
this predominantly female sample listed
muscle tone and strength in the top five
benefits of exercise. This suggests that
these issues are no longer unique to the
male gender and that men and women
may have very similar reasons for exer-
cise and sports participation.

Participation in organized intercolle-
giate or intramural sports programs was
significantly associated with regular exer-
cise habits. These findings establish the
value of social groups and organized cam-
pus activities in promoting regular exer-
cise habits among college students.

IMPLICATIONS

Since data collected in this study are
self-reported, they should be interpreted
with that in mind. Although it has
been suggested that the highest rate of

decline in physical activity occurs in late
adolescence and early adulthood in those
age 18 to 24 years,2 the results of this
study do not support a decline in physi-
cal activity level during the college years.
The critical years may occur after college
graduation when individuals are not in
school, perhaps for the first time in their
lives. Cullen et al17 explored risk behav-
iors in 5,881 students graduating from
high school, a time of major life transi-
tion. They found a significant decrease
in exercise activity among the males in
this sample as well as increases in other
risk behaviors. Graduation from college,
establishing full-time employment, and,
for many, marrying and starting a fam-
ily are also major life transitions and re-
quire a new set of responsibilities and
time management skills. This is likely to
affect risk and health-promoting behav-
iors. It is this population that needs tar-
geting for enhancing participation in ex-
ercise. Flexible work schedules adopted
by some businesses and agencies are al-
lowing for exercise time during daytime
work hours. Time management skills
could be useful when attempting to in-
corporate regular exercise into the daily
schedule.

Aspects of physical performance and
appearance were most widely reported
as benefits of regular physical activity.
In contrast, time constraints and physical
exertion/exhaustion related to exercise
were the most widely reported perceived
barriers to regular physical activity. It is
important to teach the benefits of ad-
equate rest, hydration, and nutrition in
order to recover between exercise ses-
sions. Exercisers of all ages should be
counseled on the importance of gradual

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84 FAMILY & COMMUNITY HEALTH/JULY 2002

increases in frequency, duration, and in-
tensity of exercise regimens and cau-
tioned against the “weekend warrior”
syndrome, which could lead to exhaus-
tion or injury and therefore discourage

future exercise. Associating exercise as
an integral part of a healthy, balanced
lifestyle must be stressed in educating our
youth in order to help people of all ages
incorporate exercise as a lifelong activity.

REFERENCES

1. Taylor WC, Blair SN, Cummings SS, Wun CC, Ma-
lina RM. Childhood and adolescent physical activity
patterns and adult physical activity. Med Sci Sports
Exerc. 1999;31(1):118–123.

2. US Department of Health and Human Services.
Healthy People 2010. 2nd ed. Understanding and
improving health. Volume 1. Washington, DC: US
Government Printing Office, November 2000.

3. Paffenbarger RS, Hyde RT, Wing AL, Hsieh C.
Physical activity, all-cause mortality, and longevity of
college alumni. N Engl J Med. 1986;314(10):605–
613.

4. Pender NJ. Health Promotion in Nursing Practice.
3rd ed. Stamford, CT: Appleton & Lange; 1996.

5. Sechrist KR, Walker SN, Pender NJ. Develop-
ment and psychometric evaluation of the Exercise
Benefits/Barriers Scale. Res Nurs Health. 1987;
10(6):357–365.

6. Grunbaum JA, Kann L, Kinchen SA, et al. Youth
Risk Behavior Surveillance—National Alterna-
tive High School Youth Behavior Survey, United
States, 1998. Washington, DC: National Center for
Chronic Disease Prevention and Health Promotion,
US Dept of Health and Human Services; 1999.

7. Lookinland S, Harms J. Comparison of health-
promotive behaviors among seniors: exercisers ver-
sus nonexercisers. Soc Sci Health. 1996;2(3):147–
161.

8. Swift CS, Armstrong JE, Beerman KA, Campbell
RK, Pond-Smith D. Attitudes and beliefs about exer-
cise among persons with non-insulin dependent dia-
betes. Diabetes Educ. 1995;21(6):533–540.

9. Cash TF, Novy PL, Grant JR. Why do women

exercise? Factor analysis and further validation of
the Reasons for Exercise Inventory. Percept Motor
Skills. 1994;78(2):539–544.

10. Smith BL, Handley P, Eldridge DA. Sex differences
in exercise motivation and body image satisfac-
tion among college students. Percept Motor Skills.
1998;86(2):723–732.

11. Jones M, Nies MA. The relationship of perceived
benefits and barriers to reported exercise in older
African-American women. Public Health Nurs.
1996;13(2):151–158.

12. Garcia AW, Norton Broda MA, Frenn M, Coviak
C, Pender NJ, Ronis DL. Gender and developmen-
tal differences in exercise beliefs among youth and
prediction of their exercise behavior. J Sch Health.
1995;65(6):213–219.

13. Bonheur B, Young SW. Exercise as a health-
promoting lifestyle choice. Appl Nurs Res. 1991;
4(1):2–6.

14. Sherwood NE, Jeffrey RW. The behavioral determi-
nants of exercise: implications for physical activity
interventions. Annu Rev Nutr. 2000;20:21–44.

15. Ali NS. Predictors of osteoporosis prevention among
college women. Am J Health Behav. 1996;20(6):
379–388.

16. O’Connell D. Behavior change. In: Feldman MD,
Christensen JF, eds. Behavioral Medicine in Pri-
mary Care. Stamford, CT: Appleton & Lange;
1997:125–135.

17. Cullen KW, Koehly LM, Anderson C, et al. Gender
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Research Brief

Interactive Introductory Nutrition Course Focusing on
Disease Prevention Increased Whole-Grain Consumption by
College Students
Eun-Jeong Ha, PhD; Natalie Caine-Bish, PhD, RD, LD

Family and
Address fo
Studies, Ke
2194; E-ma
�2011 SO
doi:10.1016

Journal of

ABSTRACT

Objective: To estimate current consumption of whole grains in college students and determine whether
there would be an increase in whole-grain consumption after the students completed an interactive
introductory nutrition course focusing on disease prevention.
Methods: Eighty college students, 18–24 years old, participated in the study. Grain and whole-grain con-
sumption, whole-grain food sources, and energy intake were measured before and after the nutrition
course. Repeated-measures analysis of variance was performed.
Results: After the study, whole-grain intake significantly increased from 0.37 ounces (oz) to 1.16 oz
(P < .001), whereas total grain intake remained the same (3.07 oz). The number of whole-grain food sources increased from 7 to 11 food items after the intervention. Conclusions and Implications: A general nutrition course can be used as an avenue to increase whole- grain intake by college students. KeyWords: young adults, college students, class-based nutrition, whole grains, food habits (J Nutr Educ Behav. 2011;43:

263

-267.)

INTRODUCTION

The prevalence of diet-related chronic
diseases has steadily increased over
the past few decades. Research has
revealed that a plant-based diet, spe-
cifically the regular consumption of
whole grains, aids in weight mainte-
nance1,2 and reduces the risk of type
2 diabetes,3 heart disease,3,4 and
certain cancers.5 The MyPyramid
food guidance system recommends
that at least half the dietary grain
intake should comprise whole-grain
products, that is, 3 ounces (oz) per
day for a 2,000-kcal diet.6 The average
intake of whole grains in American
adults, however, is reported at less
than 1 oz per day, with only 8% of
Americans meeting the recommenda-
tion.7

The college years are a period of
significant change in the lifestyles of
young adults. Food patterns estab-

Consumer Studies, Kent State Uni
r correspondence: Eun-Jeong Ha, Ph
nt State University, Kent, OH 442
il: eha@kent.edu
CIETY FOR NUTRITION EDUC
/j.jneb.2010.02.008

Nutrition Education and Behav

lished during college are likely to be
maintained for life and may have
long-lasting influences on college stu-
dents’ future health and that of their
families.8 Previous research has
demonstrated that college students
consume only 0.7 servings per day,
which is less than the recommended
amount of whole-grain products.1 In
spite of accumulating evidence dem-
onstrating the physiological benefits
of whole grains, nutrition interven-
tion promoting whole-grain intake
has been very limited among college
students. The purpose of the present
investigation was to estimate their
current consumption of whole grains,
identify their whole-grain food sour-
ces, and determine whether whole-
grain consumption would increase
after completing an interactive intro-
ductory nutrition course focusing on
disease prevention in college
students.

versity, Kent, OH
D, 130 Nixson Hall, Family and Consumer
42; Phone: (330) 672-2701; Fax: (330) 672-

ATION

ior � Volume 43, Number 4, 2011

DESCRIPTION OF THE
INTERVENTION AND
EVALUATION

A sample of 90 healthy college stu-
dents (between 18 and 24 years old)
participated in the study. This group
was enrolled in an introductory,
sophomore-level nutrition class at
a university in the midwestern United
States during spring 2006. Written in-
formed consent was obtained from
the students before they participated
in the study. Procedures for this re-
search were reviewed and approved
by the Institutional Review Board at
the university.

A pre- and posttest non-experi-
mental design was used in this
research. Data were collected during
the first 2 weeks and the last week of
spring semester. Body weight was
measured in kg to the nearest 0.1 kg
on an electronic scale in light clothing
without shoes. Standing height was
recorded without shoes on a portable
stadiometer to the nearest 0.1 cm
with mandible plane parallel to the
floor. Each subject’s body mass index
was calculated as weight (kg)/height
(m2). A researcher with a degree in
exercise science completed anthropo-
metric measurements.

263

mailto:eha@kent.edu

264 Ha and Caine-Bish Journal of Nutrition Education and Behavior � Volume 43, Number 4, 2011

A 3-day dietary record was col-
lected to assess dietary intake. On
the first day of class, participants
were instructed to complete the 3-
day dietary record on the typical 2
weekdays and 1 weekend day prior
to the interview. To obtain typical die-
tary intake information, participants
were advised not to include holidays,
exam days, or any day that included
a special event, such as a birthday
party or wedding reception. After
completion of the 3-day dietary re-
cord, each participant was scheduled
for an individual interview with a pri-
mary investigator to review the die-
tary information provided in the
food log. During the interview, a vari-
ety of tools and techniques was used
to increase the validity and reliability
of the data. To estimate proper por-
tion sizes, food models and standard
measuring cups and spoons were
used, and participants were also asked
to bring their own household utensils
and tableware to quantify actual
intakes.

In addition, to increase the accu-
racy of the dietary data, research asso-
ciates visited local restaurants and
campus cafeterias where the majority
of participants ate to gain accurate in-
formation about ingredients and por-
tion sizes. When dietary intake
information was insufficient and/or
was not publicly available, food items
were purchased for estimation of in-
gredients or portion sizes. In addition,
all food labels and the packaging for
products that participants consumed
during the data collection period
were gathered. Grain and whole-
grain intakes were categorized and
quantified in ounces as defined by
MyPyramid. Whole-grain products
were defined as those with whole
grains listed as the first ingredient on
the food labels. Whole-grain status of
each grain item was verified by its
food label. Dietary analysis was per-
formed by the primary investigator
using NutriBase IV Clinical (version
IV, CyberSoft, Inc., Phoenix, AZ,
2002).

A unique approach was adopted to
allow participants to understand their
current dietary practices. In tradi-
tional introductory nutrition classes,
students typically fill out and analyze
their dietary records by themselves.
In the current study, food logs were re-
viewed and analyzed by the primary

investigator during the interview
session. Each student sat down with
the researcher and discussed his or
her dietary intake with the researcher
(ie, correct portion sizes, types of
food consumed). The students also re-
ceived the results of their completed
analysis from the primary investiga-
tor. Then the students were asked to
bring their results to each class to
compare their intake to Dietary Refer-
ence Intake, dietary guidelines, and
MyPyramid recommendations. To re-
inforce dietary recommendations,
the participants completed a personal
assessment of their dietary logs at the
conclusion of the semester by stating
the strengths and weaknesses of their
diet and any dietary changes that
could be made.

The class met in 50-minute ses-
sions 3 times a week. Lectures empha-
sized the following: increasing
consumption of fruit, vegetables, and
whole-grain products; encouraging
the consumption of low-fat dairy
products; discouraging over-reliance
on dietary supplements; and promot-
ing active lifestyles. This study em-
phasized the role of a healthful
lifestyle by stressing dietary habits, ex-
ercise, and disease screening in pre-
venting chronic diseases instead of
by emphasizing food choice modifica-
tion as the only mechanism related to
disease prevention. For example, to
encourage whole-grain intake, the
participants were asked to consider
their risk for heart disease in a risk-
assessment activity. In a subsequent
lecture, the physiological benefits of
whole grains were introduced to en-
courage participants to increase over-
all diet quality by including whole
grains. Another lecture that involved
food-label contents, ‘‘What’s in
a food label other than calories?’’ was
followed to help participants identify
whole-grains products and possibly
to result in an increase of actual intake
of whole-grain products. A whole-
grain tasting activity was then con-
ducted to reduce students’ biases
against the taste and texture of
whole-grain products compared to
refined-flour products, a major barrier
to increasing whole-grain intake.7,9,10

Approximately 4 hours of total lecture
time were spent on whole grains and
related topics.

In the present study, traditional
lectures were combined with interac-

tive feedback and ‘‘hands-on’’ activi-
ties (Table 1) that incorporated
concepts from the Social Cognitive
Theory (SCT). The SCT is based on
the interaction of environment (ie,
factors that affect a person’s behav-
ior), personal factors (cognitive, affec-
tive, and biological events), and the
behaviors that an individual per-
forms.11 Many of the activities and
class lectures in the current study
were tied to SCT whereby the students
used their own dietary behaviors and
lifestyle choices as a framework to
learn the course materials. For exam-
ple, one of the class activities, ‘‘May I
take your order?’’ was based on social
constructs including behavioral capa-
bility and environment. Another ac-
tivity, ‘‘Happy Body Log,’’ reflected
constructs from SCT such as self-
control and expectation. Lectures
were piloted for 3 semesters prior to
delivery in the current program to de-
termine whether they met the study
objectives by assessing students’ suc-
cess on projects, classroom activities,
and exams.

SPSS (version 14.0, SPSS, Inc., Chi-
cago, IL, 2005) was used for all analy-
ses. Data were entered into SPSS, and
the lead investigator randomly veri-
fied data entry by checking accuracy
between the data file and a hard
copy. Means and standard deviations
were calculated for all the variables
analyzed. Repeated-measures analysis
of varince was performed to deter-
mine changes in grain, whole-grain
product fiber, and energy intake with
respect to sex. Significance was set
a priori at P # .05.

FINDINGS

Among 90 students, 80 of them com-
pleted the study. Exclusions included
students older than 25 years of age
and those with preexisting medical
conditions limiting the intake of cer-
tain foods (ie, celiac disease), illnesses
affecting food intake during data col-
lection period (ie, flu), or incomplete
or unreliable data.

Because no significant differences
in overall grain consumption were
demonstrated between males and fe-
males (P ¼ .48) at pre- and posttest
measures, the data were pooled (ie,
all participants included). On aver-
age, both grain and whole-grain

Table 1. Examples of Class Activities and Demonstrations for a Basic Nutrition Course

Title of Activity Class Topic Lesson Plan
Food label hunt: What’s on

a food label besides calories?
Food label Students learn how to interpret information on food labels,

including the ingredient list. This activity provides important
tips (ie, bread that comes in ‘‘brown bag’’ is not necessarily
‘‘whole-grain bread’’).

May I take your order? Carbohydrate and fiber Students pretend to go out for lunch and are asked to choose
fiber-rich, low-fat food items from a provided menu. Answers
are given in the order of fiber/fat contents.

Whole-grain product sampling MyPyramid Low-cost whole-grain products are introduced, and opinions
are shared. Most students cite their negative bias against
whole-grain products, believing their taste and texture to be
poor and their cost high, but they change their attitudes after
taste tests.

Let’s try something new:
Best recipe contests

Why do we eat? MyPyramid,
energy balance, vitamins,
and minerals

For homework students try new food items, such as ethnic
food, seafood, dry beans, calcium-rich and low-sodium food,
and vegetables. Contests are held for the best recipes, and
winners receive coupons/gift certificates for whole-grain
products or whole-grain baguettes from local bakeries.

Dining out quizzes Energy balance Sample quiz: Which McDonald’s sandwich contains the fewest
calories, a Chicken McGrill or a cheeseburger? Students
usually choose Chicken McGrill over the cheeseburger,
reflecting the popular assumption that chicken is a healthful
choice.

Happy body log Fitness Students list good things that they do for their bodies in a daily
log. The key to this activity is to start with small behavior
changes, such as not eating while watching TV, reducing
portions of single condiments, and choosing skim milk over
2% milk.

How much risk do I have? Lipid and calcium Students assess their risks for heart disease and osteoporosis
by completing risk assessment forms. These activities help
students realize that they are not free from chronic disease
risks merely because they are young or currently disease free.

Be an advocate! Cancer and cardiovascular
diseases

As a class assignment, students choose at least 3 people from
their family/friends and teach them about the protective
effects of healthful diet and prevention of chronic diseases.
Experiences are shared with a group.

Journal of Nutrition Education and Behavior � Volume 43, Number 4, 2011 Ha and Caine-Bish 265

consumption was low at the begin-
ning of the study. At baseline, total
mean grain consumption was 3.07
oz, and mean whole-grain consump-
tion was 0.37 oz. After the study,
mean consumption of whole-grain
products significantly increased to
1.16 oz (P < .001), but total mean grain intake remained same (3.06 oz). On average, only 12% of the total grain intake was whole grains at the pretest, well below the MyPyramid recommendation; however, by the conclusion of the study, whole-grain consumption increased to 38%. Only 49% of the class consumed some whole-grain products at the be- ginning of the semester, but at the end, 80% of the participants con- sumed whole-grain products. Only

1% of participants consumed the rec-
ommended 3 oz of whole grains at
pretest, whereas at the conclusion of
the study, 13% of individuals con-
sumed at least 3 oz of whole grains.
Average fiber intake at baseline also
increased significantly from 15.37 �
6.82 g to 18.37 � 7.88 g after the
study (P ¼ .007), whereas total energy
intake had decreased after the study
period (P < .001). Body mass index at pretest was 26.3 � 5.63 kg/m2 and 25.93 � 5.91 kg/m2 at posttest, respectively (P ¼ .44). Participants’ characteristics and pre- and posttest means and standard deviations for dependent variables are presented in Table 2.

At the beginning of the interven-
tion, 7 food items were identified as

sources of whole grains. The major
sources of whole-grain consumption
that were identified at baseline were
granola bars (36%), ready-to-eat ce-
reals (22%), yeast breads (18%), and
hot cereals (12%), with minor
whole-grain contributions from
snacks (8%), bagels (2%), and cooked
whole grains (1%). At posttest, major
food sources for whole-grain con-
sumption included yeast breads
(39%), bagels (14%), granola bars
(13%), and ready-to-eat cereals
(11%), and cooked whole grains,
snacks, and hot cereals made up
21% of total whole-grain intake.
Pasta, popcorn, pancakes, and other
cereals (ie, bulgur/barley) were new
additions to whole-grain food sour-
ces in this population.

Table 2. Participants’ Information and Pre- and Posttest Variables of Total Grain,
Whole-grain, and Energy Intakes (n ¼ 80)

Pretest Posttest P Value
Age, y (mean � SD) 20 � 1.4 . .
Female (%) 88 . .
White (%) 71 . .
Energy, kcal (mean � SD) 2,270 � 763.7 1,764 � 551.4 < .001a Total grain, oz (mean � SD) 3.1 � 3.1 3.1 � 3.0 .97 Whole grain, oz (mean � SD) 0.4 � 0.6 1.2 � 1.0 < .001a Whole grain as percentage

of total grain (%)
12 38 .

aDemonstrates significant difference (P # .05).

266 Ha and Caine-Bish Journal of Nutrition Education and Behavior � Volume 43, Number 4, 2011

DISCUSSION

The average consumption of whole
grains among college students partici-
pating in this study was 0.37 oz, 10%
of total grain intake, which is well be-
low the MyPyramid recommenda-
tions of 3 oz for a 2,000-calorie
intake. By the conclusion of the study,
whole-grain intake significantly in-
creased to 38% of the total grain
intake (1.16 oz). In addition, the total
number of sources of whole-grain
food at baseline was limited to 7
items, and the 2 major sources—gra-
nola bars and ready-to-eat cereals—
constituted 58% of total whole-grain
intake. Intake of these 2 items with
high-sugar content was reduced to
26% by the end of the program. After
the study, whole-grain food sources
expanded to 11 items, with yeast
bread and bagels as the top
choices. These findings suggest that
course topics that focused on bal-
anced, healthful eating may have
helped the participants make health-
conscious decisions concerning
whole grains.

Several factors may explain the suc-
cess of the procedures used in this
study in helping participants increase
their consumption of whole grains.
First, lectures linking whole-grain
intake and disease prevention may
have increased awareness of the
importance of whole-grain products
as part of a healthful diet. Second,
training in reading food ingredient lists
may have helped students correctly
identify whole-grain products. A body
of evidence suggests that major barriers
to increasing whole-grain intake in-
clude a low awareness of whole-

grain products and their unique
physiological benefits,12,13 consumer
confusion,14,15 texture, and taste.7,9,10

In this study, participants often
consumed products they believed to
be whole-grain products but in actual-
ity were not. For example, many indi-
viduals recorded their consumption of
‘‘whole-wheat bread’’ in food logs, but
food labels submitted along with the
dietary record listed ‘‘enriched wheat
flour’’ as the first ingredient. Many stu-
dents also reported that they consid-
ered ‘‘wheat bread’’ as a whole-grain
product, particularly when it came in
a brown bag. These findings suggest
that whole-grain confusion may result
from an inability to interpret food la-
bels and a ‘‘marketing trend adding
whole grain claims on labels although
their products have more refined flour
than whole grain.’’16 Finally, frequent
class meetings (3 times a week), com-
pared to traditional nutrition interven-
tions, which meet only once a week,
more frequently exposed participants
to health messages.

A major strength of the present
study is its focus on obtaining accurate
dietary data using various methods.
Although dietary assessment is subject
to error, the current investigation used
a 3-day dietary record to interview in-
dividuals to help them correctly iden-
tify whole-grain products and to
estimate accurate portion sizes by us-
ing various tools, such as food labels,
food packages, and household uten-
sils; interviewing managers of local
restaurants and the campus cafeteria;
and purchasing food items actually
consumed by the participants. In addi-
tion, previous researchers considered
‘‘wheat bread’’ a whole-grain bread,1

likely resulting in overestimation of
whole-grain intake. This finding helps
to explain the lower baseline intake re-
ported in this study compared to that
in the previous research, which adop-
ted self-reported whole-grain intake
either through a food frequency ques-
tionnaire or dietary record, without
verification specific to whole-grain
items.17

This study was limited, first, be-
cause of the use of a convenience
sample and self-reported dietary log.
Second, a control group was not cre-
ated. Furthermore, in this study,
whole grains were considered only
when whole grain was listed as the first
ingredient in the food label. Therefore,
overall whole-grain consumption may
have been underestimated by ignoring
food items containing smaller
amounts of whole grains. Finally,
follow-up assessments were not made
to determine whether changes in
whole-grain consumption were sus-
tained. Future studies are recommen-
ded to investigate long-term effects
of nutrition intervention on college
students’ dietary behavior.

IMPLICATIONS FOR
RESEARCH AND
PRACTICE

The present study showed that an in-
teractive introductory nutrition class
can be an effective tool in increasing
whole-grain intake by college stu-
dents. General nutrition knowledge
focused on the role of a healthful
diet for prevention of disease, and in-
teractive activities may be an effective
way to modify eating habits in this
population. These approaches require
minimal manpower and financial re-
sources. Based on the results of this
study, an introductory nutrition class
may serve as a cost-effective means
of providing nutrition education to
students on campus.

REFERENCES

1. Rose N, Hosig K, Davy B, Serrano E,
Davis L. Whole grain intake is associated
with body mass index in college
students. J Nutr Educ Behav. 2007;39:
90-94.

2. Koh-Banerjee P, Franz M, Sampson L,
Liu S, et al. Whole grain and weight

Journal of Nutrition Education and Behavior � Volume 43, Number 4, 2011 Ha and Caine-Bish 267

maintenance: Changes in whole grain,
bran, and cereal fiber consumption in
relation to 8-y weight gain among
men. Am J Clin Nutr. 2004;80:1237-
1245.

3. Jensen MK, Koh-Banerjee P, Franz M,
Sampson L, Grønbæk M, Rimm EB.
Whole grains, bran, and germ in rela-
tion to homocysteine and markers of
glycemic control, lipids, and inflamma-
tion. Am J Clin Nutr. 2006;83:275-283.

4. Jensen MK, Koh-Banerjee P, Hu FB,
et al. Intakes of whole grains, bran,
and germ and the risk of coronary heart
disease in men. Am J Clin Nutr.
2004;80:1492-1499.

5. Larsson SC, Giovannucci E, Bergkvist,
Wolk A. Whole grain consumption
and risk of colorectal cancer: a popula-
tion-based cohort of 60,000 women.
Br J Cancer. 2005;92:1803-1807.

6. United States Department of Agriculture.
MyPyramid Web site. http://www.
mypyramid.gov. Accessed June 13, 2010.

7. Cleveland LE, Moshfegh AJ,
Albertson AM, Goldman JD. Dietary

intake of whole grains. J Am Coll
Nutr. 2000;19(suppl 3): 331S–338S.

8. Wynn A. Inequalities in nutrition. Nutr
Health. 1987;5:79-94.

9. Kantor LS, Variyam JN, Allshouse JE,
Putnam JJ, Lin BH. Choose a variety
of grains daily, especially whole grains:
a challenge for consumers. J Nutr.
2001;131(suppl 1): 473S–486S.

10. Jones JM, Reicks M, Adams J, et al. The
importance of promoting a whole grain
foods message. Am J Coll Nutr.
2002;21:293-297.

11. Bandura A. Social Cognitive Theory:
An agentive perspective. Rev Psychol.
2001;52:1-26.

12. Chase K, Reicks M, Smith C, Henry H,
Reimer K. Factors influencing purchase
of bread and cereals by low-income Af-
rican American women and implica-
tions for whole grain education. J Am
Diet Assoc. 2003;103:501-504.

13. Adams JF, Engstrom A. Helping con-
sumers achieve recommended intakes
of whole grain foods. J Am Coll Nutr.
2000;19(suppl 3): 339S–344S.

14. Kennedy E, Davis C. Dietary Guideline
2000—the opportunity and challenges
for reaching the consumer. J Am Diet
Assoc. 2000;100:1462-1465.

15. Marquart L, Pham A, Lautenschlager L,
Croy M, Sobal J. Beliefs about whole
grain foods by food and nutrition pro-
fessionals, health club members, and
special supplemental nutrition program
for Women, Infants, and Children par-
ticipants/state fair attendees. J Am Diet
Assoc. 2006;106:1856-1860.

16. Look for ‘‘The Whole (Grain)
Truth’’ in Nutrition Action Health-
letter. Center for Science in the Pub-
lic Interest Web site. http://cspinet.
org/new/200604261.html. Published
April 26, 2006. Accessed June 13,
2010.

17. Harnack L, Walters SH, Jacobs DR.
Dietary intake and food sources of
whole grains among US children and
adolescents: data from the 1994–1996
continuing survey of food intakes by
individuals. J Am Diet Assoc.
2003;103:1015-1019.

http://www.mypyramid.gov

http://www.mypyramid.gov

http://cspinet.org/new/200604261.html

http://cspinet.org/new/200604261.html

• Interactive Introductory Nutrition Course Focusing on Disease Prevention Increased Whole-Grain Consumption by College Students

Introduction
Description of the Intervention and Evaluation
Findings
Discussion
Implications for Research and Practice
References

Research Article

Effect of Nutrition Intervention Using a General Nutrition
Course for Promoting Fruit and Vegetable Consumption
among College Students
Eun-Jeong Ha, PhD; Natalie Caine-Bish, PhD, RD, LD

ABSTRACT

Objective: To evaluate the effectiveness of implementing nutrition intervention using a general nutrition
class to promote consumption of fruits and vegetables in college students.
Design: 3-day food records were collected, verified, and analyzed before and after the intervention.
Setting: A midwestern university.
Participants: 80 college students, ages 18 to 24, participated in the study.
Intervention: The intervention focused on nutrition knowledge related to prevention of chronic diseases,
healthful dietary choices increasing fruit and vegetable consumption, dietary feedback, and interactive
hands-on activities.
Main Outcome Measures: Consumption of: total vegetable, fresh vegetable, starchy vegetable, french
fries, vegetable juice, total fruit, fresh fruit, canned fruit, and fruit juice.
Analysis: Dependent t test was used to analyze the differences in pre- and posttest. Analysis of variance
was used to determine differences in dietary changes between groups.
Results: Participants significantly increased consumption of not only total fruits and vegetables (P< .005), but also fresh fruits and vegetables (P< .005). Intake of french fries decreased significantly (P< .05). Females responded better to the intervention than males in increasing vegetable consumption (P < .05). Conclusions and Implications: Class-based nutrition intervention focusing on prevention of chronic diseases is a cost-effective approach to increasing fruit and vegetable consumption among college students. Key Words: college students, fruit, vegetable, nutrition class, nutrition intervention (J Nutr Educ Behav. 2009;41:

103

-109.)

INTRODUCTION

The college years are a period of signif-
icant change in the lifestyles of young
adults. Food patterns established dur-
ing college are likely to be maintained
for life and may have long-lasting in-
fluences on college students’ future
health and the health of their future
families.1 Furthermore, an inadequate
diet during the college years could re-
sult in unfavorable physiological con-
sequences that could lead to diet-
related chronic diseases.2 The dietary
patterns of college students should
be a concern to health professionals.
It is well documented that college
students have unhealthful eating be-

haviors, including skipping meals,3

frequent snacking on energy-dense
food,4 and engaging in unhealthful
weight-loss methods.5,6 In addition,
dietary intakes of college students ap-
pear to be high in fat, saturated fat,
cholesterol, and sodium,7,8 whereas
they are low in fiber; vitamins A, C,
and E; folate; iron; and calcium.9-12

An extensive body of research reveals
that a diet high in fruits and vegeta-
bles is associated with a reduced risk
of cancer and heart disease13-18 and
may aid in weight management.19

Fruit and vegetable consumption in
college students is between 2.1 and
5.5 servings,20-22 which is below the
current recommendation of 9 servings

or 41⁄2 cups.
23 Furthermore, college

students have less awareness than
older adults of the health benefits of
fruits and vegetables and the effects
of poor dietary practices.24

Although numerous nutrition edu-
cation programs promote fruit and
vegetable consumption, relatively few
efforts have targeted college students
in these nutrition interventions.20

Traditionally, the most successful
programs for diet modifications have
involved individual contact with
a registered dietitian or other primary
health care provider.25 However, these
methods may not fit with the busy life-
styles and unpredictable schedules of
most college students,4 causing poten-
tial for high attrition. Recently, alterna-
tive cost-effective interventions have
been introduced to specifically target
college students. For example, newslet-
ters were used to promote fruit and veg-
etable intake in college students, and
their participation in this intervention
was found to increase fruit and vegeta-
ble intake by 1 serving.20 Investigators

103

Family and Consumer Studies, Kent State University, Kent, OH
Address for correspondence: Eun-Jeong Ha, PhD, 130 Nixson Hall, Family and Consumer
Studies, Kent State University, Kent, OH 44242; Phone: (330) 672-2701; Fax: (330) 672-
2194; E-mail: eha@kent.edu
�2009 SOCIETY FOR NUTRITION EDUCATION
doi:10.1016/j.jneb.2008.07.001

Journal of Nutrition Education and Behavior � Volume 41, Number 2, 2009

mailto:eha@kent.edu

104 Ha and Caine-Bish Journal of Nutrition Education and Behavior � Volume 41, Number 2, 2009

in this study combined newsletters
with a motivational interview and
e-mail follow-up to increase the inter-
vention’s impact on dietary behavior
change. Shive and Morris also found
that a social marketing campaign im-
proved fruit consumption among
college students by offering a fruit fair
on campus that included distribution
of fruit samples and brochures.26

Similarly, college nutrition courses
have been used to enhance nutritional
knowledge in college students with the
goal to encourage dietary change.27,28

However, results of these investiga-
tions indicate that this type of inter-
vention appears to be successful only
in increasing nutrition knowledge
and not in changing dietary intake.
In contrast, Matvienko et al reported
that participation in a college nutrition
science course prevented weight gain
in freshmen, suggesting that class-
based nutrition education may help
college students translate nutrition
knowledge into dietary changes.29

Overall, prior research on interven-
tions targeting college students’ die-
tary behaviors suggests a need to
develop curricula targeting specific nu-
trition behaviors in college students.

The purpose of this study was two-
fold: (1) to assess the current intake of
fruits and vegetables in a sample of
college students; and (2) to evaluate
the effectiveness of participation in
a 15-week basic nutrition class in in-
creasing consumption of fruits and
vegetables in college students. The
present intervention is unique in
combining the use of conventional
education materials with interactive
feedback and ‘‘hands-on’’ activities in
promotion of increased fruit and
vegetable intake.

METHODS

During the spring of 2006, 90 healthy
college students between the ages of
19 and 35 years who were enrolled
in a basic sophomore-level nutrition
class at a midwestern university par-
ticipated in the study. Among them,
10 participants were excluded from
the final analysis. Exclusions included
students who were older than 25
years, preexisting medical conditions
limiting dietary intake (ie, celiac dis-
ease), illnesses affecting food intake
during the data collection period (ie,

flu), or incomplete or unreliable data.
The study population consisted of
undergraduate students from various
majors including Nutrition and Die-
tetics (35%), Human Development
(28%), other (30%) (ie, Hospitality
Management, Audiology, Exercise Sci-
ence), and undeclared (7%) majors.
Participants were told that the pur-
pose of the study was to obtain data
to develop a nutrition education in-
tervention program for college stu-
dents. This research was approved by
the Kent State University Institutional
Review Board, and informed consent
was obtained from each participant
before enrollment in the project.

A pretest–posttest design was used to
assess effectiveness of nutrition educa-
tion on changes in vegetable and fruit
consumption of college students. Data
were collected during the first 2 weeks
and the last week of spring semester
2006. At the time of both pre- and post-
testing, a 50-minute interview was
conducted with each participant to
gather anthropometric data, as well as
to verify completion and accuracy of
each participant’s food record. Body
weight was measured in kilograms to
the nearest � 0.1 kg using an electronic
scale (Body Fat Monitor/Scale [TBF
551], Tanita, Arlington Heights, IL),
with the student in light clothing and
without shoes. Standing height with-
out shoes was recorded on a portable
stadiometer (The Portable Adult/Infant
Measuring Unit [PE-AIM-101], Perspec-
tive Enterprises, Kalamazoo, MI) to the
nearest � 0.1 cm with mandible plane
parallel to the floor. Each subject’s
body mass index (BMI) was calculated
as weight/height2 (kg/m2).

Dietary intake was assessed using
3-day dietary records. Subjects were
instructed to record their food intake
over 2 weekdays and 1 weekend day
while adhering to their usual eating
practices. They were told to avoid
days with special events or exam
days for reporting dietary intake. A va-
riety of tools and procedures was used
to obtain reliable data:

1. Food models, measuring cups and
spoons, household utensils, and ta-
bleware were used to illustrate
proper portion sizes.

2. Participants were asked to collect
and bring all the food labels of the
products they consumed during
the data collection period.

3. To obtain the most accurate infor-
mation about ingredients and
portion sizes, research associates
visited the local restaurants and
campus cafeterias where a majority
of the participants ate.

4. Food items were also purchased by
the researchers to estimate portion
sizes when students were not able
to provide accurate information.

Dietary analyses were performed
by the trained researcher using Nutri-
Base IV Clinical software (Cyber Soft,
Inc., Phoenix, AZ, 2002).

Fruit and vegetable intake was re-
ported in cups so results could be
compared to the MyPyramid food
guidance system.23 The guidelines
provided by the Centers for Disease
Control and Prevention are based on
daily calorie needs estimated by con-
sidering age, gender, and activity
level.30 These guidelines are given in
cups of fruits and vegetables.

The class met 3 times a week for 50
minutes per session over a 15-week
period. To minimize the effect of class
materials on eating behavior and
physical activity patterns, lectures
during the baseline data collection
focused on topics not directly related
to dietary intake and health, such as
‘‘How the Body Uses Food,’’ ‘‘Food
Insecurity,’’ and ‘‘Food Safety.’’

Class lectures covered topics that
addressed overall dietary quality, in-
cluding: (1) the importance of nutri-
tion related to prevention of chronic
diseases; (2) increasing consumption
of fruits, vegetables, and whole-grain
products; (3) encouraging low-fat
dairy product consumption; (4) dis-
couraging over-reliance on dietary
supplements; and (5) promoting an
active lifestyle. In addition to the tra-
ditional approach of lectures, video-
tape watching and various class
activities were introduced. The pres-
ent educational investigation is
unique because of its combination of
conventional education materials
and ‘‘hands-on’’ activities that were
goal oriented. By focusing not only
on increasing academic knowledge,
educational materials and activities
that were goal oriented, reinforced,
and focused on students’ current envi-
ronment and social constructs were
developed.31 Many of the activities
and class lectures were tied to the Social
Cognitive Theory (SCT), whereby

Journal of Nutrition Education and Behavior � Volume 41, Number 2, 2009 Ha and Caine-Bish 105

Table 1. Class Activities for Basic Nutrition Course

Activity Description Social Cognitive Theory Conceptsa

1. Food label hunt Students bring the food label of a frequently
used product

Behavioral capability; Environment

2. May I take your order? –
Fiber-rich food

Pretending to go out for lunch, students
select fiber-rich/ low-fat food from the
menu. Answers are given in the order of
fiber/fat contents.

Behavioral capability; Environment

3. Whole-grain product sampling Whole-grain products are introduced, and
opinions are shared.

Situation; Expectancies

4. What changes do I want to make? Students list simple behavioral changes they
want to make in a couple of weeks.

Self-control

5. Happy body log Students list up to three good things that
they do for their body in a daily log.

Self-control; Expectations

6. How much risk do I have? –
Osteoporosis and cardiovascular
disease

Using risk assessment forms and food
frequency form developed for calcium
intake, students assess their risk of
osteoporosis and cardiovascular
diseases.

Behavioral capability

7. Calcium-rich food contest Students are asked to bring their favorite
recipes high in calcium. Best 5 recipes are
selected and shared with class.

Behavioral capability

8. New food challenge Students are asked to try any new dish
containing vegetables and submit written
report.

Reinforcements

9. What about my parent’s diet? Students perform dietary intake interview
with their parent using the same packet
they used at the beginning of the
semester for themselves.

Reciprocal determination

aThe following are the definitions of each of the Social Cognitive Theory concepts used for the in-class projects:
Behavioral capability: Knowledge and skills to perform behavior used to demonstrate skills training
Environment: Factors physically external to the person
Expectations: Model positive outcomes of a behavior
Expectancies: Value placed on an outcome
Reciprocal determination: Interaction of the person, environment, and behavior to demonstrate skill and personal change
Reinforcements: Responses to a person’s behavior
Self-control: Personal regulation of goal-directed behavior
Situation: Perceptions of the environment

students were using their own dietary
behaviors and lifestyle choices as
a framework to learn course materials.
Table 1 lists course activities, as well as
the constructs from the SCT that each
activity meets.32 The SCT is based
on interaction of environment (ie, fac-
tors that affect a person’s behavior),
personal factors (cognitive, affective,
and biological events), and behaviors
that an individual performs. This the-
ory is typically used in health educa-
tion and behavior programs and can
be used as a framework for developing
intervention strategies. SCT has been
successfully used in previous interven-
tions to increase fruit and vegetable
consumption.33,34

Statistical Analysis

Means and standard deviations were
calculated for all the variables ana-
lyzed. The statistical program and pro-
cedures of SPSS for Windows (version
14.0, SPSS, Inc., Chicago, Ill, 2005)
were used for all analyses. Consump-
tion variables for the current investiga-
tion included the following: total
vegetable, fresh vegetable, starchy veg-
etable, french fries, vegetable juice, to-
tal fruit, fresh fruit, canned fruit, and
fruit juice. Pre–post differences were
assessed using analysis of variance
(ANOVA). Differences in consumption
were not significantly different accord-
ing to gender, residency, and years in

college; therefore, data were pooled
and paired t tests determined overall
differences in fruit and vegetable vari-
ables. Significance was set a priori at
P # .05.

RESULTS

Eighty students enrolled in a sopho-
more level general nutrition course
participated in this investigation. Av-
erage BMI of the participants was
26.3 � 5.6 kg/m2. Average age of the
participants was 20.2 � 1.4 years.
The majority of the participants were
female (88%). The residence status of
the participants was: 16% lived at

106 Ha and Caine-Bish Journal of Nutrition Education and Behavior � Volume 41, Number 2, 2009

Table 2. Pre- and Posttest Variable Means þ Standard Deviations for Vegetable
And Fruit Consumption

Consumption (cups per day) Pretest Posttest P Value
Vegetables 0.77 � 0.62 1.52 � 1.03 < .001 Fresh vegetables 0.46 � 0.50 1.2 � 0.93 < .001 Starchy vegetables 0.30 � 0.33 0.29 � 0.40 .47 French fries 0.15 � 0.28 0.07 � 0.15 .01 Vegetable juice 0.01 � 0.07 0.02 � 0.15 .75 Fruit 0.94 � 0.92 1.33 � 0.99 .002 Fresh fruit 0.43 � 0.61 0.99 � 0.85 < .001 Canned fruit 0.06 � 0.15 0.05 � 0.15 .473 Fruit juice 0.45 � 0.64 0.32 � 0.47 .107

home, 40% lived in the dorm, 35%
lived off campus, and 6% reported an-
other living arrangement.

Pre- and posttest means and stan-
dard deviations are reported in Table
2. Since no differences in fruit
and vegetable consumption between
genders (P ¼ .13 and P ¼ .83, respec-
tively), place of residence (P ¼ .49
and P ¼ .49, respectively), and years
in college (P ¼ .27, P ¼ .79, respec-
tively) were found between pre- and
posttest measures, the data were
pooled (ie, all participants included).
Paired t tests revealed overall differ-
ences in fruit and vegetable consump-
tion from pre- to posttest regardless of
grouping variables (ie, gender, resi-
dency, year in school). Statistically sig-
nificant increases in consumption of
total vegetable, fresh vegetable, total
fruit, and fresh fruit were demon-
strated between pre- and posttest mea-
sures (Table 2). A significant decrease
in french fry consumption was also
noted between pre- and posttest mea-
sures (P ¼ .01). Fruit and vegetable
consumption data at posttest between
males and females did show a signifi-
cant difference in vegetable consump-
tion (P ¼ .036) at posttest whereby
females consumed more vegetables
than males. No differences (P ¼ .806)
in fruit consumption at posttest were
demonstrated.

Overall, vegetable consumption
and fresh vegetable consumption
were low at the beginning of the
study. Seventy-two percent of partici-
pants consumed 1 cup or less of total
vegetables, and 90% of participants
consumed 1 cup or less of fresh vege-
tables at the onset of the investiga-
tion. Both total vegetable and fresh
vegetable consumption increased by

the end of the investigation. Sixty-
five percent of the participants were
consuming more than 1 cup of vege-
tables, and over 50% of the partici-
pants were consuming more than 1
cup of fresh vegetables by the conclu-
sion of the class. The consumption of
starchy vegetables did not change
from pre- to posttest (P ¼ .47).

Overall, students participating in
this study demonstrated an increase
in fruit consumption (Table 2).
Ninety-two percent of participants
consumed 2 cups or less of fruit per
day. By the conclusion of the semes-
ter, 22% of the participants were con-
suming more than 2 cups of fruit per
day. Increases in fresh fruit consump-
tion were also shown whereby at the
time of the pretest, 90% of partici-
pants were consuming 1 cup or less
of fresh fruit, but at posttest, 39% of
participants were consuming greater
than 1 cup of fresh fruit per day. There
was only a 10% decrease in fruit juice
consumption over the semester,
which was found to be nonsignificant
between pre- and posttest measures
(P ¼.147). No change in the consump-
tion of canned fruit was demonstrated
(P ¼ .473).

DISCUSSION

The present investigation assessed the
intake of fruits and vegetables of col-
lege-age students and examined the
effectiveness of a 15-week interven-
tion in fruit and vegetable consump-
tion as part of a general nutrition
course. Numerous researchers have re-
ported that college students display
unhealthful eating patterns3,4 and
are engaged in unsound dieting prac-

tices.5,6 The typical diet of college
students is often low in fruits and veg-
etables and high in fat, simple sugar,
and sodium content.7,8 This type of
diet can be characterized by a lack of
certain vitamins and minerals and fi-
ber.9-12

The present study confirmed that
college students do not consume fruits
and vegetables at the recommended
dietary intake. This research also
found that exposure to a class-based
nutrition education intervention
driven by SCT for 18- to 24-year-old
college students may help this age
group meet the recommended serv-
ings of fruits and vegetables (ie, 4.5
cups for women and 5 cups for men).
This finding is important because in-
creasing fruit and vegetable consump-
tion would increase intake of dietary
fiber, folate, and vitamins A and C, all
of which are known to be lacking in
the typical diet of college students.9-12

Furthermore, research literature that
has shown that increases in nutrition
knowledge or general nutrition knowl-
edge does not necessarily predict
dietary change.27,28 The present inves-
tigation resulted in statistically signifi-
cant increases in the intake of both
fruits and vegetables. This finding is
consistent with previous research
that showed positive changes in
dietary behaviors after nutrition edu-
cation interventions with college stu-
dents.20,35

Limited research exists on nutri-
tion education interventions promot-
ing fruit and vegetable consumption
by college students. Mitchell reported
that basic nutrition classes changed
college students’ food choices but
failed to show increases in fruit and
vegetable consumption.36 More re-
cently, Richards et al demonstrated
that use of stage-based newsletters in
college students increased fruit and
vegetable consumption by 1 serving
a day, which is equivalent to a 0.5-
cup increase and is similar to the re-
sults in the current investigation that
nutrition education increased the
consumption of fruits and vegeta-
bles.20 However, the magnitude of in-
crease in fruit and vegetable intake
observed in this study was much
higher. A general nutrition course
may be more effective in increasing
consumption because students are
more frequently exposed to education
materials.

Journal of Nutrition Education and Behavior � Volume 41, Number 2, 2009 Ha and Caine-Bish 107

At the beginning of the investiga-
tion, participants’ consumption of
both fruits and vegetables did not
meet the recommended intake.
Consumption of both fruits and veg-
etables by the current study’s partici-
pants was lower than the intake by
college students participating in sim-
ilar studies.22,37 These differences
may be owing to varying methodolo-
gies such as method and timing of
data collection. At the conclusion of
the intervention, however, partici-
pants consumed 2.85 cups of fruits
and vegetables, which is less than
current recommendation in MyPyra-
mid of 41⁄2 cups a day, but this find-
ing demonstrated an improvement
over baseline intake.38 Furthermore,
the number of servings of vegetables
more than doubled, indicating the
current intervention may have had
a greater effect on increasing vegeta-
ble intake than increasing fruit in-
take. This result is inconsistent with
other research showing more of an
increase in fruit intake than vegetable
consumption39 or similar increases in
both fruits and vegetables.20 This in-
consistency may be because fruit
intake at baseline was closer to the
recommended dietary intake and
vegetable intake was below the rec-
ommended servings. Although in-
creases in fruit consumption were
smaller compared to the increases in
vegetable intake, it is notable that
fresh fruit intake significantly in-
creased, whereas fruit juice consump-
tion remained the same.

In the current study, fruit and veg-
etable consumption at baseline or be-
tween pre- and posttest did not differ
between genders. Other researchers
have found that females consume
more servings of fruits and vegetables
than males,37,40 indicating the result
of the current investigation might be
specific to this study population or be-
cause males’ caloric intake is higher,
inadvertently explaining their con-
sumption of vegetables to be higher.
Another possible explanation is that
the number of males in this study
was not large enough to result in
a statistically significant difference.
Furthermore, males enrolled in the
basic nutrition course may already
be more interested in and practice
healthful eating compared to typical
male students. However, posttest re-
sults showed that vegetable consump-

tion by female students was higher
than that of their male counterparts,
implying that female students may
be more receptive than male students
to interventions focused on increas-
ing vegetable consumption. The
difference in fruit and vegetable
consumption patterns at the conclu-
sion of the study is not surprising in
light of previous research. Several re-
searchers have demonstrated that fe-
males hold a more positive attitude
toward healthful eating and fruit con-
sumption, more social pressure to
consume fruit, and are more health
conscious than their male counter-
parts.41,42 This result suggests that
future nutrition education interven-
tion requires effective and specific
tools focused on motivating male stu-
dents to increase fruit and vegetable
consumption.

The results of this research also in-
dicated that, both at baseline and after
the intervention, housing status did
not appear to influence fruit and veg-
etable consumption. Chung et al
found that students living on campus
ate more fruits and vegetables.21 One
possible explanation for the inconsis-
tent findings with Chung et al21 is
that dining rooms for dorm residents
on campus for this study population
were open to nonresidents as well,
which provided the same food choice
opportunities for both residents and
nonresidents. In addition, this inves-
tigation found that fruit and vegetable
intake did not differ according to
years in college. This is not a surprising
result, because all students are ex-
posed to the same environment that
affects their food intake, such as
busy schedules both for work and
class, limited funds, and lack of cook-
ing facilities and skills.

There are several reasons why a prom-
ising outcome was seen in this particular
study population. Throughout the se-
mester, during lectures, class activities,
and projects, the importance of fruit
and vegetable consumption was ad-
dressed as well as its association with
health-related topics (ie, cardiovascular
diseases, cancer, diabetes, obesity, osteo-
porosis, and hypertension). Lectures
were also expanded to discuss mecha-
nisms by which diet can prevent the de-
velopment of certain chronic diseases.
This knowledge may have been espe-
cially interesting to the students, since
more than half of the students in class

had more than 1 family member suffer-
ing from at least 1 chronic disease,
which might be an intrinsic factor to
motivate students to change their eating
habits. In addition, class lectures not
only encouraged students to increase
fruit and vegetable consumption, but
also motivated them to change overall
eating behavior and their lifestyle by us-
ing a variety of class activities and assign-
ments. Examples include: introducing
simple recipes of fruits and vegetables;
assigning home cooking, tasting health-
ful snacks, and writing a healthful activ-
ity log; providing tips for healthful
eating when dining out; and using the
guiding principles of SCT. These activi-
ties and assignments provided students
with opportunities throughout the
course to assess their personal behaviors
and environmental factors that affected
those behaviors. Two key learning op-
portunities were dietary record analysis
and goal setting. This approach may
have helped students relate the class ma-
terial more directly to their own dietary
habits, thereby giving them more aware-
ness and motivation to change their
own dietary behaviors. Another factor
may be that the class met more fre-
quently (ie, 3 times a week) than tradi-
tional nutrition education intervention
programs of only once a week. This find-
ing is supported by research reporting
that when the frequency of meeting
was greater, dietary changes were
higher.43 Finally, students received their
baseline dietary analysis as feedback dur-
ing the first month of the class and used
it to compare their intake with recom-
mended servings for each food group
and nutrient recommendation covered
during lectures. This activity helped stu-
dents recognize their nutrient intakes in
high and low values and identify food
sources, which in turn motivated them
to be engaged in overall healthful eat-
ing, which included increasing fruit
and vegetable consumption.

Overall, this research showed the
possibility that using a class-based
nutrition education intervention in
a general nutrition course may be an
effective approach that has the poten-
tial to change eating behaviors with
minimum additional manpower and
financial resources in a college setting.
Incorporation of messages to the stu-
dents to increase fruit and vegetable
consumption blended well with cur-
rent general nutrition course material.
It allowed for class learning to move

108 Ha and Caine-Bish Journal of Nutrition Education and Behavior � Volume 41, Number 2, 2009

from knowledge to application. To
further strengthen the goal of the
class to increase fruit and vegetable
consumption, additional ‘‘hands-on’’
projects (Table 1) grounded in SCT
allowed for students to use the knowl-
edge that they gained in class and use
it toward understanding their own di-
etary behaviors. The class projects not
only made the students aware of their
dietary habits, but it also created op-
portunities to address weaknesses in
their diets, which then increased
awareness and need for dietary modi-
fication. Class activities in turn may
have had a positive impact on stu-
dents’ breadth of knowledge gained
in the course and may as well have
provided a forum necessary to create
a behavior change.

This study has several limitations.
A convenience sample rather than
a random sample was used. As a result,
the study population may not repre-
sent traditional college students
through oversampling of females
and nutrition and dietetics students.
In addition, a control group was not
used to control for possible confound-
ing factors such as seasonal variation
in intake. Students who were already
more interested in healthful eating
may be more likely to choose to take
a nutrition course. Long-term effect
of the intervention over time was
not included in the study design. Fu-
ture research should be directed to-
ward longitudinal studies to examine
long-term effect of class-based nutri-
tion education on changes in dietary
behavior.

A major strength of the present
study is that this study was focused
on obtaining accurate dietary data us-
ing various methods (ie, interviewing
campus cafeteria managers; purchas-
ing actual food from local restaurants;
having participants to bring food la-
bels, utensils, and cups used most at
home; and having only one nutrition-
ist analyze the dietary record).

IMPLICATIONS FOR
RESEARCH AND
PRACTICE

In conclusion, class-based nutrition
education intervention focused on
prevention of chronic diseases has po-
tential to increase fruit and vegetable
consumption among college students.

Furthermore, effectiveness of class-
based nutrition education in increas-
ing fruit and vegetable consumption
among college students regardless of
their major, housing status, gender,
and year in college was demonstrated.
Using a class-based nutrition educa-
tion intervention in a general nutri-
tion course may be an effective
approach that has the potential to
change eating behaviors with mini-
mum manpower and financial re-
sources in a college setting.

Male students responded differ-
ently to this nutrition education inter-
vention, indicting future research
should focus on gender-tailored nutri-
tion intervention.

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38. Havas S, Heimendinger J, Damron E,
et al. 5 A Day for Better Health: nine
community research projects to in-
crease fruit and vegetable consumption.
Public Health Rep. 1995;110:68-80.

39. Ahluwalia JS, Nollen N, Kaur H,
James AS, Mayo MS, Resnicow K.
Pathways to health: cluster-randomized
trial to increase fruit and vegetable con-
sumption among smokers in public
housing. Health Psychol. 2007;26:214-
221.

40. Patterson BH, Harlan LC, Block G,
Kahle L. Food choices of whites, blacks,
and Hispanics: data from the 1987 Na-
tional Health Interview Survey. Nutr
Cancer. 1995;23:105-119.

41. Backman DR, Ella HH, Jerry EL,
Johnston PK, Hodgkin GE. Psychoso-
cial predictors of healthful dietary be-
haviors in adolescents. J Nutr Educ
Behav. 2002;34:184-193.

42. Dennison CM, Shepherd R. Adoles-
cent food choice: an application of the
theory of planned behavior. J Hum
Nutr Diet. 1995;8:9-23.

43. Nguyen JY, Major JM, Knott CJ,
Freeman KM, Downs TB, Saxe GA.
Adoption of a plant-based diet by
patient with recurrent prostate cancer.
Integr Cancer Ther. 2006;5:214-223.

http://www.fruitsandveggiesmatter.gov

http://www.fruitsandveggiesmatter.gov

• Effect of Nutrition Intervention Using a General Nutrition Course for Promoting Fruit and Vegetable Consumption among College Students

Introduction
Methods
Statistical Analysis
Results
Discussion
Implications for Research and Practice
References

The impact of physical activity on body weight and fat gains during the
first

3

years of college

Sareen S. Gropper
a
*, Frank H. Newell

a
, Ali Zaremba-Morgan

b
, Margaret K. Keiley

b
,

B. Douglas White
a
, Kevin W. Huggins

a
, Karla P. Simmons

c
, Lenda Jo Connell

c
and

Pamela V. Ulrich
c

a
Department of Nutrition, Dietetics, and Hospitality Management, Auburn University, 101 Poultry
Science Bldg., Auburn, AL 36849, USA;

b
Department of Human Development and Family Studies,

Auburn University, 203 Spidle Hall, Auburn, AL 36849, USA;
c
Department of Consumer Affairs,

Auburn University, 308 Spidle Hall, Auburn, AL 36849, USA

Over two-thirds of students gain weight and body fat during college, especially during
the freshman year. This study examined whether participation in physical activity

during the first 3 years of college was associated with favorable changes in body weight

and percent body fat. Participants included 535 college students (345 females, 190
males). Height and weight (assessed by standard techniques) and body fat (assessed by
bioelectrical impedance analysis) were obtained at the beginning of fall semester and at
the end of spring semester of each year; in addition during the first 2 years, assessments
were also conducted at the end of fall semester for a total of eight assessments between
2007 and 2010. Physical activity participation was self-reported using a questionnaire
that included a subset of questions from the National College Health Risk Behavior
Survey. While both males and females exhibited significant increases in weight and
percent body fat over the 3-year period; for the females, participation in strength
training was associated with loss of weight and percent body fat. The results of this
study emphasize the benefits of physical activity, especially strength training, for
college females as a means to reduce or prevent body weight and fat gains during the
first 3 years of college

.

Keywords: body fat gains; college students; strength training

Introduction

Young adults entering college are at risk for obesity. About two-thirds of college students

gain weight during their freshman year, and, while few gain the notorious ‘freshman

15

(referring to pounds),’ a 1.8 to 2.3-kg (4 – 5 lb) weight gain is frequently reported

(Hajhosseini et al. 2006, Edmonds et al. 2008, Kasparek et al. 2008, Gropper et al. 2009,

Mifsud et al. 2009, Pullman et al. 2009). Coupled with weight gain is a decline in physical

activity, which begins as students transition from high school and persist throughout

college (Caspersen et al. 2000, Huang et al. 2003, Bray and Born 2004, Buckworth and

Nigg 2004, Kasparek et al. 2008, Mestek et al. 2008, Racette et al. 2008, Pullman et al.

2009, Wengreen and Moncur 2009, Gropper et al. 2011).

Several factors associated with gains in weight and/or body mass index (BMI) during

college have been identified including snacking behaviors, dieting strategies, all-you-can-

eat dining, alcohol consumption, and physical inactivity. Physical inactivity is especially

ISSN 1463-5240 print/ISSN 2164-9545 online

q 2012 Institute of Health Promotion and Education

http://dx.doi.org/10.1080/14635240.2012.724190

http://www.tandfonline.com

*Corresponding author. Email: groppss@auburn.edu

International Journal of Health Promotion and Education

Vol. 50, No. 6, November 2012, 296–3

10

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http://dx.doi.org/10.1080/14635240.2012.724190

http://www.tandfonline.com

detrimental to health, increasing the risk for stroke, hyperlipidemias, type 2 diabetes, and

hypertension (Katzmarzyk 2006, U.S. Dept of Health and Human Services 2008). Physical

activity enables muscular work and energy expenditure to maintain a healthy body

composition and decrease obesity risk. Both a healthy BMI and percentage body fat are

important; problems, including cardiometabolic abnormalities, are associated with

‘normal weight obesity’ (normal BMI – unhealthy body fat percentage) (Romero-Corral

et al. 2010). Yet, while physical activity promotes health, about one-third of young adults

in the United States are not engaged in sufficient physical activity (Schoenborn and Adams

2010). This study’s purpose was to determine whether physical activity participation

during the first 3 years of college was associated with favorable changes in body weight

and percent body fat.

Methods

Subjects

At the beginning of fall semester 2007 and 2008, two cohorts of incoming freshmen at

Auburn University, aged 17 – 19 years, unmarried, without children, and without a

diagnosed eating disorder, were recruited from introductory level courses. The sample

(n ¼ 535; 345 females, 190 males; mean age 18 years, SD ^ 0.4) was representative of

the university’s incoming freshman classes (Auburn University Office of Institutional

Research and Assessment 2007 and 2008). Around 62% of the freshmen were from

Alabama; the rest were from other U.S. states. The racial composition was Caucasian

(84%), African-American (7%), Hispanic (3%), Asian (2.5%), and other (3%). The

Institutional Review Board for the Use of Human Subjects in Research approved this

study. Informed consents were obtained from subjects prior to participation.

Study design and measures

Measures were collected from participants at eight different time points over their first 3

years (2007 – 2010) of college, including September, freshman year (Time 1 ¼ 0 months);

December, freshman year (Time 2 ¼ 2.7 months); April, freshman year (Time 3 ¼ 7.4

months); September, sophomore year (Time 4 ¼ 11.8 months); December, sophomore year

(Time 5 ¼ 14.8 months); April, sophomore year (Time 6 ¼ 19.5 months); September,

junior year (Time 7 ¼ 23.9 months); and April, junior year (Time 8 ¼ 31.3 months).

Percent body fat

Bioelectrical impedance analysis (BodyStat, BioVant Systems, Detroit, MI, USA) was

used to measure percent body fat at all eight time points. BodyStat has been validated for

accuracy against other body composition assessment methods (Fuller et al. 1994, Ghosh

et al. 1997, Benton and Swan 2007). Since hydration status affects measurement accuracy,

participants were instructed not to drink caffeine or alcohol or engage in strenuous

exercise for at least 12 h, and not to eat for 2 – 4 h before assessments (National Institutes of

Health 1996). All assessments were conducted between 8 and 11 am.

Weight and height

Weight and height were assessed using a digital scale with an attached height rod

(Healthometer, model 500KL; Pelstar, Bridgeview, IL, USA). The scale’s accuracy was

International Journal of Health Promotion and Education 297

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verified with external weights. Subjects were asked to wear similar lightweight clothing

at assessments and to remove shoes, belts, outerwear jackets/sweaters, and items from

their pockets.

Activity

Physical activity habits were assessed using a questionnaire that included questions from the

National College Health Risk Behavior Survey (Centers for Disease Control and Prevention

1995). These questions included (1) How many days per week do you participate in vigorous

physical activity? (Vigorous activities are those that cause you to sweat and breathe hard.);

(2) How many days per week do you participate in moderate physical activity? (Moderate

activities include activities such as walking or bicycling. Be sure to include walking or

biking to class, if applicable.); (3) How many days per week do you participate in

strengthening exercises? (Strengthening exercises include activities such as push-ups,

sit-ups, and weight lifting.) Immediately following each question regarding vigorous,

moderate, and strengthening exercises, participants were asked the number of minutes per

day spent in each activity. For both moderate and vigorous activity and strength training, the

number of days of subject participation was multiplied by the minutes of participation per

day and divided by 7, to obtain the hours/day spent in each of three activities.

Statistical analysis plan

Linear growth models were fitted, one for change in percent body fat and the other for

change in weight (Singer and Willett 2003). The observed variables for the growth model

were the measures of percent body fat at eight time points (described previously). Time

was centered at September, freshman year and the latent slope regression parameters were

fixed at 0, 2.7, 7.4, 11.8, 14.8, 19.5, 23.9, and 31.3 months from the beginning of freshman

year in September. The latent intercept is interpreted as the mean percent body fat value

when participants began college as freshmen. A positive slope indicates increase in percent

body fat over time, whereas a negative slope indicates decrease in percent body fat.

Predictors for change in percent body fat over time were collected in September, freshman

year (Time 0). The above analysis plan was also used to examine a separate series of

growth models for weight change over the same eight time points and its predictors

(gender, activity, strength training).

Results

Univariate and bivariate analyses

Means, standard deviations, and correlations for the four predictors (gender, moderate

activity, vigorous activity, and strength training) and outcomes (percent body fat: eight

time points; weight: eight time points) are shown in Tables 1 and 2. These variables were

normally distributed and their bivariate relationships with each other were linear. The

average weight, BMI, and percent body fat at Time 1 were 65 kg or 143 lbs (SD ^ 28),

22.6 kg/m
2

(SD ^ 3.5), and 19.6% (SD ^ 7.5), respectively.

All multivariate models were fit with Mplus, which allowed inclusion of respondents

with missing data by using full information maximum likelihood (FIML) estimation

(Little and Rubin 1987, Muthén and Muthén 2003). In FIML estimation with missing data,

observations are sorted into missing data patterns, and each parameter was estimated using

298 S.S. Gropper et al.

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300 S.S. Gropper et al.

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all available data for that particular parameter according to established guidelines (Muthén

and Muthén 2003).

Unconditional models

The first model fit was an unconditional linear growth model (with no predictors other than

time) to determine whether college students demonstrated systematic changes in percent

body fat over the first 3 years of college (eight time points). This linear model fit the data

adequately (x
2
/df ¼ 5; RMSEA ¼ 0.09, p ¼ 0.01; TLI ¼ 0.98), indicated by a x

2
/df ratio

of less than 5, root mean square error of approximate (RMSEA) less than 0.10, and a

Tucker – Lewis Index (TLI) greater than 0.90 (Wheaton et al. 1977). The change in percent

body fat was tested to see whether it was quadratic over time; it was not. At the beginning

of freshman year, students, on average, had 19.6% body fat (b0, Intercept ¼ 19.6,

p , 0.001) and increased an average of 0.06% body fat each month over the next 3 years

(b1, Slope ¼ 0.06, p , 0.001). Figure 1 illustrates the fitted trajectory of a prototypical

college-age student’s percent body fat over this period. On average, a college student has a

small but significant increase in percent body fat over the 3 years.

The second model fit was an unconditional linear growth model (a model with no

predictors other than time) to determine whether college-age students demonstrated

systematic changes in weight over 3 years (eight time points). This linear model fit the data

less well (x
2
/df ¼ 12; RMSEA ¼ 0.14, p , 0.001; TLI ¼ 0.95), but the TLI indicated it

was adequate. Change in body weight was not found to be quadratic over time. At the

beginning of freshman year, students, on average, weighed 65 kg (143 lbs) (b0 ¼ 143.2,

p , 0.001) and increased an average of 0.068 kg (0.15 lbs) each month over the next 3 years

(b1 ¼ 0.15, p , 0.001). Figure 2 illustrating the fitted trajectory of a prototypical college-

age student’s weight over this period shows that, on average, a college student has a small

but significant increase in weight from fall of freshman year until spring of junior year.

Conditional models

Significant variance existed in all of the growth parameters of the unconditional models

that could be predicted by the substantive predictors: gender, moderate and vigorous

Sept Dec

April Sep Dec April Sep April

Month

10
15

20

25

30

%
B

o
d
y

fa
t

Figure 1. Change in percent body fat over the first 3 years of college.

International Journal of Health Promotion and Education 301

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activity, and strength training. To do this, a series of nested hierarchical models was fit

testing whether physical activity (moderate – Step 1, vigorous – Step 2), strength training

(Step 3), gender (Step 4), and their interactions (Step 5) were significant in predicting

change in percent body fat or weight gain over time. Each predictor was retained if the

Dx
2

test indicated it was significant (Singer and Willett 2003). Table 3 presents the

Sept Dec April Sep Dec April Sep April

Month

125

135

145

155

165

175

W
e
ig

h
t

Figure 2. Change in weight over the first 3 years of college.

Table 3. Model fit statistics for all the models fit (N ¼ 535).

Model x
2

df TLI
a

SRMR
b

RMSEA
c

Dx
2

(Ddf)
d

Unconditional
Linear %body fat 167.6 31 0.98 0.06 0.09
Conditional
Moderate activity (Step 1) 175.9 37 0.98 0.05 0.08 11.5**

(2)

Vigorous activity (Step 2) 180.9 43 0.98 0.05 0.08 45.2*** (2)
Strength training (Step 3) 181.5 49 0.98 0.05 0.07 38.5*** (2)
Female (Step 4) 190.4 55 0.98 0.04 0.07 567.5*** (2)
Female £ moderate (Step 5) on
slope and intercept

197.2 68 0.98 0.04 0.05 9.2* (3)

Female £ strength on slope
Unconditional
Linear weight 389.0 31 0.95 0.03 0.15
Conditional
Vigorous activity (Step 1) 395.0 37 0.95 0.02 0.13 18.3*** (2)
Strength training (Step 2) 402.7 43 0.95 0.02 0.12 18.8*** (2)
Female (Step 3) 417.8 49 0.95 0.02 0.11 156.9*** (2)
Female £ strength (Step 4) on
slope and intercept

421.8 55 0.95 0.01 0.11 4.60
,

(2)

,
p , 0.10, *p , 0.05, **p , 0.01, ***p , 0.001.

a
TLI: Tucker – Lewis Index should be greater than 0.90.

b
SRMR: standardized root mean error should be #0.05.

c
RMSEA: root mean square error of approximation should be #0.10.

d
Testing the H0 that the added predictor is not significant in predicting growth. If the Dx

2
per change in degrees

of freedom is significant, then we can reject that H0 and say that the predictor is significant in predicting growth
and we retain it in the fitted model.

302 S.S. Gropper et al.

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models that were fit, their fit statistics, and the Dx
2

statistics that determine retention of the

variable added. Table 4 includes the unstandardized parameter estimates and standard

errors of the mean levels of the growth parameters and effects of the predictors on them for

the final fitted models as well as the amount of variance predicted in the intercepts and

slopes; these results are the ones customarily presented for linear growth models (Singer

and Willett 2003).

As seen in Table 4, the model does quite well in predicting variance in the intercept

of change in percent body fat and fairly well with change in weight. However, it does

not do well in predicting variance in either the slope of change in percent body fat or

weight. The final fitted models do provide very useful information about the trajectories

of change in percent body fat and weight over the first 3 years of college and how that

change is related to physical activity and gender. And, indeed, all of the predictors of

percent body fat and weight are significant in the prediction of these important markers

of the health of undergraduate students (Dx
2

statistics for every model in Table 4 are

significant).

Prototypical plot illustrations of parameter estimates

The effects of the predictors in the final fitted model (Table 4) on the growth parameters

for change in percent of body fat and weight over 3 years of college can best be illustrated

by ‘identifying a prototypical individual distinguished by particular predictor values

(Singer and Willett 2003).’ Meaningful values of the predictors were selected to substitute

into the fitted final model, obtaining the estimated value for the outcome (percent body fat

or weight), and plotting those trajectories, which will give trajectories that would be

typical for individuals in the population with those characteristics. The sample was not

divided into groups to illustrate the findings; the fitted ‘true’ or ‘population’ trajectories of

college students similar to those in our sample are presented. The meaningful values

chosen for the plots of prototypical individuals were 1.5 SDs above and below the mean

for moderate and vigorous activity and strength training. For gender the values of 0 for

male and 1 for female were used.

Table 4. Unstandardized parameter estimates and standard errors (in parentheses) of the final
conditional growth models for percent body fat and weight change in undergraduates over first 3
years of college (N ¼ 535).

Percent body fat Weight

Intercept Slope Intercept Slope

Mean level 12.4*** (0.5) 0.04* (0.01) 161.8*** (2.6) 0.18*** (0.5)
Effect of
Moderate activity 0.1 (0.5) 0.03 (0.02)
Vigorous activity 20.5 (0.4) 0.00 (0.01) 20.6 (1.9) 0.02 (0.04)
Strength training 20.9 (0.9) 0.00 (0.03) 6.2 (5.6) 0.01 (0.11)
Female 12.3*** (0.6) 0.02 (0.02) 229.4*** (2.9) 20.03 (0.06)
Female £ moderate 21.5* (0.7) 20.03 (0.02)
Female £ strength 20.09

,
(0.05) 25.5 (10.0) 20.31

,
(0.17)

R
2
¼ Amount of variance

in the growth parameter
explained by the predictors

59.8% 3.5% 27.2% 2.2%

,
p , 0.10, *p , 0.04, ***p , 0.001.

International Journal of Health Promotion and Education 303

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Overall summary

Percent body fat (%BF)

In the final model for change in percent body fat, on average, young adult males begin lower

than females at the beginning of college (Mean Interceptmale ¼ b0 ¼ 12.4%BF, p , 0.001;

Interceptfemale ¼ 12.4 [Mean Interceptmale] þ 12.3 [bfemale] ¼ 24.7%BF) on percent of

body fat. Males and females then increase or decrease (females) in their percent body fat

over the next 3 years at different rates, depending on the prototypical male or female whose

trajectory we examine (see Table 4). On average, however, for both males and females

significant growth in percent body fat over time does exist (Mean Slope: b1 ¼ 0.04%BF,

p , 0.05). Males who are vigorously exercising on entry to college have lower percent

Sept Dec

ST-strength training

MA-moderate activity

VA-vigorous activity

April Sep Dec April Sep April
Month
10
15
20
25
30
%
B
o
d
y
fa
t

No ST, No MA

No ST, High MA

High ST, High M
A

High ST, No MA

Figure 3. Change in percent body fat for males with no vigorous activity predicted by moderate and
vigorous activities and strength training over the first 3 years of college.

Sept Dec April Sep Dec April Sep April
Month
10
15
20
25
30
%
B
o
d
y

fa
t

No ST, No MA
No ST, High MA
High ST, High M
A
High ST, No MA
ST-strength training
MA-moderate activity
VA-vigorous activity

Figure 4. Change in percent body fat for males with high vigorous activity predicted by moderate
and vigorous activities and strength training over the first 3 years of college.

304 S.S. Gropper et al.

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body fat, on average, than those who engage in no vigorous activity at entry. But, regardless

of their level of vigorous activity at college entrance, males have similar trajectories over

time that only differ slightly by the levels of strength training and moderate activity. Thus,

all the trajectories for change in percent body fat for prototypical males are very similar (see

Figures 3 and 4). The story for female undergraduates is more complex.

On average, vigorous activity for females has an effect, at the entry into college, which is

similar to that for males; those females who engage in high vigorous activity at the

beginning of college, on average, have lower percent body fat at that time. Strength training

for women has a moderating effect on their change in percent body fat (Slope Effect:

bfemale£strength ¼ 20.09%BF, p , 0.10); women who engage in strength training, regardless

of the level of their moderate or vigorous activity, have a decrease in percent body fat over

Sept Dec April Sep Dec April Sep April
Month
10
15
20
25
30
%
B
o
d
y
fa
t
No ST, No MA
No ST, High MA

High ST, High MA

High ST, No MA
ST-strength training
MA-moderate activity

Figure 5. Change in percent body fat for females with no vigorous activity predicted by moderate
and vigorous activities and strength training over the first 3 years of college.

Sept Dec April Sep Dec April Sep April
Month
10
15
20
25
30
%
B
o
d
y
fa
t
No ST, No MA
No ST, High MA

High ST,High MA

High ST, No MA
ST-strength training
MA-moderate activity

Figure 6. Change in percent body fat for females with high vigorous activity predicted by moderate
and vigorous activities and strength training over the first 3 years of college.

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time. Women who do not engage in strength training increase their percent body fat over

time. In addition, women who are already involved in moderate activity at the beginning of

college have lower percent body fat at that time (Intercept Effect: bfemale£moderate ¼ 21.5%-

BF, p , 0.05) (see Figures 5 and 6). For men, neither of these effects exists.

Weight (LB)

On average, males begin at a higher weight than females at the beginning of college (Mean

Interceptmale ¼ b0 ¼ 73.5 kg or 161.8 lbs, p , 0.001; Interceptfemale ¼ 73.5 kg or 161.8

[Mean Interceptmale] 229.4 [bfemale] ¼ 60.18 kg or 132.4 lbs). Males and females then

increase or decrease (females) their weight over the next 3 years at different rates,

Sept Dec April Sep Dec April Sep April
Month
125
135
145
155
165
175
W
e
ig
h
t

No ST, No VA

No ST, High VA

High ST, High V
A

High ST, No VA

ST-strength training
VA-vigorous activity

Figure 7. Change in weight for males predicted by vigorous activity and strength training over the
first 3 years of college.

Sept Dec April Sep Dec April Sep April
Month
125
135
145
155
165
175
W
e
ig
h
t

No ST, No VA
No ST, High VA

High ST, High VA
High ST, No VA

ST-strength training
VA-vigorous activity

Figure 8. Change in weight for females predicted by vigorous activity and strength training over
the first 3 years of college.

306 S.S. Gropper et al.

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depending on the prototypical male or female whose trajectory was examined (see

Table 4). On average, however, both males and females significantly increase their weight

over time (Mean slope: b1 ¼ 0.082 kg or 0.18 lbs, p , 0.001). Few differences exist for

males relating to their strength training or vigorous activity level. The majority of the

differences are at the entry into college. Males who engage in strength training have more

body weight at intercept and across time than males who do not engage in that activity (see

Figure 7). Once again, the story for female undergraduates is more complex. On average,

women who engage in strength training, although similar to women who do not at the

beginning of college, lose weight over time, regardless of their level of vigorous activity

(Slope Effect: bfemale£strength ¼ 2 0.14 kg or 2 0.31 lbs, p , 0.10; Mean Slope:

b1 ¼ 20.08 kg or 0.18 lbs; therefore effect of strength training on change in weight is

0.18mean 2 0.31slope effect ¼ 20.059 kg or 20.13 lbs per month decrease) (see Figure 8).

Discussion

Gains in weight and body fat during the freshman year of college are well documented as

are factors associated with these gains. In the few studies that have examined changes

during the sophomore year, compared to the freshman year, college students were found to

continue to gain weight although body composition changes were more favorable with

males and females gaining less fat and females also gaining more fat-free mass (Hull et al.

2007, Gropper et al. 2011). Unique to the literature is this 3-year study on college students

examining not only weight and body composition changes but also physical activity

habits. These results showed that, on average, males and females exhibited significant

increases in weight and percent body fat. However, for females, participation in strength

training was associated with loss of weight and percent body fat, while females not

engaged in strength training exhibited increased weight and percent body fat over the 3

years. Males, while having similar trajectories for percent body fat change over time,

differed slightly by the levels of strength training and moderate activity; however, males

engaged in strength training gained more weight across time than did those males who did

not engage in that activity.

The reasons for this study’s observed gender differences are not clear, but may relate, in

part, to higher anabolic hormone levels and the longer growth spurt experienced by males

versus females. Such differences promote greater body weight and fat-free mass gains

among males than females as observed in this study. Differences because of participation in

exercise also may have contributed. Males in this sample population were found to have

met vigorous-intensity physical activity and resistance training recommendations

significantly more often than females (Newell 2011), a finding similar to other studies

(Huang et al. 2003, Buckworth and Nigg 2004, Mestek et al. 2008). Additionally, the

observed differences may be associated with initial differences in physical fitness. Mifsud

et al. (2009) found that males entering college with a greater level of physical fitness gained

more fat than those who were less physically fit. Lastly, inter-individual variations in body

composition, especially fat mass accretion, in response to the duration and intensity of

physical activity also may be responsible (Venables et al. 2005, Barwell et al. 2009).

The beneficial changes in body composition observed with participation in strength

training in this study are consistent with the results of other studies that have directly

examined the effects of strength training on body composition. Such studies generally report

that strength training stimulates fat-free mass accretion and decreases fat mass and percent

body fat (Poehlman et al. 2002, Sillanpää et al. 2008, Hanson et al. 2009, Kemmler et al.

2010). Aerobic exercise also promotes reductions in body weight, BMI, and fat mass, and

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gains in fat-free mass; however, the magnitude of the effects typically varies depending

upon exercise intensity, duration, and frequency (Donnelly et al. 2003, Jakicic et al. 2003,

Slentz et al. 2004, Irving et al. 2008, Stasiulis et al. 2010).

The strengths of this study include its relatively large sample size which was followed

for 3 years and its sophisticated statistical analysis. Further, this is one of the first studies

providing an intervention strategy for females to help minimize body weight and fat gains

normally accrued during the college years. However, a limitation to this study is that it was

conducted at a public university, and thus the results may not be applicable to those not

attending a university or those attending private universities. Subject honesty was also

required and activity logs were not kept to verify self-reported activity.

Conclusion

The results of this study underscore the benefits of physical activity, especially strength

training, for college females as a means to reduce or prevent body weight and fat gains

during the first 3 years of college. Given it is this young adult group that has experienced

the greatest increases in overweight/obesity nationally (Mokdad et al. 1999), effective

strategies to minimize these gains may help to reduce the growing obesity epidemic.

Acknowledgements

This research was supported by U.S. Department of Agriculture, Alabama Agricultural Experiment
Station (AAES) projects 013-020 and 07-020, and an AAES Initiative Grant. No financial
disclosures were reported by the authors of this paper.

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310 S.S. Gropper et al.

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J Nutr Hum Health 2018 Volume 2 Issue 113

http://www.alliedacademies.org/nutrition-human-health/Research Article

Introduction
College students’ eating habits and knowledge of nutritional
requirements
College students are at risk for making poor dietary choices that
can cause significant health problems. Brown, O’Connor, and
Savaiano [1] warned the transition to college causes significant
changes in dietary options. Majority of undergraduate students
eat at college dining facilities with limited healthy food options.
Moreover, if students do not attain adequate nutrition daily, a
decrease in academic or physical performance can result. The
purpose of the present quantitative study was to investigate
college students’ eating habits and knowledge of nutritional
requirements for health. Two research questions addressed
were RQ1: What are college students’ eating habits for health?
and, RQ2: What is college students’ knowledge of nutritional
requirements for health?

Despite the strong emphasis on meeting nutritional requirements
every day to achieve optimal health, many college students tend
to care less about or neglect their nutritional requirements. Many
factors come in to play as they transition to college life. Many of
them leave the parental home, adapt to social and environmental
changes, acknowledge new financial responsibilities, build
different social networks, and experience different time
availability [2]. Yet, meeting nutritional requirements remains
important in achieving one’s health. It is very beneficial for
college students to formulize good eating habits that lead them
to obtaining health and optimal function.

Background
As individuals transition from home to college life,
nutritional knowledge becomes more important because food
options change and dietary challenges arise. Traditional on-
campus students use college dining facilities to eat meals.
Students have the liberty of choosing a variety of healthy and
unhealthy food options. O. Brown, noted “…those attending
college can expect to gain 4-9 pounds in the first 2 years of
college”. If college students are unaware of the nutritional
requirements to maintain a healthy body weight, they can
make poor nutritional decisions, which can cause poor weight
management and health problems.

Individuals entering college are beginning to live
independently. Students’ physical activity and eating habits
usually shape or change during these years. Establishing
good eating habits during this time is critical, because these
behaviors often continue through adulthood and can be very
difficult to change once they are established. O. Brown et
al. stated, “overweight college students are more likely to
become overweight adults and are at a higher risk for diet-
related chronic diseases such as cardiovascular disease, type
2 diabetes, some cancers, and hypertension”. Educational
interventions need to be addressed to help college students
meet their daily nutritional requirements.

Review of Literature
Sources of data accessed for review included peer-reviewed
studies mostly from EBSCOhost. All the articles used for this

Background: Inadequate nutrition affects students’ health and academic success. Students
may have proficient knowledge regarding nutritional requirements; however, the transition to
college life gives them more freedom to choose the type and the amount of food they eat. Most
college campuses have dining facilities that provide a variety of food options, which can lead to
establishing either good or bad eating behaviors.

Purpose: The purpose of this study was to examine college students’ eating habits and knowledge
of nutritional requirements for health.

Method: This was a quantitative, cross-sectional study, with a descriptive design.

Results: The students are knowledgeable that consuming fast food, soda, and processed food
are unhealthy and they contain additives. They indicated strong agreement to keep themselves
hydrated and choosing food because of taste preference. Even though majority admitted eating
fresh fruits, a significant number consume processed food such as chips, cookies, and cereal
based on convenience. Smartphone resources, vending machine use, and drinking soda were
their least frequently used habits.

Conclusion: Students have a fair knowledge of nutritional requirements for health; however,
food choices they make are not necessarily healthy. Convenience and taste of food were priority.

Abstract

College students eating habits and knowledge of nutritional requirements.

Sam Abraham*, Brooke R. Noriega, Ju Young Shin
Bethel College School of Nursing, Bethel College, Mishawaka, Indiana, USA

Accepted on January 17th, 2018

Keywords: Nutritional requirements, College students’ health, Eating habits, College students’ knowledge of food.

14

Citation: Abraham S, Noriega Brooke R, Shin JY. College students eating habits and knowledge of nutritional requirements. J Nutr Hum Health.
2018;2(1):13-17

J Nutr Hum Health 2018 Volume 2 Issue 1

study were published from 2012 to 2015. Keywords used to find
relevant research articles are nutritional requirements, college
students’ health, eating habits, and college students’ knowledge
on food.

Nutritional requirements
It is crucial to meet daily nutritional requirements for one’s
body to function properly and to maintain one’s health to the
optimal level. Most nutritional values such as protein, energy,
carbohydrates, fats and most minerals can be obtained by food
sources. However, some individuals take dietary supplements
on a daily basis to ensure their nutrition level. The current
college-aged group should have had education on nutrition
back in elementary school via the Food Pyramid. Also, since
2011, people have had access to MyPlate, which is a visual
representation of nutrition requirements. Each nutrient plays an
important role in establishing health, metabolism, and proper
function of the body [1].

College students’ knowledge
It is inevitable that college students face a new environment for
meal preparation, planning, and eating as they transition to their
college life. Even though many college-aged students are aware
of the importance of meeting nutritional values, their knowledge
and attitude might hinder them from changing their behavior.
Many other factors come in to play in their decision-making,
however, the college students’ knowledge on nutrition does
not always lead to healthy food choices. Stockton and Baker
discovered college students do understand that consuming fast
food can lead to disease; however, their knowledge was not a
factor that influenced their food choices.

Interestingly, Stockton and Baker found that college students
did not think the harm from fast food was related to calories but
rather harmful chemicals and additives. The main concern the
students had was not the number of calories they were taking,
but the additives to their food. Also, the male college students
consumed more fast food than female college students. Students
thought that hamburgers were not harmful to their health [3].

Eating habits
Various poor eating habits have been noted among college groups
in many recent studies. Brown et al. conducted an experiment
in which they implemented interventions on vending machine
sales on a university campus. They stated that many college
students tended to select food according to convenience, taste,
time, and price rather than nutritional values. Many college
students tended to choose quick and tasty options, which were
usually available through vending machines [4].

In a study on correlation between perceived parenting style and
the eating practices of college freshmen, Barneset al. surveyed
264 college freshmen between the ages of 18 and 20 years.
Through this survey, they found little evidence of the effect of
parenting style on eating habits in a college group. About 44%
reported that they had the same eating practice as they did before
coming to college. The researchers concluded that parenting
style did not affect the eating habits of college students as much
as it does in the eating habit of children and youth [5].

Weight management
In a study to determine weight management knowledge in first-
year college students, Das and Evens surveyed 45 first-year
students who were recruited using a cross-sectional method.
They found that the reasons for weight gain among first-year
college students were the changes in the social and physical
environments, such as dietary intake including alcohol use,
physical activity, stress, and sleep [2].

Challenges
Das and Evans discovered that men and women reported
different types of barriers for maintaining health. Men’s
perceived barriers were limited access to healthier foods and
gym supplies, not enough time for physical activity, large
work overload, and lack of support from family and friends.
On the other hand, women reported lack of time to exercise
or eat healthy, inability to manage time, stress from different
environments, and failure to prioritize weight management as
their perceived barriers [2].

Health promotion and healthy behaviors
Healthy behaviors, such as physical activity, are often
compromised among college students. Miller et al. conducted
a survey on the effectiveness of a health promotion smartphone
application for college students. The researchers noted that
many undergraduate students had less than optimal health
nor participated in healthy behaviors on a regular basis. They
discovered that college students believed the application was
beneficial and helpful in that it promoted healthy behaviors and
raised awareness [6].

Many colleges in the United States provide nutrition-related
courses for their students. Lockwood and Wohl studied the
effectiveness of a lifetime wellness course on changing students’
global self-efficacy, physical self-efficacy, and wellness
behavior. The participants reported that the 15-week course had
a significant impact on their behavior changes. More physical
activity and exercise were initiated. Also, the students’ food
choices were more nutritious and healthier after the course [7].

Boucher et al. investigated an intervention to promote the
consumption of fruits and vegetables among young adults in
junior college. Consuming fruits and vegetables is one of the
important healthy behaviors to achieve one’s optimal physical
function. The researchers found that the intervention increased
the number of college students eating at least five servings
of fruits and vegetables. They emphasized the importance of
developing interventions tailored to college students to promote
healthy behaviors [8].

Similar results were shown in another recent study. Texting
has become one of the common communication tools in
college-aged population. Brown et al. used text messaging in
their study to provide nutrition education and inform better
food choices in college students. They found out that college
students had better understanding of nutrition and increased
their fruit and vegetable consumption. Furthermore, text
messaging appeared to be an effective education tool to
enhance nutrition related knowledge and encourage healthy
behaviors in college students [1].

Abraham/Noriega/Shin

15 J Nutr Hum Health 2018 Volume 2 Issue 1

Conclusion drawn from literature review
The researchers in the reviewed studies predominantly supported
and demonstrated evidence for nutritional requirements, college
student’s eating habits, knowledge, weight management,
barriers, healthy behaviors, and education. Many research-
based articles support that there is a need to investigate college
students’ eating habits and knowledge of nutritional requirements
for health. There is a lack of knowledge in nutritional values
among college students. Some students develop poor eating
habits and tend to select food according to convenience, taste,
time, and price available to them rather than their nutritional
values [4]. Therefore, many college students struggle managing
their weight as they gain more weight each year. Some barriers
to healthy behaviors they reported are lack of time, lack of
healthy food options, and lack of social support [2]. Various
interventions such as text messaging, Smartphone application,
and college courses appeared to be very beneficial in promoting
healthy behaviors such as exercise and eating healthy food.

Methodology
This was a quantitative, cross-sectional study with a descriptive
design. An approval from the Institutional Review Board (IRB)
was attained before surveying college students. All participants
were 18 years of age or older and currently attending college.
The sample size was 121 college students. A convenient
sampling method was used, because access was easy and all
individuals were encouraged to participate.

The survey instrument was developed after a thorough review
of the literature. The survey tool was thoroughly reviewed
by the nursing faculty and peers to obtain face-validity. The
demographics helped obtain information about the specific
aggregate. The statements related to the research question and
were directed to obtain information regarding college student’s
eating habits and knowledge of nutritional requirements for
health. The survey included five demographic and 20 Likert-
type survey statements. Statements 1-12 addressed RQ1 using
the frequency scale never (1), rarely (2), every day (3), and more
than once a day (4). Statements 13-20 addressed RQ2 using the
agreement scale.

Permission was obtained to conduct the surveys outside the
college Dining Commons. Participants had the freedom to
answer survey questions before or after their meals. A table
was set up with survey sheets and informed consents, as well
as candy to help give individuals the initiative to participate
in the study. Each individual willing to participate was given
explanation regarding the informed consent and requested to
sign it before completing the survey. The participants’ names
were not written on the survey document, therefore, keeping
information confidential. Participants received a copy of the
informed consent. Completed surveys and signed informed
consents were collected separately in envelopes to ensure
confidentiality.

Results
After collecting the required surveys, demographics and survey
statements were analyzed using the quantitative measures.
A total of 125 students took the survey. However, 4 survey

responses were incomplete, and therefore, withdrawn from the
data. The demographic results are displayed in percentages and
frequencies in Table 1.

Demographic characteristics
The sample size of this study was 121 participants with 61%
female students. All the participants were from ages 18-25.
All 4 years in college were well represented with 20 or more
from each year. Varieties of majors were represented in the
study, among which, the business-related majors, health
science, education, and sports majors dominated the group.
For weight perception, 64% reported they perceive they were
at normal weight. About one-third indicated they were from
6 to 50+ pounds overweight, whereas 7% perceived they
were underweight (Table 1).

The various factors that contribute to college students’ eating
habits are displayed in Table 2. The habit with the highest
frequency was “I keep myself hydrated with water (M=3.41,
SD=0.73). Other frequent habits included taste preference
(M=3.23, SD=0.57), eating fresh fruits (M=3.04, SD=0.70),
and consumption of processed food (M=2.72, SD=0.66). The
statement with which participants showed the least frequent
habits was related to Smartphone resource use to find the right
food to eat (M=1.51, SD=0.78) (Table 2).

The factors that contribute to college students’ knowledge
of nutritional requirements are displayed in Table 3. The
statement with which the participants agreed most strongly
was that fast food contains unhealthy additives (M=3.56,
SD=0.60). Othe agreements included unhealthiness of fast

Variable f %

Gender
Male 47 39%

Female 74 61%

Age range
18-21 102 84%
22-25 19 16%

Class

Freshmen 29 24%
Sophomore 32 26%

Junior 40 33%
Senior 20 17%

Majors

Business 23 19%
Health Science 19 16%

Education 15 12%
Sports 12 10%

Sociology 9 7%
Arts and Design 8 7%

Christian Religion 8 7%
Criminal Justice 6 5%
Sign Language 6 5%

Other 15 12%

Weight Category

Underweight 8 7%
Normal 78 64%

Overweight 6-10 lbs 17 14%
Overweight 11-20 lbs 8 7%
Overweight 21-30 lbs 6 5%
Overweight 31-50 lbs 0 0%
Overweight 51+ lbs 4 3%

Note: (N=121)

Table 1. Descriptive statistics for participant demographics and
background.

16

Citation: Abraham S, Noriega Brooke R, Shin JY. College students eating habits and knowledge of nutritional requirements. J Nutr Hum Health.
2018;2(1):13-17
J Nutr Hum Health 2018 Volume 2 Issue 1

food (M=3.30, SD=0.63), drinking soda (M=3.28, SD=0.70),
and eating processed food (M=3.15, SD=0.64). The statement
with which participants showed the least level of agreement
was that exercise is more important than the type of food they
eat (M=2.40, SD=0.77). The participants had a fair agreement
that smartphones help to find the right food (M=2.75, SD=0.65).
An important note is that even the least agreed statements
collectively had a mean of 2.5 or greater on a 4-point scale
indicating that most students have knowledge in nutritional
requirements for health (Table 3).

Discussion
Majority of the students have formed the habit of staying well
hydrated, which is a good practice. Choosing food according to
taste preference seems to be the hallmark of college students.
An interesting finding was that a large number of students
reported they ate fresh fruits frequently, while they also
consumed processed food. This contradictory health practice
may be because of the limited food options available on campus.
Students consumed a lot of processed food; however, they tried
to maintain their nutrition level by choosing fresh fruit options
available to them. Even though participants reported that
Smartphone can be used to find resources for healthy eating,
very few used the source. Vending machine use, and drinking
soda were their least frequently used habits, which indicates a
positive health direction.

Majority of the participants (85.1%) reported that they rarely
consume fast food. They also strongly agreed (61.2%) that
fast food contains unhealthy additives. This shows students
acknowledge the unhealthiness of fast food and avoid consuming
it on a regular basis. However, they claim to choose food based
on taste, convenience, and food that is processed indicating
unhealthy eating choices even though it is not necessarily from
a fast food restaurant.

The relationship between the habit and knowledge of drinking
soda was positive. A vast majority of participants (81.8%)
reported that they either rarely or never drink soda. Only a
small number of participants (18.2%) reported that they drink
soda every day or more than once a day. When asked about
their knowledge about unhealthiness of drinking soda, many
participants (87.6%) either agreed or strongly agreed. There is
a positive correlation between their eating habit and knowledge
indicating that students acknowledge the harmfulness and their
behaviors reflect their knowledge.

There was a negative correlation between eating habit and
knowledge in consuming processed food. More than half of the
participants (65.3%) indicated that they consume processed food
either every day or more than once a day. However, 89.3% either
agree or strongly agree with the statement that it is unhealthy to eat
processed food. This finding reveals that many students understand
that processed food is unhealthy; however, they continue consume
a large amount of processed food every day.

Brown et al. discovered that college students often select food
from vending machines and according to convenience, taste,
time, and price instead of nutritional values. On the contrary,
in the current study, college students did not frequently use
vending machines on campus even though they reported
choosing food based on convenience. Stockton and Baker had
warned that college students’ knowledge of nutrition does not
always correlate with their eating habits because they consumed
large amount of fast food even though they acknowledge its
unhealthiness. In contrast, in the current study, responses
from college students demonstrated low amount of fast food
consumption, yet high level of knowledge of its harmfulness to
their health. However, there was a high level of processed food
consumption despite their knowledge in its negative effect on
their health. Therefore, it can be concluded that high level of
knowledge does not necessarily mean healthy eating behaviors.

Limitations
A limitation to this study was the small sample size of 121
participants in about 2000-student population. The college
cafeteria is the main dining place on campus where most
traditional undergraduate students who have on-campus meal
plan eat every day. Non-traditional or commuters were under-
represented. Another limitation was that the responses could have
been biased because students were already going or coming from
eating at a buffet style location. Since this survey was gathering
information regarding nutritional requirements, some students’
responses might have differed compared to those students who
would cook at home or in their dorm rooms. Generalization could
be limited because this study was conducted in one Christian
college in the mid-western United States.

Variable M SD
I keep myself hydrated with water. 3.41 0.73

I choose food according to taste preference. 3.23 0.57
I eat fresh fruits. 3.04 0.7

I consume processed food (such as chips,
cookies, cereal)

2.72 0.66

I choose food according to convenience. 2.66 0.61
I eat vegetable salads. 2.55 0.76

I tend to eat unhealthy food when I am
stressed.

2.36 0.73

I tend to eat unhealthy food when I am happy. 2.3 0.65
I consume fast food. 2.07 0.41

I drink soda (regular or diet). 1.85 0.81
I eat food items purchased from a vending

machine.
1.54 0.58

I use the Smartphone to find the right food to
eat.

1.51 0.78

Note: (N=121). Items were rated on a 4-point Liker-type scale ranging from 1
(Never) to 4 (More than once a day), so higher means indicate higher levels of
frequency.

Table 2. What are college students eating habits for health?

Variable M SD
Fast food contains unhealthy additives. 3.56 0.6

It is unhealthy to eat fast food. 3.3 0.63
Drinking soda is unhealthy. 3.28 0.7

It is unhealthy to eat processed food. 3.15 0.64
Fresh salads are healthier than meat products. 3.15 0.64
Excess calories in food are harmful to health. 3.06 0.64

Smartphones help to find the right food. 2.75 0.65
Exercise is more important than the type of food. 2.4 0.77

Note: (N=121). Items were rated on 4-point Liker-type scale ranging from 1
(Strongly Disagree) to 4 (Strongly Agree), so higher means show greater level
of agreement.

Table 3. College students’ knowledge of nutritional requirements.

Abraham/Noriega/Shin

17 J Nutr Hum Health 2018 Volume 2 Issue 1

Conclusion and Recommendations
About one-third of college student participants reported that
they were from 6 to 50+ pounds of overweight. Even though
students were knowledgeable about food containing additives,
perils of fast food, unhealthiness of processed food and soda,
they intermingled processed and fast food based on taste
preference and convenience with fruits and salads. Although
many participants showed good eating habits and adequate
knowledge of nutritional requirements, the need was discovered
to investigate further about different factors that contribute to
their eating habits and their knowledge. In addition, it would
be beneficial to research what health education practices can
be placed to help guide their eating habits and promote overall
health. Providing health, nutrition related courses and using
technology as a means to educate the new generation could be
effective and used for future learning.

References
1. Brown O, O’Connor L, Savaiano D. Mobile MyPlate:

A pilot study using text messaging to provide nutrition
education and promote better dietary choices in college
students. J Am Coll Health. 2017;62:320-27.

2. Das B, Evans E. Understanding weight management

perceptions in first-year college students using the health
belief model. J Am Coll Health. 2014;62:488-97.

3. Stockton S, Baker D. College students’ perceptions of fast
food restaurant menu items on health. Am J Health Educ.
2013;44:74-80.

4. Brown M, Flint M, Fuqua J. The effects of a nutrition
education intervention on vending machine sales on a
university campus. J Am Coll Health. 2014;62:512-16.

5. Barnes S, Brown K, McDermott R, et al. Perceived
parenting style and the eating practices of college freshmen.
Am J Health Educ. 2012;43:8-17.

6. Miller T, Chandler L, Mouttapa M. A needs assessment,
development, and formative evaluation of a health
promotion smartphone application for college students. Am
J Health Educ. 2015;46:207-15.

7. Lockwood P, Wohl R. The impact of a 15-week lifetime
wellness course on behavior change and self-efficacy in
college students. Coll Stud J. 2012;46:628-41.

8. Boucher D, Gagné C, Côté F. Effect of an intervention
mapping approach to promote the consumption of fruits and
vegetables among young adults in junior college: A quasi-
experimental study. Psychology Health. 2015;30:1306-25.

*Correspondence to:
Sam Abraham, RN, MS, DHA
Assistant Professor of Nursing
Bethel College School of Nursing
Mishawaka
Indiana
USA
Tel: 269-240-7467
E-mail: abrahams383@att.net

JOURNAL OF AMERICAN COLLEGE HEALTH, VOL. 58, NO. 5

Positive Changes in Perceptions and Selections
of Healthful Foods by College Students After a
Short-Term Point-of-Selection Intervention at a

Dining Hall

Sharon Peterson, PhD, RD; Diana Poovey Duncan, MS, RD; Dawn Bloyd Null,
MS, RD; Sara Long Roth, PhD, RD; Lynn Gill, MS, RD

Abstract. Objective: Determine the effects of a short-term, multi-
faceted, point-of-selection intervention on college students’ percep-
tions and selection of 10 targeted healthful foods in a university din-
ing hall and changes in their self-reported overall eating behaviors.
Participants: 104 college students, (age 18–23) completed pre-I
and post-I surveys. Methods: Pre-survey collected at dining hall in
April 2007, followed by 3-week intervention then post-survey col-
lected via email. Healthy choice indicators, large signs, table tents,
flyers and colorful photographs with “benefit-based messages” pro-
moted targeted foods. Response rate to both surveys was 38%.
Results: Significantly more participants reported that healthful
choices were clearly identified in the dining hall after the inter-
vention. Over 20% of participants reported becoming more aware
of healthful food choices in the dining hall after the intervention.
Significant increases in self-reported intake were reported for cot-
tage cheese and low-fat salad dressing, with a trend toward increased
consumption of fresh fruit. Seven of the 14 assessed eating behaviors
had significant changes in the desired direction. Increased aware-
ness of healthful foods was the top reason for self-reported changes
in overall eating behaviors. Conclusion: Short-term, multi-faceted,
point-of-selection marketing of healthful foods in university dining
halls may be beneficial for improving college students’ perceptions
and selections of targeted healthful foods in the dining hall and may
improve overall eating behaviors of college students.

Keywords: food choices, college students, point-of-selection,
marketing, eating behaviors

Dr Peterson, Ms Bloyd Null, and Dr Long Roth are with the
Department of Animal Science, Food and Nutrition at the Southern
Illinois University Carbondale in Carbondale, Illinois. Ms Poovey
Duncan is with the Department of Human Environmental Studies
at Southeast Missouri State University in Cape Girardeau, Mis-
souri. Ms Gill is with the Student Health Center at Southern Illinois
University Carbondale, Illinois.

Copyright © 2010 Taylor & Francis Group, LLC

Entering college brings about numerous life changes,with many decisions involving food choice. Many oftoday’s college dining halls offer a large variety of
food items presented in a self-service format, similar to an
“all-you-can-eat” restaurant. Thus, it is well-established that
college students need guidance on making healthful food
choices.1–10 They typically consume high intakes of total fat,
saturated fat, and cholesterol, and low fiber intake.6 Few col-
lege students eat 5 or more servings of fruits and vegetables
per day.3 Frequently, there is a disproportionate consumption
of higher-fat foods at the expense of more healthful foods.5

Healthy People 2010 aims to increase the proportion of col-
lege students who receive information on dietary practices,
nutrition, and disease prevention.11 To meet these national
objectives, action must be taken to increase education re-
lated to food choice, nutrition, and disease prevention within
the college setting.

Social marketing may be an effective environmental strat-
egy to promote nutrition knowledge and awareness to college
students. Whereas traditional marketing aims to satisfy con-
sumer needs and wants, social marketing seeks to change the
target group’s attitudes and/or behaviors, including thoughts,
actions, or values. Social marketing attempts to influence the
acceptability of an idea in a population. The goal of social
marketing when applied to human health is to “change a spe-
cific behavior by influencing voluntary health behaviors.”12

Nutrition labeling at the point of food selection is considered
a type of social marketing, and has been used to promote
healthful eating in various settings.13

When college students are provided with relevant informa-
tion regarding benefits of healthy eating that are specifically
catered to their values, while given clear indication as to

425

Peterson et al

which foods are considered “healthful” selections, it is as-
sumed that students will become more aware of healthy food
choices available to them. In fact, a recent study concluded
first-year college students can be positively influenced by nu-
trition information at point-of-selection.14 Two thirds of re-
spondents reported they were aware of nutrition labels posted
within the college dining hall, whereas one third reported
using them to guide their food choices. A social marketing
campaign designed to increase fruit intake among community
college students reported increased awareness and increased
fruit intake over a 10-week intervention period.15

Buscher et al reported improved food selections and rec-
ommend use of “benefit-based messages” in college dining
hall point-of-purchase interventions.16 Benefit-based mes-
sages are positive phrases that address specific motivations
of the target population such as taste, body leanness, having
more energy, or overall health. Effectiveness was attributed to
colorful graphics and a “professional look.” Because college
students have grown up with flashy, high-quality marketing,
it is essential that “marketing” of healthy food selections on a
college campus compares to promotions they are accustomed
to in other settings. Strategic placement of benefit-based mes-
sages was also a key to success.16

Successful interventions must be attention-grabbing and
visible to all persons entering the college dining hall. Thus, it
appears routine exposure to nutrition information at point-of-
selection during college years could have a positive impact
on nutrition knowledge and eating behaviors of college stu-
dents.8,14–17 However, current research on successful point-
of-selection interventions for college students in dining halls
is very limited.

A multifaceted approach using a variety of attention-
grabbing approaches holds great promise for increasing in-
take of healthful food choices among college students. There-
fore, the purpose of this study was to evaluate the impact of
benefit-based messages with a multifaceted approach (use of
table tents, posters, flyers, and point-of-selection symbols)
on college student’s perceptions and selections of healthful
foods at a university dining hall. Our hypothesis was that
participating students would report increased selection of the
targeted foods and improved overall eating behaviors follow-
ing the intervention.

METHODS
This study was conducted at a large Midwestern public

university with a total student population of 19,878. The uni-
versity’s undergraduate population is diverse, with 17.5% of
undergraduates from self-reported minority groups (African
American, Hispanic, Native American, Asian American,
and/or Pacific Islander). The study design for this pilot study
was a preintervention (pre-I)/postintervention (post-I) writ-
ten survey, with a 3-week intervention at a campus dining
hall. The dining hall serves approximately 900 people at any
given lunch/dinner period. Students purchase a prepaid meal
plan and choose foods from a variety of food stations, thus

cost of the individual food items would not be a factor in this
study.

Survey Development
To measure student perceptions and selections of healthful

food choices in the dining hall, a short survey was developed.
Content validity was assessed by 4 Registered Dietitians who
evaluated the survey for appropriate content and appropriate-
ness for this university setting. Content validity was further
assisted in a small pilot study (n = 29) with college stu-
dents who indicated the items were appropriate and easy to
understand.

The survey (see Appendix A) evaluated student’s percep-
tions of availability of healthful foods in the targeted dining
hall while also inquiring about their weekly dining frequency.
A short food frequency questionnaire assessed student’s self-
reported intake of the 10 targeted healthful foods that were
available in the dining hall at lunch and dinner every day (see
Table 3 for intake questionnaire items).

Recruitment
This research was approved by the university’s Human

Subjects Committee (Institutional Review Board). Due to
poor response rate (∼10%) received from this population by
previous researchers who only used e-mail contact to collect
data,18 it was decided pre-I surveys would be distributed in
person. Pre-I surveys were distributed for 3 days by a team
of researchers onsite (once during lunch hours and 3 times
during dinner hours). Every person who entered the cafeteria
was invited to participate in exchange for a chance to win a
mountain bike or Ipod shuffle. By the 3rd day of recruitment,
it appeared that most of the students who were interested in
participating had been approached by the research team. E-
mail addresses were collected upon completion of the pre-I
survey and participants were informed that they would re-
ceive a post-I survey via their e-mail account. Participants
were required to be between the ages of 18 and 23, have a
meal plan with residence hall dining, and consume at least
3 meals per week at the targeted dining hall to ensure they
were adequately exposed to the intervention.

Intervention
The 3-week intervention started 1 month after the pre-I

surveys were collected to avoid overlapping activities during
National Nutrition Month in March. Due to the end of the
semester in early May, 3 weeks was the maximum time
frame that was possible for this intervention. Although
this time frame was short, previous studies of college
students have reported positive behavior changes following
short-term interventions for alcohol abuse19,20 and for
improved attitudes toward older adults.21

Healthy choice indicators at point-of-selection were used
to increase perceptions of availability of healthy food choices
and increase selection of the 10 targeted healthy foods. A
colorful logo was created which featured a slogan describ-
ing the targeted foods as “The Right Stuff!” All promotional

426 JOURNAL OF AMERICAN COLLEGE HEALTH

Perceptions and Selections of Healthful Foods

materials featured “The Right Stuff!” logo to tie all compo-
nents together. Ten different 12′′ × 18′′ signs hung above the
10 targeted food items on the tray line. These colorful signs
used humor and benefit-based messages to draw attention to
the 10 targeted food items. In addition, a 4′′ × 5′′ card with
the same healthy choice indicator was placed directly in front
of the targeted foods on the tray line and corresponding table
tents were placed on dining tables. Flyers were distributed
throughout the dining hall and a large sign was placed at
the entrance to encourage selection of the 10 targeted foods
identified by “The Right Stuff !” healthy choice indicators.
All signage directly corresponded with the 10 targeted foods
on the survey.

Procedures
After the 3-week intervention, post-I surveys were dis-

tributed via e-mail, and were accepted for 10 days following
the distribution. Researchers sent a reminder e-mail prior
to the post-I survey distribution, and also sent 3 reminder
e-mails to participants following distribution of the post-I
survey. Upon return of the completed post-I survey, re-
searchers matched it with participant’s pre-I survey and then
she/he was entered into the drawing.

Data Analysis
Data were analyzed using the Statistical Package for So-

cial Sciences (SPSS) version 15.0 for Windows, Student Ver-
sion (2006; SPSS, Chicago, IL). Descriptive statistics were
used to describe demographic information and Wilcoxon’s
matched pairs test was used to measure participant responses
when comparing pre-I surveys to the post-I surveys. The
Wilcoxon test is the nonparametric model of the paired t
test that evaluates differences between matched pairs/groups
and determines the direction of difference between matched
pairs.22

RESULTS
Two-hundred eighty-eight students completed the pre-I

survey, which represents approximately 1/3 of those who
entered the dining hall on a given day. However, 16 were
excluded due to age disqualification. Of these 272 students,
107 returned the post-I survey. Three post-I surveys were
excluded due to infrequent weekly meals at the dining hall,
leaving the total post-I sample size at 104. Therefore, re-
sponse rate for the post-I survey was 38%, which is over 3 1

2
times the response rate received when sampling the same
population the previous year.18 This improved return rate
may be attributed to face-to-face contact during the pre-I
survey distribution, numerous reminder e-mails sent out to
participants, and/or the chance to win a prize.

Demographics of student participants are presented in
Table 1. Compared to other racial/ethnic backgrounds, the
overall response rate from African American students de-
clined by half for the post assessments. Of the 114 partici-
pants who responded to the open-ended question about per-
ceived barriers that could prevent them from selecting healthy

TABLE 1. Demographics of College Students
Surveyed in a University Dining Hall Setting
Preintervention (pre-I) and Postintervention
(post-I)

Total pre-I survey

Both
pre-I/post-I

survey
(n = 272) (n = 104)

Demographics Mean SD Mean SD

Age (years) 19.58 1.365 19.97 1.882
Gender (%)

Male 63.8 56.7
Female 36.2 43.3

Race/ethnicity (%)
Caucasian/White 60.5 68.3
African American 26.6 13.5
Hispanic American 3.0 4.8
Asian American 4.4 8.7
Native American 1.8 2.9
Other 3.7 1.9

Class rank (%)
Freshman 55.9 53.8
Sophomore 18.6 16.4
Junior 15.9 17.3
Senior 9.6 12.5

Note. Pre-I/Post-I = participants who completed both the preinter-
vention (pre-I) and postintervention (post-I) surveys.

foods in the dining hall, approximately 24% mentioned “lim-
ited selection of healthy choices” (data not shown).

For the question: “Are healthy food choices easily identi-
fied at the dining hall (such as labels, symbols, or signs)?”
44 participants’ responses significantly changed in a positive
direction after the intervention (Table 2). Of the 104 post-I
survey respondents, 83 indicated that healthy choices were
easily identified at least some of the time. Of those, 52 par-
ticipants successfully acknowledged some component of the
intervention and 20 specifically identified materials promot-
ing healthy foods as the “The Right Stuff!” A question on
the post-I survey asked: “If your eating habits have changed

TABLE 2. Frequency of Self-Reported Changes
for College Students’ Belief That Healthy Foods
are Easily Identified (Symbols, Signs or Labels) at
the College Dining Hall

Positive
Change in
Response

Negative
Change in
Response

No
Change in
Response

Observed 44 21 38
Expected 32.5 32.5 —
Residual +11.5 −11.5 —

Note. n = 104. χ 2(df = 1) = 8.162, p = .004∗∗
∗∗indicates that the correlation was significant at the .01 level.

VOL 58, MARCH/APRIL 2010 427

Peterson et al

TABLE 3. Frequency of College Students’
Self-Reported Changes in Various Eating Habits
Following a Point-of-Selection Dining Hall
Intervention (n = 104)

No Yes

“I’m eating more fast food” (n = 104)
Observed 99.0 5.0
Expected 77.0 27.0
Residual 22.0 −22.0
χ 2(df = 4) = 24.276, p = 0.000

“I’m eating more junk food” (n = 104)
Observed 95.0 9.0
Expected 67.0 37.0
Residual 28.0 −28.0
χ 2(df = 4) = 32.938, p = 0.000

“I’m drinking more soft drinks” (n = 104)
Observed 98.0 6.0
Expected 81.0 23.0
Residual 17.0 −17.0
χ 2(df = 1) = 16.111, p = 0.000

“I’m eating larger portions” (n = 104)
Observed 96.0 8.0
Expected 63.0 41.0
Residual 33.0 33.0
χ 2(df = 1) = 43.847, p = 0.000

“I’m eating one large meal a day” (n = 104)
Observed 81.0 23.0
Expected 50.8 53.3
Residual 30.2 −30.2
χ 2(df = 4) = 35.126, p = 0.000

“I’m eating less junk food” (n = 104)
Observed 76.0 28.0
Expected 86.0 18.0
Residual −10.0 10.0
χ 2(df = 4) = 6.732, p = 0.009

“I’m eating smaller portions” (n = 104)
Observed 88.0 16.0
Expected 99.0 5.0
Residual −11.0 11.0
χ 2(df = 4) = 25.498, p = 0.000

since April 1st, what is the reason for this change?” The most
common reason was: “I am now more aware of healthy food
choices in the dining hall.” Thus, 22% of participants became
more aware of healthy food choices in the dining hall, which
led to a self-reported change in eating habits.

Seven of the 10 assessed eating behaviors had significant
changes in the desired direction (Table 3). For example, a sig-
nificant decrease in “I’m eating more fast food,” was found in
the post-I survey responses. Significantly fewer participants
also reported: “I’m eating more junk food,” as well as “I’m
drinking more soft-drinks.” Eleven more participants in the
post-I survey indicated they were eating smaller portions of
food. These eating habits were combined into either “posi-

tive” or “negative” responses to determine whether responses
had changed overall for the “positive” or the “negative” after
the intervention. The post-I responses revealed significantly
fewer participants were reporting “negative” changes in their
eating behaviors than were previously seen (Table 4).

To assess whether the intervention increased self-reported
selection of targeted healthful foods, the food frequency por-
tion of the survey was analyzed. Self-reported use of cot-
tage cheese and low-fat salad dressing increased significantly
(Table 5) in the post-I surveys. Although it was predicted that
students would significantly increase selection of deli sand-
wiches, the opposite results were found. The analysis of fresh
fruit did not reach significance; however, a trend was seen
towards more frequent consumption of fresh fruit. No signif-
icant results were found for self-reported changes in intake
of steamed vegetables, chicken breast, tossed salad, or skim
milk.

COMMENT
It appears that specific food choices in the dining hall and

overall eating behaviors of college students can be improved
through a short-term, multifaceted, benefit-based, point-of
selection intervention in a dining hall setting. “Limited avail-
ability of healthy foods within the dining hall” was the most
common reason why these students said they did not choose
healthful foods in the dining hall on the pre-I survey. Stu-
dents’ top reason for why their eating habits changed since
the beginning of the intervention was, “I am now more aware
of healthy food choices within the dining hall.” Significantly
more students in the post-I survey indicated that healthy
choices were easily identified in the dining hall. Half of the
post-I participants described some component of the inter-
vention, whereas 19% specifically described materials pro-
moting healthy foods as the “The Right Stuff!” These results
are comparable to previous studies.14,16

Increased awareness of healthy foods appears to have
prompted some students to report improved overall eating
behaviors. Students reported significantly fewer “negative”
eating behaviors and significantly greater “positive” ones af-
ter the intervention. There was also a significant increase
in self-reported consumption of cottage cheese and low-fat
salad dressing, with a trend for increased reports of consump-
tion of fresh fruit after the intervention. Drawing attention to
nutrition and health in a dining hall surrounded with abundant
food choices appears to have positively impacted students’
food behaviors. This is consistent with previous research that
has shown healthy choice indication at point-of-selection to
be moderately effective in improving selection of targeted
foods.14,16

Just as businesses, products, and concepts must be mar-
keted in today’s world to succeed, nutrition and health mes-
sages must receive the same marketing vigor if expected to
gain attention in a college population. The success of this
intervention can be attributed to the abundance of marketing
materials placed within the dining hall, including colorful
graphics, sign placement, and relevant messages specifically

428 JOURNAL OF AMERICAN COLLEGE HEALTH

Perceptions and Selections of Healthful Foods

TABLE 4. Self-Reported Changes in College Students’ “Positive” and “Negative” Eating Behaviors From Pre-I
to Post-I Surveys (n = 104)

Mean SD t df Significance (2-tailed)

Pre-I “negative” eating behaviors versus post-I
“negative” behaviors

1.106 2.038 5.533 103 .000∗∗∗

Pre-I “positive” eating behaviors versus post-I
“positive” behaviors

−.279 1.830 −1.554 103 .123

Post-I “negative” behaviors versus post-I
“positive” behaviors

−.202 1.375 −1.498 103 .137

Note. Pre-I = preintervention survey response. Post-I = postintervention survey response.
∗∗∗The correlation was significant at the.001 level.
“Positive” behaviors included eating smaller portions, eating more vegetables, drinking fewer soft drinks, eating less “junk” food, eating less fast
food, eating 5 to 6 small meals/day, and skipping fewer meals.
“Negative” eating behaviors included eating less fruits and vegetables, eating larger portions, drinking more soft drinks, eating more fast food, eating
more “junk” food, eating one large meal/day, and skipping more meals.

designed to appeal to college students. These messages pro-
moted taste, satiety, body leanness, energy value, and overall
health as reasons to select our targeted foods and improve
eating habits.

These results may not be indicative of the full impact of
the intervention. Participants could have experienced non-

TABLE 5. Frequency of Self-Reported Changes
for College Students From Pre-I to Post-I for
Targeted Healthful Foods at a College Dining Hall

Positive
change in
response

Negative
change in
response

No change
in response

Cottage cheese
Observed 26 8 67
Expected 17 17 —
Residual 9 −9 —
n = 104. χ 2(df = 4) = 10.253, p = 0.001 (the correlation
was significant at the p ≤ .001 level)

Low-fat salad dressing
Observed 34 19 45
Expected 26.5 26.5 —
Residual 7.5 −7.5 —
n = 104. χ 2(df = 1) = 3.876, p = 0.049 (the correlation
was significant at the p ≤ .05 level)

Deli sandwiches
Observed 27 51 22
Expected 39 39 —
Residual −12 12 —
n = 104. χ 2(df = 1) = 6.959, p = 0.008 (the correlation
was significant at the p ≤ .01 level)

Fresh fruit
Observed 34.0 23.0 46
Expected 28.5 28.5 —
Residual 5.5 −5.5 —
n = 103. χ 2(df = 1) = 3.65, p = 0.056

measurable changes such as a more positive attitude towards
healthful eating to move them closer to an actual behav-
ior change. For students who were already making healthful
food choices, the intervention may have helped to maintain
or reinforce their healthy behaviors. A review by Glanz et al
proposed that providing nutrition information to target pop-
ulations has benefits even if there is no measurable behavior
change.23 It is well known that knowledge and awareness of
healthy foods does not always translate into healthful eat-
ing behavior. Awareness created by the marketing materials
may have encouraged students to select at least some of the
targeted foods and direct them towards improved eating be-
haviors.

Although the celebration of National Nutrition Month
(NNM) in the dining hall did not occur simultaneously with
this intervention, it is possible that participant responses
could have been impacted by having NNM between the
pre-I and post-I surveys. Specifically, increased awareness
of healthful food choices in the dining hall might have be
related to NNM; however, it is important to note the NNM
efforts made no attempt to identify healthy food choices in
the dining hall.

A significant limitation of this study was the short length
of the actual intervention period (3 weeks). Ideally, the inter-
vention would last longer and would occur at the beginning
of fall semester to avoid National Nutrition Month in March.
However, the intense nature of our graduate program is not
conducive to data collection during the fall semester. Another
weakness was the inability to collect food production data to
substantiate self-reported food frequency intake.

Use of 24-hour dietary recalls may have better captured the
impact of the intervention and would have strengthened the
study design. In addition, survey questions may have been
misunderstood. Some students may have difficulty remem-
bering how many times a week they consumed the targeted
foods. Because the pre-I survey was given to participants
in the dining hall, they may have been distracted or rushed,
leading them to misreport their answers. Moreover, the sea-
son in which this research took place may have affected the

VOL 58, MARCH/APRIL 2010 429

Peterson et al

results because early spring is a time when some students
may attempt to eat healthier in anticipation of summer.

Future research efforts should incorporate 24-hour dietary
recalls into the study design and use a control group with
randomly selected participants. Further evaluation of gender
and racial/ethnic differences may provide insight into why
some college students respond and others do not to point-
of-selection promotions. Future studies should provide for a
longer intervention period and also attempt to measure “un-
intended consequences” resulting from the intervention. For
example, in a college dining hall population, when certain
foods are promoted as “healthy,” researchers should con-
sider how the rest of the food choices may be perceived by
participants. Those who are prone to categorizing foods as
“good” or “bad” (such as those with disordered eating pat-
terns) might use the point-of-purchase labels as an excuse
to restrict/avoid certain foods or the labels may trigger a
set-back in those recovering from an eating disorder.

Development of a close partnership with food service
staff would increase the likelihood of adding more health-
ful choices to the cycle menu. Improving variety, taste, and
visual appearance of healthy food choices within the dining
hall could improve results of point-of-selection interventions.
By incorporating taste testing of the “new” healthy choices,
their selection may be further increased. Rearranging the
food service area to highlight healthful choices could also
improve healthful food selection. Placement of visually ap-
pealing, healthful choices near the entrance and relocating
less healthful options away from the entrance may have ben-
eficial results. Seeing healthful items first may entice students
to choose these foods instead of less healthful items.

ACKNOWLEDGMENTS
This research was supported by the Illinois Soybean As-

sociation, and was completed in partial fulfillment of the re-
quirements for a Masters Degree in Nutrition from Southern
Illinois University Carbondale.

NOTE
For comments and further information, address correspon-

dence to Sharon Peterson, PhD, RD, Assistant Professor,
Department of Animal Science, Food and Nutrition, 875
S. Normal Avenue, Mail code 4317, Southern Illinois Uni-
versity Carbondale, Carbondale, IL 62901, USA (e-mail:
sharonp@siu.edu).

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430 JOURNAL OF AMERICAN COLLEGE HEALTH

Perceptions and Selections of Healthful Foods

Appendix A. Survey Instrument

1. How many days per week do you typically eat LUNCH at this dining hall?

(please circle one) 0 1 2 3 4 5 6 7

2. How many days per week do you typically eat DINNER at this dining hall?

(please circle one) 0 1 2 3 4 5 6 7

3. Please put an “X” by the THREE MOST IMPORTANT factors that influence your food choices when eating at this dining hall?

� Appearance of food � Convenience � Calorie content
� Taste � Nutrient content/health � Food cravings
� Safety of food � Hunger level � Other?

4. Do you think this dining hall offers a variety of healthy food choices for Lunch and/or Dinner?

� Yes � No � Sometimes

5. Is it possible for you to select healthy food choices at this dining hall for Lunch and/or Dinner?

� Yes � No � Sometimes
6. What are your barriers, if any, to selecting healthy food choices at this dining hall?

7. Are healthy food choices easily identified at this dining hall (such as labels, symbols, or signs)?

� Yes � No � Sometimes

8. When you eat at this dining hall, how often do you choose each of the following?
(Put an “X” by the answer that best suits each food choice)

Food Item Almost Daily 2–4 X’s/week Once/wk Once/month Never
Deli Sandwiches
Grilled chicken breast
Tossed salad
Low-fat salad dressing
Steamed vegetables
Fresh fruits
Yogurt
Fat-free (skim) milk
Cottage cheese
Whole grain bread

Let’s talk about YOU: How old are you? Gender? � Male � Female
What is your class rank? � Freshman � Sophomore � Junior � Senior
Which of the following best describes your ethnicity/racial background?

� African American/Black � Hispanic American � Other?
� Asian American/Asian � Caucasian/White
� Native American � Pacific Islander/Native Hawaiian

VOL 58, MARCH/APRIL 2010 431

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Copyright c© 2019 Wolters Kluwer Health, Inc. All rights reserved.

DIETARY INTERVENTIONS AND EDUCATION

Promoting Wellness on College
Campuses
Identifying and Addressing the
Wellness Needs of College Students

Jenna Christianson, BS;
Kendra Kattelmann, PhD, RDN, LN;
Kristin Riggsbee, PhD; Lauren Moret, PhD;
Melissa J. Vilaro, PhD, MPH;
Melissa D. Olfert, DrPH, MS, RDN;
Anne E.W. Mathews, PhD, RDN;
Makenzie Barr, PhD, RDN, LD; Sarah Colby, PhD, RD

The objective of this study was to explore the health-related knowledge, beliefs, and attitudes of
college students. An online and in-class course was offered at 4 universities. As part of the course,
focus groups consisting of 102 students met virtually for 5 sessions to answer questions aimed
at addressing students’ wellness issues. Students indicated that college campuses need to offer
more services centered on student health while addressing the barriers to student wellness overall.
Students also indicated that universities better met their needs when communicating about various
health-related services and facilities that were available to students. Key words: community-
based participatory research, emerging adulthood, first-year college students, health education,
obesity prevention, stress management, wellness

Author Affiliations: Health and Nutritional Science
Department, South Dakota State University,
Brookings (Ms Christianson and Dr Kattelmann);
Nutrition Department (Ms Riggsbee and Dr Colby)
and Educational Psychology and Counseling
Department (Dr Moret), University of Tennessee at
Knoxville; Food Science and Human Nutrition
Department, University of Florida, Gainesville (Drs
Vilaro and Mathews); and Division of Animal and
Nutritional Sciences (Dr Olfert) and Davis College of
Agriculture, Natural Resources, and Design (Dr
Barr), West Virginia University, Morgantown.

Jenna Christianson received a grant from the South
Dakota State University Honors Grand Challenges
Undergraduate Research Mentorship. Funding for the
research was provided by the Agriculture and Food Re-
search Initiative grant no. 2014-67001-21851 from the
USDA National Institute of Food and Agriculture.

The authors have disclosed that they have no signif-
icant relationships with, or financial interest in, any
commercial companies pertaining to this article.

P ROMOTING a healthy transition intoadulthood during college may be a useful
approach to increasing wellness and health
for many emerging adults; thus, universities
have a unique opportunity to influence stu-
dents’ overall wellness.1 The World Health Or-
ganization defines and divides wellness into
2 basic concerns for an individual: (1) re-
alizing one’s fullest potential and (2) fulfill-
ing one’s role expectations.2 Both concerns
are dependent on multiple factors including

Correspondence: Kendra Kattelmann, PhD, RDN, LN,
Health and Nutritional Science Department, South
Dakota State University, Box 2275A, SWG 425, Brook-
ings, SD 57006 (Kendra.Kattelmann@sdstate.edu).

DOI: 10.1097/TIN.0000000000000169

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125

mailto:Kendra.Kattelmann@sdstate.edu

126 TOPIC IN CLINICAL NUTRITION/APRIL–JUNE 2019

physical, psychological, social, spiritual, and
economic characteristics.3-5 Wellness pro-
grams on college campuses need to provide
support for all of these factors to facilitate life-
long wellness characteristics among emerging
adults.3-7 A focus on the physical components
of wellness programs on college campuses of-
ten includes nutrition, physical activity, and
stress management.8

Nutrition is one of the key components
of wellness, but many college students
(83%) lack knowledge regarding nutritional
guidelines1 and fall short of the US Dietary
Guidelines for fruit and vegetable intake (con-
suming only 2 or less servings of fruits and veg-
etables per day).9,10 Research has found sup-
portive evidence to link adequate student fruit
and vegetable intake to an overall increase in
both health and quality of life.11 However,
even if students are aware of the perceived
health benefits of adequate intakes of the
fruits, vegetables, and fiber, most (∼84% of
males and females aged 19+ years) fall short
of meeting fruit, vegetable, and fiber recom-
mendations. Protein (53.7% males and 24.6%
females), refined grains (71% males and fe-
males), and solid fats and added sugars (81%
males and 87% females) are overconsumed.12

This overconsumption of the aforementioned
foods combined with low fruit, vegetable, and
fiber intakes contributes to major health chal-
lenges including obesity, cardiovascular dis-
ease, high blood pressure, type 2 diabetes,
some cancers, and poor bone health.13 How-
ever, these specific, negative health outcomes
may be preventable with adequate nutrition
and physical activity.13

Physical activity is another component of
wellness known for benefiting college stu-
dents’ overall health.14 However, similar to
nutrition recommendations, many (70%) stu-
dents fall short of exercising on a daily basis.15

In fact, most college students report exercis-
ing less than 3 days during the week, and
students older than 20 years report partici-
pating in less physical activity than those 19
years and younger.16 In the United States, less
than half of all college students are achieving
the recommended levels of physical activity
(150 minutes of moderate-intensity aerobic

physical activity throughout the week).16,17

Also, with the increased use of cell phones
and other mobile technology in the college-
aged population, increasingly more college
students are engaging in sedentary behav-
iors than in physical activity.18 Balancing time
to relax, socialize, and complete academic
responsibilities is reported as one of the
main barriers for students looking to achieve
and maintain a healthy weight.18 In addition,
a study addressing weight-related behavior
change reported that students were looking
for greater support from others to find the mo-
tivation to exercise.19 This observation sug-
gests that college campus wellness programs
may have a prominent role in promoting and
implementing recommended physical activity
habits.20

Another element of wellness is stress man-
agement. Besides nutrition, stress is another
area of wellness where college students lack
knowledge.8 Fifty-two percent of college stu-
dents have high levels of stress.8 High stres-
sors are negatively correlated to both col-
lege students’ body mass index and quality
of life.7,11 Students associate these high levels
of stress with the instability that occurs while
adjusting to living life on one’s own during
emerging adulthood.8 Freedom from parental
control can sometimes cause increased stres-
sors, but it also presents an opportunity for
students to explore their own values and
identity.8 With the right tools and knowledge,
students may be able to avoid unnecessary
stresses associated with emerging adulthood.
They could use their college years to grow
and learn as individuals to embrace a healthy
lifestyle during the college experience.8

Wellness habits that students develop
in college extend far beyond their years at
university.21 Investigations could address
how multiple concurrent health behaviors
affect student health.8 Understanding the
wellness needs and issues of college students
will allow more effective development of col-
lege campus wellness programs to promote
wellness and reduce future health compli-
cations for students.6,7,19,22 The objective of
this study was to explore the health-related
knowledge, beliefs, and attitudes of a group

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Promoting Wellness on College Campuses 127

of college students engaged in the Get
Fruved research project to better understand
what approaches college wellness programs
should consider when developing wellness
promotion interventions. Get Fruved is a
social marketing and environmental change
intervention (SMEI) that uses community-
based participatory research (CBPR) methods
to develop and implement an intervention to
prevent unwanted weight gain among older
adolescents.

METHODS

Research design and recruitment

CBPR is a type of research approach that
actively involves the community being stud-
ied in the research process.23 Using these par-
ticipatory methods, college students were re-
cruited and trained as student investigators
and became equal partners with faculty re-
searchers to identify priorities and develop
interventions to promote health on college
campuses.

Student researchers (n = 102; 88 women
and 14 men) varying in age (freshmen to
seniors) were recruited at 4 land-grant uni-
versities and divided into the respective fo-
cus groups: University of Florida (n = 29;
25 women and 4 men); South Dakota State
University (n = 8; 6 women and 2 men);
University of Tennessee (n = 35; 31 women
and 4 men); and West Virginia University
(n = 30; 26 women and 4 men). Recruitment
occurred through posters, announcements in
classes across campus, table events, and by
word of mouth. The recruits were then in-
vited to enroll in a hybrid (using both online
and in-class activities) class to develop an SMEI
for college campuses and become the student
researchers for this project. One SMEI hybrid
class was offered at each of the 4 intervention
universities.

After informed consent was obtained as
part of the SMEI class, student researchers
participated in 5 class discussions that took
place in the classroom and were audio and
video recorded. In these discussions, the stu-
dent researchers were provided with a list
of questions on 5 major discussion topics

(definition of health, changing behavior, so-
cial marketing, changes on campus, and tai-
loring messages) (see Table 1) and were asked
to discuss their thoughts within the focus
group. The questions for each class topic
were developed and reviewed for content
validity24 by a multistate panel of nutrition ed-
ucation researchers with experience in ado-
lescent and college obesity prevention. The
questions were refined and reviewed for face
validity25 by graduate and undergraduate col-
lege students of various health majors, in-
cluding kinesiology, public health, and nu-
trition before being posed to the student
researchers enrolled in the SMEI class. As
a tenant of CBPR,26-33 with all student re-
searchers participating in the process, a dif-
ferent SMEI student researcher at each univer-
sity volunteered each week to facilitate their
focus group. Each week the 4 leaders (one
from each university) wrote summaries that
reflected the discussion in the focus. Each
summary was then reviewed with the focus
group for feedback and consensus. Student
leaders amended the group’s summary to re-
flect most accurately the discussions and en-
sure inclusion of all student researchers’ per-
spectives. Summaries from each focus group
were then posted on a shared Web site, allow-
ing the SMEI student researchers across all 4
campuses to access the summary documents.
There were 20 focus group discussions in to-
tal (5 from each university) that occurred with
20 summarizations posted to inform future in-
tervention design activities that would take
place later in the semester. All procedures
for the University of Tennessee, South Dakota
State University, and West Virginia University
were reviewed and approved by the institu-
tional review board at the University of Ten-
nessee, and all procedures for the University
of Florida were reviewed and approved by the
institutional review board at the University of
Florida.

Data analysis

Each group summary (n = 20) served as the
main data source for analyses, and 1 student
researcher (not involved in the SMEI course)
conducted a review of the summaries in

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128 TOPIC IN CLINICAL NUTRITION/APRIL–JUNE 2019

Table 1. College Student Health Questions Used in Class Discussions

Questions

Discussion 1: Definition of Healthy
What do you think it means to be healthy?
Why do you think it is important to be healthy?
What things make it harder to be healthy?
What things make it easier to be healthy?
What do you think needs to change to better this campus?
Discussion 2: Changing Behavior
Does “behavior” or “changing behavior” matter in a social marketing campaign?
Does “behavior” or “changing behavior” matter in the environment college students function in?
You are a college student, what works when “changing behavior”?
How do we work to engage those who have not thought about their environment or

communication through social marketing?
What events may really get the attention of your peers?
Discussion 3: Social Marketing
What did you like about those (social marketing) campaigns?
What did you dislike about those (social marketing) campaigns?
Discussion 4: Changes on Campus
What are some things that need to be changed on your campus to improve student quality of life?
What is the first step in making these (life-improving) changes?
Besides nutrition, what are some areas of health that we can target?
How will you promote health in these (target) areas?
What are some strategies that you can use to change behavior at the individual, community, and

policy levels?
Discussion 5: Most Important Message and Tailoring
What do you think is the most important thing to communicate to college students as part of Fruved?
How will you tailor messaging to students?

comparison with recorded videos of focus
group discussions to become familiar with the
context as well as content. This review pro-
cess led to the development of a codebook
to be used for thematic analysis of the class
summaries.

The same student researcher and 1 fac-
ulty researcher independently read each
summary and identified themes using the
codebook. The codes from the 2 indepen-
dent researchers were compared for coding
to reach consensus. Discrepancies between
codes were adjusted with a third researcher,
eliminating any conflicts.

RESULTS

A total of 7 themes emerged from the
student researchers’ class discussions. Four
major themes defined and described compo-

nents of college student wellness: what it
means to be healthy, importance of health,
barriers of health, and facilitators of health
(see Table 2). Three major themes emerged,
with suggestions on what needs to be done
on campuses for programing and implement-
ing behavior change: alterations that col-
lege campus wellness programs need to ad-
dress, approaches for college campus well-
ness programs to change student behavior,
and approaches for college campus wellness
programs to grab students’ attention (see
Table 3).

What it means to be healthy

The students in the focus groups reported
both positive and negative definitions of
what it means to be healthy. The students
stated that being healthy includes a positive
perspective on both physical and mental

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Promoting Wellness on College Campuses 129

Table 2. Themes and Respective Quotes From College Student Health Discussion Questions

What It Means to be Healthy Student Quotes

1. Exercising regularly
2. Consuming a variety of nutrients and

healthy foods
3. Limiting time spent sedentary
4. Sleeping an adequate amount of time
5. Practicing self-confidence
6. Having the ability to manage stress and

emotions
7. Having knowledge of wellness issues
8. Reading food labels
9. Being consistent with health behaviors

10. Not abusing substances
11. Challenging to become healthy
12. Challenging to maintain being healthy

“Don’t be sedentary.”
“Vary your diet to get a variety of nutrients.”
“People that are able to manage their stress are

healthier than those who have poor stress
management skills.”

“(Healthy) can have a negative connotation
because it can be viewed as too hard.”

Importance of Health

1. Physical benefits
2. Mental benefits
3. Financial benefits
4. Environmental benefits

“Exercising and eating well is not only food for
your body but for your mind as well.”

“Your health affects other aspects of your life.”
“Being healthy can save money in the long run.”
“Helps the environment.”

Barriers of Health

1. Unhealthy foods on campuses (fast food,
convenience food, and chocolate)

2. Misinformation from media outputs,
advertisements, food marketing
campaigns, and peers

3. Stress
4. Time in students’ schedules
5. Knowledge of health topics
6. Finances
7. Lack of healthy food on campuses
8. Lack of sidewalks on campuses
9. Peer pressure

10. Taste
11. Lack of cooking skills
12. Past experiences with unhealthy habits
13. Conflicting priorities

“Misleading advertising claiming unhealthy things
are good for you.”

“Stress and being busy with school (makes it
harder to be healthy).”

“Very few middle school or high school
curriculums require nutrition courses, stress
management courses, or other courses teaching
overall health and wellness. Because of this,
many incoming college students lack adequate
knowledge on how to be healthy.”

Facilitators of Health

1. Having healthy friends
2. Having access to gyms
3. Having access to healthy foods
4. Having access to health education
5. Implementing a daily routine
6. Finding physical and mental balance

“Finding peers that are interested in being
healthy.”

“Becoming educated on nutrition and health.”
“Balance makes life easier.”

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130 TOPIC IN CLINICAL NUTRITION/APRIL–JUNE 2019

Table 3. Themes and Respective Quotes From Discussion Questions About Changing College
Students’ Behaviors

Changes College Campus Wellness
Programs Need to Address Student Quotes

1. Increase healthy food options
2. Increase health education
3. Promote truthful marketing
4. Promote health-related student

organizations
5. Increase encouragement through support

systems
6. Increase the number of sidewalks
7. Decrease the cost of healthy foods
8. Decrease the amount of greasy foods served

“The food court needs to be seriously looked
at, reviewed, and revised to include
healthier foods and to make it so the healthy
choice becomes the easy choice.”

“Market foods truthfully.”
“Vegetables in dining hall need to be cooked

with less grease.”

Approaches for College Campus Wellness
Programs to Change Student Behavior

1. Provide health education on all health
topics

2. Provide students with a support system
3. Accommodate students’ schedules

“Having knowledge of nutrition and its
benefits can inspire a change.”

“When the people around you make smart
choices, it becomes a lot easier to make
smart choices yourself.”

“(Offer) healthier options at later times of
operation.”

Approaches for College Campus Wellness
Programs to Grab College Students’
Attention

1. Host guest speakers
2. Support wellness oriented clubs
3. Play games
4. Offer cooking classes
5. Hold farmers’ markets
6. Offer free events
7. Allow sponsorship
8. Advertise events
9. Utilize technology

10. Offer themed events
11. Keep events convenient for students

“Move farmers’ market to pedestrian
walkway.”

“Having local businesses come down with
food/coupons/etc.”

“Free food, free anything.”
“(Have a) famous person come in.”
“Ask for sponsorship from Traders

Joes/Lucky’s.”
“Schedule events toward when students would

be more available. Convenience is key.”

health. The characteristics of being physically
healthy were reported as exercising regularly,
limiting amount of time being sedentary, in-
corporating a variety of nutrients and healthy
foods into one’s diet, and receiving adequate
sleep. The characteristics of being mentally
healthy were reported as having the ability
to manage stress, practicing self-confidence,
being happy with oneself, and being able to
properly address other emotions. Not only

was being healthy looked at from a physical
and mental standpoint but the students also
described being healthy in terms of being
holistic. For example, the idea of “balance”
was mentioned to explain that multiple areas
of health must complement each other and
work together to allow a student to reach
optimal health. The concept of being knowl-
edgeable regarding health was reported as a
characteristic of being healthy. The students

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Promoting Wellness on College Campuses 131

in the focus groups shared that “knowing
how to read nutrition labels and make
sustainable choices influence one’s ability
to be healthy.” However, being healthy was
not just discussed in a positive context. The
class discussions reported that being healthy
can be viewed as being too hard to obtain.
Students specifically mentioned that being
healthy can even sometimes be considered a
“chore.”

Importance of health

The students described the importance of
being healthy in the context of physical, men-
tal, financial, and environmental benefits. Stu-
dents noted that taking care of one’s physical
health prevents disease and improves one’s
quality of life. The mental importance of being
healthy was noted frequently by the students.
The students reported the mental benefits
as including having higher levels of concen-
tration, a healthier body image, improved
self-confidence, and an increase in academic
performance. The students also pointed out
that the importance of being healthy extends
beyond one’s own personal benefits. For ex-
ample, one can save money and economically
benefit society by avoiding potential medical
costs due to an unhealthy lifestyle. The stu-
dents in this study reported, “A lot of students
say it is expensive to eat healthy; however,
the medical bills resulting from future doc-
tors’ visits will be far more expensive than
eating healthy food now.” The benefits of be-
ing healthy were even reported as extending
as far as improving one’s own community
and environment. The students reported that
healthy individuals can influence the com-
munity for the greater good by helping the
environment, stimulating the local economy,
and purchasing from local farmers.

Barriers of health

The students listed the barriers of being
healthy in terms of either a lack or an excess
of certain resources present in one’s daily life.
Lack of time, knowledge, finances, healthy
food, and sidewalks were listed as the barriers
to college students’ health. When discussing
lack of knowledge, the students reported col-

lege curricula not offering enough classes in
both stress management and nutrition. Also,
the participants reported that college students
often do not have enough knowledge on gro-
cery shopping. Along with a lack of knowl-
edge, a lack of having proper finances to sup-
port healthy living was noted. In particular,
the students expressed the high expense of
college meal plans being required by college
campuses was a barrier to being healthy. In
addition to the expensive meal plan being a
barrier to college students’ health, the lack of
access to healthy foods on campus made it
harder for college students to be healthy. The
cost of healthy food items on campus was re-
ported by the students as being a barrier to
health on campus. In particular, the students
noted that “a salad costs (on average) $7.00
versus a bag of chips and soda, which costs
(on average) $3.50.”

The excess of unhealthy foods available on
campus including fast food restaurants and
convenience foods was listed as contribut-
ing to an environment that did not support
healthful behavior. The students also noted
chocolate as a specific unhealthy food when
consumed in excess. In addition, an excess of
general misinformation from media outputs,
advertisements, food marketing campaigns,
and peers were listed as negative attributes of
college campuses when trying to promote stu-
dent health. The last barrier that students re-
ported hindering college students’ health per-
tains to the excessive amounts of stress that
accompanies a college student’s class sched-
ule. For example, the students communicated
having excess stress related to struggles with
scheduling their time between classes, home-
work, social events, eating, and working out.
The students noted that being healthy was
stressful because finding the time to do health-
ful activities was viewed as a chore in a stu-
dent’s everyday schedule.

Facilitators of health

The facilitators to improving college stu-
dents’ wellness involved increasing support
through peers who have healthy habits, places
to be active, healthy food, and health ed-
ucation. The student researchers reported

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132 TOPIC IN CLINICAL NUTRITION/APRIL–JUNE 2019

that students should both have a routine and
find balance in their life to reach optimal
wellness. The student researchers also re-
ported that these actions would not only help
students reach small, obtainable goals but also
help them learn how to satisfy potentially un-
healthy cravings in healthier ways.

Changes college campus wellness
programs need to address

The students reported multiple changes
that college campus wellness programs could
make to address the health of college stu-
dents on campus. The students reported that
campus wellness programs need to increase
supports for various healthy components of
everyday life while also decreasing the barri-
ers that inhibit student health. Specific rec-
ommendations with descriptions for college
campus wellness programs are summarized
as follows:

Healthy food options: Students requested that
campuses need to offer healthier food op-
tions, ensuring that these options are avail-
able later in the evening and on weekends.
The students reported a need to increase
the number of salad bars located on cam-
pus along with the healthy food options
offered at various, existing salad bars. One
example included the need for healthier
salad dressing choices. Convenience stores
were also listed as places where college
campuses could increase accessibility to
healthier food options. In particular, stu-
dents expressed a need for more fresh fruits
and requested that college campuses offer
healthier juices along with adding smooth-
ies to menus. Not only did the students
suggest increasing the amount of healthy
food on campus but also the access to nu-
trition information for each item/dish being
offered. They proposed the use of a ranking
system to inform students of how their food
choices rank in nutrition healthfulness. The
students recommended that campuses pro-
vide better placement for healthier food
options than those that are unhealthy to
increase visibility and opportunities so

that students would opt for the healthier
choices. The students reported that the
price of healthy food options needed to be
reduced to increase consumption. For ex-
ample, the price of fruits was noted for be-
ing a high expense on college campuses by
the students. The amount of high-fat, fried
foods offered on campus was also noted as
an item that needed to be decreased to re-
duce the consumption of unhealthy fat in
students’ diets. In particular, the students
wanted colleges to reduce the amount of
fats used when cooking vegetable dishes.

Education: The students listed a variety of
health topics for which they desired more
education. Topics included mental health,
sleep, stress, time management, substance
abuse prevention (alcohol and drugs),
smoking, sexual health (sexually transmit-
ted disease prevention), food safety, phys-
ical activity, and nutrition. The students
specifically reported that campuses should
offer healthy food demonstrations along
with offering recipe booklets that students
could use.

Truthful marketing: Students reported a
need for truthful marketing regarding the
food products offered on campuses. Fur-
thermore, they requested that all messages
must be clearly represented to avoid any
potential misinterpretations.

Student organizations: The students desired
an increase in the number of student orga-
nizations focused on health. This was re-
ported as a strategy to increase the number
of peers learning and sharing about well-
ness.

Support systems: The students reported the
importance of mental health on overall
health. The students specifically identi-
fied receiving personal encouragement to
be a beneficial tool that could be used
to improve students’ quality of life on
campus.

Sidewalks: Sidewalks were listed by the
student researchers as locations that could
be used to increase and provide opportu-
nities for students to engage in physical
activity

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Promoting Wellness on College Campuses 133

Approaches for college campus wellness
programs to change student behavior

The students identified not only what
changes college campus wellness programs
needed to make but also how such changes
should be implemented. The recommenda-
tions on approaches for college campus well-
ness programs to change student behavior are
summarized as follows:

Education: The student researchers men-
tioned that education on both nutrition and
physical activities must be provided for stu-
dents to change their behaviors. For ex-
ample, students noted that detailed guides
(workout and cooking) were identified as
a potential tool that universities could pro-
vide to students when imparting educa-
tion on a particular health topic. In addi-
tion, students reported that they need to
be taught about the rewards of changing
their behaviors before a change is actually
made.

Support systems: Students could be encour-
aged to make a lifestyle change by having a
support system of friends and/or peers who
want to see their fellow students succeed
at reaching lifestyle goals. In particular, the
students in the class discussions reported
that changing behavior is easier when stu-
dents are motivated by friends and/or peers
who value their own health and wellness
needs. In addition, the students wanted the
support of the student government and fac-
ulty. They stated that they were interested
in knowing the services provided by din-
ing representatives, dietitians, and student
organizations focusing on health and well-
ness issues.

Scheduling: The participants identified that
students must understand the importance
of scheduling time to maintain a healthy
lifestyle. For example, students must be
committed to scheduling “out” the re-
quired amount of time to eat each meal
throughout the day. Students must also un-
derstand how to create health goals that
are both small and attainable to encourage
lifestyle changes.

Approaches for wellness programs to
grab college students’ attention

The student researchers also identified cer-
tain aspects of the college environment and
events on campuses more likely to grab
college students’ attention. The recommen-
dations for college campus wellness pro-
grams to grab students’ attention are as
follows:

Guest speakers: Students reported being
drawn to celebrity speakers.

Club events: Students expressed an interest in
clubs promoting wellness needs and issues.

Intramural activities: Students shared an in-
terest in engaging in “games” through in-
tramural sporting events such as kickball,
powder puff football, and ultimate Fris-
bee. The students wanted to interact with
the school’s mascot during these gaming
events.

Cooking classes: Students were interested in
learning various cooking skills to prepare
healthier meals and suggested having more
cooking classes on campuses.

Farmers’ markets: Students recommended
having farmers’ markets on campus to in-
crease access to “fresher produce.”

Cool factor: Students noted the following
characteristics of an event for their abil-
ity to grab students’ attention: free, con-
venient, themed, sponsored, advertised,
and high-tech. For example, the students
quoted that “schedule events towards
when students would be more available.
Convenience is key.” Finally, the students
reported that universities must specifically
tailor to the needs of this target audi-
ence to help facilitate healthy lifestyle
changes.

In addition, the students suggested that col-
lege campuses must show both the benefits
of choosing a healthy lifestyle and the conse-
quences of engaging in unhealthy behaviors.
Sharing such information with students who
are uninformed or unaware may enable the
use of the college environment as a tool to
initiate health changes.

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134 TOPIC IN CLINICAL NUTRITION/APRIL–JUNE 2019

DISCUSSION

The objective of this study was to explore
the health-related knowledge, beliefs, and
attitudes of a group of college students to
better understand what approaches college
wellness programs should consider when
developing wellness interventions. In partic-
ular, the results from this study focused on
the components of college campus wellness
programs including nutrition, physical activ-
ity, and stress management. The results from
this study showed that the focus groups’ atti-
tudes, knowledge, and beliefs toward overall
health and wellness involve multiple facets
of everyday life. Finding balance between
all areas of wellness was mentioned by the
student researchers in this study. They further
confirmed that they want universities to set
priorities that address more than 1 area of
wellness at a time (nutrition, stress, physical
activity, etc). The student researchers also
suggested that college campus wellness pro-
grams should continue to facilitate providing
physical, emotional, and social supports for
students and their wellness goals.

The student researchers reported that col-
lege campuses should offer more education
on both the wellness areas of nutrition and
stress. These results from the class discussions
are similar to the results reported by LaFoun-
taine and colleagues8 in the Wellness Evalua-
tion of Lifestyle (WEL). Results from the WEL
indicated that college students reported be-
ing the least knowledgeable on the wellness
areas of stress management and nutrition.8

The student researchers from this project rec-
ognized the need for college wellness pro-
grams to offer more nutrition and stress ed-
ucation in order to address students’ lack
of knowledge related to wellness. The stu-
dent researchers also recommended that if
college campus wellness programs provide
wellness information to students, the well-
ness information should not be confusing or
misleading. For example, misleading adver-
tising that claims unhealthy foods are good
for students fosters a confusing environment

that hinders students’ abilities to make edu-
cated decisions regarding one’s health. These
requests suggest the importance of college
campus wellness programs providing clear
wellness education and helpful resources for
students.

In addition to educating students on
physical wellness and behavioral change, col-
lege wellness programs need to understand
how to best approach changing students’
behaviors.8 Similar to the results reported
by Walsh and colleagues34 and Plotnikoff
and colleagues,6 the students in this study
suggested the importance of students having
strong support systems when changing
behavior. Also, peers play an influential role
in affecting students’ decisions. Nelson and
team19 reported that during emerging adult-
hood, a student’s support system changes
from his or her parents to peers. This change
in support showcases how peers become the
most important source of change in students’
behaviors related to both alcohol and exer-
cise while parents remain the most influential
over their children’s health beliefs through
modeling.35

College campus wellness programs must
recognize the physical wellness needs of a
peer-focused support system and offer mul-
tiple wellness opportunities for students to
engage with each other. Further confirming
the findings of Plotnikoff and colleagues,6 not
only did the students report that they wanted
to find support from their peers but also from
the campus student governance and faculty.
This suggestion indicates that college campus
wellness programs can utilize prominent cam-
pus figures as support for student behavioral
change. The studies referenced earlier, in ad-
dition to results from this study, showcase the
importance of college campus wellness pro-
grams offering students a strong social sup-
port system when looking to alter students’
wellness behaviors.8,11,19,35

Students in this study and previous stud-
ies reported the need to find additional
ways to cope with stress.8,11 In particu-
lar, the students reported the need to learn

Copyright © 2019 Wolters Kluwer Health, Inc. Unauthorized reproduction of this article is prohibited.

Promoting Wellness on College Campuses 135

how to handle stress associated with bal-
ancing multiple facets of a busy student
schedule. These same findings were also
reported by Greaney and colleagues18 in
a study analyzing college students’ barriers
and enablers for healthful weight manage-
ment. Greaney and colleagues18 reported that
the time constraints involved with the busy
schedules of students were noted as being
a main barrier for achieving and maintain-
ing a healthy weight. Not only have stud-
ies noted high stress associated with busy
student schedules as a barrier to healthful
weight management8,11 but also a study by
Kattelmann and colleagues36 aimed at design-
ing an online intervention to prevent weight
gain in young adults indicated that stress and
time management played key roles in affect-
ing a student’s overall quality of life and well-
ness. These results further suggest that college
campus wellness programs must provide re-
sources and social support for multiple facets
of students’ busy schedules.

The strengths of this study included a stu-
dent body from 4 land-grant universities dis-
persed geographically throughout the United
States working together using a CBPR ap-
proach. The CBPR process gathered college
students interested in wellness together to
develop a health intervention for college stu-
dents. The class discussion questions and de-
sign were created and refined by a group of
nutrition researchers specializing in college
obesity prevention in partnership with gradu-
ate and undergraduate students attending the
same universities.

The limitations of this study relate to
the students’ demographics and must be
considered. Recruitment used convenience
sampling of students interested in wellness,
which might not represent the diversity of
a college campus’s student body. The study

consisted of a high female to male ratio, limit-
ing gender perceptions on wellness needs and
issues. The mixed gender focus groups also
did not allow for the development of well-
ness interventions targeted for each individ-
ual gender. Finally, although the universities
involved in this research were from different
states, the results may not be generalizable to
all universities due to geographic differences
(South Dakota State University = rural area;
University of Tennessee = urban area; West
Virginia University = urban area; and Univer-
sity of Florida = urban area).

CONCLUSION

Given that overall wellness of college
students is influenced by multiple factors
within a student’s environment, administra-
tors must be aware of the physical wellness
needs and issues students want addressed to
improve long-range, comprehensive health
and wellness on campus. Targets identified
for enhancing student wellness included in-
creasing healthy food options, education on
health topics, truthful marketing, student or-
ganizations focusing on wellness, and support
systems. Concurrently, decreases are desired
in cost of “healthy food options, availability
of unhealthy food options, misinformation
regarding health, and stress.”37(pS63) The
priorities for facilitating behavioral changes
focused on communicating health-related
services and facilities available to students,
providing a support system for students,
educating students on health topics, accom-
modating students’ schedules, and offering
attention-grabbing events. Future studies
should evaluate the impact of addressing
these wellness needs and issues of college stu-
dents on students’ overall physical wellness.

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360

Assessing HeAltHy nutrition AwAreness Among
College students And tHe role of HeAltH

eduCAtion in Promotion

Hanaa Alamari
Kuwait University

The purpose of this study was to assess the level of healthy nutrition
awareness between college students, and its effect on their health.
Healthy Nutrition plays an important role in preventing health prob-
lems associated with unhealthy diet especially overweight and obesity.
Three hundred and forty undergraduate college students participated in
the study, The test scores were analyzed by using quantitative methods.
Computer data entry and analysis were done by using SPSS (Statistical
Package for the Social Sciences). The results of the study revealed
that, a high percentage of college students at Kuwait University were
unaware about healthy nutrition, and the risk factors resulting from
eating unhealthy. Major variations among students were found accord-
ing to their gender, age, level of study, and major. Statistical Analysis
using T-Test showed that significant differences exist in the level of
nutrition awareness among students, and male students tended to have
a higher level of awareness than their counterparts in all aspects. Most
of the students agreed that health education plays an important role in
promoting a healthy nutrition among them.

Keywords: Healthy nutrition; awareness; college students; health ed-
ucation; promotion

Introduction
Nutrition is the science that investigates

the relationship between physiological func-
tions and the essential elements of the food we
eat. Nutrition is the area of health that focuses
on selecting foods that contain nutrients, eval-
uating food labels, eating the recommended
daily amounts of foods from My Pyramid,
following the Dietary Guidelines, planning
a healthful diet that reduces the risk of mal-
nutrition disease, and developing healthful
eating habits.

Following the Dietary Guidelines in stu-
dents diet is very important, in order to protect

them against foodborne illness, maintaining a
desirable weight and body composition, and
developing skills to prevent eating disorders,
( Laura &Kirsten, 2006). Nutrition education
provides knowledge and increases awareness
about healthy eating habits between college
students. The food provided in the university
is characterized by high level of fats, sugars,
and carbohydrates, and low in nutrient con-
tents. College students are not aware of what
is the best and healthiest food to select, there
are no nutrition labels on any package of each
type of food offered at the university. Ingre-
dients and components of any food should be

Assessing Healthy Nutrition Awareness Among College Students / 361

stated on it, with the dietary guidance in order
for the students to adopt healthy eating habits
(Francis &Eleanor, 2005).

For a good health, college students should
increase complex carbohydrates consumption
to 48 percent of total calories, and reduce
proteins to 12 percent, simple sugars to 10
percent, and less than 30 percent fat Low
intake of vitamins, minerals, and water can
also affect health, because they are necessary
for healthy body function, but do not supply
calories to our diet. Most diet-related diseases
results from excess calories and an increased
consumption of fat, (Cottrell, Girvan & McK-
enzie, 2015).

The National Academy of Science, the
National Cancer Institute, and the American
Cancer Society (2007), have examined the
role of diet in preventing cancer, To reduce
the risk of developing cancer, college students
should practice dietary guidelines by main-
taining a healthy weight to prevent obesity,
eat a variety of foods rich in antioxidants, eat
foods rich in fibers, such as fruits, and vegeta-
bles that are cruciferous, cut fat consumption,
salted and smoked food, and avoid the con-
sumption of alcoholic beverages.

Importance of Health Education in
Promoting Healthy Nutrition

Health education programs help increase
awareness between college students about
healthy eating habits and its effect on their
health. The interventions may target food pol-
icy or nutrition education. Health education
helps students develop the skills and knowl-
edge about nutrition to modify their eating
habits and avoid leading causes of death,
illness, and injury, (US Department of Health
and Human Services, 2010). Health educa-
tors and nutritionists play an important role
at the university level. In order to implement
nutrition lectures among college students,
teaching health education is important to help
influence their knowledge about nutrition.

Health education campaigns offer programs
to promote healthy nutritional attitudes,
knowledge and behavior, including healthy
eating among college students. (Eun-Jeong &
Natalie, 2009).

Health education plays an important role
in promoting healthy eating habits, It provides
the health information needed to teach young
people important life skills for nutrition. Diet
affects a person’s health status in the present
as well as in the future. A healthful diet is
needed for growth and development. It pro-
vides energy for daily activities, including ex-
ercise. College students should eat a healthful
diet to reduce the risk of developing certain
diseases like obesity, high blood pressure,
cardiovascular diseases, diabetes, and osteo-
porosis. (Powers &Todd, 2006).

The main purpose of this study was to
assess the differences in the levels of healthy
nutrition awareness among college students,
and their opinion about the importance of
health education in promoting healthy eating
habits between them.

Review of the Literature
In their study, (Anding, Suminiski &

Boss, 2001), show the differences in the
levels of perception among college students
about a healthy nutrition and its effect on
their health according to their gender. The
study revealed that, the perception about
healthy nutrition differs significantly among
college students regarding the following as-
pects: vitamin A deficiency affects growth
and causes malnutrition diseases (p<0.05), protein deficiency affects growth (p<0.01), bread and whole grains are good sources of fibers and provide energy (p<0.005), dairy products are good source of vitamins, calci- um, and protein (p<0.005), Fruits and veg- etables are rich with vitamins and minerals (p<0.001).Female students are found to have a higher level of perception than their coun- terparts in all these aspects, while perception

362 / College Student Journal

about meat and beans are rich with protein
and iron (p<0.05) differed significantly, and it was found to be higher among male col- lege students than their counterparts,

There should be more emphasis on improv-
ing dietary habits between college students, and
this emphasis should be given in the college
level, because the eating habits that a person
develop in the college will usually, continue
into adulthood. College campuses should have
programs that target the student’s daily food
consumption, such as the types of food they are
eating, the amount of the food, and how often
they are eating, to reduce health problems that
they encounter, (Center for Disease Control
and Prevention, 2012).

Haberman & luffy (2008) defined mal-
nutrition as,” lacking the proper nutrition for
the body to function at its best, and is much
more common around college students, and
Obesity is the body weight that is 20 percent
or more over desirable body weight.

A study, at the department of food resourc-
es at Kuwait Institute of Scientific Research
(KISR), Al Houti, (2010) in coordination
with the Ministry of Health in Kuwait, was
designed to examine Kuwaiti’s eating habits
and its relationship with some widespread
diseases. The study showed that 70 percent of
Kuwaiti men and 75 percent of Kuwaiti wom-
en are overweight. There is a need to examine
the eating habits and investigate the causes of
widespread diseases and illnesses, namely:
diabetes, obesity, cardiac troubles and high
blood pressure. Such knowledge is vital to
change the wrong eating habits. Those suf-
fering from diabetes amounted to 15 percent.
The spread among females was slightly high-
er than among males. The study revealed that
22% of males and 27% of females suffered
from low metabolism and 30% suffered from
high cholesterol which reached 60 % among
females aged 50 and above. Obesity threatens
to become the leading health problem in the
21st century.

Potential health implications related to
overweight may occur between college stu-
dents, and affect their health, health education
courses should be required at the university
level to improve their nutritional knowledge.
Justification is based on known linkages be-
tween health education and nutrition. (Paul,
Insel & Walton, 2006).

Obesity is described as epidemic in the
United States, (Contento, Koch &Lee, 2010),
and (the World Health Organization, 2012)
describes obesity as an “escalating global ep-
idemic “in many parts of the world”. College
obesity is increasing, college students should
achieve and maintain a healthy eating pattern
that consisted of the four food groups, they
should take adequate amounts of nutrients,
and not to exceed energy needs, to maintain
health and to prevent the development of
high blood pressure, cardiovascular diseas-
es, stroke and overweight, Being overweight
is associated with the development of car-
diovascular disease. Overweight students
should work on losing weight to lower their
risk cardiovascular disease. (Linda, Philip,
&Randy, 2007).

The food guide is a nutrition education
tool translating scientific knowledge and
dietary standards and recommendations into
understandable and practical for use by those
who have little or no training in nutrition,
(Lee, & Nieman, 2013). Food guides are
problem oriented and address specific nutri-
tional problems identified within the college
students. Typically, foods are classified into
basic food groups according to similarity of
nutrient content or other criteria. If a certain
number of servings from each groups is con-
sumed, a balanced and adequate diet is likely
to result a healthy nutrition. Health education
programs related to nutrition offers basis for
calculating the daily values.

According to American College Health
Association (ACHA, 2007), studies show
that most students at the college level are not

Assessing Healthy Nutrition Awareness Among College Students / 363

aware about the effect of iron deficiency and
low vitamin (B12) intake on health. It is im-
portant for college students to eat a variety of
food, to get the right combination of antioxi-
dants. Fruits and vegetables contain vitamin
C.E, and A, which are antioxidants that help
prevent cancer of the colon, rectum, prostate,
stomach, esophagus, and lung. (Rebicca &
Lorraine, 2009).

Healthy nutrition play an important role in
preventing diseases. The risk of developing
health problems between college students,
can be reduced by eating a variety of foods,
because when a person consumes a variety
of foods, the body will have a combination
of nutrients, which help in reducing the risk
of cancer.eg various foods contain antioxi-
dant-substances that protect cells from being
damaged by oxidation, ( Ann, 2006).

Method
The objective of this research was to assess

college student’s awareness about healthy nu-
trition, and their opinion about the importance
of health education in promoting healthy eat-
ing habits between them. This research pres-
ents a qualitative approach. For the purpose
of the study, information were collected via
a nutrition questionnaire, a standard survey
tool by (Harris, 2004). Computer analysis
and data entry were scored by using SPSS/PC
(statistical package for the social sciences).
Data analysis included frequency distribution,
percentages, mean, standard deviation, t-test
and Pearson Correlation Analysis test.

Sample
The sample used to conduct the research

was made up of three hundred and forty
undergraduate college students attending
Kuwait University, their age ranges between
18-26 years, and from different levels of edu-
cation, and majors.

Instruments
A questionnaire was administered to

the students at different colleges at Kuwait
University.

The questionnaire consists of three sec-
tions. The first section requested demographic
data including student’s gender, age, level of
education, and college of study (Arts or Sci-
ence). The second section of the questionnaire
measures the differences in Perception among
college students regarding their awareness
about healthy nutrition, it consists of twelve
aspects. The third section is about the role of
Health Education in promoting healthy nutri-
tion between college students, it consists of
four aspects.

Reliability and Validity
Cronbach’s alpha coefficients were calcu-

lated for the following scales: Healthy Nutri-
tion, and Health Education. Alpha coefficients
were evaluated using the guidelines suggested
by (George & Mallery, 2016). The items for
Healthy Nutrition Awareness had a Cron-
bach’s alpha coefficient of 0.76, indicating
acceptable reliability. The items for Health
Education had a Cronbach’s alpha coefficient
of 0.74, indicating acceptable reliability.

Results
Frequencies and Percentages for Student’s
Demographic

Three hundred and forty college students
completed the questionnaires, the most fre-
quently observed category of gender was
female (n = 203, 60%). The most frequently
observed category of level of education was
Second (n = 128, 38%). The most frequently
observed category of age was 18-22 (n = 214,
63%). The most frequently observed category
of college of study was Arts College (n = 252,
74%). Frequencies and percentages are pre-
sented in Table 1.

364 / College Student Journal

Table 1. Frequency Distribution
&Percentage for Various Nominal
Independent Variables

Variable N %

Gender

Female 203 60

Male 137 40

Level

First 108 32

Second 128 38

Third
Fourth 38 11

58 17

Missing 2

Age

18-22 214 63

22-24 84 25

24-26 35 10

Missing 7 2

College

Arts 252 74

Science 88 26

Missing 0 0

Table 2. Perception of college students
about Healthy Nutrition:

Variable M SD N

Performing daily exercise
increase the body ability to
prevent overweight

4.25 0.93 340

Food additives and chemicals
are related to cancer

4.23 1.21 339

Freezing food help to pre-
serve it for a longer time

3.26 1.49 339

Eating healthy food helps
prevent the spread of over-
weight and obesity and is
very important for a person’s
overall health

4.06 1.05 335

Fruits and vegetables are rich
with vitamins, minerals and
fibers

4.00 1.02 339

Variable M SD N

Dairy products are rich with
vitamin D, Calcium and
phosphorus

4.15 1.02 340

People with high cholesterol
should eat less fatty food

2.61 1.41 340

Protein deficiency cause
mal-nutrition diseases

3.88 1.38 340

Vitamin A deficiency cause
eye sight weakness

2.63 1.42 340

Vitamin D, Calcium, and
phosphorus deficiency are the
main cause for bone diseases

2.32 1.62 339

Vitamin B12 helps prevent
blood Anemia

2.22 1.50 339

Bread and beans food group
contain a high percentage of
carbohydrates

3.59 1.32 339

Perception of college students about
Healthy Nutrition:

As seen in table 2, the results according to
ranking from the highest mean to the lowest
Mean were as the following:

Performing daily exercise increase the
body ability to prevent overweight”, (Mean
=4.25, SD = 0.93). “Food additives and chem-
icals added to food, are related to cancer”,
(Mean = 4.23, SD = 1.21), “dairy products
are rich with vitamin D, Calcium and phos-
phorus, it helps the healing process of frac-
tured bones” (Mean = 4.15, SD = 1.02), “eat-
ing healthy food helps prevent the spread of
overweight and obesity and is very important
for a person’s overall health” ( Mean = 4.06,
SD = 1.05), “fruits and vegetables are rich
with vitamins, minerals and fibers” ( Mean
= 4.00, SD = 1.02), “protein deficiency cause
mal-nutrition diseases” ( Mean = 3.88, SD =
1.38), “Bread and beans food group contain
a high percentage of carbohydrates and it’s a
high source of energy” ( Mean = 3.59, SD =
1.32), “freezing food help preserve it for lon-
ger time” ( Mean = 3.26, SD = 1.49), “vita-
min A deficiency cause eyesight weakness ”(

Assessing Healthy Nutrition Awareness Among College Students / 365

Mean = 2.63, SD = 1.42), “people with high
cholesterol should eat less fatty food” ( Mean
= 2.61, SD = 1.41). “Vitamin D, Calcium, and
phosphorus deficiency are the main cause for
bone diseases” (Mean = 2.32, SD = 1.62),
“vitamin B12 helps prevent blood Anemia”,
(Mean = 2.22, SD = 1.50).

The study revealed that a high percentage
of college students at Kuwait University are
not aware about the health problems and the
risk factors associated with unhealthy eating
habits. The majority of the students did not
know the fact that protein deficiency and
vitamins deficiency cause malnutrition prob-
lems and other health related problems e.g.,
Obesity, High blood pressure, High choles-
terol, Anemia, Eyesight weakness, Bone
illnesses. The results showed that college
students at Kuwait University are unaware
about vitamin A deficiency effects eyesight,
fatty food increase the level of cholesterol,
vitamin D deficiency causes osteoporosis
and other bones illnesses, The students
didn’t of know the importance vitamin B12
in protecting from Anemia.

Table 3. Perception of College Students
about Health Education Promotion

Variable M SD N

Teaching health education to
college students is very import-
ant to develop healthy eating
habits

4.14 1.19 340

Health education increases
knowledge about malnutrition
diseases

4.04 1.09 334

Teaching health education helps
in promoting knowledge about
human health and nutrition

3.18 1.42 337

Health education follows dif-
ferent educational methods that
increase healthy Nutrition

3.53 1.28 340

Perception of students about Health
Education Promotion:

As seen in table 3,The results according to
ranking from the highest Mean to the lowest
Mean were as the following: “Teaching health
education to college students is very import-
ant to develop healthy eating habits”, (Mean=
4.14, SD = 1.19), “health education increas-
es knowledge about malnutrition diseases”,
(Mean= 4.04 and, SD = 1.09), “health educa-
tion follow different educational methods that
increase Healthy Nutrition ”, (Mean) = 3.53,
SD = 1.28), “teaching health education at the
university level helps in promoting knowl-
edge about human health and nutrition”,
(Mean = 3.18, SD = 1.42).

Independent Sample t-Test for Healthy
Nutrition according to gender

An independent sample t-test (George et
al 2016) was conducted, to detect if there is
a significant difference between male and
female college students in their levels of
awareness about healthy Nutrition according
to their gender.

Table 4 shows that t-test was not sig-
nificant, t (338) = -0.04, p >.05 ( p =, 968),
suggesting that the mean value of Healthy
Nutrition, was not significantly different be-
tween male and female students, but the mean
values show that male students (M=3.36,
SD=.58) possessed higher levels of knowl-
edge about healthy nutrition, than female stu-
dents (M=3.35, SD=, 64 ), but this difference
is not statistically significant at, 05 level.

Independent Sample t-Test for Health
Education promotion according to gender

An independent sample t-test was conduct-
ed to detect if there is significant a difference
between male and female college students in
their opinion about the importance of Health
Education in promoting healthy nutrition.

As seen from table 5, the result of the

366 / College Student Journal

independent sample t-test was not significant,
t (338) = 0.03, p>.05 (p =, 980) suggesting
that the mean average of health education was
not significantly different between male and
female at, 05 level, but the mean values show
that male students (M=3.72, SD=.90) and
female students (M=3.72, SD =.97) agreed
equivalently about the importance of Health
Education in promoting healthy nutrition.

Pearson Correlation Analysis between
Health Awareness and Student’s Health Ed-
ucation Pearson correlation analysis was
conducted between Health Education, and
Healthy Nutrition. Table 6 presents the results
of the Pearson Correlation Matrix between
Health Educations and Healthy Nutrition.

Table 6 Pearson Correlation Matrix
between (Health Educations) and (Healthy
Nutrition)

Variable 1 2

1. (Health Education) –

2. (Healthy Nutrition) 0.25 –

Note. The critical values are 0.11, 0.14, and 0.18 for
significance levels, 05,, 01, and, 001 respectively.

Cohen’s standard test (1988), was used
to evaluate the strength of the relationship,

where coefficients between, 10 and, 29 repre-
sent a small association, coefficients between,
30 and, 49 represent a moderate association,
and coefficients above, 50 indicate a large
association (Westfall & Henning, 2013).

As seen in table 6 there is a significant pos-
itive correlation between Health Education
and Healthy Nutrition (r = 0.25, p <, 001). The correlation coefficient between Health Education and Healthy Nutrition was 0.25 indicating a small relationship. This indicates that as Health Education increases, Healthy Nutrition Awareness, tends to increase. The study showed That Health Education is very important in increasing awareness of healthy eating habits between college students.

Discussion
Teaching health education is important to

develop healthy eating habits and improve
nutrition, it plays an important role in the
promotion and prevention of malnutrition
diseases, and promote physical and psycho-
logical health and improve nutrition, and ac-
cordingly, there should be more emphasis on
improving the levels of knowledge between
students about different methods conducting
a healthy nutrition between them. The results

Table 4. Independent Sample t-Test for perception about Healthy Nutrition according to
gender

Female Male

Variable M SD M SD T P D

Healthy Nutrition 3.35 0.64 3.36 0.58 -0.04 .968 0.00

Note. Degrees of Freedom for the t-statistic = 338. d represents Cohen’s d.

Table 5. Independent Sample t-Test for Health Education Promotion according to
gender

Female Male
Variable M SD M SD T P D

Health Education Promotion 3.72 0.97 3.72 0.90 0.03 .980 0.00

Note. Degrees of Freedom for the t-statistic = 338. D represents Cohen’s

Assessing Healthy Nutrition Awareness Among College Students / 367

of the study, showed that there is a need to
promote healthy nutrition between college
students at Kuwait University, as proposed by
the world health organization (WHO, 2012).
Students should be involved in promoting a
healthy nutrition and live a healthy life. These
results supported the likely value of including
nutrition knowledge as a target for health edu-
cation campaigns aimed at promoting healthy
eating. Colleges in general should institute
a special department for nutrition education
to help their students develop healthy eating
habits, and increase the knowledge about
healthy nutrition between them, improve their
health and reduce the spread of malnutrition
diseases among t

Conclusion
We conclude from the study that a high

percentage of college students are not meeting
dietary guidelines. Male students were found
more aware about healthy nutrition than fe-
male students. The study revealed that, most
male and female students agreed equivalently,
that teaching health education courses will
increase the knowledge about nutrition and
should be required for graduation. These re-
sults cause serious impact on student’s health
and should be taken into considerations

The findings of the study pave the way for
other researchers, physicians, and health care
specialists, to conduct future research of nu-
tritional importance between college students.
Interventions should be conducted by facilita-
tion group sessions where nutritional educa-
tion is promoted in order to spread awareness.
There should be further studies to investigate
the effect of malnutrition on college student’s
health, their academic performance and the
difficulties they will encounter because of un-
healthy diet. (Trockel, Barnes &Egget, 2002).

Future efforts should include nutrition
knowledge as a target for health education
campaigns aimed at promoting healthy nutri-
tion between college students.

368 / College Student Journal

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nutrients

Article

College Students and Eating Habits: A Study Using
An Ecological Model for Healthy Behavior

Giovanni Sogari 1,2,* , Catalina Velez-Argumedo 3, Miguel I. Gómez 2 and Cristina Mora 1

1 Department of Food and Drug, University of Parma, 43124 Parma, Italy; cristina.mora@unipr.it
2 Charles H. Dyson School of Applied Economics and Management, Cornell University, Ithaca, NY 14850,

USA; mig7@cornell.edu
3 Tecnológico de Monterrey, EGADE Business School, San Pedro Garza García 66269, Mexico;

catalina.velez@gmail.com
* Correspondence: giovanni.sogari@unipr.it

Received: 15 September 2018; Accepted: 20 November 2018; Published: 23 November 2018 �����������������

Abstract: Overweightness and obesity rates have increased dramatically over the past few decades
and they represent a health epidemic in the United States (US). Unhealthy dietary habits are among
the factors that can have adverse effects on weight status in young adulthood. The purpose of
this explorative study was to use a qualitative research design to analyze the factors (barriers and
enablers) that US college students perceived as influencing healthy eating behaviors. A group of
Cornell University students (n = 35) participated in six semi-structured focus groups. A qualitative
software, CAQDAS Nvivo11 Plus, was used to create codes that categorized the group discussions
while using an Ecological Model. Common barriers to healthy eating were time constraints, unhealthy
snacking, convenience high-calorie food, stress, high prices of healthy food, and easy access to junk
food. Conversely, enablers to healthy behavior were improved food knowledge and education, meal
planning, involvement in food preparation, and being physically active. Parental food behavior and
friends’ social pressure were considered to have both positive and negative influences on individual
eating habits. The study highlighted the importance of consulting college students when developing
healthy eating interventions across the campus (e.g., labeling healthy food options and information
campaigns) and considering individual-level factors and socio-ecological aspects in the analysis.

Keywords: young adults; focus group; USA; interventions; overweight; qualitative studies

1. Introduction

Overweightness and obesity rates have dramatically increased over the past few decades and
they represent a health epidemic in the United States, as well as in many other areas of the world [1–3].
According to a scoping review of risk behavior interventions in young men, Ashton, Hutchesson,
Rollo, Morgan & Collins [4] identified obesity as a serious health risk with an incidence rate of obesity
reaching 29% of the population aged 20–39 years old [5,6]. Physical inactivity and unhealthy dietary
habits are among the main behaviors that potentially have adverse effects on weight status in young
adulthood, and consequently, the future health of adults [3,7].

As reported by the World Health Organization (WHO) [8], the adult disease burden is due to
health risk behaviors that start during adolescence (e.g., unhealthy eating practices). For example,
most of the United States (US) population does not consume the recommended daily amount of fruit
and vegetables, nuts, and seeds. On the other hand, the consumption of added sugars, processed
meats, and trans fats is higher than the recommended daily intake [9]. It has been shown that after
the transition from adolescence to young adulthood, when independency increases, young adults are
continuously challenged to make healthful food choices [2,10]. Along with unhealthy eating behaviors,

Nutrients 2018, 10, 1823; doi:10.3390/nu10121823 www.mdpi.com/journal/nutrients

http://www.mdpi.com/journal/nutrients

http://www.mdpi.com

https://orcid.org/0000-0002-2561-571X

http://www.mdpi.com/2072-6643/10/12/1823?type=check_update&version=1

http://dx.doi.org/10.3390/nu10121823

http://www.mdpi.com/journal/nutrients

Nutrients 2018, 10, 1823 2 of 16

a new series of weight-related behavioral patterns begins throughout this period, such as excessive
alcohol consumption and a low level of physical activity.

Substantial life-changing transitions happened when young adults finish high school to start
college or a working life [10]. According to the literature [11–13], university is a critical period for
young adults regarding food choices and their relationship with weight gain. Some studies have even
shown that college students tend to gain more weight than those who do not attend university [14].
In order to design and support healthy nutrition campaigns (e.g., less meat options) across campuses,
it is critical to improve knowledge of dietary behaviors in the university-age population [15].

In the last decades, there has been growing interest in the development and implementation of
health promotion interventions in the workplace [16]. Studies exploring eating behavior in children [17],
adolescents [18,19], and young adults [20] have been done in recent years; however, theories to explain
such behaviors are still moving from the nascent to the mature stage [21].

Recently, the so-called Ecological Model has been considered as an acceptable framework to link
individual and social behaviors with environmental determinants, to reduce serious and prevalent
health problems [22].

The aim of this study is to explore the barriers and enablers of healthy eating behaviors among
US college students, using focus groups that foster open discussion between a small number of
participants. This study is the first stage of a larger research project called “CONSUMEHealth. Using
consumer science to improve healthy eating habits”, funded bythe European Union’s Horizon 2020
research and Innovation programme (Marie Sklodowska-Curie grant agreement No 749514).

2. Materials and Methods

2.1. Focus Groups

We selected focus group interviewing as a key methodology for the study, the elements of which
include participant observation, formal and informal interviewing, filming, and recording, among
others [23]. Focus groups are used to obtain insights and in-depth information on why and how people
think (perceptions, attitude, opinions, experience) about a topic of interest [24] used to unlock the
complexity of the decision-making process [25] and unencumbered by what we expect to find [26].
In our study, a focus group was suitable, since we were aiming to obtain cultural insights from a
group of individuals, and to explore their beliefs and behaviors [27], allowing for us to examine the
context of healthy eating behaviors [16,28]. Various studies have demonstrated that focus groups are
an appropriate research method to study eating habits, particularly among students [2,29]. Since the
definition of this population was not just a matter of age, but of lifestyle and identity, a focus group
could help us to better understand the meanings of healthy eating behaviors and its contexts.

2.2. Participants

Eligible participants were college students aged 18 to 25 years, who were transitioning from
adolescence to young adulthood, who lived in the USA, and who were enrolled at Cornell University
in the town of Ithaca (New York, NY, USA). Similar to previous studies [29], no first-year university
students were included in the study, due to their limited college experience. In addition, we excluded
students from nutrition classes or any other disciplines that might transmit a greater overall knowledge
or awareness of healthy eating. The final group consisted of students from different disciplines
(humanistic and scientific). These young adults were recruited via flyers that were distributed across
the University facilities, and via email using a college student database. In the advertisement sheet,
a link to an online survey was provided to facilitate recruitment, and to give subjects the essential
statement outline of the study (aim, benefits, and risks that are associated with time, incentives, other).
One advantage of our approach was that it allowed us to recruit participants from different disciplines
and years of study. In addition, we chose to have mixed-gender groups, which could produce a
greater variety of responses and better discussion [29]. The interview guideline was designed to take

Nutrients 2018, 10, 1823 3 of 16

participants on a journey, starting from a broader concept of health, to more specific questions on past,
present, and future diet behavior practices.

2.3. Procedure

The recruitment of participants was carried out using an online system at Cornell University.
A recruitment rate of between six and eight participants per focus group session was planned, in order
to have at least four people in each focus group session, therefore, an over-recruitment of two students
was planned in the case of ‘no-shows’.

Following the literature [29], a semi-structured question guide was developed to identify the key
questions for the research problem (eating habits, physical activity levels, and weight change). Enough
flexibility and side-questions allowed for open discussions within the group, to obtain more in-depth
information from participants.

Projective techniques were used both at the beginning of the sessions for “ice-breaking”, and
later on to understand better emotional connections and cognitions towards the topic of interest [30].
Specifically, the photograph response test technique was used, which consists of showing a series of
photographs that are related to the topic under investigation. A stimulus (images of obese/overweight
individuals) was presented to the group, and the participants were asked to answer with the first
words that came to their mind.

As reported by Guerrero and Xicola [24], the integration of different qualitative techniques (e.g.,
projective stimuli as in this study) within the same focus group was considered to be a mixed approach.
The study was approved by the Institutional Review Board (IRB) of the Office of Research Integrity
and Assurance of Cornell University (Protocol ID: 1709007406).

2.4. Data Collection Outline

During the online prescreening registration, all of the participants completed a short questionnaire,
providing self-reported socio-demographic information, physical activity, height, weight status, and
perceived body image.

Before beginning the focus group, an information sheet about the study and a consent form
for anonymity and confidentiality were signed by each participant. Drinks and a few snacks were
provided in order to make the environment as much comfortable as possible. In addition, the room
that was used to carry out the discussions was modified to look like a living room of a house.

As suggested in the literature [28], each focus group lasted around 90 min, and it was held in
a comfortable and quiet place. The sessions were video-audio recorded with the permission of the
participants, and were facilitated by a well-trained and experienced moderator (female moderator
with five years of experience in focus groups in the field of food, both in the public and private context).
The principal investigator was an observer, and stayed in another room that was connected with audio
and video recording systems during the focus group discussion. The moderator directed the flow of
the discussion, and ensured that all of the important issues were covered. We opted for small groups
(4–6 people), which was considered to be more appropriate when the topic of investigation is seen as
complex and personal [31]. Both the principal investigator and the moderator did not have any type of
relationship with the participants; we strongly believe that no bias or conflict of interest exist between
the research team, the subjects, and the focus of the study.

The semi-structured questions guide (Table 1), as developed following Krueger and Casey [27],
aimed to investigate the main factors influencing eating behaviors among college students. First,
a projective technique was first used for “ice breaking”, and to facilitate the group discussion. At the
beginning, all of the participants were asked to list “five healthy eating habits” and “five unhealthy
eating habits”, and afterwards to read the list out and share it with group. In this way, the whole
group was actively involved in the discussion, and participants became acquainted with, and felt
connected with each other. The main questions focused on factors influencing students’ health and
weight-related behaviors. Before ending each of the focus group sessions, the moderator and principal

Nutrients 2018, 10, 1823 4 of 16

investigator decided whether further questions were needed. At the very end of the focus group, all
of the subjects chose to either receive a monetary payment ($ 15) or university course credit (1.5) for
their participation.

Table 1. Short version of the Focus Group questions guide.

Question Type Questions

Opening and warm-up questions
Presentation of the research topic and participants (demographic characteristics

and some general eating habits like “what do you have for breakfast?)

Introduction/Projective techniques
Could you list five habits related to healthy and unhealthy eating?

Could you mention the first types of food/food products you consider healthy?

Transition questions (to move into
and between key questions)

How do you think the concept of healthy eating has changed?
Were you involved in cooking preparations in the past?

What changes happened in your cooking habits since you started college?

Main key questions

What different eating behaviors do you have between eating out and at home?
What is for you the meanings of the word “healthy” and “unhealthy”?

What is your eating behavior to stay healthy?
What are the consequences of having a healthy eating behavior?

How may have the community (e.g., colleges)
impacted on your healthy and unhealthy habits?

How can a parent/guardian positively/negatively
influence on children’s eating behavior?

Projective technique
(i.e., showing images of

overweight/underweight
adults/children)

What comes into your mind (e.g., thoughts) when you see these images on obesity,
overweightness, and a healthy body weight?

Ending
Are there any other opinions related to the topic? Is there anything else you would

like to share?

2.5. Data Analysis

In the field of health studies, the use of focus groups for research is a relatively recent
phenomena [28]. The information resulting from focus groups is usually analyzed throughout a
process of categorizing and coding the data in a systematic manner.

At the end of the six focus group sessions, the audio tapes were transcribed verbatim in Microsoft
Word by an independent transcription agency, and they were double-reviewed by two researchers.
Second, the data collected were analyzed by the principal investigator and two research assistants
who were trained in qualitative analysis. All quotes were encoded using the computer-assisted
qualitative data analysis software Nvivo11 Plus Version 11 (QSR International Pty Ltd., Melbourne,
Australia) [32]. This software helped the researchers at the stage of data analysis, marking, and coding
the transcription, and helped them to identify the relations between categories (concepts, themes, and
ideas) and individuals [28].

An inductive thematic approach, which is useful for identifying core meanings that were relevant
to the research objects, was used for data analysis, in which quotes were coded and categorized into
themes and subthemes [25,33]. These themes were organized into individual, social, and environmental
categories using an Ecological Model framework [16,22], and were successively described. A Microsoft
Excel package was used to analyze the characteristics of the sample using responses from the
questionnaire (descriptive statistics).

3. Results

3.1. Descriptive Results

In our study, six focus group discussions were conducted until saturation of new information was
reached. The final sample consisted of 35 students (23 females), with a mean age of 20.4 ± 1.5 years
and a mean body mass index (BMI) of 23.2 (SD ± 4.52), which was calculated as weight (kg) divided

Nutrients 2018, 10, 1823 5 of 16

by height squared (m2). Most participants considered themselves to have a healthy weight status,
and few of them indicated current or past eating disorders. The characteristics of the sample are
summarized in Table 2. Participants were also from a variety of study disciplines and different college
years (from junior to senior). This variety in participant characteristics enormously contributed to
gather more insights (e.g., diverse experiences and opinions) into the relationship between behaviors
and healthy eating.

Table 2. Characteristics of focus group participants (n = 35).

Group Characteristics Responses % Mean ± SD

Race/ethnicity

White/Caucasian
Asian (excluding South Asian)

African American
South Asian

80
11
6
3

Gender (female) 66
Age (years) 20.4 ± 1.5

Body Mass Index (BMI) 23.2 ± 4.5

Field of study

Business
Scientific

Humanistic
Info not provided

42.9
34.3
14.3
8.5

Students with an extra job
Job

No job
Info not provided

42.9
40

17.1

Physical activity
No exercise 15.2

Exercise 1 time per week 21.2
Exercise 3 times per week 42.4
Exercise 5 times per week 21.2

Self-assessment weight status
Underweight 9.1

Normal weight 66.7
Overweight 21.2

Population Area (size of the city)
<5000 inhabitants 15.1

Between 5000 and 50,000 inhabitants 27.3
>50,000 inhabitants 57.6

3.2. Qualitative Results

Following the literature [31], the researchers reviewed the transcript line-by-line encoding and
classified the text. As a first step, the questions that were enclosed in the script were used as initial
categories, then during a rigorous and systematic reading of the transcript, the main categories started
to emerge [33]. The researchers used an inductive coding method to find meaningful messages to
categorize into main themes and sub-themes.

The information was then analyzed in conjunction with the Ecological Model conceptual
framework. The importance of the Ecological Model in the social sciences is the consideration
of interactions between the people’s behavior and the environment (sociocultural, policy, and
physical) [16,29].

With the results from this model, we developed a list of factors influencing healthy eating
behaviors among college students, based on content analysis of the focus groups (Figure 1). We adapted
a model by Deliens, Clarys, Bourdeaudhuij & Deforche [29], and then developed the following main
levels for the analysis: individual (intrapersonal), social (interpersonal relationship), and university
environment (community settings), and some main attributes of the students (e.g., gender). The most

Nutrients 2018, 10, 1823 6 of 16

significant quotes by respondents were reported to illustrate each (sub)theme. We also decided to
incorporate some basic information of the participants by using an ID for the quotes: e.g., FG1_F21
(Focus Group 1, Female, age 21 years old).

Nutrients 2018, 10, x FOR PEER REVIEW 6 of 16

interactions between the people’s behavior and the environment (sociocultural, policy, and physical)
[16,29].

With the results from this model, we developed a list of factors influencing healthy eating
behaviors among college students, based on content analysis of the focus groups (Figure 1). We
adapted a model by Deliens, Clarys, Bourdeaudhuij & Deforche [29], and then developed the
following main levels for the analysis: individual (intrapersonal), social (interpersonal relationship),
and university environment (community settings), and some main attributes of the students (e.g.,
gender). The most significant quotes by respondents were reported to illustrate each (sub)theme. We
also decided to incorporate some basic information of the participants by using an ID for the quotes:
e.g., FG1_F21 (Focus Group 1, Female, age 21 years old).

Figure 1. Factors influencing healthy eating behaviors of college students.

Source: Authors’ creation

3.2.1. Individual Level (Intrapersonal)

Intrapersonal factors are represented mainly by attitude, behavior, self-concepts, and skills [16].

3.2.1.1. Healthy Eating: Meaning, Perception, and Consequences

Research shows that individuals’ beliefs about a healthy diet is shaped by their psychology.
Understanding what healthy eating means is crucial to making healthy food choices across and
within product categories. Participants seemed to be aware of healthy eating habits: “For me, healthy
eating is eating clean. So, lots of fresh veggies and fruits and some sort of protein” (FG1_F20); however, they
were also aware that they did not necessarily follow this suggestion: “Things (healthy food) that help
fulfil your daily nutrition requirement, even though I obviously don’t do that” (FG1_F20).

There was a gap between having knowledge and actually practicing it: “… now I feel like I’m more
aware of it (healthy eating), I just don’t pay attention to it” (FG5_F21). In addition, they highlighted how

Individual level

Healthy eating: meaning,
perception, and consequences

Eating habits (healthy and
unhealthy)

Food preferences

Healthy activities

Food preparation and
knowledge

Time, price, and state of mind

Barriers and
enablers to a healthy

diet

University
environment and

student life

Social level

Parental feeding behavior

Diet at home, school, and
eating out

Friends and media
pressure

Figure 1. Factors influencing healthy eating behaviors of college students.

3.2.1. Individual Level (Intrapersonal)

Intrapersonal factors are represented mainly by attitude, behavior, self-concepts, and skills [16].

Healthy Eating: Meaning, Perception, and Consequences

Research shows that individuals’ beliefs about a healthy diet is shaped by their psychology.
Understanding what healthy eating means is crucial to making healthy food choices across and within
product categories. Participants seemed to be aware of healthy eating habits: “For me, healthy eating is
eating clean. So, lots of fresh veggies and fruits and some sort of protein” (FG1_F20); however, they were also
aware that they did not necessarily follow this suggestion: “Things (healthy food) that help fulfil your
daily nutrition requirement, even though I obviously don’t do that” (FG1_F20).

There was a gap between having knowledge and actually practicing it: “ . . . now I feel like I’m more
aware of it (healthy eating), I just don’t pay attention to it” (FG5_F21). In addition, they highlighted how
the meaning of healthy eating had changed over the past decades: “when I was a kid, I definitely thought
it was more … just eating less, … now I understand that it’s more eating the right things, and not necessarily
eating less, but just eating different stuff ”(FG1_F21).

During the focus groups, the term “healthy” itself proved to be quite elastic: “I think about getting
a lot of balance” (FG3_M23) and it was perceived to have changed overtime: “before, it was all about
portion control, eating smaller things, but now, it’s focused more on eating healthy things” (FG1_F20). Most
participants considered their generation to be more health-aware and more health-conscious than the
previous ones. However, others believed that today, it is harder for people to eat healthy because there
is so much fast food available. For someone whose parents taught them during childhood, healthy

Nutrients 2018, 10, 1823 7 of 16

eating remained an important factor for the future: “my mom told me when I was a kid, healthy eating is if
your plate is colourful, so sometimes when I went through that little phase where I was trying to eat really well
at the dining halls I’d be like, carrots, orange, tomatoes, red, I’d get a bowl of blueberries, blue. You’d try to get
every colour on your plate and that’s healthy” (FG5_F19).

Participants were aware of the long-run consequences of not maintaining a healthy diet: “It’s
risk for diseases, increasing your risk of dying earlier” (FG4_F19); “you have less health problems, for the most
part, that are related to your diet. You probably have more energy, honestly, because processed stuff sort of slows
you down” (FG1_F20). In particular, a male participant reported: “I think that America has this epidemic,
which is obesity. And I know that leads to a whole bunch of complications, especially the demographic that I am.
I understand that our life expectancy isn’t as high as other demographics, and that’s due to obesity, diabetes,
heart disease and stuff like that” (FG2_M20).

They also considered “eating healthy” as something that was related to a lifestyle with positive
consequences to the general mindset of the individual: “I think healthy is feeling good about yourself,
having energy, and not being exhausted all day” (FG2_F18); “I think healthy goes beyond just food, you
have to be mentally healthy and physically healthy” (FG2_F19); “I tend to like healthy food, it makes me feel
better” (FG6_M22). More generally, people related the concept of being healthy to both physical and
psychological status: “I think being healthy is both your physical appearance and your mindset . . . exercised
and eating food, as well as balancing it out with your mental state” (F2_M20).

We used a projective technique to create more interaction and interest on the topic. Images of
overweight/obese people were shown, and participants were then asked what thoughts came into
their mind. Most participants felt uncomfortable with describing these images. Some of them thought
that being heavily overweight or obese could be attributed to not having control over their own
lifestyle: “I feel bad for them, because I know the probably inside, they are not happy with themselves, but it’s all
your personal choice” (FG3_M19). At the same time, there was a feeling both of sadness for them, but
also a willingness to not judge other people’s weight status. Only one person mentioned that body
image was a motivator in maintaining healthy eating: “I want to be in a good shape, and I think that’s what
motivates me” (FG4_M21).

Eating Habits (Healthy and Unhealthy)

Every participant was asked to list five healthy and five unhealthy eating habits on post-it notes
and then share it among the groups (Table 3). First, snacking was associated most of the time with
unhealthy eating, as mentioned by several participants: “I’m trying to eat a heavier breakfast so that I snack
less throughout the day” (FG1_F21); “I have snacks late night, mostly, if I’m going to snack at all, it’s generally
junk food” (FG4_M19). Only a few of them tried snacking with an healthy option: “I don’t mindlessly
snack, but when I do snack, it’s always something healthy like nuts or fruit” (FG4_F19). Some participants
did not seem conscious of having three meals a day, but preferred to have smaller snacks consistently
throughout the day and being portion-aware: “I try to eat like four to five times a day like smaller meals
as opposed to just like breakfast, lunch and dinner” (FG3_M23). Regarding drinking habits, surprisingly,
alcohol consumption was not mentioned as an unhealthy drinking habit; but more attention was
focused on the most common daily drinks (i.e., water, coffee, and soda). One female participant said:
“I like carbonated drinks, like sugary drinks that I should probably stay away from” (FG1_F21). Many people
were aware that a high sugar-sweetened beverage intake was associated with greater weight gain.

The participants were asked about why American consumers do not follow the dietary guidelines
given by the United States Department of Agriculture (USDA). Most of them mentioned that nowadays
there is a greater availability of unhealthy foods: “I think there’s a lot more junk food now than there
was then, and it’s also way cheaper than getting healthy food” (FG1_F20); “I think junk food is way more
accessible than going out to get healthy food” (FG1_F21); “sometimes people just don’t have access to food in
their neighbourhood” (FG6_M22).

Nutrients 2018, 10, 1823 8 of 16

Table 3. Top 12 self-reported healthy and unhealthy eating habits of the participants.

Healthy Eating Habits Frequency (n) Unhealthy Eating Habits Frequency (n)

Consumption of fruit and vegetables 26 Irregular meals 25

Drinking water 13
Sweet food (i.e., dessert, ice-cream, candy,

chocolate)
21

Balanced diet 12 Unhealthy snacks 15
Portion control 8 High salty and fat food (i.e., fried food) intake 13

Having breakfast 8 Overeating 10
No sweet food 8 Skipping breakfast 10

No oils/fat (e.g., less sauces) 7
Over protein consumption (i.e., too much meat,

eggs)
5

No processed food (i.e., whole food) 7 Eating disorders 5
Regular meals 7 Low water consumption 5

Protein consumption 7 Drinking soda 4
Self-prepared meals 6 Low fruit and vegetable consumption 4

Healthy snack (i.e., nuts) 5 Coffee consumption 3
Other Other

Notes: “Other”: eating habits that have been mentioned only one or two times. The researchers decided not to
report them.

Food Preferences

Food preferences are highly complex, personal, and influenced by a broad variety of factors,
especially physiological. Even if health seemed to be important for everyone, when choosing food,
students did not take health into consideration as the most important factor, but usually pleasure and
taste. As one participant said: “I think unhealthy food just tastes better. I don’t know, if a food tastes good to
me, I have thoughts of, “Is this unhealthy?” Because I feel like healthy food just doesn’t taste as good” (FG2_F19).
Likeability as a first factor for choosing food was confirmed by another student: “I think unhealthier
food just tastes better to everybody” (FG2_M20). Another participant highlighted the importance of the
pleasure of eating: “I really like pasta, like a lot, it’s pretty much what I eat every day. I put hot sauce on
everything” (FG5_F19).

Healthy Activities

Almost all of the participants mentioned that they had been very busy since they started tertiary
education, and that this was a barrier to maintaining a healthy lifestyle. They remembered that
exercising was as a big part of family time: “ . . . me and my two brothers and my dad, we started going
to the gym. So we’d go to the gym like every weekend” (FG2_M20); “I play a lot of soccer with my dad”
(FG3_M19). It is clear the role of parents in incentiving activities to stay healthy: “my parents were
also very encouraging of me and my other siblings with doing sports” (FG6_M21). Nowadays, due to time
constraints associated with being a college student, it was more difficult to stay active. The statement
“not keeping junk food in the house” was repeated by several students as a way to avoid the temptation of
eating unhealthy foods, as was having small snacks throughout the day rather than designated meals.
They were also aware about overeating, and few of them believed themselves to be good at controlling
portion sizes: “I try to get individual packages, so I have portion control” (FG2_F18).

Food Preparation and Knowledge

In order to eat healthy, consumers must have some knowledge about food, healthful products,
and the composition of a meal, among others. During the focus groups, participants were asked about
changes that they had made in their cooking habits since they had moved from home. Some of them
realized how negative the changes were in terms of eating healthy: “the first time I lived outside of home
wasn’t good. I ate out twice a day, every day, which is really unhealthy and really expensive. So now I’m trying
to cook more, which is good. I feel like I’m healthier when I’m cooking it myself ” (FG1_F21). Others confirmed
how expensive it is to eat out frequently: “Well I didn’t cook at all when I was at home. So just off campus,
it’s cheaper to cook than eating out every night, so I’m just trying to cook more” (FG1_F21).

Students were asked their involvement in preparing food when living with their parents,
the majority declared to have never helped in the kitchen or only during holiday meals. One participant

Nutrients 2018, 10, 1823 9 of 16

shared a personal experience: “Only for Thanksgiving or Christmas I would usually make a dessert or
something like that. Cake or cookies” (FG6_M21).

When asked to elaborate more on a healthy diet and give examples, few students had a vague
idea of what the Mediterranean diet was about: “I’ve definitely heard of it before, but I don’t … is it like,
only eating certain Greek, Mediterranean ingredients?” (FG2_F19), and most of them had not even heard
of the term before.

Time, Price and State of Mind

The transition from living at home to the college experience was considered to be stressful. Most of
the participants mentioned a problem with stress eating, especially when studying; as one participant
said: “ . . . I definitely snack too much when I’m stressed” (FG4_F19). Another one: “I work too much. I don’t
take the down time to exercise. I like to snack a lot. I use food to regulate my mood” (FG6_M22). Almost all
participants believed that they did not have enough time to prepare healthy meals. The “lack of time”
appeared to be an important barrier: “I don’t have time to be going to the grocery store to just get fruit and
healthy things” (FG1_F20). Time constraints also made students skip meals: “ . . . then sometimes I will
eat at random hours during the day, including sometimes I’ll have to skip lunch if I just don’t have enough time,
which I can see the effects, it just makes me really tired, it’s not good for working out” (FG4_F19).

Also, the relative perception of the high costs of buying healthy food (i.e., fruits and vegetables)
was one of the main barriers to a varied diet [2,34]. For many students: “junk food is way cheaper than
getting healthy food”; as one female participant specified: “it can be hard to afford healthy food, because
no matter what healthy eaters say about how easy is to find cheap, healthy food, it’s always probably gonna be
cheaper to find heavily processed junk food” (FG1_F20). Another female participant with Asian origin
confirmed with her personal experience that: “it’s very abnormal in America that the fruit and the vegetables
are much expensive than the meat, because back in China the vegetables and fruits are very cheap, so everyone
can have access to that” (FG5_F24).

3.2.2. Social Level (Interpersonal Relationships)

Social relationships in early adulthood are predominantly formed with roommates and friends at
college, as well as with family members, even if with a lower frequency with the latter. The perception
of social pressure was a strong determinant in supporting and maintaining a healthy diet [35]. As one
participant said in relation to healthy eating: “What you eat and who you’re around is really influential”
(FG2_F20). Another one confirmed this point: “Seeing if someone’s eating really unhealthy, you can be
like: “I’m going to be the one to eat healthy tonight”, or if everyone’s eating healthy, you feel more inclined to
eat healthy” (FG2_F20). Sometimes, it was also the influence of the partner that could make a person
change their dietary habits.

Parental Feeding Behavior

Respondents were asked about how parents can negatively and positively impact a child’s eating
behavior. They agreed that it was difficult for kids and adolescents to learn about eating healthy if their
parents did not influence and teach them: “I think as a child, you look up to your parents a lot, so instead of
verbally saying, “Eat healthy, blah blah blah . . . ” you actually have to show it” (FG2_F19).

One student explained that sometimes there was a risk that the parents were too busy to take care
of their children’s diet: “If parents are too busy or they don’t have the income and also the time, if they’re
working too many jobs, you know, they’ll just get packaged food or processed foods and that could definitely have
a very negative effect” (FG6_M21). As a result, the parents prefer to give them money to buy food away
from home and most of them choose junk food or fast food: “if I’m with my friends, I can kind of get away
with my mom not knowing what I’m eating. So I tend to eat what I can’t eat at home, so always unhealthy”
(FG1_F21).

These young adults believed that parents should give a good example (i.e., not going to a fast
food place). Most of the students mentioned the role of the mother as a relevant figure for giving good

Nutrients 2018, 10, 1823 10 of 16

recommendations: “my mom has always ingrained the healthy eating thing in me” (FG1_F20); “when I was
younger … even now, my mom only has healthy food available for me. And if I ever shop with her, she doesn’t let
me buy snacks or sweets” (FG1_F21). The participants who mentioned that their parents were good at
cooking, and liked preparing foods from different cultures, also realized that they should not be really
picky in their food choices. Others reported that their parents used some tricks to make their children
to eat healthy food: “I think my parents just seasoned my vegetables so it would taste better. And that way I
wouldn’t really have to think about me eating vegetables” (FG4_M20). Other students experienced a more
ambiguous and controversial approach with food: “We weren’t allowed to leave the table until I finished
my food” (FG4_F21); in this case, sometimes their mothers were part of the “Clean Plate Club”, a club
where parents are used to asking their children to finish everything on their plates.

Dietary Aspects of Home, School, and Eating Out

Respondents were asked what different eating behaviors they had between eating out and at
home. Even if young adults ate in a variety of different settings, especially after living with their
parents, the number of times eating out strongly increased. For instance, eating at home was usually
correlated with higher fruit and vegetable intakes. However, many participants said that eating out
was a kind of relief where all food desires could be satisfied: “I tend to eat what I can’t eat at home, so
always unhealthy” (FG1_F21); “when I’m eating out “I might as well treat myself” and treat myself for nothing”
(FG4_F21); “when I lived at home, I would always eat really healthy, so whenever I go out, I tend to eat a lot of
junk food” (FG1_F21). One participant’s personal experience confirmed that: “usually when I go out with
my friends or family, I eat just such trash food. And restaurant food to begin with is already so caloric, and then
you just add on top of it, let’s get appetizers and desserts” (FG4_F19).

High school had also a strong determinant on eating habits; most of the time, eating in secondary
school was related with a negative experience: “a lot of times in high school I just ate chips, because I just
hated my school lunch, it was pretty bad. But I think if the school lunch is the only thing that’s available to you,
it’s definitely going to affect what you’re eating and how you’re eating” (FG4_F21). Several students reported
that they did not feel that the school meal was healthy, due to limited choices. One remembered: “we
always used to joke about saying that pizza counted as a vegetable, we had to get a vegetable but pizza counted,
so we’d always get pizza” (FG4_M19). However, almost all of the participants agreed that nowadays,
schools are getting more involved in providing healthy options than in the past: “I think our school
definitely they had healthier options” (FG5_F19).

Friends and Media Pressure

Young adults are often influenced by their peers for many habits, and also when eating behaviors
are involved [29]; as one male participant, who had a high frequency in activity level and played in
a team, said: “there is just so much social pressure to eat healthy around other people” (FG3_M23). As one
female student reported: “I think every girl has this kind of thing and you have some pressure from your
friends and if you will see them wearing beautiful dresses you want to lose weight or something” (FG5_F24).
Another explained: “I think general rule of thumb, if you see people [friends] that look healthy, that we tend to
ask someone, what do you eat? How do you do that?” (FG5_M21). Usually, meals with friends tended to be
not healthy: “when I’m with my friends in the evening we do tend to eat heavier meals, which make me feel
pretty sick the next morning”. However, for someone else, the experience was the opposite: “I think the
big thing that changed for me was when I came here at Cornell, I saw other people and their eating habits, and
some of them were eating lean or eating healthier, and I tried to pick up on some of those too” (FG4_M21).

Many participants raised concerns about the role of television and other mass media on how an
adolescent or young adult should look: “I just feel like in the media, you see all these images of celebrities and
their body type is glorified, so you just want to eat healthier to look like that” (FG4_F19). In addition, they also
considered advertisement on TVs for candies and other sweet foods to be negative communication on
what to eat, as one participant said: “ . . . there’s all these ads on TVs for candies and stuff like that . . . kids
would rather have the bright colors, the fun candies and stuff that aren’t necessarily healthy” (FG1_F21).

Nutrients 2018, 10, 1823 11 of 16

3.2.3. University Environment and Student Life

Besides human physiology, the physical environment is also another element that can strongly
shape our food choices [36]. In general the surroundings where you are living can strongly determine
your diet: “I also think like your environment that you’re in and that like you’re constantly in really affects how
you eat” (FG3_M19).

The university environment could have both a positive and negative influence on eating habits, as
one participant explained: “I think if the community is driven to be healthier, then I think once you’re in that
environment, it tries to influence you to be healthier. And seeing other people around you eat healthy and want
to be healthier is a big influencer on changing your habits. And vice versa” (FG2_F20). For example, most
of the students thought that the dining halls strongly influenced their eating habits. Some students
started to eat irregularly when starting college: “I eat irregularly, like sometimes for dinner I just don’t want
anything in the dining halls and I’ll just eat cookies or the ice cream” (FG5_F19); “I probably eat more meat at
college, I don’t know, just a lot of food” (FG5_F19). When asked what events could make a person gain or
lose lots of weight, someone said that going to college made people gain weight: “having that sort of
unrestricted freedom of being able to choose whatever you want to eat, and also having a meal plan where it’s like
an “all-you-can eat” buffet” (FG1_F20). One participant shared a personal experience and said: “I need to
go eat every meal at the dining hall. And once you’re at the dining hall, you have unlimited food, so I feel like
I overate a lot in the dining halls. And now living off campus, I’m able to just buy what I want to cook, and
sometimes I cook all my food at once. So I can plan, this is for lunch, this is for dinner. So I can do better with
portion control” (FG1_F21).

For some other students, especially athletes, having the dining hall always available and close to
the dormitory or workplace was instead an advantage: “it was good to have the dining halls right there
so you could kind of eat whenever you wanted to. So it helped me stay healthy and had a good eating pattern
for that kind of lifestyle. And then, I think once when I got off campus, it’s like harder to keep up with good
eating patterns” (FG6_M21). Student life could be a critical period regarding unhealthy changes in
lifestyle behaviors: “I also sometimes skip lunch when I have class or studying to do, and a lot of times when
I’m studying I also eat junk food, try to keep myself awake” (FG4_M21).

Table 4 summarizes the main barriers and enablers that are associated with health decisions
during college life.

Table 4. Summary of the main barriers and enablers to a healthy diet among college students (n = 35).

BARRIERS ENABLERS

Individual-level
Not exercising

Not eating healthful food
Time constraints

Unhealthy snacking
Convenience food
Bad mood & stress

High prices
Junk food home availability

Individual-level
Maintenance of healthy lifestyle

Healthy eating habits
Food knowledge and education

Meal planning
Involvement in food preparation

Physical activity
Being portion-aware

Social-level

Parental food behavior and influence

Friends pressure and influence
Low food culture

Social-level
Friends pressure and influence

Parental food behavior and influence

University Environment
College’s dining services

Availability of high-calorie food and fast food

Environmental-level
College’s dining services

Source: own elaboration.

Nutrients 2018, 10, 1823 12 of 16

4. Discussion

Using an adapted version of an Ecological Model used by Deliens et al. [29], we developed a
framework that included individual (intrapersonal), social (interpersonal), university environment
(community settings), and students’ life factors as influences affecting eating habits. This model
integrated individual healthy and unhealthy eating patterns, in combination with the main barriers
and enablers that are associated with health decisions during college life. Many researchers [4,15,37–39]
identified a great number of factors that may contribute to the malnutrition epidemic, and related
health problems (e.g., weight gain and other dietary disorders) in emerging adulthood: unhealthy
eating habits increased when young adults leave their home circumstances, such as lower consumption
of healthy options (i.e., fruit and vegetables), irregular meals (e.g., breakfast skipping), and increasing
intakes of unhealthy snacks and other “junk food” (e.g., fried food). For college students, the transition
phase from living at home to living alone/with roommates during the period of postsecondary
education, is one of the most important life changes, and many food choices are deeply involved in
this change.

As indicated by other authors [2–4,35], the most common factors that are reported as barriers to a
healthy diet are time constraints, the high price of food items, and their availability, followed by the
lack of motivation in food preparation, which is strongly related to intention. Regarding the latter
barrier, as reported by Menozzi, Sogari & Mora [35], intention is the main factor in predicting behavior
regarding the consumption of healthy foods, such as fruits and vegetables. Therefore, we believe
that nutrition professionals within the university community should design programs and tools that
can help students to be more motivated in choosing healthy food. During the focus groups, students
realized the strong role of college facilities in influencing their eating habits. In fact, when students
start college, they will face a new (food) environment (e.g., all-you-can-eat formula dining), which can
have strong impact on their eating habits and intention to perform a healthy behavior. Interventions
across campus dining facilities should decrease the potential barriers to healthy food, and increase
self-efficacy and behavioral controls, to encourage students to embrace a better diet [40].

Among the social enablers, students found that having the support of friends to be active in
healthy eating was an important stimulus. We also observed that students who have a higher frequency
of physical activity believe that social pressure helps them to stay healthy. Parents also have a crucial
role, both positive and negative, in shaping the concept of healthy eating and in encouraging children
in healthy activities, both related to eating (e.g., food preparation) or more physical (e.g., sport,
outdoor activities). We noticed how perceived benefits of healthy eating also influence the intention to
consume healthier food [41], which seems to be more easily achieved if students start planning their
meals (self-control technique). Moreover, university characteristics, such as living arrangements (i.e.,
dormitory, off-campus, with parents) or academic schedules (e.g., classes, exams, etc.), also influence
the relationships between individuals and their eating behaviors [18,29,42], and they should be taken
into account when designing effective and tailored multilevel intervention programs.

Finally, it should be noted that some barriers for certain individuals, might be perceived
as potential drivers by others. For instance, and not surprisingly, some students stated that
“all-you-can-eat” formulas have a negative impact on the amount and quality of food consumed,
whereas others believed that these types of dining halls facilitated their ability to have a healthy diet.

The focus groups confirmed that both lifestyle and behavioral factors are strongly associated with
dietary patterns among college students: participants were aware that “being a healthy person” was
not just exercising and eating healthy foods, but also taking time for yourself and being an overall
happy individual.

One of the methodological limitations to the current study is that these results cannot be
automatically generalized to the whole population of university students, when considering the
specific and limited sample of participants (i.e., US college environment, healthy BMI status, other).
Another limitation is related to the presence of students who might have been more interested in this
topic, and decided to participate at the focus group, leading to “selection bias”.

Nutrients 2018, 10, 1823 13 of 16

5. Implications

More precision in the relationship between food and health is a topic of growing importance on
the public agenda [43]. Nevertheless, even with wide recognition that the food that we consume has a
strong impact on our health, consumers’ food preferences do not always lead to the best nutritional
choices. A better understanding of the link between diet and health among college students is
important for developing programs and behavioral change strategies to improve their lifestyle in
general, and to reduce diet-related diseases in particular [9].

This study highlights the importance of consulting college students when developing healthy
eating interventions across the campus for dining services or programs. As suggested by Stok et al., [10],
researchers in the food and nutrition field should not only focus on individual-level factors, but they
should also integrate socio-ecological aspects into the analysis. Dining halls and other University
facilities should ensure the availability of healthy food choices, as well as promoting physical activity
practices regularly. They should also provide food education and food preparation classes, to make
students more knowledgeable on how to cook and better plan meals.

Giving college students the necessary skills to be more aware of what a healthy diet style means
would empower them to make better food choices throughout their life. As suggested by many
authors [4,44], interventions should be specific for the targeted population (i.e., young adults) in order
to help individuals to behave accordingly with their healthy intentions. For instance, social media
facilitates the interaction between individuals and organizations (e.g., university administrators and
food researchers), in order to provide tailor-made information [29,45]. This aspect can be helpful in
promoting healthy diets without creating eating disorders. In addition, price reductions for high-cost
foods in campus facilities, such as dining halls and cafeterias, should also facilitate the purchase of
more healthy options (e.g., fruits and vegetables). Environmental modifications can include changing
and/or labeling healthy food options to make them more appealing, while creating a point of nutrition
information where students can see healthy food options.

6. Conclusions

The aim of this study was to identify factors driving healthy lifestyle behaviors among US college
students. Opinions and recommendations for effective and tailored-made intervention programs
or environmental modifications that support healthy eating were presented, using an ecological
framework that combined psychological, social, and environmental strategies.

Consumer behavior scientists typically do not contribute to the scientific debate about what is
best to eat from a nutritional point of view or give recommendations about dietary components for the
specific amounts and limits for food groups. In this study, we instead tried to understand the individual,
social, and environmental factors that influenced students’ healthy eating choices. Our results suggest
that participants were influenced by individual, social, and university environmental factors.

The Ecological Model can help university communities to gain more insights into how and why
students make certain food choices, and support them in staying healthy.

Colleges and dining halls on campuses should acknowledge their crucial role in guiding healthy
eating behaviors, and be the first subjects to be interested in creating a healthy environment for
the students. Unless they start understanding the reasons behind unhealthy eating behaviors of
young adults, effective policies and managerial strategies to fight malnutrition (obesity, anorexia,
micro-deficiency) cannot be developed.

The next step of this research will include the collection of a larger and more representative
sample size, especially when taking into consideration the socio-cultural differences of college
students between the US and other Western countries. Considering that the same negative trend of
overweightness and unhealthy eating behavior among children, adolescents, and young adults is
emerging in Europe, and also in Mediterranean countries [46], discussions on potential and future
studies addressing this problem in a national context are advised. In addition, further research should

Nutrients 2018, 10, 1823 14 of 16

evaluate whether specific tailor-made interventions are effective in changing behaviors towards a
healthy lifestyle.

Author Contributions: G.S. took lead in writing the manuscript and was overall responsible for the study design,
data collection and analysis. C.V.-A. has contributed in the study design and in the data collection (Focus Group
moderator). C.M. and M.I.G. contributed in the result interpretation and made suggestions and comments of the
final version of the manuscript. All authors read and approved the final manuscript.

Acknowledgments: This study, which is part of a wider project called “CONSUMEHealth. Using consumer
science to improve healthy eating habits”, has received funding from the European Union’s Horizon 2020 research
and Innovation programme under the Marie Sklodowska-Curie grant agreement No 749514. We appreciate the
assistance of Liam Wickes-Do and Zekun Ma, two research assistants, for the contribution in data collection,
cleaning and transcription of the focus groups. The authors also thank all students participating in this study
and the staff members of the Cornell Institute for Social and Economic Research (CISER). We also sincerely
appreciate the feedbacks and insightful comments of the anonymous reviewers who helped improve and clarify
this manuscript.

Conflicts of Interest: None of the authors or affiliated institutions associated with this manuscript submission
has any financial or personal relationship or affiliation that could influence the present work.

References

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Homepage

http://creativecommons.org/licenses/by/4.0/.

• Introduction
• Materials and Methods

Focus Groups
Participants
Procedure
Data Collection Outline
Data Analysis
Results
Descriptive Results
Qualitative Results
Individual Level (Intrapersonal)
Social Level (Interpersonal Relationships)
University Environment and Student Life

• Discussion
• Implications
• Conclusions

References

50 The Nurse Practitioner • Vol. 41, No. 7 www.tnpj.com

he high rate of obesity in the United States cur-
rently poses a serious threat to the health of college
students. Students who are obese face a greater risk

of future chronic conditions, such as type 2 diabetes mellitus
(T2DM), coronary heart disease, certain types of cancer,
long-term disability, and death.1 In 2013, the American
College Health Association (ACHA) reported that one-third
(33.7%) of college students self-reported being either over-
weight (21.9%) or obese (11.8%), and numerous sources

indicate that most college students are not meeting dietary
or physical activity guidelines.2-5

Only 6.3% of students reported eating five or more
servings of fruits and vegetables per day, and only 20.0%
actually participated in moderate-intensity exercise for
30 minutes or longer, 5 or more days per week.2 Government
agencies such as the Institute of Medicine (IOM) recom-
mend that all healthcare providers adopt standards of
practice (evidence-based or consensus guidelines) for

By Maria Estela Salcido, DNP, APRN and Diane B. Monsivais, PhD, RN, CNE

T

Screening and management of
overweight and obesity at a university

student health center
Abstract: This article discusses a quality improvement project focused on developing, implementing,

and evaluating an evidence-based best practice protocol for screening and management of

overweight and obesity in college students in a university-based student health center.

Keywords: overweight and obesity guidelines, quality improvement, student health services, university health promotion

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Copyright © 2016 Wolters Kluwer Health, Inc. All rights reserved.

Screening and management of overweight and obesity at a university student health center

www.tnpj.com The Nurse Practitioner • July 2016 51

prevention, screening, diagnosis, and treatment of over-
weight and obesity in all populations.6

■ Local problem
The need for a protocol that met best practices for preven-
tion, screening, diagnosis, and treatment of overweight
and obesity was identifi ed as a quality gap in the student
health center (SHC) at a university in the Southwest Unit-
ed States. The students primarily attended on a part-time
basis and did not live on campus. Setting apart the adult
population on a college campus from the adult population
in general was a recommendation from Healthy Campus
2020, which recommends making health promotion a
collaborative effort between student health services and
campus initiatives.7

In 2010, the obesity prevalence rate for the city where
the university is located was 31.2%. Body mass indexes
(BMIs) documented for the SHC population were higher
than those reported by the 2013 ACHA national survey.2 In
the SHC population, 30% had a BMI of 25% to 29.9% (over-
weight) and 13% had a BMI greater than 30% (obesity)
compared to 21.9% overweight and 11.8% obese from the
national survey. These BMI rates underscored the need for
an effective protocol to manage overweight and obesity at
the SHC.8

■ Intended improvement
The PICO question: (P [population or problem]) In pa-
tients ages 18 and over with a BMI of 25 or greater, (I [in-
tervention]) is the implementation of a clinical practice
guideline for initial screening, assessment, and management
of overweight and obesity versus (C [comparison]) no stan-
dardized guideline or program in place (O [outcome]) a
feasible and acceptable option at the SHC? The aim of this
project was to identify and implement a best practice
protocol for prevention, screening, diagnosis, and treatment
of overweight and obesity for patients seen at the SHC.
The global aim was to decrease the risk of overweight and
obese students developing obesity-related chronic condi-
tions, such as T2DM, coronary heart disease, certain types
of cancer, and long-term disability. Measurement of the
global aim was outside the scope of this quality improvement
project.

■ Choosing the guidelines and supporting material
To determine the evidence-based, best-practice protocol that
would most effectively serve the needs of the student popu-
lation, PubMed and CINAHL databases were searched us-
ing the following keywords: guideline implementation,
evidence-based guidelines for obesity, and college health.
Literature focusing on obesity prevention programs for

college populations published in English from 1998-2013
was reviewed.

Several organizations provide information and guid-
ance on evaluation and management of overweight and
obese patients including the National Heart, Lung, and
Blood Institute, the American Obesity Association, the
American Academy of Family Physicians, the U.S. Preven-
tive Services Task Force (uspstf ), the IOM reports, and the
World Health Organization. There were no specifi c guide-
lines addressing the college population, and therefore,
guidelines and resources for the general adult population
were used.

The uspstf recommendations were the best match
with the student population at the SHC as well as an initia-
tive titled, “Americans in Motion—Healthy Interventions”
(AIM-HI).1,9 The uspstf recommends screening all adults
for obesity at their preventive health services and to refer
all patients with a BMI of 30 or higher to intensive, mul-
ticomponent, behavioral interventions.1 The American
Academy of Family Physicians, in endorsing this recom-
mendation from the uspstf for primary care, developed
AIM-HI.

The AIM-HI program uses the multifaceted approach
of physical activity, healthy diet, and emotional well-being
as the key to prevention and management of many chronic
conditions.9 The program was designed to be used for offi ce-
based and community interventions, making it ideal for an
SHC. The toolkit includes educational manuals for staff and
patients, an online module on motivational interviewing,
and a fi tness inventory questionnaire (assesses readiness for
change behavior, eating pattern, physical activity type and
frequency, time spent on computer, and emotional well-
being). The questionnaires were developed and normed as
part of the entire AIM-HI project, which was evaluated
during a 3-year research study.9

Patient outcomes from baseline to 4 months showed
decrease in BMI, improved eating habits, improved fi tness,
and increased exercise levels.9 Implementation of evidence-
based clinical practice guidelines can be facilitated through
the use of an algorithm to address key points of care and
guide timing of referral to specialty services.10,11 Therefore,
the “Prevention and Diagnosis Algorithm” from the Institute
of Clinical Systems Improvement was selected for its ease of
use and adapted for use with the student population.12

■ Methods
Ethical issues. This quality improvement project was ap-
proved by the Institutional Review Board at the University
and the Director of the SHC. If students met the inclusion
criteria of BMI of 25 or greater and were ages 18 and older
(and not pregnant) when they presented for preventive

Copyright © 2016 Wolters Kluwer Health, Inc. All rights reserved.

52 The Nurse Practitioner • Vol. 41, No. 7 www.tnpj.com

Screening and management of overweight and obesity at a university student health center

health screenings, they would be provided with an informa-
tion sheet describing the quality improvement project. If
they agreed to participate, they were given a consent form
to sign prior to fi lling out the AIM-HI Fitness Inventory
questionnaire. The student could fi ll out the questionnaire
anonymously, and the questionnaire was not included in
the student’s patient chart but instead was kept in a locked
cabinet. Results were presented as aggregate data to protect
patient confi dentiality.

Setting. The project setting was a public university in
the Southwest area of the United States with a student pop-
ulation of 23,003 students. Three providers (one MD and
two NPs) at the college health center performed an average
of 60 preventive health screenings during the fi rst month of
each new semester. Both NPs were female, and the physician
was male. Each provider had over 12 years of experience in
student health. Because of the small provider group and
frequent interaction, no formal survey was carried out prior
to the program implementation. Everyone involved ex-
pressed enthusiasm for project implementation. Through
informal discussion with providers, uninsured students’
inability or unwillingness to pay for any fee-related referrals
was the main potential challenge identifi ed.

■ Intervention planning, implementation, and evaluation
The intervention was a best practice protocol for prevention,
screening, diagnosis, and treatment of overweight and obe-
sity in a student health setting. Recommendations from the
uspstf and the initiative AIM-HI were used as best prac-
tices for this protocol. Outcome measures were as follows:
• Documentation of BMI and waist circumference at all

preventive health services
• Provider use of the algorithm based on best practice

protocol
• Referral of students to campus programs based on the

Fitness Inventory Questionnaire (from the AIM-HI
toolkit).

Cullen and Adams’ framework of creating awareness,
building knowledge, promoting action and adoption, and
pursuing integration and sustained use was used in this
project.13 The following sections provide a description of
how the steps were implemented.

Step 1: Create awareness. Education and training
included all levels of staff, including providers, nursing
personnel, support staff, and the nutritionist. From prior
experience, the SHC staff prefers small group discussions
for educational dissemination, and this was easily accom-
plished at the weekly staff meetings on Fridays.

Step 2: Build knowledge. Two weekly in-services were
conducted for this project. The staff was introduced to the
components of the AIM-HI toolkit, which included online

training for motivational interviewing, evidence-based
weight management interventions, and an overview of the
Dietary Guidelines for Americans, 2010 (available at http://
health.gov/dietaryguidelines). The second educational ses-
sion included education on the calculation of BMI and waist
circumference measurements (if BMI was 30 or greater),
documentation of these parameters as part of vital signs for
all patients, and review of the components of the algorithm.
Readiness for change was to be assessed through the use of
the fi tness inventory questionnaire.

The campus wellness coordinator was invited to discuss
initiatives on campus available for both staff and students.
She introduced the staff to the SHAPE (Students Helping
Actively Participate in Exercise) Fit Challenge sponsored by
a university organization.14 The SHAPE Fit Challenge is a
movement to invite others to personally commit to increase
their physical activity by exercising 30 minutes a day (or 150
minutes a week) per the recommendations of the U.S. De-
partment of Health and Human Services.15 In addition, the
campus wellness coordinator provided a list of food venues
on campus that offer healthy food choices.

Step 3: Promote action and adoption. The algorithm
was posted in the triage area for easy access by all clinic staff
and as a reminder of the implementation of the guideline.
Easy-tear BMI charts were placed in all exam rooms so
patients could record their own BMIs to take with them
after the visit. The nutritionist provided her own preferred
nutrition record (food diary) to be given to the patient
prior to the meeting.

Step 4: Pursue integration and sustained use. Report-
ing results of project implementation and revisions based
on evaluative data and NP feedback can facilitate addi-
tional commitment to sustained use of new practices.13
Practitioners and staff provided feedback that the BMI chart
and algorithm posted in the triage area were helpful as a
reminder that the BMI for all patients coming in for preven-
tive health services had to be calculated and documented.
In addition, uninsured patients provided feedback that they
did not want to pay the established fee out of pocket for
nutritional referrals and counseling. Follow-up consultation
fees with the nutritionist were reduced to a more affordable
level to encourage continuity of care.

■ Outcome measure documentation
Outcome 1: Documentation of BMI and waist circumfer-
ence at all preventive health services. Baseline data were
obtained from the period of January 3 to 31, 2013 that
identifi ed 66 charts with the diagnosis of preventive health
services, such as school physical, sports physical, occupa-
tional health evaluation, and routine gynecological exam.
There were 23 males and 43 females with the age range

Copyright © 2016 Wolters Kluwer Health, Inc. All rights reserved.

Screening and management of overweight and obesity at a university student health center

www.tnpj.com The Nurse Practitioner • July 2016 53

between 20 and 43 years. The information was entered in
an Excel spreadsheet created for this project. The categories
created for the data collection included BMI, waist circum-
ference, age, and gender.

A total of 29 charts fi t the criteria for inclusion (BMI
of 25 or greater) in this review. All charts were completed
by the NPs. Analysis of the data indicated discrepancies in
recording BMIs as part of routine vital signs. Of the 29
charts, 93% (n = 27) had no BMI recorded and had to be
calculated retrospectively. Only 7% (n = 2) of the charts
had BMI measurements documented. Of these two charts
(n = 2), only one was provided a referral to the SHC nutri-
tionist, and no screening for comorbid conditions related
to obesity was done.

Chart audit 2: Postintervention. Data from a chart re-
view in March 2014 were compared to preintervention base-
line data from January 2013. Sixty-two charts were identifi ed
with the diagnostic code for preventive health services; of
these, six were excluded as these consisted of employee oc-
cupational health evaluations. Of the 56 charts identifi ed, a
total of 24 charts fi t the criteria (BMI of 25 or greater) for
inclusion in the review. However, only 18 participants con-
sented to complete the anonymous fi tness inventory.

■ Outcome 2: Provider use of the
algorithm based on best practice protocol
The category “algorithm implementation” was added for the
postintervention audit in order to demonstrate algorithm
adherence. This category was the consistent use of the clin-
ical practice guidelines algorithm by all clinical providers.
A total of 53 charts were audited (audit 1 = 29, audit 2 =
24). Descriptive statistics were used to analyze the chart
audit data before and after intervention (see Chart audit
summary). The difference in the proportion of charts that
recorded BMI as part of routine vital signs went from 7%
to 100% between chart audit 1 and 2. The proportion of
charts identifi ed as consistently using the guideline went
from less than 1% in the preintervention group to 100% in
the postintervention group.

■ Outcome 3: Referrals to campus programs based on
the fi tness inventory questionnaire
Fitness inventory questionnaire results: Sixty-seven per-
cent (n = 12) of students surveyed spent more than 2 hours
per day watching TV or on the computer. Another fi nding
revealed that 44.4% (n = 8) were participating in less than
3 days per week of physical activity. According to the latest
physical activity guidelines, adults should do at least 150
minutes a week (30 minutes a day) of moderate-intensity
physical activity.15 The last fi nding revealed that only 11.1%
(n = 2) were eating fi ve or more servings of fruits and veg-
etables per day as recommended by the Dietary Guidelines
for Americans, 2010.16

The SHC providers found that the fi tness survey pro-
vided an excellent starting point for discussion of goals and
management of weight loss with the patient and provided
a basis from which to make appropriate referrals to campus
wellness resources. Clinic personnel followed up with phone
calls every 2 weeks for 3 months to assess patient progress.

■ Discussion
Low participation in physical activity (documented on the
fi tness inventory questionnaire) is an area of concern and
suggests that this is a potential area to target in prevention
interventions. Since physical activity and nutrition are two
of the best modifi able factors to decrease obesity, the recom-
mendation was made to refer all students to the SHAPE Fit
Challenge to personally commit to increase their physical
activity. Enlisting other members of the campus commu-
nity, such as the campus wellness coordinator, the SHAPE
Fit Challenge created awareness not just for the staff but for
the students of the wide range of recreational facilities avail-
able on campus for little or no cost.

A wellness fair (once per semester) for the 2014 year was
planned as part of the wellness promotion on campus. A
wellness fair was held on April 1, 2014. A variety of strategies
were utilized to bring the students out, including games with
prizes, speaker presentations, and food (prepackaged,
healthy choices) as well as home-baked, low-calorie snacks

Chart audit summary

Guideline Preintervention

chart audits (N = 29)

Postintervention

chart audits (N = 24)

Correct Incorrect Correct Incorrect

BMI documentation 2 27 24 0

Algorithm implementation 0 0 24 0

Total chart audits 2 27 24 0

Percentage 7% 93% 100% 0%

Copyright © 2016 Wolters Kluwer Health, Inc. All rights reserved.

54 The Nurse Practitioner • Vol. 41, No. 7 www.tnpj.com

Screening and management of overweight and obesity at a university student health center

with recipes provided. There were 246 attendees, and 41
consented to be screened through the use of BMI. The results
of the BMI screening demonstrated that 54% of the students
were within the normal of 24.9, 24% were within the range
of 25 to 29.9, and 22% had a BMI of 30 or greater. The results
correlated with the ones previously seen at the SHC. Of the
nine students with the BMI of 30 or greater, two returned
to the SHC for further evaluation.

■ Summary
This quality improvement project resulted in important
practice changes related to screening and management of
overweight and obesity at the SHC. The project resulted in
consistent documentation of BMI and waist circumference
as routine vital signs, implementation of the uspstf recom-
mendations for overweight and obesity, identifi cation of risk
factors for comorbidities, and referral of students to campus
programs based on the fi tness inventory questionnaire com-
pleted by the students. Consistent use of screening guidelines
is intended to be the fi rst step toward meeting the more
global, long-term aim of decreasing the risk of overweight
and obese students of developing obesity-related chronic
conditions, such as T2DM, coronary heart disease, certain
types of cancer, and long-term disability.

■ Limitations
Planning for program sustainability became a limitation
when the liaisons to the campus wellness programs were
unable to continue at their original level of involvement due
to competing demands. This meant the number of wellness
classes were limited. Another factor that impacted participa-
tion was the protocol requirement for participants to com-
plete a formal written consent form prior to fi lling out the
anonymous fi tness inventory questionnaire. This require-
ment may have deterred some students from participating,
as there was extra time involved in the visit, and it may have
restricted the number of participants who could benefi t
from educational resources and referral to campus services.

■ Implications for practice
Educational sessions tailored to the needs of the staff, an
easily accessible algorithm, and cost-effective campus re-
sources for referral resulted in consistent protocol use in the
screening and management of overweight and obesity in a
college-age population. Health promotion in higher educa-
tion should be guided by the principle of facilitating a wide
range of campus and community partners for collective
action.17

NPs who hold a Doctor of Nursing Practice (DNP) degree
are ideally suited to lead this type of initiative. The ability to
effectively lead teams for the development, implementation,

and evaluation of practice guidelines is one of the founda-
tional skills of the DNP Essentials, with improved patient and
population health outcomes as the ultimate goal.18

REFERENCES
1. US Preventive Services Task Force. Screening for and management of

obesity in adults: Clinical Summary of U.S. Preventive Services Task
Force Recommendation. 2012. www.uspreventiveservicestaskforce.org/
Page/Document/UpdateSummaryFinal/obesity-in-adults-screening-and-
management.

2. American College Health Association. American College Health
Association-National College Health Assessment II: Reference Group
Executive Summary. 2013. www.acha-ncha.org.

3. Jaffe R. Confronting the obesity epidemic at community colleges. 2011.
http://ctl.laguardia.edu/journal/v5/pdf/InTransit_Spring11_v5_jaffe .

4. Shah N, Amirabdollahian F, Costa R. The dietary and physical activity habits
of university students on health and non-health related courses. J Hum Nutr
Diet. 2011;24(3):303-304.

5. University of New Hampshire. College students face obesity, high
blood pressure, metabolic syndrome. ScienceDaily. 2007, June 18. www.
sciencedaily.com/releases/2007/06/070614113310.htm.

6. National Research Council (NRC). Accelerating Progress in Obesity
Prevention: Solving the Weight of the Nation. 2012. http://images.nap.edu/
openbook/13275/png.

7. American College Health Association. Implementing healthy campus: MAP-
IT framework. 2010. www.acha.org/HealthyCampus.

8. Center for Health Statistics. Texas Behavioral Risk Factor Surveillance
System Survey Data. Texas Department of State Health Services. 2010. www.
dshs.state.tx.us/Layouts.

9. American Academy of Family Physicians (AAFP). Americans in Motion—
Healthy Interventions (AIM-HI). 2013. www.americansinmotion.org.

10. Cincinnati Children’s Hospital Medical Center: Best Evidence Statement
(BESt). Initial screening and referral for comorbidities in pediatric obese
patients. 2010. www.Endocrinology/Obesity/ComorbidityEvaluation/
BEST073 .

11. Registered Nurses Association of Ontario. Toolkit for implementation
of clinical practice guidelines. 2002. rnao.ca/sites/mao-ca/fi les/BPG.
Toolkit-o .

12. Fitch A, Everling L, Fox C, et al. Institute for Clinical Systems Improvement.
Prevention and Management of Obesity for Adults. 2013. www.icsi.org/_
asset/s935hy/Obesity-Interactive0411 .

13. Cullen L, Adams SL. Planning for implementation of evidence-based
practice. J Nurs Adm. 2012;42(4):222-230.

14. SHAPE: Educate, Advocate & Achieve Fitness & Healthy Living. 2013. http://
utep.edu/cce.

15. US Department of Health & Human Services. Physical activity guidelines
for Americans. 2008. www.health.gov/paguidelines.

16. US Department of Agriculture/Department of Health and Human Services.
Dietary Guidelines for Americans, 2010. www.health.gov/dietaryguidelines/
dga2010/DietaryGuidelines2010 .

17. American College Health Association. ACHA Guidelines: Standards of
Practice for Health Promotion in Higher Education. 2012. www.acha.org/
Publications/docs.

18. American Association of Colleges of Nursing. The Essentials of Doctoral
Education for Advanced Nursing Practice. 2006. www.aacn.nche.edu/
education-resources/essential-series.

Maria Estela Salcido is an advanced practice registered nurse at the Student
Health Center, University of Texas at El Paso, El Paso, Tex.

Diane B. Monsivais is the director of the MSN Nursing Education program in
the Graduate Nursing Program at the University of Texas at El Paso, El Paso,
Tex.

The authors have disclosed that they have no fi nancial relationships related to
this article.

DOI-10.1097/01.NPR.0000472251.51574.fd

Copyright © 2016 Wolters Kluwer Health, Inc. All rights reserved.

232

EffEctivEnEssof intuitivE Eating intErvEntion

through tExt MEssaging
aMong collEgE studEnts

Tessa J. Loughran
Illinois State University

Tammy Harpel
Illinois State University

Rachel Vollmer
Bradley University

Julie Schumacher
Illinois State University

This study examined the effects of an intuitive eating (IE) text mes-
saging intervention on the IE habits, perceived stress, and perceived
self-efficacy of college students in comparison to an electronically
emailed handout with the same information. Undergraduate students at
a Midwestern university (n=300) completed a pre-intervention survey
online which assessed IE practice (Intuitive Eating Scale), perceived
stress (Perceived Stress Scale), and self-efficacy (General Self-Effi-
cacy Scale and Eating Habits Confidence Survey). Participants were
randomly divided into a control (n=150) and intervention (n=150)
group. The intervention group received five weeks of intervention with
weekly IE texts, and the control received the same IE information in
one emailed handout. Following the intervention, all participants com-
pleted the post-intervention survey with the same measures. A total of
146 (n = 99 intervention, n = 47 control) participants completed the
pre-and post-intervention surveys. Paired t-tests and linear regressions
were used for analyses. The results showed the IE texting interven-
tion significantly increased total IE habits. Additionally, IE texting was
found to increase GSE scores and to limit increases in PSS levels. The
results of this study provide evidence that texting can be a successful
platform for increasing IE behaviors among college students.

KEYWORDS: Intuitive Eating, Mindful Eating, Nutrition, Perceived
Stress, Self-Efficacy, Texting Intervention

Effectiveness Of Intuitive Eating Intervention Through Text Messaging / 233

The prevalence of obesity and overweight
among adults in the United States is alarm-
ing, with more than one-third of Americans
classified as obese and more than two-thirds
as overweight (Ogden, Carroll, Kit, & Flegal,
2014). Overweight and obese adults are at
high-risk for developing a number of health
complications such as type II diabetes, cardio-
vascular disease, and some types of cancers
(Hruby et al., 2016). A critical time period to
prevent overweight may be the college age
years given that college students experience
newfound food independence, which may
result in the formation of poor eating habits,
such as overeating, or binge eating, that place
them at high risk for obesity (Kelly-Weed-
er, Phillips, Leonard, & Veroneau, 2014;
Smith-Jackson & Reel, 2012). Additionally,
college students with high perceived stress
levels are more likely to experience emotion-
al eating, which may also lead to weight gain
(Wilson, Darling, Fahrenkamp, D’Auria, &
Sato, 2015).

The most common form of weight loss
intervention used to combat weight gain is
calorie restriction; however, this weight loss
method is short-term, often resulting in the re-
gaining of the lost weight (Mann et al., 2007).
Recently, intuitive eating (IE) has gained
recognition as a successful weight loss inter-
vention method. IE can be defined by three
main principles: (a) eating for the purpose
of providing the body with energy, not due
to emotional cues, (b) listening to the body’s
hunger and satiety signals, and (c) uncondi-
tional permission to eat (Mathieu, 2009). The
use of IE methods with college students has
been associated with weight loss, lower blood
lipid levels, and improved cardiovascular risk
(Hawks, Madanat, Hawks, & Harris, 2005).

Text messaging intervention programs
may be an appropriate platform for changing
health behaviors among college students be-
cause these programs have been described as
effortless, but effective, due to the automated

text message reminders (Obermayer, Riley,
Asif, & Jean-Mary, 2004). One pilot texting
program for MyPlate nutrition education
showed that participants had a better un-
derstanding of the MyPlate guidelines and
increased fruit consumption in comparison
to the control group that received a mailed
brochure containing the same information
(Brown, O’Connor, and Salvaiano, 2014).
These results suggest that a text messaging
IE nutrition intervention may be an effective,
efficient way to reach the college population
in order to prevent excessive weight gain.

Currently, to the authors’ knowledge,
no studies have evaluated the success and
overall effectiveness of using text messaging
as a platform for IE intervention to combat
obesogenic behaviors among college stu-
dents. Compared to traditional intervention
methods, reaching college students through a
text messaging platform may increase success
and adherence to IE guidelines in a relatively
cost-effective manner. In turn, increased ad-
herence to IE methods may promote healthy
eating habits in college students, decreasing
the overall risk for excessive weight gain.
The current study addresses this gap in the
literature, with the intent of informing inter-
ventions for the college student population.
The results of this study can help determine
if IE intervention through text messaging
can improve eating habits and, subsequent-
ly, provide evidence that text messaging is a
successful platform for IE intervention in the
college student population.

Specifically, the study investigated the
following research questions:

(a) Does intuitive eating intervention
through text messaging have a greater
influence on the overall intuitive eating
habits of college students than an elec-
tronically emailed handout with the
same information?

234 / College Student Journal

(b) Does intuitive eating intervention
through text messaging have a greater
influence on college students’ per-
ceived self-efficacy in relation to diet
than an electronically emailed handout
with the same information?

(c) Does intuitive eating intervention
through text messaging have a greater
effect on college students’ perceived
stress than an electronically emailed
handout with the same information?

Methodology
Participants and Recruitment

After receiving approval from the Uni-
versity Institutional Review Board, potential
participants were identified and contacted
through the University’s listserv of students
who had indicated they were willing to re-
ceive emails regarding research. In order to
participate, individuals must have met the
following criteria: (1) a current university
student, (2) between 18 to 24 years of age, (3)
possess a personal smartphone with the abil-
ity to receive standard text messages, and (4)
live within a 15-minute walking distance to
campus. No exclusion criteria existed for gen-
der, race, ethnicity, or income level. The goal
sample size was 300 participants, which was
randomly divided into the control (n=150)
and intervention (n=150) groups.

Procedure
From the time the initial recruitment

email was sent, participants had two weeks
to volunteer and consent for the study and
complete the pre-intervention surveys. A
reminder email was sent one week after the
initial recruitment email was sent. Informed
consent was obtained from each participant
using the online platform utilized by the re-
searcher’s university. Cell phone numbers
and email addresses were obtained from par-
ticipants upon their consent for participation.

Following consent, participants completed
several questionnaires, including the Intuitive
Eating Scale (IES), Perceived Stress Scale
(PSS), General Self-Efficacy Scale (GSE),
and Eating Habits Confidence Survey, again
using the secure online survey system. It was
estimated that the online surveys would take
each participant approximately 15 to 30 min-
utes for completion.

Once the pre-intervention surveys were
completed and cell phone numbers and email
addresses were obtained from participants,
150 participants were randomly assigned
to the texting intervention group, and 150
participants were randomly assigned to the
control group. The control group received a
general Healthy Eating Behaviors interven-
tion email one week after the pre-intervention
survey closed. The intervention group began
the Healthy Eating Behaviors Text Messaging
Program one week after the pre-intervention
survey closed.

Intervention program. The texting pro-
gram was five weeks long and consisted of
a total of 10 text messages, at a rate of two
per week (Table 2). Text messages, based on
the 10 IE principles, were constructed in 160
characters or less. The text messages were
sent to participants through a mass text mes-
saging provider, EZ Texting, Monday through
Saturday, at either 11:00am or 5:00pm CST.
Participants in the intervention group were
able to opt out of the study at any time by
texting STOP.

The control group received the same
messages as the texting intervention group,
however, all the messages were delivered at
one time as a PDF handout through email.
Control group participants were able to reply
“STOP” by email at any time to withdraw
from the study.

One week after the five-week intervention
was completed, both intervention and control
participants were sent an email with a link to
complete the online post-intervention survey.

Effectiveness Of Intuitive Eating Intervention Through Text Messaging / 235

Table 1. Intuitive Eating Principles Descriptions

IE Principles Description

Principle 1 Reject the diet mentality: Ignore magazine articles and diet books that provide short-term weight
loss. Give all of your attention to IE methods.

Principle 2 Honor your hunger: Prevent excessive hunger and rebuild trust with yourself and food.

Principle 3 Make peace with food: Give yourself unconditional permission to eat. Do not deprive yourself in
order to prevent uncontrollable cravings and binges.

Principle 4 Challenge the food police: There are no “good” or “bad foods”.

Principle

5 Feel your fullness: Listen to the internal body signals of hunger and fullness.

Principle 6 Discover the satisfaction factor: Eating foods that you like, in a comfortable environment, allow-
ing you to feel satisfied.

Principle 7 Cope with your emotions without using food: Resolve emotional issues without using food.

Principle 8 Respect your body: Do not be overly critical or unrealistic of your body type and genetic makeup.

Principle 9 Exercise–feel the difference: Participate in activities that promote exercise that you enjoy.

Principle 10
Honor your health–general nutrition: Choose foods that are good for your health and that you
enjoy the taste of.

Table 2. Intuitive Eating Texts

Text Message Description

1 Forget all methods of dieting today! Focus on listening to what your body is telling you.

2 Ask yourself before eating today, is your body telling you it’s hungry? You may be experiencing a
behavior or emotion urging you to eat, and no actual hunger.

3 Sometimes our bodies crave sweet treats. Remind yourself that it’s okay to enjoy your favorite
foods in moderation.

4 Remember: There are no good or bad foods! Aim for a well-balanced diet.

5 Feel your fullness: Listen to the internal body signals of hunger and fullness.

6 Sit down and enjoy your dinner today. Turn off the television and focus on the meal you’re eating
without distractions.

7 Feeling stressed or upset? Eating when you’re not hungry won’t help you manage your emotions.

8 Respect and love yourself. Remember you are different from all others and you should appreciate
your genetic makeup!

9 Get out and exercise today! Whether it’s an extra-long walk or perhaps a new fitness class at the
gym–your body will thank you for it.

10 Honor your health–choose foods that make you feel good and taste good!

236 / College Student Journal

Upon completion of the post-intervention sur-
vey, participants were able to click a link that
redirected them to a separate webpage where
they could provide their name, email address,
and mailing address to enter into a random
drawing for one of four $25 gift cards.

Measures
The online pre-intervention survey mea-

sured intuitive eating behaviors, perceived
stress, general self-efficacy, and self-efficacy
related to diet. Additionally, participants an-
swered demographic questions (age, gender,
year in college, ethnicity, distance live from
school, and employment) and reported height
and weight (which were used to calculate
BMI). The online post-intervention survey
contained all of the measures from the pre-in-
tervention survey, with the exception of the
demographic-related questions that were
unlikely to change during the five-week inter-
vention period.

The Intuitive Eating Scale—2 (IES) is
a 23-item measure that assesses the partici-
pants’ eating habits and use of IE (Tylka &
Kroon, 2013). The IES has four subscales,
which include unconditional permission to
eat, eating for physical rather than emotion-
al reasons, reliance on hunger and satiety
cues, and body-food choice congruence. The
questionnaire uses a 5-point Likert scale (1 =
strongly disagree, 5 =strongly agree). Each
of the four subscales are scored by averaging
each subset of questions in each subscale,
while the total IES is scored by averaging
all items. The average IES scores may range
from one to five, with five indicating high
intuitive eating practice. The Intuitive Eat-
ing Scale has been found to be reliable and
valid for the undergraduate college student
population (Tylka & Kroon, 2013). Cron-
bach’s alpha scores for the pre-intervention
survey (.813) and post-intervention survey
(.849) showed high levels of reliability for
this study.

Perceived stress was measured by the
Perceived Stress Scale (PSS) (Cohen, Bruner,
Kuryluk, & Whitton, 2015). The Perceived
Stress Scale (PSS) is a 10-item questionnaire
that evaluates participants’ stress levels in re-
lation to their feelings within the past month
using a 4-point scale (0 = never, 4 = very
often). The items of this scale are summed,
with scores ranging from 0 to 40, and a high-
er score indicating a higher perceived stress
level (Cohen et al., 2015). This scale has been
found to have considerable reliability and
validity when used within the college popula-
tion to assess perceived stress levels (Cohen,
Kamarck, & Mermelstein,1983). The scales
used for this study showed high levels of
reliability based on Cronbach’s alpha scores
for the pre-intervention survey (.877) and
post-intervention survey (.892).

The Eating Habits Confidence Survey
(EHCS) was used to evaluate participants’
confidence in accomplishing a certain be-
havior for the next six months (Sallis, Pins-
ki, Grossman, Patterson, & Nader, 1988).
Ultimately, such confidence helps determine
possible behaviors that might occur during di-
eting. The scale contains 20 items, with four
subscales that assess resisting relapse, reduc-
ing calorie intake, reducing salt consumption,
and reducing fat consumption (Sallis et al.,
1988). Using a 5-point Likert scale (1 = I
know I cannot; 5 = I know I can), participants
report how confident they feel that they can
accomplish a given eating behavior, with
a maximum summed score of 100 points.
Greater scores indicate higher self-efficacy
levels. This scale was previously found to be
reliable and valid among adult women (Deck-
er, & Dennis, 2013). The Cronbach’s alpha
coefficients were adequate for the pre-inter-
vention survey (.795) and post-intervention
survey (.856).

The General Self Efficacy (GSE) ques-
tionnaire was used to assess participants’
general confidence levels in completing

Effectiveness Of Intuitive Eating Intervention Through Text Messaging / 237

or coping with difficult tasks or issues
(Schwarzer & Jerusalem, 1995). The scale
contains 10 items and uses a 4-point Likert
scale (1= not true at all; 4 = exactly true).
The scores were summed together ranging
from 10 to 40, with higher scores indicating
greater self-efficacy. This scale was found to
be reliable and valid when measuring gener-
al self-efficacy in adult individuals aged 18
to 87 years (De las Cuevas & Peñate, 2015).
The results of the Cronbach alpha scores for
this survey showed high reliability for the
pre-intervention survey (.853) and post-in-
tervention survey (.887).

Data Analysis
IBM SPSS Statistics Version 24 soft-

ware was used for data analysis. Descriptive
statistics were calculated for participant
characteristics and variables of interest. To
evaluate the effects of the intuitive eating
texting intervention on college students’
overall intuitive eating habits, perceived
stress, and diet self-efficacy in comparison
to the control, paired t-tests were used with a
significance value of p<0.05. Control and in- tervention groups’ pre- and post-intervention Intuitive Eating, Perceived Stress, General Self-Efficacy, and Eating Habits Confidence were compared with paired t-tests to assess if the intervention program was associated with significant change in these variables. Additionally, linear regression was used to assess if change in Eating Habits Confidence and Perceived Stress was associated with the intuitive eating texting intervention. In each regression, participant change in BMI, race, gender, and ethnicity were entered as covari- ates and pre-intervention scores were used as control variables in the equation.

Results
Participants

A total of 6,035 students, between the ages
of 18 to 24 years, were recruited for the study.
For the pre-intervention survey, 526 individ-
uals opened the survey, 412 consented to par-
ticipate, and 300 fully completed the survey.
Individuals who completed the survey were
randomly assigned to either the intervention
or control group. Eight participants in the in-
tervention texting group and one in the email
control group replied “STOP” and opted out
of the study.

After the five-week intervention was com-
pleted, 227 participants opened the post-inter-
vention survey, and 146 (n = 99 intervention,
n = 47 control) fully completed the survey.
Of the 146 participants who fully completed
both the pre-intervention and post-interven-
tion surveys, the majority of participants were
18 years of age (70%), white (90%), female
(85%), college freshmen (75%), and currently
unemployed (75%).

IES Pre- and Post-Test Score Means
Within Groups

Mean IES scores were calculated for both
the control and intervention groups. Table 3
shows the pre-intervention and post-interven-
tion survey means for both groups, as well as
changes between the means. Within the inter-
vention group, scores for the IES total score
significantly increased by .096, t(98) = 2.564,
p <.05. Additionally, for the intervention group, change in one of the four subscales within the IES scale was found to be signif- icant. Specifically, the IES subscale Reliance for Hunger and Satiety Cues significantly in- creased by .201, t(98) = 2.866, p <.005. Both of these significant findings within the inter- vention group show an increase in intuitive eating behavior at post-intervention. Changes within the intervention group for IES scores were significant; however, within the control

238 / College Student Journal

group, there were no significant differences
between the pre-intervention and post-inter-
vention survey IES scores.

PSS Pre- and Post-Test Score Means
Within Groups

Mean PSS scores were calculated for both
the control and intervention groups. Table 3
shows the pre-intervention and post-interven-
tion survey means for both groups, as well
as changes between the means. Scores sig-
nificantly increased by 1.303 for PSS scores,
t(98) = 2.214, p <.05, among the intervention group, indicating an increase in perceived stress from pre- to post-intervention. While not significant, the PSS score also increased for the control group, with an increase of 1.660, t(46) = 1.926, p>.05. Although per-
ceived stress increased in both groups, the
control group post-intervention score was
higher than the intervention group post-inter-
vention score. Therefore, despite the fact that
the increase was not significant, the control
group had a greater increase in perceived
stress than the intervention group.

EHCS and GSE Pre- and Post-Test Score
Means Within Groups

Mean GSE and EHCS scores were cal-
culated for both the control and intervention
groups. Table 3 shows the pre-intervention
and post-intervention survey means for
both groups, as well as changes between the
means. Although the results were not statis-
tically significant, the results indicated an
increase in both EHCS and GSE scores for
the intervention group. Average EHCS scores
increased by .333, and GSE scores increased
by .534, indicating both perceived self-effica-
cy and perceived diet-efficacy increased from
pre- to post-intervention. Within the control
group, GSE scores decreased by -.987, while
EHCS scores increased by 1.617.

Intervention Effects on IE
Linear regression was used to assess if the

IE intervention was associated with change in
IES scores when controlling for pre-interven-
tion scores and other variables. Specifically,
participants’ change in BMI, age, gender, year
in college, and race were entered as covariates

Table 3. Intuitive Eating Intervention Means and Paired Sample T-Test Results

Pre-Survey Post-Survey Change

Scales INT CNT INT CNT INT CNT

IE Total 3.27 3.24 3.37 3.24 .096* .001

3.01 3.06 2.97 3.05 -.037 -.011

3.21 3.06 3.32 3.10 .106 .040

3.50 3.41 3.70 3.39 .201** -.011

3.52 3.75 3.63 3.70 .114 -.057

EHCS 48.06 52.30 48.39 53.92 .333 1.617

GSE 30.83 32.81 31.36 31.83 .534 -.987

PSS 18.01 17.98 19.31 19.64 1.303* 1.660

Note. N=146, Intervention, N=99, Control N=47. *p<.05, **p<.005. a. Unconditional permission to eat b. Eating for physical rather than emotion reasons c. Reliance on hunger and satiety cues d. Body-Food Choice Congruence

Effectiveness Of Intuitive Eating Intervention Through Text Messaging / 239

in the regression model, with the IE pre-inter-
vention survey score used as a control vari-
able and IE intervention (texting intervention
versus control group membership) serving as
the independent variable.

The regression model, excluding the in-
dependent variable (IE intervention), was
statistically significant, R2 = .528, F(6, 139)
= 25.093, p <.0005. Pre-intervention IES sur- vey scores were significantly correlated with post-intervention IES scores. The pre-inter- vention survey scores remained significantly correlated after the independent variable was added to the model; however, the independent variable was not significantly related to IES post-intervention survey scores. See Table 4 for detailed regression results. Therefore, the results of the full regression analysis indicat- ed the IE texting intervention did not relate to post-intervention survey IES scores.

Intervention Effects on Self-Efficacy
A linear regression was used to assess if

IE intervention was associated with change
in EHCS scores among groups when con-
trolling for pre-intervention scores and other
variables. Participants’ change in BMI, age,
gender, year in college, and race were also
entered as covariates in the regression model.
The pre-intervention EHCS survey score was
used as a control variable.

The results of the regression model,
when excluding the independent variable (IE
intervention), was statistically significant,
R2 = .327, F(6, 139) = 11.234, p <.0005. Pre-intervention EHCS survey scores were significantly correlated with post-intervention EHCS scores. The pre-intervention survey scores remained significantly correlated after the independent variable was added to the model; however, the independent variable was not significantly associated with EHCS post-intervention survey scores. See Table 5 for full details on this regression. Thus, the

Table 4. Intervention Effects on IE

Post-Survey IES Scores

Model 2

Variable B Beta t p

Constant -.589 -.695 .488

IES Pre-Survey .741** .683 11.423 .000

Age .083 .232 1.795 .075

Race -.014 -.019 -.320 .750

Year in School -.100 -.211 -1.610 .110

Gender -.058 -.043 -.720 .473

Change in BMI -.043 -.083 -1.411 .160

IE Intervention .097 .094 1.498 .137

R2 .528

F 22.021**

Change in R2 .008

Change in F 2.243

Note. N=146. *p<.05, **p<.001.

Table 5. Intervention Effects on EHCS

Post-Survey EHCS Scores

Model 2
Variable B Beta t p

Constant 11.450 .668 .505

IES Pre-Survey .571** .500 7.040 .000

Age .661 .109 .711 .478

Race -1.892* -.153 -2.154 .033

Year in School -1.530 -.189 -1.208 .229

Gender 1.554 .067 .952 .343

Change in BMI -.433 -.050 -.707 .481

IE Intervention -1.528 -.087 -1.159 .248

R2 .333

F 9.844**

Change in R2 2.99

Change in F 1.343

Note. N=146. *p<.05, **p<.001.

240 / College Student Journal

results of the full regression analysis indicat-
ed IE texting intervention did not correlate
with post-intervention survey EHCS scores.

Intervention Effects on Perceived Stress
Finally, linear regression was used to as-

sess if IE intervention was associated with
change in PSS scores among groups. Par-
ticipant change in BMI, age, gender, year in
college, and race were entered as covariates in
the model, with the pre-intervention PSS sur-
vey entered as a control variable, and IE inter-
vention serving as the independent variable.

The results of the regression model, with-
out the independent variable included, were
statistically significant, R2 =.414, F(6, 139) =
16.394, p <.0005. Pre-intervention PSS sur- vey scores were significantly associated with post-intervention PSS scores. The pre-inter- vention survey scores remained significantly associated after the independent variable was added to the model; however, the indepen- dent variable did not result in a significant

relationship between PSS post-intervention
survey scores. See Table 6 for full details on
this regression. Thus, the results of the full
regression analysis indicated the IE texting
intervention was not associated with post-in-
tervention survey PSS scores.

Discussion
This study examined the effectiveness of

an intuitive eating texting program in compar-
ison to an emailed handout among a college
student population. The intervention effects
on IE practice, eating habit confidence, and
perceived stress levels were examined in the
study. The results of the study showed sig-
nificant increases in total IES scores within
the intervention group, with greater changes
for the intervention group than the control
group. These findings suggest that IE texting
intervention over a five-week period may be
more effective in improving IE practice than
receiving an emailed IE handout containing
the same information. A similar study also
found increases in targeted nutrition-related
behaviors and knowledge when evaluating the
effectiveness of a nutrition education based
texting program, Mobile MyPlate (Brown et
al., 2014). Brown et al. found that once the
Mobile MyPlate intervention was complete,
participants showed increases in fruit con-
sumption and MyPlate nutrition knowledge
when compared to the control group that
received a mailed handout with the same in-
formation. The results of the Mobile MyPlate
study (Brown et al., 2014) and the current
study both suggest texting intervention pro-
grams can increase desired nutrition-related
behaviors more than a handout delivered at a
single time.

In addition to the significant increase in
total IES scores, the IES subscale Reliance on
Hunger and Satiety Cue scores significantly
increased among the intervention group. In-
terestingly, this subscale actually decreased in
the control group that received the one-time

Table 6. Intervention Effects on PSS

Post-Survey PSS Scores

Model 2
Variable B Beta t p

Constant 27.060 -1.974 .050

IES Pre-Survey .646** .602 -9.022 .000

Age -1.157 -.227 -1.571 .118

Race .163 .016 .237 .813

Year in School 1.328 .195 1.336 .184

Gender 2.261 .117 1.764 .080

Change in BMI -.321 -.044 -.666 .507

IE Intervention -.410 -.028 -.396 .692

R2 .415

F 13.989

Change in R2 .001

Change in F .157

Note. N=146. *p<.05, **p<.001.

Effectiveness Of Intuitive Eating Intervention Through Text Messaging / 241

email version of the IE information. These
findings suggest that texting IE reminders are
a more effective way to remind individuals of
their hunger and satiety cues versus reading
the information within an emailed handout. In
particular, this finding may be explained by the
timeliness of the texts sent in the intervention.
The texts for this study were purposely sent
slightly before meal times, with the intention
of reminding participants to focus on their IE
skills before consuming meals. On the other
hand, at mealtimes, those in the control group
would have to remind themselves of what
they had read about IE earlier in the emailed
handout. Having texts sent before meal times
may have been more successful at increasing
IE behavior than the emailed handout because
participants did not have to recall information
or motivate themselves to reference the hand-
out at meal times. This finding is consistent
with the findings of Obermayer et al. (2004)
and colleagues, which showed a texting smok-
ing cessation program was successful due to
the fact that participants did not have to seek
information (i.e., open an email, log into a
website). Taken together, the results of our
study and other research, suggest that texting
programs may increase desired behaviors due
to the timing of the text and the minimal effort
required to seek or recall information that may
promote the targeted behavior.

The current findings showed the interven-
tion group reported significantly higher PSS
scores in the post-intervention survey. The
results for the control group’s average PSS
scores were not significant. This suggests an
IE texting intervention may have a greater im-
pact on helping one to manage their perceived
stress level than information that is delivered
one time via email. Yet, both intervention and
control PSS scores increased from pre- to
post-intervention. Perhaps, the increases in
scores among both groups can be attributed to
the university environment of the population.
The majority of the students who participated

in this study were freshman (75%). Therefore,
this study took place during the first semester
of college for many of the participants, which
can be a very stressful time for students, due
to the adjustment of being away from home
and course workload. Students are also expe-
riencing newfound food independence, which
can create poor eating habits, such as over-
eating, or binge eating (Kelly-Weeder et al.,
2014; Smith-Jackson & Reel, 2012). College
students with high perceived stress levels are
more likely to experience emotional eating
(Wilson et al., 2015). In turn, emotional eat-
ing and poor eating habits due to the new col-
lege setting may be related to high levels of
perceived stress, possibly altering PSS scores.
Our results may further be explained by a
study that examined the relationship between
IE and perceived stress among Finnish obese
adults (Järvelä-Reijonen et. al, 2016), with the
results showing increased levels of perceived
stress to be correlated with low IE practice. In
terms of our study, the IE texting intervention
had a slight impact on PSS scores among the
intervention group; however, the IE texting
intervention may have had a larger impact in
a less stressful context.

In contrast to previous research, the results
involving the GSE scores and EHCS scores
among both groups were not significant. In
particular, Annessi and Gorjala (2010) found
that following the implementation of a 6-month
intervention program involving nutrition and
exercise, increases in self-efficacy were re-
lated to increases in desired health behaviors
and outcomes among an adult population.
Our study, on the other hand, did not provide
evidence that IE intervention correlated with
increased self-efficacy. It is possible that the
length of our intervention played a role in the
discrepancy between our findings and those of
Annessi and Garjala. Perhaps our IE texting
program would have resulted in greater in-
creases in self-efficacy if the intervention pro-
gram was longer-term, such as the Annessi and

242 / College Student Journal

Gorjala intervention, versus only five weeks in
duration. In order to further examine this re-
lationship, future research would benefit from
utilizing longer interventions.

Although the results were not statistically
significant for GSE, it is interesting that the
GSE scores among the control group de-
creased, while the intervention group GSE
scores increased. A study by Moeini and col-
leagues (2008) may provide some explanation
for these findings. The researchers examined
the relationship between PSS and GSE lev-
els in Iranian male high school students and
found that increased amounts of PSS led to de-
creased amounts of GSE (Moeini et al. 2008).
In our study, the intervention group reported a
smaller increase in PSS when compared to the
control group. Given that PSS levels showed
a greater increase among the control group
when compared to intervention group scores,
this may explain why GSE scores decreased
in the control group and increased in the in-
tervention group.

While this study provides evidence to
support the use of IE texting interventions,
the study is not without limitations. First, it
is unknown if the participants opened the text
messages that contained the IE intervention
material and, if so, how they used the texts.
Based on how the participants handled the
text messages, it may be that the intervention
group did not receive the full benefits of the
texts. In support of this, other studies have
successfully included interaction via text be-
tween participants and researchers (de Niet et
al., 2012; Bauer, de Niet, Timman, & Kordy,
2010). Such interaction between researchers
and participants may help monitor and en-
courage participant engagement and may,
subsequently, increase intervention effec-
tiveness. Future research would benefit from
including a mechanism that motivates partici-
pants to open and read the text messages.

Participant attrition was another issue
in this study. A portion of participants who

completed the pre-intervention survey did
not complete the post-intervention survey,
resulting in more participants within the in-
tervention group than in the email (control)
group. The variance between group sizes
may have produced unbalanced results. The
higher attrition of the control group from
pre- to post-intervention may, at least in part,
be due to the intervention group receiving a
reminder text to complete the survey, while
the control received an email reminder. Oth-
er studies have found the automated text re-
minder to be a better form of communication
than email or mail for the college-aged, likely
because texts do not require self-motivation
to seek information by opening an email or
logging onto a website (Brown et al., 2014;
Hebden et al., 2014; Obermayer et al., 2004).
Therefore, the likelihood of attrition may be
decreased in future studies through the use of
texting reminders for both intervention and
control groups.

Additionally, all data in this study were
self-reported by participants. Questions re-
lated to food behaviors, stress levels, and
weight may have caused discomfort for some
participants, and participants may not have
answered the questions honestly. Finally,
given that participants were not screened for
pre-existing knowledge of IE prior to this
study, it is unknown if participants were ever
exposed to IE or had knowledge of IE prior
to this study. Pre-existing knowledge and use
of IE prior to the study may have affected the
degree of change in scores.

Conclusion
Previous research suggested texting pro-

grams are effective for delivery of nutrition
related information; however, this study
was the first to implement IE education in
a texting platform. The results of this study
found an IE texting intervention significantly
increased total intuitive eating habits within
a college student population. In addition, our

Effectiveness Of Intuitive Eating Intervention Through Text Messaging / 243

findings also indicated that while all college
students in the sample reported greater stress
from pre- to-post intervention, the texting
intervention was associated with a smaller
increase in stress than what was observed in
the control group. Therefore, the results of
the study provide evidence that texting can
be a successful platform for increasing IE
behaviors among college students, while also
suggesting that a texting intervention may
play a moderating role in the perceived stress
of college students. Based on the results of
other research, we believe texting programs
of longer duration would likely produce even
greater benefits among the college population
than what was observed in our study. Over-
all, we believe our results suggest that the
implementation of IE eating texting programs
on college campuses can increase student
healthy eat habits, which may reduce obesity
and promote healthy lifestyles.

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Drug Invention Today | Vol 10 • Issue 8 • 20181408

Awareness on balanced diet and eating practices among
college students – A survey
A. Swetaa1, R. Gayathri2*, V. Vishnu Priya2

INTRODUCTION
A balanced diet is one that provides the body with all
the essential nutrients, vitamins, and minerals required
to maintain cells, tissues, and organs as well as to
function correctly.

A diet that is lacking in nutrients can lead to many
different health problems ranging from tiredness and
lack of energy to serious problems with the function of
vital organs and lack of growth and development. The
number of calories in a food is a measurement of the
amount of energy stored in that food.[1-3]

Your body uses calories from food for walking,
thinking, breathing, and fatigue, and poor
performance. Children with a poor diet run the risk
of growth development problems and poor academic
performance, and bad eating habits can persist for

Research Article

1Department of Biochemistry, Saveetha Dental College, Saveetha Institute of Medical and Technical Science, Saveetha
University, Chennai, Tamil Nadu, India, 2Department of Biochemistry, Saveetha Dental College, Saveetha Institute of
Medical and Technical Science, Saveetha University, Chennai, Tamil Nadu, India

*Corresponding author: R. Gayathri, Department of Biochemistry, Saveetha Dental College, Saveetha Institute of
Medical and Technical Science, Saveetha University, 160, Poonamallee High Road, Chennai – 600 077, Tamil Nadu, India.
Phone: +91-9710680545. E-mail: gayathri.jaisai@gmail.com

Received on: 28-03-2018; Revised on: 29-04-2018; Accepted on: 21-05-2018

Access this article online

Website: jprsolutions.info ISSN: 0975-7619

the rest of their lives. Dietary habits are the habitual
decisions of individuals or group of people regarding
what foods they eat. Proper dietary choices require the
consumption of vitamins, minerals, carbohydrates,
proteins, and fats.[4-7]

Milk products play a significant role in human health.
An unhealthy diet is a major risk factor for a number
of chronic diseases including pressure diabetics,
abnormal blood lipids, overweight/obese, and cancer.
There are two therapies, one is the dietary therapy
to maintain a good and a healthy life and another
one is medical nutritional therapy, and we have also
traditional Chinese method.[8-11]

In addition to dietary recommendations for the general
population, there are many specific diets that have

primarily been developed to promote better health in
specific population groups. At the core of a balanced
diet are foods that are low in unnecessary fats and sugars
and high in vitamins, minerals, and other nutrients.[12]

In addition to dietary recommendations for the general
population, there are many specific diets that have

ABSTRACT

Introduction: A balanced diet is one that provides the body with all the essential nutrients, vitamins, and minerals required to
maintain cells, tissues, and organs as well as to function correctly. A diet that is lacking in nutrients can lead to many different
health problems ranging from tiredness and lack of energy to serious problems with the function of vital organs and lack of
growth and development. The number of calories in a food is a measurement of the amount of energy stored in that food.
Materials and Methods: Questionnaire was prepared and survey was conducted among college students about balanced diet
and eating practices, and data were collected. The survey was prepared on survey planet and was circulated among students.
The survey was conducted among 110 students. Results: About 82.4% of the college students are already aware of balanced
diet. Most of the college students say that their snacking is always junk and fast food, but still, they wanted to maintain a
balanced diet to stay healthy. Hence, many students are aware of balanced diet and their eating practices. Conclusion: The
survey was conducted among 110 students from that most of the college students are aware of balanced diet but still consume
junk as their snack, which is about 59.6%, and have unhealthy eating practices. I think they should consult a nutritionist so
that they would attain a balanced diet.

KEY WORDS: Balanced diet, College students, Eating practices, Nutrition

A. Swetaa, et al.

1409Drug Invention Today | Vol 10 • Issue 8 • 2018

primarily been developed to promote better health in
specific population groups such as people with high
pressure blood (as in low sodium diets or the more
specific DASH diet), or people who are overweight
or obese (in weight control diets). However, some of
them may have more or less evidence for beneficial
effects in normal people as well.[13-15]

A balanced diet is one that provides an adequate
intake of energy and nutrients for maintenance of the
body and therefore good health. A diet can easily be
adequate for normal bodily functioning yet may not
be a balanced diet. An ideal human diet contains fat,
protein, carbohydrates, vitamins, minerals, water, and
fiber all in correct proportions. These proportions vary
for each individual because everyone has different
metabolic rates and levels of activity.[9]

Malnutrition results from an unbalanced diet, this can
be due to an excess of some dietary components and
lack of other components, not just a complete lack of
food. Too much of one component can be as much
harm to the body as too little. Deficiency diseases
occur when there is a lack of a specific nutrient,
although some diet-related disorders are a result of
eating in excess. An adequate diet provides sufficient
energy for the performance of the body to function.[4]

Carbohydrates, fats, and proteins provide energy.
Proteins are a provider of energy in an emergency
but are primarily used as building blocks for growth
and repair of many body tissues. We also need much
smaller amounts of other nutrients such as vitamins
and minerals. Despite the small quantities needed,
these are essential to provide a healthy diet.[16-17] The
aim of the study is to create awareness on balanced
diet and eating practices among college students.

MATERIALS AND METHODS
The sample size of this study is 110. The study group
consists of students in the age group between 17 and
23 years. This was a questionnaire-based study. The
survey questions were prepared and administered
through survey planet using an online link. The
questions basically analyzed on different aspects
of their eating practices. Results were statistically
analyzed.

RESULTS AND DISCUSSION
The discussion covers various aspects of the
student’s diet and their eating practices. The students
who are aged between 17 and 23 years are very much
aware about balanced diet [Figure 1], but still, they
lack good eating practice. They consume more junk
during their college break time (59.6%) [Figure 2].
Most of the participants lack a regular exercise
[Figure 3].

Most of them have only two meals per day and intake
of nutritional food is inadequate. Most of them prefer
potatoes as their favorite vegetable, but potatoes
contain high amount of carbohydrates and fats which
unhealthy for an individual [Figure 4]. 33% of them
eat chips rather than fruits and nuts.

39.4% of the participants suffer from headache.
Most of the participants are stressed and have lack
of confidence in their college life. It is evident fear
that of facing difficulties in academics, extracurricular

Figure 1: Are you aware of balanced diet?

Figure 2: Do you exercise?

Figure 3: When you feel stressed, do you feel any of the
symptoms?

Figure 4: What vegetable you like the most?

A. Swetaa, et al.

Drug Invention Today | Vol 10 • Issue 8 • 20181410

activities, and other college activities are more
[Figure 5].

Principle of balanced diet article says that balanced
diet and eating practices are different among students.
This survey concludes that students have unhealthy
balanced diet because of consumption of junk and
their eating practices are poor.[18-20] From this survey,
awareness on balanced diet and eating practices
among college students was created.

CONCLUSION
The aim of this is to create awareness on balanced diet
and eating practices among college students. Most of
the college students are aware of balanced diet but still
fail to attain. From this survey, awareness on balanced
diet and eating practices among college students was
created.

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Figure 5: What about your snaking?

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AfricanJournal for Physical, Health Education, Recreation and Dance (AJPHERD)

September 2011 (Supplement 1), pp. 56-69.

Perceived benefits and barriers to physical exercise participation

of first year university students

V .T NOLAN, M. SANDADA AND J. SURUJLAL

Faculty of Management Sciences, Vaal University of Technology, Private Bag X021,

Vanderbijlpark 1900, Republic of South Africa E-mail: babs@vut.ac.za

Abstract

Regular participation in exercise is associated with disease prevention and provides many

benefits. Physical exercise plays a key role in the promotion of good health. However, very few

young people participate in physical exercise. The purpose of this study was to identify the

perceived benefits and barriers to participation in physical exercise among first year students in a

South African university. A quantitative approach was used. Academics at three different

campuses at Vaal University of Technology (South Africa) administered an adapted version of

the Exercise Benefits Barriers Scale (EBBS) to first year entering university students.

Descriptive, one-way repeated measure ANOVA and independent t-tests were applied to

determine the perceived benefits and barriers associated with participation in physical exercise.

The study identified lack of facilities and tiredness as the constraining factors. Improved health,

physical performance, psychological outlook and life enhancement were perceived as the

strongest benefits. First year students perceive participating in exercising to have more benefits

than adverse effects. As a result, students will be more motivated to overcome the barriers they

face and hinder them in their participation in physical exercise in order to make physical

exercise part of their daily lives.

Key words: Benefits, barriers, physical exercise, university students, health, participation

How to cite this article:

Nolan, V.T., Sandada, M. & Surujlal, J. (2011). Perceived benefits and barriers to physical

exercise participation of first year university students. African Journal for Physical, Health
Education, Recreation and Dance, September (Supplement 1), 56-69.

Introduction

Regular participation in exercise activities is associated with disease prevention

and has many physical, psychological and physiological benefits (Lopez,

Gallegos & Extremera, 2010). However, despite the extensive body of literature

that documents the health benefits of physical activity participation among

young people, evidence shows that very few young people participate in physical

exercises (Daskapan, Tuzun & Eker, 2006). Schutzer and Graves (2004) have

noted that although there are perceived benefits of exercises, there are several

barriers that hinder exercise participation. Given the high prevalence of obesity

and obesity-related health problems (Lovell, Ansari & Parker, 2010) increased

research efforts are needed to explore the perceived benefits and barriers of

exercise participation by university students. Information gleaned from such

research could contribute to designing relevant intervention policies and

programmes to promote the health consciousness of the young population.

mailto:babs@vut.ac.za

Perceived benefits and barriers to physical exercise participation 57

Schutzer and Graves (2004) define physical activity as any movement of the

body that is produced by skeletal muscles and requires the use of energy.

Physical activity sub-categories include exercise, leisure-time physical activity,

and lifestyle activity (Henderson & Ainsworth, 2003). Exercise is viewed as

planned, purposeful and structured training to gain physical fitness, whereas

leisure-time physical activity comprises any physical activity carried out during

one‟s spare time, and lifestyle activity comprises activities such as gardening,

yard work, house work and climbing stairs (Henderson & Ainsworth, 2003).

This study adopts a definition of physical exercise suggested by Slattery,

Edwards, Khe-ni, Friedman and Potter (1997), where both moderate and

vigorous activities are considered as categories of physical activities. Moderate

activities consist of brisk walking, raking, gardening, cleaning windows,

mopping floors, swimming, canoeing, volleyball, jogging, walking uphill

leisurely, mowing the lawn and weeding. Some of the vigorous physical

activities include strenuous physical activities such as weightlifting, jogging or

running fast, aerobic dance, fast swimming, shovelling, chopping wood,

gardening with heavy tools, tennis, basketball, soccer, and cross country at

increased speed (Slattery et al., 1997).

There are varied benefits that are associated with participation in physical

activities. Using evidence of a cross-sectional study in Finland, Hassmen,

Koivula and Uutela (2000) showed that active participation in physical

exercise

results in lower depression, anger, cynical distrust, and stress. The study further

indicated that regular exercisers enjoy healthier lives and general well being as

well as higher levels of physical fitness and a stronger feeling of social

integration and higher levels of coherence than those who do not exercise. The

importance of physical exercise was also reinforced by Salmon‟s (2001) study

that reported that exercise training has antidepressant effects because exercisers

develop enduring resilience to stress and consequently reduce premature

mortality. Furthermore, Dubbert (2002) identified several benefits associated

with physical exercise participation which include: reducing obesity, maintaining

healthy joints, controlling pain, building stronger bone mass as well as

improving endurance, strength and balance. Arora, Stoner and Arora (2006)

enumerated the gains of exercise such as reducing risk of heart disease, reducing

cholesterol levels, regulating sugar levels thereby reducing risk of developing

diabetes, maintaining body weight or reducing body fat and hence obesity,

building and maintaining healthy muscles, bones and joints, and for preventing

cancer. Cokerill (1995) posits that physical exercise promotes self-esteem and

self-efficacy. This is resonated by Henderson and Ainswoth (2003) who claim

that physical exercise is associated with feeling good, being with others,

maintaining a healthy life, and experiencing spiritual and psychological gains.

Six major perceived benefits to physical exercise are reported in the study by

O‟Dea (2003). First are the social benefits that include fun or enjoyment,

58 Nolan, Sandaba and Surujlal

socialising with friends and team mates, and developing life skills. Second are

the psychological enhancement gains such as sense of achievement, pride and

self-esteem, mood invigoration, discipline development, and enjoyment of goals

and challenge realisation. The third group of benefits are the physical sensation

gains that enable exercisers to feel good physically in aspects such as feeling of

refreshment, energy boosting, fatigue reduction, increased fitness and strength as

well as enhanced sleep. An enhanced performance in sports is the fourth benefit

and it covers skill development and an improvement in fitness, strength,

endurance, agility, muscle flexibility, and reflexes. The fifth class of benefits are

cognitive gains that consist of mind clearing and thinking, enhancement of

concentration and brain function. Finally the coping benefits are those gains of

physical exercise that encompass stress relief, relaxation, distraction from

worries, reduction in aggression, frustration, and anger.

The benefits of physical exercise are further highlighted by Blomstrand,

Björkelund, Ariai, Lissner and Bengtsson (2009) whose cross-sectional analysis

of Swedish women reports strong associations between physical exercise activity

and the experience of well-being. The study found that increased physical

exercise participation promotes both health and well-being of exercisers.

Similarly, in their study of around 8000 respondents in the Netherlands, Stubbe,

de Moor, Boomstra, and de Geus (2007) report that exercisers are more satisfied

with their life and are happier than non-exercisers. The study concluded that

there is a strong relationship between physical exercise participation and high

levels of life satisfaction and happiness. Recent evidence about the importance of

exercises in producing positive physiological and health benefits is presented by

Lovell et al. (2010) and Burke and McCarthy (2011). According to Lovell et al.

(2010), the perceived benefits from exercise were: physical performance,

psychological outlook, preventive health, life enhancement, and social

interaction. This is consistent with Burke and McCarthy‟s (2011) findings on the

lifestyle behaviours and exercise beliefs of undergraduate student nurses in

Ireland which noted that physical exercise is beneficial as it helps to achieve

ideal body images and a sense of personal accomplishment.

With regard to barriers to physical exercise participation, substantial evidence

exists to show that many perceived barriers exist to inhibit people from adopting

and maintaining an active lifestyle (Schutzer & Graves, 2004; Daskapan et al.,

2006; Burke & McCarthy, 2011). A barrier to physical exercise is described by

Henderson and Ainsworth (2003) as a constraint that inhibits a person from

being physically active. In their study of perceptions about physical activity

among older African American and American Indian women, Henderson and

Ainsworth (2003) reported that the perceived constraints include a range of

problems such as finding time and space to exercise, time constraint, job

demands, physical tiredness and illness, major life changes or traumas, safety

issues, disabling weather and environment, the bother of personal care such as

Perceived benefits and barriers to physical exercise participation 59

hair dressing and showering, lack of facilities and opportunities. It is interesting

here to note that Henderson and Ainsworth (2003) report about physical

tiredness as one of the barriers found in their study but O‟Dea (2003) reports that

physical exercise is beneficial at it reduces tiredness.

Higgins, Lauzon, Yew, Bratseth and McLeod (2010) found that issues of time,

finances, workload, and lack of accessible and affordable facilities were major

barriers inhibiting Canadian university students from wellness programmes.

Lopez et al. (2010) identified both internal and external perceived barriers by

Spanish university students in the practice of physical exercise activities. While

on one hand external barriers refer to constraints such as time constraints and

lack of social support, on the other hand internal constraints are barriers ranging

from disliking exercise, ignorance about the benefits of exercise, laziness, and

lack of confidence in one‟s ability to exercise (Lopez et al., 2010). Similarly,

high school students interviewed in Canada by Allison, Dwyer and Makin (1999)

reported that time constraints due to schoolwork, existence of other interests and

family activities were barriers that were considered as important. Other

constraints were mood, lack of energy, lack of self-discipline, discomfort, cost,

stress, lack of family and friends support, illness and injury (Allison et al., 1999).

Reichert, Barros, Domingues and Hallal‟s (2007) study found a strong

association between physical inactivity and lack of money, feeling too tired,

dislike of exercising, and lack of company. Besides time constraints and lack of

social support, the other major barriers identified by O‟Dea (2003) were

preference for indoor activities, low level of energy, lack of self-motivation, low

levels of motivation from others and low perceived rewards. Schutzer and

Graves (2004) found environmental constraints including absence of sidewalks,

parks, recreation centres, fitness facilities and security, as barriers to exercise.

The authors also state that lack of physician advice and lack of knowledge can

also negatively affect exercise participation. The other barriers to physical

exercise include lack of information and expensive facilities (Samir, Mahmud &

Khuwaja, 2011), long distance to exercise facilities (Burke & McCarthy, 2011)

and family discouragement (Lovell et al., 2010).

Very few studies have been conducted to identify first year university students‟

perceived benefits and barriers of exercise activity which is lacking. This study

attempts to fill the gap in research. Universities are well-positioned

health

promotion stakeholders since they offer the „last opportunity‟ in the provision

health and wellness education especially to incoming or first year university

students (Higgins et al., 2010).

The study is necessary because physical exercise is now regarded to play a key

role in the promotion of good health. In order to promote a healthy young

population, it is crucial for stakeholders to have an insight the perceived benefits

60 Nolan, Sandaba and Surujlal

and barriers to physical exercise in South Africa. The outcome of this study

could inform policy holders and other stakeholders about the development of

policy and intervention strategies to promote the health of young adults. The

current study is the first phase of a longitudinal study. The researchers intend to

replicate the study with the same sample in their third year of study to ascertain

whether similar perceived barriers and benefits still exist. It would be interesting

to determine if and how the perceived benefits and barriers have changed as

students‟ progress towards their final year and to enter the workforce. Steptoe, et

al. (2002) found that beliefs about the importance of regular exercise remained

stable overall over a decade.

The purpose of this study was to identify perceived benefits and barriers to

physical exercise in first year university students in South Africa. To achieve the

purpose of the study, the following objectives are formulated:

To ascertain the perceived benefits and barriers to exercise among first year
university

students

To identify if differences exist between male and female university
students‟ perception of benefits and barriers to exercise.

Methodology

Sample

Purposive sampling was used to select 507 first year university students from

three university campuses in the Gauteng province of South Africa. Patton
(2002) describes a purposive sample as a non-representative subset of so

me

larger population (in this study first year freshman university students) and is

constructed to serve a very specific need or purpose. In this study, the

participants were incoming students who were doing their freshman year

regardless of academic programmes.

Of the 507 questionnaires administered, 462 (91%) completed questionnaires

were returned. The mean age of the first year students was 19.98±2.25 years and

almost all the students were single (99%). More female students (58%) than male

students (42%) participated in the study. Most students (45%) perceived their

fitness level as moderate active, 30% followed an on-off active lifestyle; 22%

were very active and only 3% indicated that they lead sedentary lifestyle.

Instrument and procedures

In order to identify the perceived benefits and barriers associated with physical

exercise participation among first year university students, an adapted version of

a reliable and validated instrument, the Exercise Benefits Barriers Scale (EBBS)

Perceived benefits and barriers to physical exercise participation 61

(Sechrist, Walker & Pender, 1987) was used. The questionnaire comprised two

sections. Section A requested the participants to provide demographic

information. Section B of the questionnaire sought responses regarding benefits

and barriers associated with participation in physical exercise. Items in Section B

of the questionnaire were scored on a four-point Likert scale ranging from

strongly disagree (1) to strongly agree (4).

The benefit component comprised 29 questions categorised into five subscales:

life enhancement (8 questions), physical performance (8 questions),

psychological outlook (6 questions), social interaction (4 questions) and

preventative health (3 questions). The barrier component comprised 14 questions

categorized into four subscales: exercise milieu (6 questions), time

expenditure

(3 questions), physical exertion (3 questions) and family discouragement (2

questions). For the current study, the internal consistency for the benefit

component was α=0.866 and α=0.692 for the barrier component. The overall

internal consistency for Section B was α=0.737. Both these consistencies were

within the suggested benchmark of 0.7 (Nunnally, 1978:245) indicating that the

benefit and barrier scale are reliable.

Academics at three different campuses were requested to administer the

questionnaires to first year students. While a non-probability convenience

sampling procedure was utilized, randomisation of data collection was ensured

by administering questionnaires at three different campuses. This also allowed

for a large number of questionnaires to be administered to respondents in a

relatively short period of time (Churchill, 2001).

Data analysis

The returned questionnaires were subjected to editing and coding for input into

the Statistical Package for the Social Sciences (PASW Statistics – version 18 for

Windows). To ascertain the perceived benefits and barriers of participation in

physical exercise, percentages were calculated for each EBBS question. To

establish the greatest perceived benefit of exercise as well as barrier(s) with

strongest inhibiting effect to exercise, a one-way repeated measure ANOVA was

applied. The independent t-test was applied to test if there is a significant

difference between female and male students‟ perception of benefits and barriers

to exercise activities. Cohen‟s D was also applied to determine the practical

effect of the statistical significance difference. A value larger than 0.8 indicates a

large effect, 0.5 – 0.8 a medium effect and 0.2 – 0.5 a small effect (Cohen, 1988).

Results

The perceived benefits to exercise according to EBBS and percentages to each

question in the benefit sub-scale are provided in Table 1. Most students agreed

62 Nolan, Sandaba and Surujlal

with the statements reflecting benefits that are associated with participation in

regular exercise.

Table 1: The exercise benefits scale: percentage for each questionnaire item

Perceived Benefit Item Strongly

dis

agree

Disagree Agree Strongly

agree

Preventive Health Subscale

I will prevent heart attacks by exercising 2.8 9.7 46.8 40.7

Exercising will keep me from having high blood

pressure

4.3 8.9 48.9 37.9

I will live longer if I exercise 5.4 11.7 38.1 44.8

Physical Performance Subscale

Exercise increases my muscle strength 1.5 11.9 49.4 37.2

Exercising increases my level of physical fitness 2.4 5.8 42.2 49.6

My muscle tone is improved with exercise 3.5 13.2 60.6 22.7

Exercising improves functioning of my

cardiovascular system

3.2 11.7 62.8 22.3

Exercise increases my stamina 3.2 9.1 50.4 37.2

Exercise improves my flexibility 1.9 4.5 47.8 45.7

My physical endurance is improved by

exercising

0.9 12.1 63.4 23.6

Exercise improves the way my body looks 1.9 7.4 39.0 51.7

Psychological Outlook Subscale

I enjoy exercise 2.4 10.0 61.7 26.0

Exercise decreases feelings of stress and tension

for me

1.9 8.9 48.1 41.1

Exercise improves my mental health 1.9 7.4 48.3 42.4

Exercise gives me a sense of personal

accomplishment

1.7 12.8 61.5 24.0

Exercising makes me feel relaxed 3.7 13.6 53.2 29.4

I have improved feelings of well-being from

exercise

3.7 10.4 60.2 25.8

Life Enhancement Subscale

My disposition is improved by exercise 5.6 26.4 59.5 8.4

Exercising helps me sleep better at night 3.7 11.5 47.2 37.7

Exercise helps me decrease fatigue 4.8 21.9 56.5 16.9

Exercising improves my self-concept 1.7 10.4 67.7 20.1

Exercising increases my mental alertness 3.7 13.0 57.1 26.2

Exercise allows me to carry out normal activities

without becoming tired

2.2 13.9 51.7 32.3

Exercise improves the quality of my work 5.0 17.7 55.4 21.9

Exercise improves overall body functioning for

me

1.9 6.9 60.6 30.5

Social Interaction Subscale

Exercising lets me have contract with friends and

persons I enjoy

11.3 25.3 41.1 22.3

Exercising is a good way for me to meet new

people

11.3 22.1 45.2 21.4

Exercise is good entertainment for me 5.8 17.5 46.3 30.3

Exercising increases my acceptance by others 13.2 35.1 40.7 11.0

Perceived benefits and barriers to physical exercise participation 63

In particular, the students strongly agreed with the following statements:

„exercising increases my level of physical fitness‟, „exercise improved the way

my body looks‟ and „I will live longer if I exercise‟.

Although most of the students either agreed or strongly agreed with the

statements classified under the social interaction subscale, approximately 23%

and 48% either strongly disagreed or disagreed with the statements. Specifically,

48.3% of the students did not perceive that „exercising increased their acceptance

by others‟ as a benefit.

The perceived barriers to exercise according to EBBS and responses to each

question expressed as a percentage in the barrier sub-scale are provided in Table

2. Most students disagreed with the statements that reflected barriers that are

associated with participation in regular exercise. Specifically, the students

strongly disagreed with the statements: „I am embarrassed to exercise‟, „it cost

too much money to exercise‟ and „my family members do not encourage me to

exercise‟. These were not viewed as barriers that hinder them in their exercise

participation.

Table 2: The exercise barriers scale: percentage for each questionnaire item.

Perceived Barriers Item Strongly

disagree

Disagree Agree Strongly
agree

Physical Exertion Subscale

Exercise tires me 10.4 38.3 38.1 13.2

I am fatigued by exercise 14.3 47.4 34.6 3.7

Exercise is hard work for me 31.0 43.5 18.2 7.4

Exercise Milieu Subscale

Places for me to exercise are too far away 20.3 45.7 20.1 13.9

I am too embarrassed to exercise 61.3 32.3 4.3 2.2

It costs too much money to exercise 50.2 35.3 9.7 4.8

Exercise facilities do not have convenient

schedules for me

12.6 51.3 32.3 3.9

I think people in exercise clothes look funny 33.3 38.7 19.3 8.7

There are too few places for me to exercise 13.9 32.9 38.3 14.9

Time Expenditure Subscale

Exercising takes too much of my time 22.5 58.9 12.1 6.5

Exercise takes too much time from family

relationships

33.5 50.2 10.8 5.4

Exercise takes too much time from my family

responsibility

31.8 52.2 10.8 5.2

Family Discouragement Subscale

My spouse (or significant other) does not

encourage exercising

37.2 42.2 16.0 4.5

My family members do not encourage me to

exercise

40.9 35.1 15.2 8.9

The overall disagreements of students with the statements in the barrier scale

indicate that there are very few barriers to participation in exercise for the

64 Nolan, Sandaba and Surujlal

students. “There are too few places for me to exercise” was perceived as a barrier

that hinders most students (38.3%) in participating in exercise.

The greatest perceived benefits from exercise was preventative health

(mean=3.23) and physical performance (mean=3.22), followed by psychological

outlook, life enhancement and social interaction (Table 3). Preventative health

and physical performance was rated significantly higher than life

enhancement

and social interaction. Physical performance was also rated significantly higher

than psychological outlook. However, life enhancement and social

interaction

were rated significantly lower than the other benefits subscales. The average

mean for preventative health, physical performance, psychological outlook and

life enhancement was greater than 3, which suggests that the students agreed

with the statements in the benefits subscales.

The barrier with the strongest inhibiting effect to physical exercise participation

is physical exertion, followed by exercise milieu, time expenditure and family

discouragement. Physical exertion was rated significantly higher than the other

barriers listed in Table 3. Exercise milieu was also rated higher than time

expenditure and family discouragement. No significant difference was found

between time expenditure and family discouragement. The average mean for the

four barriers was approximately 2, which indicate that the students disagreed

with the statements in the subscales implying that the students do not perceive

physical exertion, exercise milieu, time expenditure and family

discouragement

as barriers that hinder their participation in exercise.

Table 3: Descriptive statistics for the perceived benefits and barriers sub-scale

Mean (SD) Sub-scale

1 2 3 4 5

Benefits subscale

1. Preventative health 3.23 (0.55) – 1.000 0.108 0.000* 0.000*

2. Physical performance 3.22 (0.43) – 0.013* 0.000* 0.000*

3. Psychological outlook 3.16 (0.44) – 0.000* 0.000*

4. Life enhancement 3.02 (0.39) – 0.000*

5. Social interaction 2.76 (0.62) –

Barriers sub-scale

1. Physical exertion 2.28 (0.56) – 0.000* 0.000* 0.000*

2. Exercise milieu 2.05 (0.46) – 0.000* 0.000*

3. Time expenditure 1.93 (0.60) – 1.000

4. Family discouragement 1.90 (0.70) –

* Significant at p≤ 0.05

A comparison of the means (Table 4) across the first year students for each

perceived benefit to exercise activities revealed that both female and male

students agreed that preventative health, physical performance, psychological

outlook and life enhancement are benefits associated with participation in

exercise. If the mean for the social interaction benefit is rounded numerically, the

mean will be 3, indicating that students agreed that social interaction is a benefit

Perceived benefits and barriers to physical exercise participation 65

to participation in exercise. The mean of the responses for male students were

slightly higher for all the benefits, except for preventative health. A statistical

significant difference was found between male and female students for the

physical performance benefit, indicating that male students perceived physical

performance more important than female students. No statistical significant

difference was found for the preventative health, psychological outlook, life

enhancement and social interaction benefit, indicating that female and male

students share similar views. The Cohen‟s D value was less than 0.2 indicating

that no practical effect was found for the physical performance benefit or any

other benefit in Table 4.

Table 4: Descriptive statistics, statistical and practical significance of the perceived benefits of

exercise

Preventative

health

Physical

performance

Psychological

outlook

Life

enhancement

Social

interaction

Mean

(SD)

Female

students

3.25 (0.54) 3.16 (0.43) 3.15 (0.44) 3.01 (0.37) 2.71

(0.59)

Male

students

3.20 (0.56) 3.31 (0.42) 3.17 (0.44) 3.03 (0.41) 2.82

(0.65)

t-test t value 0.927 3.705 0.312 0.372 1.917

Significance 0.354 0.000* 0.755 0.710 0.056

Practical

effect

Cohen‟s D 0.002 0.029 0.000 0.000 0.008

Effect None None None None None

* Significant at p≤ 0.05

A comparison of the means (Table 5) across the first year students for each

perceived barrier to exercise activities revealed that both female and male

students disagreed that physical exertion, exercise milieu, time expenditure and

family discouragement are barriers associated with participation in exercise.

Table 5: Descriptive statistics, statistical and practical significance of the perceived barriers to

exercise
Physical

exertion

Exercise

milieu

Time

expenditure

Family

discouragement

Mean (SD) Female students 2.26 (0.54) 2.01 (0.45) 1.97 (0.63) 1.94 (0.712)

Male students 2.29 (0.58) 2.10 (0.47) 1.91 (0.57) 1.87 (0.70)

t-test t value -0.513 2.107 1.109 1.033

Significance 0.608 0.036* 0.268 0.302

Practical
effect

Cohen‟s D 0.001 0.010 0.003 0.002

Effect None None None None

* Significant at p≤ 0.05

The mean responses for male students were slightly higher for physical exertion

and exercise milieu barrier, whereas the mean responses for female students were

higher for time expenditure and family discouragement barrier. A statistical

significant difference was found between male and female students for the

exercise milieu barrier, indicating that male students disagreed more towards the

barrier than female students. No statistical significant difference was found for

66 Nolan, Sandaba and Surujlal

the other barriers listed in Table 5, indicating that male and female students share

similar views. The Cohen‟s D value is less than 0.2 indicating that no practical

effect was found for the physical exertion, exercise milieu, time expenditure and

family discouragement barrier.

Discussion

In contrast to findings in previous studies (e.g. Allison, Dwyer, Goldenberg,

Fein, Yoshida Boutilier, 2005; Gyurcsik, Bray & Brittain, 2004; Grubbs &

Carter, 2002) which reported that lack of time was the greatest barrier to

participation in physical exercise, the current study identified the lack of

facilities or infrastructure and tiredness as constraining factors. Being first year

students and the fact that most of these students may be living in areas away

from their home environment; the possibility exists that they may not have fully

acclimatised to their new environment. They thus perceive that there is a lack of

facilities at which they can exercise. With regard to the tiredness that they

experience, it should be acknowledged that these students are fresh from the

school environment where they were literally „spoon fed‟. The new environment

that they find themselves in is much more demanding in that they are expected to

work independently as well as accustom themselves to a new environment. It

may also be possible that the cultural background from which these students

come could have an influence on their participation in physical exercise (Lovell

et al., 2010). If the norm, according to the student‟s background was not to

exercise, this would impact on his/her attitude towards participation which would

ultimately influence his/her fitness levels. This may also be related to the

responsibility that students have towards maintaining a healthy lifestyle. Lee and

Yuen Loke (2005) found that students had a limited sense of health

responsibility and relatively few engaged in physical activity and practised

nutritional habits.

The strongest perceived benefits for students were related to improved health and

physical performance, enhanced psychological outlook and life enhancement.

This finding echo Dreyer, Strydom and Malan‟s (1988) finding that participating

in physical activity and a healthy lifestyle are considered to be the most

important factors in maintaining optimal health. The benefit of exercise on the

psychological outlook of students, indicate that student participate in exercise

because it is fun, reduce their stress levels and improve their mental well-being.

This is consistent with studies by Nolan and Surujlal (2009, 2010). It is also

supported by Alla, Olubayo-Fatiregun and Adeniran (2007) that exercise diverts

one‟s mind from stress, provides one with a positive outlook on life and impacts

positively on work-related stress.

A surprising finding was that social interaction scored lowly as a benefit, while it

is understandable taking into account the context in which the student is

Perceived benefits and barriers to physical exercise participation 67

functioning. Being in the first year of study in a totally new environment the

student is still finding his/her feet and may therefore not focus on social

interaction. One may, however, argue that being in a new environment students

would more than likely attempt to make new friends and socialise. Huddleston,

Mertesdorf and Araki (2002) highlighted the strong social interaction benefits

associated with participation in physical exercise. In addition, the World Health

Organisation posits that engaging in physical activities give young people

opportunities to have social interaction and integration as well as for learning the

spirit of solidarity and fair play, among others (WHO, 2002).

Conclusion

Participation in physical exercise poses many benefits and if a student engages in

an exercise that he/she enjoys a healthy life. This study found that, amongst first

years students, benefits regarding participation in exercise prevail over barriers

experience in participation in exercise activities. First year students recognise the

benefits of participating in exercises and face barriers that hinders them in their

participation in physical exercise. To help students face the barriers, they need to

understand the barriers and apply creative strategies to overcome them and this

could help students to make physical exercise part of their daily lives.

Universities are ideal settings, because they are transitional periods that offer

good conditions for the acquisitions of healthy lifestyles.

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Contents lists available at ScienceDirect

Eating Behaviors

journal homepage: www.elsevier.com/locate/eatbeh

Starting university with high eating self-regulatory skills protects students
against unhealthy dietary intake and substantial weight gain over 6 months

Nathalie Kliemanna, Helen Crokera, Fiona Johnsona, Rebecca J. Beekena,b,⁎

a Department of Behavioural Science & Health, University College London, London, UK
b Leeds Institute of Health Sciences, University of Leeds, Leeds, UK

A R

T

I C L E I N F O

Keywords:
Weight change
Eating behaviours
Self-regulation
Population studies
Freshman year

A B S T R A C T

Background: There is consistent evidence that suggests first year students are at risk of weight gain, but the
reasons for this vulnerability are still unclear. This study aimed to explore whether the ability to regulate eating
behaviours is a predictor of weight and dietary changes in first year undergraduate students.
Methods: First year undergraduate students from universities situated in London were invited to complete a
survey at the beginning of the academic year and at 6-month follow-up. Each survey included the Self-Regulation
of Eating Behaviour Questionnaire, food frequency questions, socio-demographic questions and anthropometric
questions. Linear and logistic regressions were performed to explore the associations between baseline eating
self-regulatory skills and weight and dietary changes.
Results: 481 first year undergraduate students took part in the study. Students who entered university with
higher eating self-regulatory skills were more likely to maintain or achieve a higher fruit and vegetable
(OR = 1.8, p = 0.007) and a lower sweet and salty snack (OR = 1.9, p = 0.001) intake over the course of the
first 6 months in university. Higher baseline eating self-regulatory skills were also related to lower weight
changes (β = −0.15, p = 0.018) and lower likelihood of gaining 5% initial body weight (OR = 0.52, p = 0.006)
at 6-month. Additionally, self-regulatory skills moderated the relationship between baseline BMI and weight
changes (β = −0.25, p ≤0.001) and between baseline BMI and 5% weight gain (OR = 0.82, p = 0.008).
Conclusions: Starting university with higher eating self-regulatory skills may help students to maintain or
achieve a healthy diet and protect them against substantial weight gain, especially among students with over-
weight.

1. Introduction

The transition to university is a period characterised by changes in
lifestyle, environment and responsibilities. In the late 1990’s, a belief
that this period leads to dramatic weight gain, identified as the
‘Freshman 15 pounds (6.8kg)’ was widely spread by newspapers and
academic articles (Brown, 2008; Graham & Jones, 2002). More recent
studies have indicated a lower, but still significant, weight gain among
students starting university (Crombie, Ilich, Dutton, Panton, & Abood,
2009; Morrow et al., 2006). A review and meta-analysis (Vella-Zarb &
Elgar, 2009) found students gain on average 1.75 kg (95%CI 1.73; 1.77)
over the course of their first year.

However, the reasons for this vulnerability to weight gain and in-
dividual differences in the experience are still unclear. Reviews suggest
weight gain in first year undergraduate students is associated with high
baseline weight, dietary changes, decreases in physical activity, living

in residential halls, level of stress, and dietary restraint (Crombie et al.,
2009; Vella-Zarb & Elgar, 2009). Genetic influences may also play a role
(Meisel, Beeken, van Jaarsveld, & Wardle, 2015). However, higher
baseline weight is not always a predictor of weight gain. A study con-
ducted with 120 first year students from the UK found that students
with a lower baseline weight actually gained the most weight over a 12-
month period (Finlayson, Cecil, Higgs, Hill, & Hetherington, 2012).
Regarding the relationship between dietary changes and weight gain, a
study with first year students from the United States found that weight
gain in male students (N = 140) was predicted by an increase in alcohol
consumption whereas in female students (N = 256) it was predicted by
lower fruit and vegetable intake (Economos, Hildebrandt, & Hyatt,
2008). In contrast, some studies have found that dietary behaviours
neither change nor predict weight gain in first year undergraduate
students (Boyce & Kuijer, 2015; Nikolaou, Hankey, & Lean, 2015).
These inconsistencies may be due to a lack of power to detect changes

https://doi.org/10.1016/j.eatbeh.2018.09.003
Received 22 December 2017; Received in revised form 10 August 2018; Accepted 14 September 2018

⁎ Corresponding author at: Leeds Institute of Health Sciences, University of Leeds, Leeds LS2 9JT, UK.
E-mail address: r.beeken@leeds.ac.uk (R.J. Beeken).

Eating Behaviors 31 (2018) 105–

112

Available online 15 September 2018
1471-0153/ © 2018 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license
(http://creativecommons.org/licenses/BY-NC-ND/4.0/).

T

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https://doi.org/10.1016/j.eatbeh.2018.09.003

https://doi.org/10.1016/j.eatbeh.2018.09.003

mailto:r.beeken@leeds.ac.uk

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http://crossmark.crossref.org/dialog/?doi=10.1016/j.eatbeh.2018.09.003&domain=pdf

or due to the use of different measures to assess weight, physical ac-
tivity and dietary behaviours.

However, it is important to note that weight gain over the first year
at university may not always represent a concerning change. Small
weight gains may represent natural daily weight fluctuation (Orsama
et al., 2014) or even be a positive change for people who had a very low
body mass index (BMI). There is also evidence that some students may
experience weight loss during this transition (Gillen & Lefkowitz, 2011;
Vadeboncoeur, Foster, & Townsend, 2016). Thus, further research into
the mechanisms of weight change (as opposed to just the drivers of
weight gain) during the transition to university is warranted.

It has been suggested that stress may increase both risk of weight
loss and weight gain (Serlachius, Hamer, & Wardle, 2007). According to
Boyce and Kuijer (2015) people who enter university with higher levels
of stress and lower BMI may lose weight, while those with higher BMI
may gain weight. Studies have also shown that increased social support
may be a possible buffer of the negative effect of stress on weight gain
over the freshman year, especially among men (Darling, Fahrenkamp,
Wilson, Karazsia, & Sato, 2017). Increases in physical activity and de-
creases in calorie intake may also lead to weight loss during the tran-
sition to university (Hootman, Guertin, & Cassano, 2017). However, the
transition to university has also been linked to an increased risk of
developing eating disorders (Delinsky & Wilson, 2008; Striegel-Moore,
Silberstein, & Rodin, 1986). Delinsky and Wilson (2008) found that
women with higher dietary restraint and concerns about their weight
during the first year at university were more likely to lose weight and
show disordered eating symptoms.

However, with respect to dietary restraint, that is – the intention to
eat less in order to stay in shape (Herman & Polivy, 1975), and its re-
lationship with weight changes, other studies have shown conflicting
results. For example, Provencher et al. (2009) found in a cohort of first
year students (N = 2921) from Canada that high levels of dietary re-
straint were related to both weight loss and weight gain. Researchers
have suggested that some restraint scales, such as the Restraint Scale
(Herman & Polivy, 1975), assess a range of personality traits and eating
tendencies (such as the susceptibility to overeat and weight fluctuation)
rather than the intent to exercise dietary restraint, and that this may
have contributed to mixed results (Hagan, Forbush, & Chen, 2017;
Laessle, Tuschl, Kotthaus, & Pirke, 1989; Williamson et al., 2007). As a
result, researchers have developed psychometric scales assessing just
dietary restraint and no other traits, but this has not solved the issue of
inconsistent results for the relationship with weight control (Johnson,
Pratt, & Wardle, 2012; Williamson et al., 2007). Some authors have
argued that inconsistent results may be because some restrained dieters
have higher eating self-regulatory skills than others and may be more
capable of maintaining or losing weight (Hays & Roberts, 2008;
Johnson et al., 2012; Phelan et al., 2009).

Self-regulatory skills are often conceptualized as the individual’s
ability to alter their behaviour, thoughts, feelings and attention in the
pursuit of their personal goals (Boekaerts, Maes, & Karoly, 2005; Carver
& Scheier, 2001; De Vet et al., 2014; Moilanen, 2007), for example, the
ability to inhibit a desire to have a sweet in order to stay healthy. Most
theoretical models define self-regulatory skills as a continual and multi-
level process involving self-monitoring; appraising progress and at-
tempting to approach or maintain the desired goal; making adjustments
to it when necessary or giving up (Bandura, 1991; Baumeister, Vohs, &
Tice, 2007; Rasmussen, Wrosch, Scheier, & Carver, 2006; Schwarzer,
2008).

Given the dramatic changes in routine, environment and social life
experienced by first year undergraduate students, some level of self-
regulatory skills may be required to keep healthy habits and/or build
new ones due to disruptions of old habitual behaviours. The new en-
vironment may also increase demands on self-regulation to inhibit
impulses towards food temptations, since students can experience a
high exposure to unhealthy food options at university (Grech, Hebden,
Roy, & Allman-Farinelli, 2016).

A recent online study conducted with 923 adults in the UK showed
that higher eating self-regulatory skills were related to higher fruit and
vegetables intake and to lower unhealthy snack intake and sugary
drinks intake, as well as lower BMI (Kliemann, Beeken, Wardle, &
Johnson, 2016). Similar results were found in studies conducted spe-
cifically with undergraduate students (Price, Higgs, & Lee, 2017;
Schroder, Ollis, & Davies, 2013; Tomasone, Meikle, & Bray, 2015).
However, the majority of these studies had cross-sectional designs,
which cannot indicate causality. Additionally, although the transition
to university tends to promote weight gain and unhealthy dietary
changes (Vella-Zarb & Elgar, 2009), no study has assessed the asso-
ciations between self-regulatory skills and weight and dietary changes
among first year undergraduate students.

Therefore, this study aimed to examine relationships between eating
self-regulatory skills and changes in weight and dietary behaviours over
6 months in an online longitudinal cohort of undergraduate students
from London, UK. This study hypothesised that high eating self-reg-
ulatory skills at baseline would prevent weight gain and be related to
weight loss, as well as, help people to achieve or maintain healthier
dietary behaviours over the first 6 months at university. People who
worsened their dietary behaviours and those who maintained an un-
healthy diet over the first 6 months at university would have lower
eating self-regulatory skills at baseline.

2. Material and methods

2.1. Participants

Participants were first year undergraduate students from 13 uni-
versities within London, chosen based on convenience and having at
least one university representing each of the seven regions of London.
The Departments and/or Faculties within each university were in-
dividually contacted and invited to take part in the study. All interested
students aged between 18 and 30 years able to give informed consent
and willing to complete the online survey twice over a 6-month period
were eligible. Participants who were 30 years old or over were ex-
cluded, as older students might not be as susceptible to weight gain as
younger students (Hulanicka & Kotlarz, 1983). A criterion for height
changes was established to allow for reporting errors ( ± 1 cm); parti-
cipants with a height change ≤−1 or ≥4 cm were excluded from the
analyses.

2.2. Procedure

The Departments or Faculties that agreed to take part in the study
invited all of their first year undergraduate students to complete the
online survey at the beginning of the academic year (September/
October 2015) through an email circular. Interested students who
consented to participate were directed to the online survey on Survey
Monkey (2015). At 6-month follow-up (March/April 2016), partici-
pants were invited to complete the online survey for the second time. As
an incentive, participants had the chance to enter a draw to win a £20
high street voucher. Ethical approval was granted by the University
College London Research Ethics Committee.

2.3. Measures

2.3.1. Predictor variable
Eating self-regulatory skills at baseline was assessed using the valid

and reliable 5-item Self-Regulation of Eating Behaviour Questionnaire
(SREBQ) (Kliemann et al., 2016). Response options ranged from 1
(never) to 5 (always). Total mean score was calculated. The SREBQ
demonstrated good internal reliability at baseline (Cronbach’s
alpha = 0.73).

N. Kliemann et al. Eating Behaviors 31 (2018) 105–112

106

2.3.2. Outcome variables
Weight and height were self-reported, as first year students tend to

provide reliable anthropometric data (Vella-Zarb & Elgar, 2009).
Changes from baseline to 6-month follow-up were calculated for ab-
solute weight in kg and categorised into 1) ≥5% initial body weight
gain (substantial weight gain) or < 5% initial body weight gain and; 2) ≥5% initial body weight loss or < 5% initial body weight loss. These criteria for categorising weight changes were based on the current evidence suggesting health benefits of losing 5% of initial body weight, such as improvements in blood pressure, blood cholesterol, and blood sugars (Brown, Buscemi, Milsom, Malcolm, & O'Neil, 2016; Van Gaal, Mertens, & Ballaux, 2005; Vidal, 2002). Following the same principle, gaining 5% of initial body weight could be considered a significant amount of weight since it may increase individuals' risk for these health issues, especially among individuals with overweight and obesity. Ad- ditionally, BMI was calculated and categorised into underweight (BMI < 18.5 kg/m2); normal weight (BMI 18.5 to 24.9 kg/m2) or overweight or obese (BMI 25 kg/m2 or over) (WHO, 2015).

Participants were asked to answer the question ‘How frequently do
you typically eat fruit and vegetables (FV)’ in both surveys (baseline
and 6 months) via a valid 7-point scale that ranged from ‘less than once
a week’ to ‘3 or more a day’ (Cappuccio et al., 2003). This scale was
then adapted to assess the frequency of sweets and salty snacks (SSS),
and sugary drinks (SD) intake. Answers were recoded to represent daily
intake, for example, ‘2-3 times a week’ was coded as 0.36. High and low
intake were defined using percentile ranks of the scores at baseline. For
FV, the 75th percentile was the cut-off point for high intake, while
scores that fell below this percentile represented a low intake. Re-
garding SSS and SD, the 25th percentile was the cut-off point for low
intake, and scores above this percentile were classified as high intake.
Participants who presented a high FV and a low SSS and SD at
6 months, where categorised as those who managed to maintain or
achieve healthier dietary behaviours over 6 months.

2.3.3. Socio-demographic and other variables
Data on age, gender, ethnicity (White; Black; Asian; Mixed or

Other), and living arrangements (living in college/university halls,
renting from the local authority or privately, living with parents or
owning their home) were collected.

2.4. Sample size

A sample of at least 286 participants was aimed for to detect a
medium effect (R2 = 0.15) of eating self-regulatory skills on weight or
dietary behaviours, when running multiple regression tests with up to
10 predictors (Field, 2012). The sample size calculation ensured 95%
power, a significance level of 0.01% and allowed for 50% attrition,
based on a previous online study (Boyce & Kuijer, 2015). The calcula-
tion was performed using G*Power 3.1.5 software.

2.5. Statistical analysis

Descriptive analyses were used to characterize the sample. Baseline
differences between completer and drop-out participants were checked
using Chi-square tests for categorical variables, and t-test or Mann-
Whitney tests for continuous variables. Completers were defined as
those participants with data at baseline and follow-up, while drop-outs
were those with missing data at follow-up.

Pearson’s or Spearman’s correlations were carried out to assess as-
sociations between eating self-regulatory skills, weight, dietary intake
and socio-demographic characteristics at baseline. Ethnic origin was
dichotomised into white ethnicity or other ethnicity; and living ar-
rangements into living in college/university halls or not; living with
parents or not; and renting or owning a home or not.

Change in weight between baseline and 6-month follow-up was
explored using paired t-tests. Cohen’s effect size was calculated. Chi-

square tests were used to assess differences in dietary behaviours
(percentage of high and low intake) over 6 months.

Hierarchical multiple linear regression analyses explored the asso-
ciation between eating self-regulatory skills and weight changes. The
first step included only eating self-regulatory skills, while age, gender,
ethnic origin, baseline BMI and height changes were entered in step 2
and interactions between eating self-regulatory skills and covariates
were entered in step 3. Only significant interactions were included.

Binary logistic regression was performed to explore the associations
between eating self-regulatory skills and risk of gaining 5% of initial
body weight; likelihood of losing 5% of initial body weight and main-
taining or achieving the three healthy dietary behaviours at 6-month
follow-up. Separate models were run for each outcome. Following the
same order as in the linear regression, binary models included eating
self-regulatory skills in step 1, covariate variables in step 2 and inter-
action terms between self-regulatory skills and covariates in step 3.

All analyses were performed using IBM SPSS statistics version 22
(SPSS Inc., Chicago, IL, USA). Due to the number of analyses, a more
stringent p-value of ≤0.01 was considered statistically significant for
this study.

3. Results

A total of 815 students were interested in taking part in the study
and provided baseline data. Of these, 334 had to be excluded for the
following reasons: did not accept to be contacted a second time
(N = 186); were not a first year undergraduate student (N = 85); re-
ported a height change outside the acceptable range (N = 38); were
from a university based outside London (N = 13); or were 30 years or
over (N = 12). The final sample consisted of 481 students, and 262
completed the 6-month follow-up survey (54.3%).

The sample’s characteristics at baseline are presented in Table S1.
The majority was female (76.5%), white (59.7%), living in halls
(70.7%) and had a healthy weight (73.4%). The mean age was 19 years
old and mean weight was 60 kg. Students reported consuming on
average < 2 servings of FV per day and having SSS 4–6 times per week and SD 2–3 times a week. A total of 262 participants provided data at 6- month follow-up and they did not differ significantly from non-com- pleters at baseline for the majority of the variables, with the exception of gender, ethnicity and sugary drink intake. The completer group had a significantly higher proportion of female (80.9% vs 71.2%, p = 0.01) and white (64.9% vs 53.4%, p = 0.012) participants and tended to drink sugary drink less frequently at baseline (0.28 vs 0.37, p = 0.020).

At baseline, higher eating self-regulatory skills was associated with
consuming more servings of FV (r = 0.30, p < 0.01), fewer SSS occa- sions (r = −0.34, p < 0.01) and lower SD intake (r = −0.22, p < 0.01). There were no significant correlations between baseline eating self-regulatory skills and baseline weight, gender, age, ethnicity or living arrangements (Table S2).

3.1. Change in weight and dietary behaviours over 6 months

Over 6 months a mean weight change of 0.66 kg (sd = 3.83) was
observed, and this was statistically significant (t(254) = 2.752,
p = 0.006), representing a small-sized effect (d = 0.17). The range of
weight change varied widely (−11.3 kg to +26.2 kg). No changes were
reported in a small number of participants (19.6%, N = 50), while
about a third lost weight (30.6%, N = 78) and about half gained weight
(49.8%, N = 127). Among students whose weight increased over
6 months (N = 127), the mean weight gain was 3.30 kg (sd 3.16).
Around a quarter of participants (23.5%, N = 60) gained 5% or more of
their initial body weight.

The percentage of people with a high FV intake from baseline to 6-
month follow-up did not significantly change (25.4 to 30.5%,
p = 0.14). The percentage of people with a high frequency of SSS intake
increased significantly (50.1 to 59.9%, p = 0.01) over 6 months.

N. Kliemann et al. Eating Behaviors 31 (2018) 105–112

107

Conversely, there was a significant decrease (55.9 to 46%, p = 0.01) in
the percentage of people with a high frequency of SD intake over
6 months. About 30% of participants managed to achieve or maintain a
higher intake of FV, while about 40% and 50% of participants managed
to achieve or maintain a low intake of SSS or SD, respectively, over the
first 6 months at university.

3.2. Eating self-regulatory skills and weight changes at 6 months follow-up

Table 1 shows that the adjusted regression model (Model 2) ac-
counted for 6.8% of the variance in weight changes (p = 0.009).
However, only baseline BMI was a significant predictor (β = −0.21,
p = 0.002). The inclusion of interaction terms between Self-Regulation
of Eating Behaviour (SREB) and covariates (Model 3) significantly im-
proved the model fit by 7% (ΔF = 9.986, p < 0.001). Here, eating self- regulatory skills significantly predicted weight changes (β = −0.15, p = 0.01), alongside baseline BMI (β = −0.30, p < 0.001). There was also an interaction between baseline BMI and eating self-regulation (β = −0.25, p < 0.001) and between ethnicity and eating self-reg- ulatory skills (β = 0.16, p = 0.01).

Fig. 1 illustrates that higher eating self-regulatory skills (> 3.6)
predicted decreases in weight among students with overweight
(BMI ≥ 25 kg/m2), while those with normal weight (BMI between 18.5
and 24.9 kg/m2) and underweight (BMI < 18.5 kg/m2) showed in- creases in weight regardless of their baseline level of eating self-reg- ulatory skills. Lower eating self-regulatory skills predicted increases in weight among white students, while no association was found for other ethnicities (Fig. 2).

3.3. Eating self-regulatory skills and likelihood of gaining or losing 5% of
initial body weight at 6 months follow-up

The results for the likelihood of losing 5% of initial body weight,
presented in Table 2, were not statistically significant for any of the 3
models. In line with this, the results for the likelihood of gaining 5% of
initial body weight were not statistically significant for Model 1 un-
adjusted nor Model 2 adjusted for covariates. However, the model fit
improved significantly with the inclusion of an interaction between
eating self-regulatory skills and baseline BMI (ΔΧ2(6) = 7.23,
p = 0.007). Since the inclusion of interactions between SREB and socio-
demographics did not improve the model fit, these were excluded from
the final model. The final model (Model 3) explained from 7% to 11%
of the variance in risk of substantial weight gain, correctly classifying
77% of cases. Lower eating self-regulatory skills and BMI at baseline
were associated with an increased likelihood of gaining at least 5% of

initial body weight (ORSREB = 0.52, p = 0.006 & ORBMI = 0.80,
p = 0.003).

These results also suggest that self-regulatory skills moderated the
relationship between baseline BMI and 5% weight gain (OR = 0.82,
p = 0.008). As shown in Fig. 3, students with overweight
(BMI ≥ 25 kg/m2) and normal weight (BMI between 18.5 and 24.9 kg/
m2) had higher baseline eating self-regulatory skills (> 3.6), and lower
risk of gaining at least 5% of their initial body weight over the first
6 months at university than those who had lower baseline eating self-
regulatory skills.

3.4. Eating self-regulatory skills and dietary behaviours at 6 months follow-
up

Table 3 shows the results for the logistic regressions. The interac-
tions were not significant for any model, and therefore, only the results

Table 1
Predictors of changes in weight at 6-month follow-up.

Weight changes model 1 unadjusted Model 2 adjusted Model 3 adjusted

B (SE) β p B(SE) β p B(SE) β p

Constant 0.58 (0.22) 0.009 0.59 (0.22) 0.008 0.49 (0.22) 0.025
SREBa −0.41 (0.32) −0.07 0.194 −0.64 (0.32) −0.13 0.045 −0.73 (0.30) −0.15 0.018
Age 0.09 (0.13) 0.04 0.491 0.04 (0.13) 0.02 0.748
Genderb −0.46 (0.56) −0.06 0.413 −0.54 (0.55) −0.06 0.327
Ethnicityc −0.70 (0.46) −0.09 0.130 −0.73 (0.45) −0.10 0.103
Baseline BMI −0.23 (0.07) −0.21 0.002 −0.32 (0.07) −0.30 < 0.001 Height changes 0.47 (0.23) 0.13 0.037 0.43 (0.22) 0.12 0.049 Ethinicity ∗ SREB 1.58 (0.62) 0.15 0.011 BMI ∗ SREB 0.38 (0.09) −0.25 < 0.001 Model fit R2 = 0.007 & R2 adj = 0.003

F = 1.694, p = 0.194
R2 = 0.068 & R2 adj = 0.044
F = 2.909, p = 0.009
ΔR2 = 0.061, ΔF = 3.137, p = 0.009

R2 = 0.14 & R2 adj = 0.11
F = 4.842, p < 0.001 ΔR2 = 0.07, ΔF = 9.986, p < 0.001

P-value of ≤0.01 was considered statistically significant.
a Eating self-regulatory skills at baseline.
b Gender, Male = 0 and Female = 1.
c Ethnicity, White = 0 and Other = 1.

Fig. 1. Interaction between baseline BMI and baseline eating self-regulatory
skills as a predictor of changes in weight at 6-month follow-up
Note: SREB = baseline eating self-regulatory skills, where low SREB indicates a
score ≤ 3.6 and high SREB indicates a score > 3.6. Weight changes from
baseline to 6-month follow-up. Underweight indicates a BMI < 18.5 kg/m2; Normal weight indicates a BMI between 18.5 and 24.9 kg/m2 and Overweight indicates a BMI 25 kg/m2 or over. Mean weight changes adjusted for age, gender, ethnicity and height changes.

N. Kliemann et al. Eating Behaviors 31 (2018) 105–112

108

for the two-step models are presented. In the unadjusted model, eating
self-regulatory skills at baseline significantly predicted higher FV intake
(p = 0.008). The inclusion of socio-demographic variables improved
the model fit significantly (ΔΧ2(4) = 18.907, p = 0.001), and this final
model explained from 9% to 14% of the variance in FV intake and
classified 66% of the cases correctly. Greater baseline eating self-

regulatory skills (OR = 1.8, p = 0.007) and being female (OR = 4.3,
p = 0.002) were associated with an increased likelihood of maintaining
or achieving a higher consumption of FV at 6 months follow-up.

Fig. 2. Interaction between ethnicity and baseline eating self-regulatory skills
as a predictor of changes in weight at 6-month follow-up
Note: SREB = baseline eating self-regulatory skills, where low SREB indicates a
score ≤ 3.6 and high SREB indicates a score > 3.6. Weight changes from
baseline to 6-month follow-up. Mean weight changes adjusted for age, gender,
baseline BMI and height changes.

Table 2
Predictors of gaining or losing 5% of initial body weight or over at 6-month follow-up.

Model 1 unadjusted Model 2 adjusted Model 3 adjusted

B(SE) OR (95%CI) p

B(SE) OR (95%CI) p B(SE) OR (95%CI) p

5% Weight gain
Constant −1.2 (0.15) < 0.001 −1.2 (0.16) < 0.001 −1.4 (0.18) < 0.001 SREBa −0.39 (0.21) 0.68(0.44;1.03) 0.071 −0.50 (0.22) 0.60(0.39;0.94) 0.025 −0.66 (0.24) 0.52(0.32;0.83) 0.006 Age −0.04 (0.10) 0.96(0.78;1.17) 0.684 −0.04 (0.10) 0.96(0.78;1.17) 0.697 Genderb 0.16 (0.40) 0.85(0.38;1.88) 0.696 −0.17 (0.41) 0.84(0.37;1.9) 0.679 Ethnicityc 0.28 (0.33) 0.75(0.40;1.45) 0.402 −0.36 (0.34) 0.69(0.36;1.35) 0.288 Baseline BMI −0.13 (0.06) 0.87(0.77;0.99) 0.032 −0.21 (0.07) 0.80(0.70;0.93) 0.003 Height changes 0.14 (0.15) 1.15(0.85;1.5) 0.365 0.13(0.16) 1.14(0.84;1.5) 0.392 BMI ∗ SREB −0.20 (0.07) 0.82(0.70;0.95) 0.008 Model fit R2 = 0.013 to 0.020

Χ2(1) = 3.290, p = 0.070
R2 = 0.043 to 0.064
Χ2(6) = 10.799, p = 0.095
ΔΧ2(5) = 7.509, p = 0.185

R2 = 0.070 to 0.11
Χ2(7) = 18.036, p = 0.012
ΔΧ2(1) = 7.237, p = 0.007

5% weight loss
Constant −2.02 (0.19) < 0.001 −2.09 (0.29) < 0.001 −2.08 (0.21) < 0.001 SREBa 0.123 (0.28) 1.13(0.65;1.97) 0.664 0.24 (0.29) 1.27(0.70;2.28) 0.420 0.166 (0.30) 1.18(0.65;2.15) 0.587 Age 0.05 (0.11) 1.05(0.85;1.31) 0.637 0.073 (0.11) 1.07(0.86;1.34) 0.516 Genderb −0.08 (0.50) 0.93(0.34;2.47) 0.873 −0.17 (0.51) 0.98(0.36;2.67) 0.973 Ethnicityc 0.07 (0.42) 1.07(0.47;2.47) 0.861 0.05 (0.43) 1.05(0.45;2.44) 0.911 Baseline BMI −0.11 (0.06) 1.11(0.99;1.25) 0.060 0.16 (0.63) 1.17(1.03;1.32) 0.012 Height changes −0.27 (0.24) 0.760(0.47;1.22) 0.255 −0.26(0.24) 0.77(0.48;1.23) 0.274 BMI ∗ SREB 0.22 (0.10) 1.24(1.00;1.54) 0.042 Model fit R2 = 0.001 to 0.001

Χ2(1) = 0.189, p = 0.664
R2 = 0.024 to 0.046
Χ2(6) = 5.874, p = 0.437
ΔΧ2(5) = 5.87, p = 0.338

R2 = 0.042 to 0.081
Χ2(7) = 10.52, p = 0.161
ΔΧ2(1) = 4.64, p = 0.031

R2 = ‘Cox & Snell R2’ to ‘Nagelkerke R2’. Mean self-regulatory skills among students who gained 5% of their initial body weight or over was 3.30 (sd = 0.71). Mean
eating self-regulatory skills among students who did not gain 5% the mean was 3.50 (sd = 0.70). P-value of ≤0.01 was considered statistically significant.

a Eating self-regulatory skills at baseline.
b Gender, Male = 0 and Female = 1.
c Ethnicity, White = 0 and other = 1.

Fig. 3. Interaction between baseline BMI and baseline eating self-regulatory
skills as a predictor of gaining 5% of initial body weight or over at 6-month
follow-up
Note: SREB = baseline eating self-regulatory skills, where low SREB indicates a
score ≤ 3.6 and high SREB indicates a score > 3.6. Underweight indicates a
BMI < 18.5 kg/m2; Normal weight indicates a BMI between 18.5 and 24.9 kg/ m2 and Overweight indicates a BMI 25 kg/m2 or over. Predicted probability of gaining 5% of initial body weight adjusted for age, gender, ethnicity and height changes.

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109

With respect to the logistic regression model for maintaining or
achieving a low consumption of SSS, the unadjusted model showed that
eating self-regulatory skills was a significant predictor (OR = 1.9,
p = 0.001). Although the inclusion of socio-demographic variables did
not significantly improve the model fit (ΔΧ2(4) = 1.035, p = 0.904),
the likelihood ratio test increased. Model 2 explained from 4.8% to
6.5% of the variance in SSS intake and correctly classified 62% of the
cases. The results indicated that higher baseline levels for eating self-
regulatory skills was related to a greater likelihood of maintaining or
achieving a lower consumption of SSS over 6 months. None of the
covariates were found to be related to the outcome.

Finally, the results for the unadjusted model for a low SD intake at
6-month follow-up indicated that greater eating self-regulation was
related to an increased chance of maintaining or achieving a low SD
intake (OR = 1.45, p = 0.041), however this did not reach the stringent
cut-off for significance established for this study (p ≤ 0.01). The in-
clusion of covariates (Model 2) did not improve the model fit
(ΔΧ2(4) = 6.935, p = 0.139). The model explained from 4.4% to 5.8%
of the variance in SD intake and classified 59% of cases correctly.

4. Discussion

This is the first study to assess eating self-regulatory abilities using a
valid scale and to examine the impact of self-regulation on weight gain
and healthy dietary behaviours among first year undergraduate

students. As hypothesised, students who entered university with higher
eating self-regulatory skills were more likely to maintain or achieve a
healthier diet over the course of the first 6 months in university.
Additionally, higher eating self-regulatory skills were related to de-
creases in weight and lower likelihood of gaining a substantial amount
of weight among students with overweight.

Although weight gain (0.6 kg) was modest, around a quarter of the
students gained a substantial amount of weight. This is in line with a
recent study in which 301 first year students in London were weighed
and measured over 7 months and found a weight gain of 0.54 kg, and
that one in five gained at least 5% of their initial body weight (Meisel
et al., 2015). However, this still conflicts with results from other studies
(Vella-Zarb & Elgar, 2009) and there is also little consistency around
whether weight gain is related to a lower or higher baseline BMI in first
year students (Finlayson et al., 2012; Mihalopoulos, Auinger, & Klein,
2008; Vella-Zarb & Elgar, 2009). According to a recent study, a po-
tential explanation for these inconsistencies is the fact that baseline BMI
appears to interact with other factors in order to promote weight gain
(Boyce & Kuijer, 2015). This is in line with findings from the present
study, which showed that higher eating self-regulatory skills protected
against substantial weight gain among students with overweight and
normal weight. On the other hand, students with underweight gained
weight regardless of their level of eating self-regulatory skills.

However, it is important to note that weight gain in the under-
weight and normal weight group could represent a positive outcome.

Table 3
Predictors of maintaining or achieving a healthier dietary intake at 6-month follow-up.

Maintained or achieved healthier dietary behaviours

Model 1 unadjusted Model 2 adjusted

B(SE) OR (95%CI) p B(SE) OR (95%CI) p

High F&V intakea

Constant −0.79 (0.14) < 0.001 −0.987 (0.16) < 0.001 SREBd 0.54 (0.20) 1.71 (1.1; 2.5) 0.008 0.59 (0.22) 1.8 (1.1; 2.7) 0.007 Age −0.19 (0.10) 0.82 (0.66; 1.0) 0.060 Gendere 1.4 (0.47) 4.3 (1.7; 10.9) 0.002 Ethnicityf −0.57 (0.31) 0.56 (0.30; 1.0) 0.066 BMI baseline 0.03 (0.05) 1.0 (0.93; 1.13) 0.511 Model fit R2 = 0.029 to 0.041

Χ2(1) = 7.402, p = 0.007
R2 = 0.09 to 0.14
Χ2(5) = 26.308, p < 00.001 ΔΧ2(4) = 18.907, p = 0.001

Low SSS intakeb

Constant −0.43 (0.13) 0.001 −0.43 (0.13) 0.001
SREBd 0.64 (0.19) 1.9 (1.2; 2.7) 0.001 0.64 (0.20) 1.9 (1.3; 2.8) 0.001
Age −0.05 (0.08) 0.95 (0.80; 1.1) 0.551
Gendere −0.24 (0.34) 0.78 (0.40; 1.5) 0.479
Ethnicityf −0.09 (0.28) 0.91 (0.52; 1.6) 0.737
BMI baseline 0.01 (0.04) 1.0 (0.93; 1.1) 0.789
Model fit R2 = 0.044 to 0.059

Χ2(1) = 11.307, p = 0.001
R2 = 0.048 to 0.065
Χ2(5) = 12.343, p = 0.030
ΔΧ2(4) = 1.035, p = 0.904

Low SD intakec

Constant 0.19 (0.13) 0.140 1.44 (0.13) 0.275
SREBd 0.37 (0.18) 1.45 (1.0; 2.1) 0.041 0.36 (0.18) 1.4 (0.99; 2.01) 0.053
Age 0.03 (0.08) 1.0 (0.88; 1.2) 0.688
Gendere 0.80 (0.34) 2.2 (1.1; 4.3) 0.017
Ethnicityf −0.15 (0.27) 0.86 (0.50; 1.5) 0.581
BMI baseline −0.02 (0.04) 0.98 (0.90; 1.0) 0.685
Model fit R2 = 0.017 to 0.023

Χ2(1) = 4.291, p = 0.038
R2 = 0.044 to 0.058
Χ2(5) = 11.226, p = 0.047
ΔΧ2(4) = 6.935, p = 0.139

R2 = ‘Cox & Snell R2’ to ‘Nagelkerke R2’. P-value of ≤0.01 was considered statistically significant.
a Maintaining or achieving a consumption at least 2.25 servings of fruit and vegetable per day.
b Maintaining or achieving a consumption of a maximum of 0.36 occasions of sweet and salty snacks per week.
c Maintaining or achieving a consumption of a maximum of 0.1 occasions of sugary drinks per week.
d Eating self-regulatory skills at baseline.
e Gender – Male = 0 and Female = 1.
f Ethnicity – White = 0 and Other = 1.

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110

On the other hand, weight gain could represent a negative outcome for
those with a BMI on the borderline of normal weight/overweight or for
those with overweight and obesity. Therefore, the prevention of weight
gain in this group is particularly relevant, since people with higher
BMIs may be more genetically predisposed to gain weight in an obe-
sogenic environment (Kautiainen, Rimpela, Vikat, & Virtanen, 2002;
Wardle & Boniface, 2008). Self-regulation is therefore a potential target
for interventions seeking to prevent substantial weight gain among
people predisposed to obesity.

Although no association between self-regulation and the likelihood
of losing at least 5% of initial body weight was found, the results for
weight gain suggest that higher eating self-regulatory skills are related
to lower likelihood of 5% weight gain in individuals with overweight
and normal weight. Further studies should explore this in samples that
include more participants affected by overweight and obesity. It is
possible that among people with normal weight, a lower likelihood of
5% weight gain may have occurred as a consequence of factors other
than their capacity to regulate eating behaviours. Studies have sug-
gested that eating disorders may affect 8 to 49% of undergraduate
students (Eisenberg, Nicklett, Roeder, & Kirz, 2011; Lipson &
Sonneville, 2017; Prouty, Protinsky, & Canady, 2002). These disorders
usually involve symptoms such as concern about body image, body
image distortion and worrying about losing control over their eating
(Eisenberg et al., 2011). This group of people tend to present rigid
control over their eating, rather than flexible control. The latter is more
representative of the ability to self-regulate eating behaviours (Johnson
et al., 2012) and may explain why self-regulation was not found to be a
predictor of weight loss among those with lower BMIs.

Previous studies have shown that ethnicity does not predict weight
changes (Gillen & Lefkowitz, 2011; Roane et al., 2015), and this was
also the case in the present study. However, a significant moderating
effect of eating self-regulatory skills on the relationship between eth-
nicity and weight changes was found. White students who had lower
eating self-regulatory skills experienced greater increases in their
weight compared to those with higher eating self-regulatory skills,
while a smaller association was found for people classified as ‘other
ethnicities’. A previous study found that white female students tend to
be more concerned about gaining weight during the first year of uni-
versity than black students (Webb, Butler-Ajibade, Robinson, & Lee,
2013). It is possible, therefore, that white students tend to apply more
self-regulatory skills to control their weight and their capability may
reflect their level of success.

With respect to dietary behaviours, the level of eating self-reg-
ulatory skills at baseline was related to higher baseline FV intake and
lower baseline SSS and SD intake, in line with results found in a cross-
sectional study with UK adults (Kliemann et al., 2016). As anticipated,
higher baseline eating self-regulatory skills also predicted higher FV
and low SSS intake at 6-month follow-up. Although lower SD intake
was also related to higher eating self-regulatory skills, it did not reach
the significance established for this study. However, this study only
assessed differences in the frequency of SD intake. A systematic review
has suggested that sugary drinks tend to be consumed in large portion
sizes, due to their lower satiety effect compared to solid foods of the
same energy density (Malik, Schulze, & Hu, 2006). Therefore, future
studies should explore the effect of eating self-regulatory skills on the
amount of sugary drinks consumed.

This study had limitations. For convenience, only students from
universities based in London were included. As a consequence, the
sample may not be representative of UK first year students, because
London tends to have a lower percentage of students with overweight
and obesity compared to other regions of the UK (Public Health
England, 2015). In fact, individuals with overweight and obesity were
under-represented in the sample, which may explain the modest weight
gain found in this study. Men were also under-represented, suggesting
that the participants who decided to take part in the study may differ
from the general student population regarding their interest in a

healthy diet and weight control.
The use of self-report measures to assess dietary intake is also a

limitation. Although the FV measure has been validated (Cappuccio
et al., 2003), the SSS and SD measures have not, although they have
been used in several previous studies (Croker, Lucas, & Wardle, 2012;
Kliemann et al., 2016; McGowan, Croker, Wardle, & Cooke, 2012). In
order to promote high retention rates, the online surveys were kept
short and only four questions on food frequency were included. How-
ever, they lacked portion size information, were related to groups of
foods rather than specific foods, and responses options ranged from 1 to
7. Also, as a retrospective measure, this food frequency questionnaire is
also limited in that it relies on individuals’ memory. However, its un-
announced and self-administered features as well as the fact that it
captures habitual behaviours are important strengths of this method
(Walton, 2015). Additionally, previous studies using these questions
have shown that they can provide valid data on habitual dietary intake
(Kliemann et al., 2016; McGowan et al., 2013).

Although there are still several aspects about the susceptibility to
weight gain among first year undergraduate students that need to be
further investigated, this study provides some initial evidence for the
role of eating self-regulatory skills in protecting students against sub-
stantial weight gain and unhealthy dietary changes. There is some
evidence that interventions using goal-setting, planning, self-mon-
itoring and feedback on performance techniques may potentially pro-
mote self-regulatory skills and weight loss among adults with over-
weight and obesity (Annesi, Johnson, Tennant, Porter, & McEwen,
2016; Crane, Ward, Lutes, Bowling, & Tate, 2016; Kolodziejczyk et al.,
2016; Norman, Kolodziejczyk, Adams, Patrick, & Marshall, 2013). Also,
a recent study showed that habit-based interventions promoting the
repetition of target behaviours in a consistent context hold promise for
enhancing self-regulatory skills among adults with obesity (Kliemann
et al., 2017). Habit-based interventions are of particular interest be-
cause they are considered to be scalable, and are designed to promote
lasting behaviour changes. Future studies should investigate whether
these techniques may also enhance self-regulatory skills among un-
dergraduate students and the effect of improving these skills on their
weight and diet over the course of their studies at university. Ad-
ditionally, future powered studies should further investigate the po-
tential impact of ethnicity on the relationship between self-regulation
and weight changes, exploring this relationship in different ethnic
groups.

5. Conclusions

This study provides evidence that higher baseline eating self-reg-
ulatory skills may help students to maintain or achieve a healthy diet
and protect them against substantial weight gain, especially among
students with overweight. Weight gain prevention initiatives that in-
clude eating self-regulatory skills training should be tested among in-
dividuals with overweight or predisposed to overweight and obesity.

Acknowledgments

RJB, FJ and HC were funded by Cancer Research UK when this
research was carried out, and NK by CAPES/BRASIL. RJB is now sup-
ported by Yorkshire Cancer Research Academic Fellowship funding.

Appendix A. Supplementary data

Supplementary data to this article can be found online at https://
doi.org/10.1016/j.eatbeh.2018.09.003.

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• Starting university with high eating self-regulatory skills protects students against unhealthy dietary intake and substantial weight gain over 6 months

Introduction
Material and methods
Participants
Procedure
Measures
Predictor variable
Outcome variables
Socio-demographic and other variables
Sample size
Statistical analysis
Results
Change in weight and dietary behaviours over 6 months
Eating self-regulatory skills and weight changes at 6 months follow-up
Eating self-regulatory skills and likelihood of gaining or losing 5% of initial body weight at 6 months follow-up
Eating self-regulatory skills and dietary behaviours at 6 months follow-up
Discussion
Conclusions
Acknowledgments
Supplementary data
References

Drug Invention Today | Vol 10 • Issue 7 • 20181094

Effect of regular exercises and health benefits among
college students
M. Meenapriya1, R. Gayathri2*, V. Vishnu Priya2

INTRODUCTION
Health is a universal trait; the World Health
Organization defines health as a “state of complete
physical, mental, and social well-being, and not
merely the absence of disease. Health contributes to
general well-being and overall lifestyle.[1] Obesity
has become a major public health concern. There is
a rapid increase in the number of young adults (20-
39) turning obese (30%). The increasing prevalence
of obesity among young people is combined with a
concomitant low rate of physical activity, with nearly
43% of college undergraduates reporting that they do
not participate in either moderate or vigorous physical
activity.[2]

Doing exercises can eliminate anxiety, tension, and
stress under pressure conditions. The use of habitual
exercise as a stress management technique has the
benefits of mood enhancement, increased self-
esteem, and reduced psychological and physical

1Department of Biochemistry, Saveetha Dental College, Saveetha Institute of Medical and Technical Science, Saveetha
University, Chennai, Tamil Nadu, India, 2Department of Biochemistry, Saveetha Dental College, Saveetha Institute of
Medical and Technical Science, Saveetha University, Chennai, Tamil Nadu, India

*Corresponding author: R. Gayathri, Department of Biochemistry, Saveetha Dental College, Saveetha Institute of
Medical and Technical Science, Saveetha University, 160, Poonamallee High Road, Chennai – 600 077, Tamil Nadu, India.
Phone: +91-9710680545. E-mail: gayathri.jaisai@gmail.com

Received on: 19-02-2018; Revised on: 27-03-2018; Accepted on: 29-05-2018

Access this article online

Website: jprsolutions.info ISSN: 0975-7619

stress reactions. According to Graham, Holt, and
Parker (1998), physical activities such as basketball,
tennis, racquetball, weight-lifting, self-defense, and
swimming help students to improve and maintain
physical, mental health, and the quality of lives.[3]

Colleges and universities are potentially important
settings for reducing the prevalence of overweight in
the adult population through the promotion of healthy
weight management practices. While overweight and
obesity appear to track from childhood into adulthood,
overweight during late adolescence is most strongly
associated with an increased risk of overweight in
adulthood. Colleges and universities provide numerous
opportunities to positively influence physical activity,
nutrition, and weight management behaviors of large
numbers of older adolescents and young adults in an
educational setting.[4]

An important developmental task for college students
is learning to manage excess or unnecessary distress
while actively engaging with healthy, age-appropriate
challenges that promote growth. Studies were
conducted earlier for evaluating the effectiveness
of meditation-based intervention for reducing

Research Article

ABSTRACT

Background: Health contributes to the general well-being and overall lifestyle. Exercise is another aspect that is important
for a person’s healthy lifestyle. This survey aims at finding the effects of exercise on college students. Objective: The
objective of this study is to create awareness on the effect of regular exercises and health benefits among college students.
Materials and Methods: Sample size for this study was 100. The survey questionnaire was filled in survey planet, and the
link was sent to 100 college students to find the effect of daily exercises among them. The result of the survey was statistically
analyzed. Results: Students with healthy lifestyle patterns have the benefits of losing weight, managing stress, and improving
memory, focus, and concentration. The survey results indicate that most of the college students follow unhealthy lifestyle
patterns and they do not regularly exercise. Conclusion: Since most of the college students do not spend much time for
exercise, the unhealthy lifestyle still persists. This study has created awareness among the college students about the benefits
of daily exercises on college students.

KEY WORDS: Diet, Exercises, Health, Stress, Weight

M. Meenapriya, et al.

1095Drug Invention Today | Vol 10 • Issue 7 • 2018

distress and enhancing well-being among college
undergraduate populations.[5]

The main purpose of this study is to explore the
major lifestyle factors among college students, in an
effort to improve their behavior and reduce the risk
factors for major diseases.[6-14] This study contributes
significantly in improving the quality of college
student’s life and helps them live longer, free from
diseases, and illnesses.

MATERIALS AND METHODS
The study was conducted among college students
in an effort to find the effects of daily exercises on
general well-being. Data were collected through
survey questionnaire that were filled in survey planet,
an online forum.

On the whole, 100 participants took part in the survey.
The questions based on their lifestyle, food habits, and
daily workout were asked to the participant through
the link. Among these questions, seven questions
come under yes or no type. Other questions are given
with appropriate options. The results of the survey
were statistically analyzed.

RESULTS
The number of participants who took part in the
survey was 100. Among the 100 participants, 61%
were female and 39% were male. Age groups of
participants who took part in the survey were from
16 to 20 (68%) and 21 to 25 (32%). The participants
who does the exercise daily was about 30% [Figure 1],
and they perform the exercises for <1 h. Most of the students do not regularly exercise (57%), and they perform only light exercises occasionally (63%). The survey among college students has revealed that the students perform the exercises to lose weight and maintain physique (60%) [Figure 2]. About 63% of the students know about the benefits of daily exercises among them [Figure 3]. And after doing the exercises, they think that their weight has reduced (63%) and they are able to manage stress (78%) [Figure 4]. Students who regularly exercise believe that exercise has a positive role in improving their memory, focus, and concentration (80%) and it boosts their mood and relieves stress (87%) [Figure 4] but does not improve their marks or GPAs (63%) [Figure 5]. Awareness on the benefits of regular exercise was created among the students who took part in the survey [Figure 3].

DISCUSSION
Many of the college students are placing their health at
risk through lifestyle choices that include insufficient
physical activity and unhealthy food choices.[15-19] Several
studies show that a high percentage of the students do

not exercise frequently and suffer from increased body
weight. College students can ensure both physical and
mental health by focusing on consuming a balanced diet,
staying hydrated, and getting adequate amount of sleep
together with exercise and a healthy lifestyle.

Figure 1: Do you exercise daily?

Figure 2: Why do you work out?

Figure 3: Benefits of regular exercise

Figure 4: After doing exercises

M. Meenapriya, et al.

Drug Invention Today | Vol 10 • Issue 7 • 20181096

Filip manual study has concluded that there was
prevalence of physical activity among college students
during weekends.Regular exercises certainly has a
role in relieving stressand anxiety. A study conducted
by Richard Lowry shows that the U.S college students
used exercise and diet for their weight control. A
study conducted by Doug oman shows that there is
a evidence for meditation based stress management
for the college students. Apart from managing stress,
there is evidence for reduced rate of hopelessness,
depression, suicidal behavior and alcohol consumption
among college students.[20-22]

CONCLUSION
Although there is a sufficient knowledge about the
benefits of daily exercises on college students, they
do nott spend much time for regular workouts, this
could be due to their addiction towards gadgets, online
games, etc. Therefore, awareness about the benefits
of daily exercises among the college students was
created using this survey.

REFERENCES
1. Al-Amari HG, Al-Khamees N. The perception of college

students about a healthy lifestyle and its effect on their health. J
Nutr Food Sci 2015;5:437.

2. Sailors MH, Jackson AS, McFarlin BK, Turpin I, Ellis KJ,
Foreyt JP, et al. Exposing college students to exercise: The
training interventions and genetics of exercise response
(TIGER) study. J Am Coll Health 2010;59:13-20.

3. Akandere M, Tekin A. The effect of physical exercise on
anxiety. Sport J 2008;104:306-14.

4. Lowry R, Galuska DA, Fulton JE, Wechsler H, Kann L,
Collins JL, et al. Physical activity, food choice, and weight
management goals and practices among US college students.
Am J Prev Med 2000;18:18-27.

5. Oman D, Shapiro SL, Thoresen CE, Plante TG, Flinders T.
Meditation lowers stress and supports forgiveness among
college students: A randomized controlled trial. J Am Coll
Health 2008;56:569-78.

6. Stults-Kolehmainen MA, Sinha R. The effects of stress on
physical activity and exercise. Sports Med 2014;44:81-121.

7. Clemente FM, Nikolaidis PT, Martins FM, Mendes RS.
Physical activity patterns in university students: Do they follow
the public health guidelines? PLoS One 2016;11:e0152516.

8. Azizi M. Effects of doing physical exercises on stress-coping
strategies and the intensity of the stress experienced by
university students in Zabol, South-Eastern Iran. Proced Soc
Behav Sci 2011;30:372-5.

9. Ranjita M, Michelle M. College students academic stress
and its relation to their anxiety, time management and leisure
satisfaction. Am J Health Stud 2000;16:41-51.

10. Hudd SS, Dumlao J, Erdmann-Sager D, Murray D, Phan E,
Soukas N. Stress at college: Effects on health habits, health
status and self-esteem. Coll Stud J 2000;34:217.

11. Keating XD, Guan J, Piñero JC, Bridges DM. A meta-analysis
of college students’ physical activity behaviors. J Am Coll
Health 2005;54:116-25.

12. Taliaferro LA, Rienzo BA, Pigg RM Jr., Miller MD, Dodd VJ.
Associations between physical activity and reduced rates of
hopelessness, depression, and suicidal behavior among college
students. J Am Coll Health 2009;57:427-36.

13. Wilson-Salandy S, Nies MA. The effect of physical activity on
the stress management, interpersonal relationships, and alcohol
consumption of college freshmen. SAGE Open 2012;2012:1-7.

14. Racette SB, Deusinger SS, Strube MJ, Highstein GR, Deusinger
RH. Weight changes, exercise, and dietary patterns during
freshman and sophomore years of college. J Am Coll Health
2005;53:245-51.

15. Kilpatrick M, Hebert E, Bartholomew J. College students’
motivation for physical activity: Differentiating men’s and
women’s motives for sport participation and exercise. J Am
Coll Health 2005;54:87-94.

16. Deliens T, Deforche B, De Bourdeaudhuij I, Clarys P.
Determinants of physical activity and sedentary behaviour
in university students: A qualitative study using focus group
discussions. BMC Public Health 2015;15:201.

17. Behrens TK, Dinger MK. Ambulatory Physical activity patterns
of college students. Am J Health Educ 2005;36:221-7.

18. Trockel MT, Barnes MD, Egget DL. Health-related variables
and academic performance among first-year college students:
Implications for sleep and other behaviors. J Am Coll Health
2000;49:125-31.

19. Thome J, Espelage DL. Relations among exercise, coping,
disordered eating, and psychological health among college
students. Eat Behav 2004;5:337-51.

20. James CH, William M. Effect of systematic physical fitness
training combined with counselling on the self-concept of
college students. J Couns Psychol 1979;26:427-36.

21. Mahendran S, Gayathri R, Priya VV. A correlative relation on
the amount of calories consumed by students of different age
groups–a survey. Int J Pharm Sci Rev Res 2017;44:33-5.

22. Mahendran S, Priya J. A comparitive study on 2nd year syndrome
among dental, medical and nursing students. Int J Curr Adv Res
2017;6:2954-7.

Figure 5: Do you think that your marks have increased after
doing exercises?

Source of support: Nil; Conflict of interest: None Declared

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articles for individual use.

Systematic Review

Factors Influencing Efficacy of Nutrition Education
Interventions: A Systematic Review

Mary W. Murimi, PhD, RDN; Michael Kanyi, PhD; Tatenda Mupfudze, PhD;
Md. Ruhul Amin, MPH, MS; Teresia Mbogori, MS; Khalid Aldubayan, PhD

College
Conflict o
with this
the JNE
Editor-in
Address
Sciences
TX 7940
�2016 S
reserved
http://dx

142

ABSTRACT

Objective: To examine systematically factors that contribute to the efficacy of nutrition education inter-
ventions in promoting behavior change for good health based on their stated objective. In a departure from
previous reviews, the researchers investigated factors that lead to success of various types of interventions.
Critical analysis of these factors constituted the outcome of this review.
Methods: This study followed Preferred Reporting Items for Systematic Reviews and Meta-analysis
criteria. A total of 246 original articles published between 2009 and 2015 in PubMed, Medline, Web of Sci-
ence, Academic Search Complete, Science Direct, Cochrane Reviews, ERIC, and PsychLIT were initially
considered. The number was screened and scaled down to 40 publications for the final analysis. Quality
assessment was based on the Cochrane Handbook for Systematic Reviews of Intervention. Studies were rated
as having low risk of bias, moderate risk, or high risk.
Results: Efficacy of nutrition education interventions depended on major factors: interventions that
lasted $5 months; having #3 focused objectives; appropriate design and use of theories; fidelity in inter-
ventions; and support from policy makers and management for worksite environmental interventions.
Conclusions and Implications: Intervention duration of $5 months, #3 focused objectives, random-
ization, use of theories, and fidelity are factors that enhance success of interventions based on the results of
this study.
Key Words: efficacy, interventions, nutrition education, systematic review (J Nutr Educ Behav. 2017;49:142-
165.)

Accepted September 8, 2016. Published online November 1, 2016.

INTRODUCTION

Nutrition education can be viewed as
any set of learning experiences designed
to facilitate the voluntary adoption of
eating and other nutrition-related be-
haviors conducive to health and well-
being.1 Efficacy describes the ability
to yield intended outcome; for the effi-
cacy of an intervention to be evalu-
ated, it must be adequately described.2

Efficacy of nutrition education inter-
ventions depends on several factors
including the duration and frequency
of intervention, the number and relat-

of Human Sciences, Texas Tech Unive
f Interest Disclosure: The authors’ confli
article on www.jneb.org. The first auth
B staff as Associate Editor. Review of
-Chief to minimize conflict of interest
for correspondence: Mary W. Murim
, College of Human Sciences, Texas T
9; Phone: (806) 834-1812; Fax: (806) 7
ociety for Nutrition Education and Beh
.
.doi.org/10.1016/j.jneb.2016.09.003

edness of the study objectives, study
design and theory, and fidelity in inter-
vention.

The specific characteristics of the de-
terminants of success of interventions
are still unclear.2 However, several
studies have been conducted to ascer-
tain determinants of efficacy of nutri-
tional education interventions. For
example, another systematic review3

concluded that educational interven-
tions that are sustained for a longer
time, >5 months, and offer personal-
ized feedback on dietary behavior and
related health risk factors, are more

rsity, Lubbock, TX
ct of interest disclosures can be found online
or of this article (M. W. Murimi) served on
this article was handled, exclusively, by the
.
i, PhD, RDN, Department of Nutritional
ech University, PO Box 41240, Lubbock,
42-3042; E-mail: mary.muimi@ttu.edu
avior. Published by Elsevier, Inc. All rights

Journal of Nutrition Education and Beh

likelytobeeffectivethanthoseconduct-
edforashortperiod,<5months,anddo not offer personalized feedback. Other studies concluded that expert-led inter- ventions as well as studies that used behavioral theories, social support, and an educational approach to guide die- tary interventions were more likely to be successful.4 Despite previous studies on the wider area of nutrition educa- tion, there is still inadequate literature on the efficacies of the various nutrition education interventions that were im- plemented in recent years. In a departure from previous reviews that concen- trated primarily on a single type of intervention and its related outcome, the current review investigated several factors that led to success of various types of interventions. The purpose of this review was to examine system- atically the factors that contribute to the efficacy of nutrition education in- terventionsinpromotingbehaviorchange for good health and well-being based on their stated objective. To achieve this purpose, the researchers used population, intervention, comparison, and outcomes criteria to frame the research questions.5

avior � Volume 49, Number 2, 2017

Delta:1_given name

Delta:1_surname

Delta:1_given name

Delta:1_surname

Delta:1_given name

Delta:1_surname

http://www.jneb.org

mailto:mary.muimi@ttu.edu

http://dx.doi.org/10.1016/j.jneb.2016.09.003

Journal of Nutrition Education and Behavior � Volume 49, Number 2, 2017 Murimi et al 143

METHODS
Literature Search

This systematic review was conducted
in accordance with recommendations
and criteria outlined in the Preferred Re-
porting Items for Systematic Reviews
and Meta-analysis statement.6,7 Articles
on studies that conducted nutrition
education interventions on dietary be-
haviors were identified by performing
literature searches in: PubMed, Medline,
Web of Science, Academic Search Comp-
lete, Science Direct, Cochrane Reviews,
ERIC and PsychLIT. The search was
limited to articles published between
2009 and 2015. Key search words were
nutrition education, nutrition education
interventions, dietary behavior, food,
and health living. References of all
retrieved studies were used to determine
the source of information, whether they
were primary, secondary, or website
based, and to understand better the
basis for conclusions of the studies
that were reviewed.

All 6 members of the research team
were independently involved in re-
viewing the references. Inclusion and
quality measures were determined by
the 3 senior researchers who conduct-
ed an independent evaluation of each
article; afterward, several discussions
were held to reach a consensus, hence
monitoring bias. A total of 246 original
studies published since 2009 and tar-
geting healthy individuals without
preexisting medical conditions were
reviewed. This initial number was
screened and scaled down to 40 publi-
cations for the final analysis. Screening
criteria for inclusion and elimination
are illustrated in the Figure.

Members of the Research Team

The research team was composed of 6
members, 3 of whom held doctoral de-
gree; the others had a master’s degree
in nutrition. The lead researcher was a
full professor of nutrition and a regis-
tered dietitian. Two other researchers
were faculty members in recognized in-
ternational universities with wide expe-
rience in nutrition, education, and
research.Eachofthe3seniorresearchers
paired with 1 junior researcher in each
database for article search and retrieval.
All 6 members were independently
involved in reviewing the articles and
initially screening them.

Inclusion/Exclusion Criteria

The authors included in the review
research articles published in English
that examined nutrition education in-
terventions in adults aged >18 years.
Studies were excluded if they were re-
view articles, poster abstracts, or quali-
tative, cross-sectional studies, or if the
target population had special nutri-
tional needs (eating disorders, dia-
betic, hospitalized, etc). In addition,
studies that failed to achieve any of
their objectives were excluded. In the
cases where multiple studies were con-
ducted on the same data set, only the
most recently published study was
included. There were 2 reviewers per
database. Trained reviewers evaluated
whether articles met inclusion criteria
and determined the quality of the
study. All researchers except the lead
researcher went through group training,
conducted by a systematic review and
meta-analysis expert, which also involved
watching a webcast.

Assessment of Study Quality/
Risk of Bias

In the initial part of work of the current
review, researchers worked in pairs in
which data were extracted by 1
reviewer and verified by a second
reviewer. The risk of bias in any re-
ported evidence should be at mini-
mum and evidence that is likely to
have high risk of bias serves a negli-
gible purpose and thus should not be
included in a systematic review even
if there is no better evidence.8 In this
review, determination of the quality
of studies was guided by the Grading
of Recommendations Assessment,
Development, and Evaluation system
of rating quality of evidence.9 A thor-
ough assessment of the study’s fidelity,
perceived conflict of interest regarding
outcome owing to sponsorship, study
design, imprecision, inconsistency,
appropriate use of theories, reasonable
duration of intervention, and whether
a study achieved the stated objectives
formed criteria for quality assessment.
Rating scores ranged from 1 to 6. Any
discrepancies were discussed until an
agreement was reached. Based on
these criteria of assessment of study
quality, studies were rated as having a
low risk of bias (5–6 scores), moderate
risk (3–4 scores), or high risk (1–2

scores) (Tables 1 and 2). Fidelity as a
factor in this systematic review was
assessed from authors’ declaration of
limitation in their respective studies.

Reviewers completed a detailed data
extraction form. Extracted data were
transferred to a spreadsheet (Tables 1
and 2).

Analysis Approach

The primary analytic goal was to deter-
mine the overall effectiveness of nutri-
tion education interventions to modify
dietary and exercise behaviors. To deter-
mine whether an intervention was suc-
cessful, the outcome of the study was
compared with the stated purpose
and/or objectives of the study. Once a
study was classified as having achieved
its intended purpose, the contributing
factors were assessed. Assessed factors
included: (1) the design of the study
including randomness, (2) the type of
intervention and activities imple-
mented, (3) the duration and dosage of
the interventions, (4) number of objec-
tives in a study, (5) fidelity in interven-
tion implementation, and (6) the use
of theory in directing the studies. These
factors were identified through a thor-
ough review of published nutrition edu-
cation interventions. They were found
to be common in almost all published
studies. The duration of intervention
wascategorizedasshortifithadacumu-
lative length of >5 months and long if a
study lasted for an accumulated period
of $5 months. This classification of
duration was deemed appropriate based
on the descriptions authors used of the
respectiveoriginal studies.Thereviewed
studies rarely reported the dosage and
frequency of interventions. Therefore
it was reasonable to report the total
amount of time spent in intervention
in months.

Another factor that emerged during
the review was worksite environment
interventions. Worksite environments
differ from one site to another. There
are various worksite environment in-
terventions for health living. These
include the provision of health mes-
sages around cafeterias, the provision
of healthy food in cafeterias, encour-
aging and providing walking space as
part of exercise for healthy living,
and schedules and amounts regarding
eating, among others. The analysis of
worksite environment interventions

Figure. Flow diagram illustrating the article filtering process as part of the systematic
review. GEMs indicates Great Educational Materials.

144 Murimi et al Journal of Nutrition Education and Behavior � Volume 49, Number 2, 2017

is therefore case specific. Data on work-
site environment interventions were
analyzed and reported along with other
initially identified factors.

A semiquantitative approach was
used to summarize findings from nutri-
tion education interventions. Results
fromnutritioneducationinterventions
were dichotomized based on whether
they reported a statistically significant
(P < .05) improvement in diet intake, exercise, or other related risk factors for obesity and diet-related chronic diseases. The researchers used this approach to allow for the diverse range of reported statistics, outcomes, and measurement units.2

RESULTS

A majority of studies (68%; n ¼ 27)
were conducted in the US; the remain-
ing studies (33%; n ¼ 13) were con-
ducted in other parts of the world. In

the current review, the duration of
intervention, number of objectives, fi-
delity in intervention, use of theories,
and use of worksite environment
were identified as determinants of the
efficacy of nutrition education inter-
ventions. This review provides the re-
sults and a discussion of these factors.

Slightly over half of the nutrition ed-
ucation interventions (53%; n ¼ 21)
were successful in modifying knowl-
edge,behavior,orphysiologicoutcomes
based on their primary objectives. Of
the 21 successful studies, 14 used the-
ories and 7 did not. Another 48% of
the interventions (n ¼ 19) met some
but not all of their primary objectives;
9 of them used theories and 10 did not.

Study Designs in the Reviewed
Studies

Randomized control trials accounted
for 70% of the studies (n ¼ 28), the pre-

test–posttest/quasi-experimental design
accounted for 23% (n ¼ 9), and the
non-experimental design constituted
8% (n ¼ 3) of the total articles included
in the analysis. Over half of the total
number of studies (58%; n ¼ 23) were
based on theory.

Interventions in the Reviewed
Studies

This review considered studies con-
ducted in person or face-to-face inter-
ventions at the individual or group
level. A majority of the studies (68%;
n ¼ 27) used a single type of interven-
tion. For example, a study whose
objective was to create education
materials using the target audience’s
preferences and to implement a
heart-healthy diet education program
used a single intervention in which 2
registered dietitians led in-home edu-
cation sessions.49 Table 1 lists more ex-
amples of studies that employed a
single intervention. Another 33% of
studies (n ¼ 13) employed multiple in-
terventions including cooking, watching
videos, attending exercise classes, and
gardening. An example is a study
whose objective was to examine the
extent to which participants in a com-
bined physical activity and dietary
intervention achieved changes in mul-
tiple health behaviors.36 Multiple in-
terventions involved the provision of
opportunities for physical activity and
healthy eating before, during, and af-
ter church services. Table 2 provides
more examples on studies that used
multiple interventions.

Effect of Duration of
Intervention

For the purposes of this systematic re-
view, the duration of the intervention
was categorized as short if it had a cu-
mulative length of <5 months and long if a study lasted for an accumu- lated period of $5 months. This was informed by the way studies reported their time. Therefore, for convenience in reporting of the results, the terms short duration and long duration were adopted. The amount of time spent in intervention was reported in months. The reviewed studies largely left out dosage and frequency of intervention.

In this review, two thirds (n ¼ 12) of
nutrition education interventions that

Table 1. Face-to-Face, Individual, Group, and Peer Counseling/Nutrition Education Studies (n ¼ 27)

Authors Study

Population Study Objective
Study Design/
Intervention

Length and
Frequency

Behavioral
Theory/

Construct
Achieved
Objectives

Risk of
Bias

(Quality
Measure) Major Findings

Arrebola
et al, 201110

60 patients with grade
II overweight and
non-morbid grade
I–II obesity (age
range, 18–50 y)

To evaluate effects of
lifestyle
modification
program focused
on diet, exercise,
and psychological
support on health-
related quality of life

Pre–post.
Intervention:
physical activity
recommendations
and psychological
support. Group
sessions led by
doctor, nurse, or
dietitian.

No control

11 sessions
conducted
every 2 wk for
6 mo

No theory All Low Intervention was associated
with significant
improvements in physical
functioning (80.37 � 18.90
vs 89.40 � 13.95;
P < .001) and role of physical (20.37 � 9.10 vs 23.14 � 6.67; P < .05), vitality (58.71 � 21.98 vs 70.91 � 26.56; P < .01), social functioning (79.62 � 27.76 vs 86.57 � 25.45; P < .03), and general health (61.03 � 19.13 vs 69.42 � 18.80; P < .001) factors.

Auld et al,
201511

723 adults To analyze impact of
weight
management
intervention on
physical activity/
exercise and body
weight and
composition

Pre–post.
Intervention: ESBA
curriculum
delivered in group
settings.

No control

9 lessons taught
for 8–12 wk

SCT and
Adult
Learning
Theory

Some Moderate ESBA elicited mean positive
behavior change for food
resource management
(P < .01), food safety (P < .001), nutrition (P < .001), and physical activity level in participating states (P < .01) except New York. There was an increase in dairy, fruit, and vegetable intake in Arkansas and California (P < .05) but not in Colorado, New York, and Ohio.

Babatunde
et al, 201112

110 African American
adults aged
50–93 y

To assess
effectiveness of
osteoporosis
education program
to improve calcium

RCT.
Sessions of #15
people; short
presentations/
lectures, hands-on

6 sessions (30–
45 min/wk) for
6 wk

Health Belief
Model

All Low Overall, an educational
program developed with a
theoretical background
was associated with an
improvement in calcium

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Table 1. Continued

Authors Study Population Study Objective
Study Design/
Intervention
Length and
Frequency
Behavioral
Theory/
Construct
Achieved
Objectives
Risk of
Bias
(Quality
Measure) Major Findings

intake, knowledge,
and self-efficacy

activities, and
demonstrations to
help participants
increase self-
efficacy.

Control: Delayed NE

intake (mean increase,
556 mg dietary calcium;
P < .001), knowledge (P < .001), and self- efficacy (P < .001).

Backman
et al, 201113

327 participants (156
treatment; 171
control), 75% of
whom were low-
income African
American women
aged 18–54 y

To evaluate
effectiveness of
fruit, vegetable, and
physical activity
toolbox for
community
educators in
changing
knowledge,
attitudes, and
behavior among
women of low-
income

Quasi-experimental
design with
treatment and
control groups.
Control group did
not receive NE.

Intervention was
1-h nutrition and
physicalactivity
education
classes per
week for 6 wk

SCT All Low Women in the treatment
group reported significant
changes in 9 measures of
attitude, compared with 1
measure in the control
group (P < .05). Compared with those in the control group, women in the treatment group were also more likely to make behavioral changes to meet recommendations for fruit and vegetable consumption (P < .001) and physical activity (P < .001).

Brennen and
Williams,
201314

16 African American
women aged 25–
63 y

To evaluate effects of
culturally sensitive
lifestyle intervention
on blood pressure
and weight

Quasi-experiment.
Intervention:
Counseling and
education on
increasing physical
activity and dietary
intake of fruits and
vegetables while
decreasing dietary
intake of salt and
fat.

No control

10 individual
sessions,
30 min each,
and 11 group
sessions,
60 min each, for
12 wk

No theory All Moderate Both systolic and diastolic
blood pressure
decreased from a mean of
151/90 pre-intervention
to 131/76 post-
intervention. There was a
significant decrease from
pre- to post-intervention
systolic (P ¼ .03) and
diastolic (P ¼ .001) blood
pressures. There was a
statistically significant
improvement in self-
efficacy to exercise if
bored (P ¼ .02) or if busy
with other activities

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(P ¼ .008). There was a
statistically significant
improvement in self-
perception as valuable/
worthless (P ¼ .02) and
choice as superficial/
profound (P ¼ .04).

Clifford et al,
200915

101 college students To determine whether
a series of 4 15-
min, theory-driven
(SCT) cooking
programs aimed at
college students
living off campus
improved cooking
self-efficacy,
knowledge,
attitudes, and
behaviors
regarding fruit and
vegetable intake

RCT. Subjects in
intervention group
viewed 4 15-min
cooking programs
over 4 wk. Subjects
in control group
viewed 4 5-min
programs on sleep
disorders.

4 weekly 15-min
episodes

SCT Some Moderate There were significant
improvements in knowledge
of fruit and vegetable
recommendations in the
intervention group
compared with the control
group post¼intervention
and at 4-mo follow-up
(P < .05). There were no significant changes in fruit and vegetable motivators, barriers, self-efficacy, or intake.

Craigie et al,
201116

75 adults aged $40 y To assess the
feasibility of a
lifestyle intervention
focusing on diet
and activity,
participating in
cardiovascular
screening

RCT. Lifestyle
intervention
composed of 3
personalized
counseling
sessions plus
telephone contact.

12 wk

No theory
reported

Some Low 82% successfully
maintained or lost weight
(mean loss 1.1 kg, and
2.6 cm waist
circumference) and 85%
reported eating 5 portions
of fruits and vegetables
compared with 56% at
baseline. No behavior
changes were detected in
control group.

Davis et al,
200917

46 individuals aged
>16 y

Assessment of peer-
led approach to
improving diet of
South Asians in
Southampton

Quasi-experiment.
Intervention: 10 taster
sessions and 28
cookery club
sessions.

No control

Length not
specified but
follow up was
conducted after
1 y

No theory All Moderate There was increased intake
of low-fat dairy products
and reduced fat and salt
intake. 80% and 75%
made positive changes to
cooking practices and
eating patterns,
respectively.

Duncan
et al, 201318

286 adults enrolled in
English as a
Second Language,
aged 18–73 y

To conduct pre–post
feasibility trial of
Healthy Eating for
Life, which

Pre–post design.
Intervention: Healthy
Eating for Life
curriculum for at

At least 2 h/wk for
12 wk

Social
Learning
Theories

All Low There was a significant
increase in fruit, vegetable
intake, nutrition
knowledge, action

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Table 1. Continued
Authors Study Population Study Objective
Study Design/
Intervention
Length and
Frequency
Behavioral
Theory/
Construct
Achieved
Objectives
Risk of
Bias
(Quality
Measure) Major Findings

integrates content
about healthy
nutrition to
decrease cancer
health disparities

least 2 h/wk of
classroom
instruction for
12 wk.

No control group

planning, and coping
planning among
participants (P < .05 for all).

Endevelt
et al,
201119

127 older adults aged
$75 y

To determine impact
of intensive
nutritional
intervention
program on health
and nutritional
status of
malnourished
community

Partial RCT.
DIT: Intervention led
by dietitian or
medical treatment.
A physician led a
standard care
group with an
educational
booklet.

Nonrandomized
‘‘untreated
nutrition’’ group.

5 visits for 6 mo No theory All Low DIT group showed
significant improvement in
cognitive function and
depression score
compared with the
change in the other 2
groups. DIT group
showed a significant
improvement in intake of
carbohydrates, protein,
vitamin B6, and vitamin
B1 and had a significantly
lower cost of physician
visits than did the other 2
groups (P < .05 for all).

Francis
and Taylor,
200949

58 women aged
54–83 y

To create education
materials using
target audience’s
preferences and
implement heart-
healthy diet
education program
designed using
needs and
preferences

RCT.
Intervention: 2
individual
registered dietitian–
led in-home
education sessions

Control: 2 education
material mailings

3 mo Social
Marketing
Theory

All Low Intervention and control mini-
nutritional assessment
scores improved
(P < .001). Intervention subjects consumed more fiber than did control subjects (P ¼ .01) and reduced sodium intake (P ¼ .02). Controls reduced energy (P ¼ .01) and cholesterol intakes (P ¼ .03), likely because of decreased food intake.

Ha and
Caine-Bish,
201120

80 college students
aged 18–24 y

To determine whether
there would be an
increase in whole-
grain consumption
after students

Pre–post.
Intervention: General
nutrition class.

No control

3 times/wk for
50 min for
semester

SCT Some Moderate At baseline, total mean grain
consumption was 3.07oz
and mean whole-grain
consumption was 0.37 oz.
After the study, mean

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completed an
interactive
introductory
nutrition course
focusing on
disease prevention

consumption of whole-
grain products significantly
increased to 1.16 oz
(P < .001) but total mean grain intake remained the same (3.06 oz).

Hsu et al,
201321

25 adults aged >18 y To examine feasibility,
acceptability, and
preliminary results
of exercise
intervention with a
Healthy at Every
Size orientation

RCT.
Intervention: Exercise
training and weekly
behavioral
intervention.

Control: Exercise only

Project CHANGE
was 8-wk
randomized,
controlled trial
with follow-up at
4 wk

SDT All Low Both interventions showed
large effect sizes on
changes in weekly energy
expenditure, moderate PA,
and brisk walking. Both
interventions showed
small effect sizes for all
fitness variables, including
body mass index, waist–
hip ratio, predicted
VO2max, 1RM machine
chest press, and leg press.
Adherence to PA goal was
better for the intervention
group at follow-up. The
Self-Determination
Theory based exercise
intervention with a Healthy
at Every Size resulted in
larger effect sizes for
changes in key
motivational variables,
including self-
determination, autonomy,
and goal-setting,
planning and scheduling
self-efficacy (P not listed).

Ireland, et al,
201022

43 healthy adults
aged 20–75 y

To investigate
whether dietary
education enabled
reduction in salt
consumption

RCT.
2 different education
methods using
either Australia’s
National Heart
Foundation Tick
symbol or Food
Standards Australia
and New Zealand’s
low-salt guideline of
120 mg sodium/
100 g food.

8 wk No theory Some Low After 8 wk, urinary sodium
excretion decreased from
121 � 50 to
106 � 47 mmol/24 h
(7.3 � 3.0 to 6.4 � 2.8 g
salt/24 h) in the Tick group
and from 132 � 44 to
98 � 50 mmol/24 h
(7.9 � 2.6 to 6.0 � 3.0 g
salt/24 h) in the Food
Standards Australia New
Zealand group (P < .05,

(continued)
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Table 1. Continued
Authors Study Population Study Objective
Study Design/
Intervention
Length and
Frequency
Behavioral
Theory/
Construct
Achieved
Objectives
Risk of
Bias
(Quality
Measure) Major Findings

with no between-group
difference).

Kannan et al,
201023

102 low-income
African American
women aged
18–45 y

To reduce nutrition
risks and enhance
protective nutrition
and analyze
changes in self-
efficacy

Pre–post.
Intervention: peer-led
nutrition
curriculum.

No control

13 lessons taught
at 1-wk intervals
for 13 wk

PEN-3
model
and TTM

All Low 77% reported adopting at
least 1 healthy eating
behavior (moderating
sodium or serving more
fruits and vegetables to
their families), 23%
adopted at least 2 such
behaviors, and 45%
adopted both dietary and
biomedical behaviors
(self-monitoring blood
pressure, and exercising).

Kontogianni
et al,
201224

126 individuals aged
45–67 y

To evaluate impact on
dietary and activity
habits of non-
intensive,
community-based
lifestyle intervention
for type 2 diabetes
prevention in high-
risk Greek
individuals

Pre–post.
Intervention: NE with
dietician.

No control

6 bimonthly
sessions for 1 y

No theory Some Moderate There was decreased
consumption of whole-fat
dairy and processed meat
(P ¼ .02 and .02,
respectively), sugar
(P ¼ .006), and refined
cereals (P ¼ .05). There
was improved diet,
decreased body weight
(P ¼ .04), plasma
triglycerides (P ¼ .02), and
2-h post-load plasma
glucose (P ¼ .05)
compared with those who
had worsened dietary
habits. Total time spent
daily on physical activity
remained unchanged
throughout the
intervention.

Kreausukon
et al,
201225

114 full-time
undergraduate
students (aged 18–

To improve fruit and
vegetable
consumption

RCT.
Intervention group
received
psychological

Not specified but
follow-up at
6 wk

SCT All Low A social-cognitive
intervention to improve
fruit and vegetable
consumption was

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25 y) at Chiang Mai
University, Thailand

program that
addressed self-
efficacy and
strategic planning.

Control group
received handouts
about general
nutrition guidelines

superior to a knowledge-
based education session
with significantly greater
intention, planning, and
self-efficacy for fruit and
vegetable consumption
(P < .05 for all). Both the intervention and control groups demonstrated an increase in fruit and vegetable consumption from baseline to 1 wk after the intervention (P < .001 for both) but the increase was greater in the intervention group (P ¼ .01).

Mendonca
Rde and
Lopes,
201226

167 adults aged
40–65 y

To determine effects
of health
interventions on
dietary habits and
physical
measurements

Quasi-experimental/
pre–post

Intervention: Guided
physical exercise,
nutrition
intervention,
nutritional
education groups,
individual nutritional
care.

No control

4 sessions, 60 min
each for 7 mo

No theory All Low There was a reduction in
systolic blood pressure
(P ¼ .02) and use of
animal fats (P < .01) as well as an increase in the percentage of individuals with a normal waist circumference and daily consumption of greens/ vegetables and milk/dairy products (P < .01)

Milliron et al,
201227

153 adults aged
20–65 y

To promote healthy
eating behavior and
weight
management

RCT.
Intervention: 10 min
face-to-face.

Control: Received no
education on Eat
Smart shelf tags
posted in store.

10 min No theory Some Moderate No significant differences
between the 2 groups on
purchased total,
saturated, or trans-fat and
servings of total
vegetables. However, the
intervention group
purchased significantly
more servings (per
1,000 kcal) of whole fruit
and dark green/bright
yellow vegetables
compared with the
control group.

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Table 1. Continued
Authors Study Population Study Objective
Study Design/
Intervention
Length and
Frequency
Behavioral
Theory/
Construct
Achieved
Objectives
Risk of
Bias
(Quality
Measure) Major Findings

Nakade et al,
201228

226 overweight/
obese adults in
Japan, aged
40–65 y

To evaluate effects of
behavioral
approach that
emphasized
tailored behavior
counseling, diet,
weight loss, and
weight
maintenance

RCT.
Intervention: 30 min
individual
counseling and
20 min group
sessions about
effective exercise
provided by
registered dietitians
and exercise
instructors.

Control: No NE

5 counseling
sessions for 1 y
and follow-up
1 y after
intervention

No theory All Low The intervention group lost
significantly more weight
than the control group
(– 5.0 kg vs 0.1 kg for men
and –3.9 kg vs –0.2 kg for
women). Dietary intake
and number of walking
steps improved in the
intervention group. After
1-y follow-up, the
intervention group
maintained significantly
lower weight, lower energy
intake, and improvement
in irregular eating habits
(P < .05 for all variables).

Pimentel et al,
201029

67 Brazilian adults
aged 50–69 y, with
impaired glucose
tolerance and at
least 1 other risk
factor for diabetes
mellitus 2

To evaluate
effectiveness of
nutritional
education program
on anthropometric,
dietary, and
metabolic
parameters with
impaired glucose
tolerance

RCT.
Intervention:

individual and
group counseling
once and twice per
month, respectively
with team of
nutritionists

Control: No NE

36 sessions over
12 mo

No theory Some Low The intervention group
showed a significant
decline in body weight
(�3.4%), body mass index
(�5.7%), cholesterol
intake (�49.5%), fasting
glycemia (�14.0%), fasting
insulin (�9.0%),
postprandial glycemia
(�21.0%), postprandial
insulin (�71.0%), total
serum cholesterol
�23.0%), and glycated
hemoglobin (�24.0%). A
decrease in energy intake
(5%; P ¼ .06) and low
density lipoprotein
cholesterol (25%; P ¼ .07)
was observed in the
interventional group,
although it did not reach
statistical significance.

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Plawecki and
Chapman-
Novakofski,
201330

69 adults aged
55–75 y

To enhance physical
activity and
nutritional
behaviors

RCT.
Intervention: lectures
and hands-on
active learning

Control: intervention
was delayed

3 sessions
(duration not
mentioned) for
8 wk

Health Belief
Model and
Theory of
Reasoned
Action

Some Low Comparison of week 1 and
week 8 data indicated
significant improvement
for the treatment but not
the control group for
calcium, and vitamin D
(P < .05). There was limited response to the exercise outcome variables, with many not participating in that section of the program.

Ritchie et al,
201031

3,015 and 3,004
before and after
NE; pregnant or
postpartum
women/caregivers
of children enrolled
in WIC, aged
22–36 y

To explore impact of
WIC on family
behavior regarding
fruits and
vegetables, whole
grains, and lower-
fat milk

Pre–post cross-
sectional design.
Caregivers
received education
intervention in a
group (class) or in
individual (one-on-
one counseling)
format.

No control

3 sessions over
6 mo

TTM All Low After nutrition education,
women and caregivers
reported increased
recognition of education
messages, positive
movement in stage of
change for target food
items, increased family
consumption of fruits and
whole grains, and
replacement of whole milk
with lower-fat milk.

Shahnazari
et al,
201332

84 US veterans aged
25–80 y

To determine
effectiveness of
nutrition-based
wellness coaching
using multiple
contacts and
simple educational
tips on health
eating and weight
management

RCT.
Intervention: 9
individualized NE
sessions. Coaching
consisted of 15-min
sessions with final
60-min session at
end of 6 mo. Total of
3.75 h educational
contact for
intervention and 1 h
for control group by
same nutrition
coach for each
veteran throughout
study

Control: 1 h
individualized NE
session.

9 NE sessions for
6 mo

Stage of
Change
Model

All Low Multiple coaching contacts
decreased intake of
energy, fat, and
carbohydrates by 31%
(P < .001). Weight loss of 5% from baseline (92.8 to 88.2 kg; P < .01) was observed in the intervention group with mean body mass index decreasing from 30.4 to 28.9 (P < .05). The control group showed a decrease in fat intake by 20% (P < .01) but no statistically significant changes in intake of other nutrients or body weight (88.7 to 87.4 kg). Veterans’ readiness to change eating behavior

(continued)
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Table 1. Continued
Authors Study Population Study Objective
Study Design/
Intervention
Length and
Frequency
Behavioral
Theory/
Construct
Achieved
Objectives
Risk of
Bias
(Quality
Measure) Major Findings

for weight loss improved
with nutrition coaching.

Silva et al,
201033

239 women aged
30–45 y

To analyze impact of
weight
management
intervention on
theory-based
psychosocial
mediators, physical
activity/exercise,
and body weight
and composition

RCT.
Intervention: NE
sessions covering:
physical activity,
eating/nutrition,
body image, and
other cognitive and
behavioral contents

Control: General
health education

30 sessions, 2 h
each weekly or
bimonthly for 1 y

SDT All Low At 12 mo, the intervention
group showed increased
weight loss (–7.29%,) and
higher levels of physical
activity/exercise
(þ138 � 26 min/d of
moderate plus vigorous
exercise; þ2,049 � 571
steps/d) compared with
control subjects (P < .001).

Sorensen
et al,
201134

56 individuals aged
22–55 y

To compare effect of
behavior
modification
consisting of either
a gourmet cooking
course or NLP
therapy on weight
regain

RCT.
The first step was 12-
wk weight loss
program.
Participants
achieving at least
8% weight loss
were randomized
to 5 mo of either
NLP therapy or
course in gourmet
cooking.

8 mo No theory Some Low The NLP therapy group lost
1.8 kg and the cooking
group lost 0.2 kg during
the 5 mo of weight
maintenance. The
dropout rate was lower
during the active cooking
treatment compared with
the NLP group. There was
no difference in weight
maintenance after 2 and
3 y of follow-up.

Wieland
et al,
201235

34 women (Hispanic,
Somali, and
Cambodian) aged
22–68 y

To evaluate socio-
culturally
appropriate
physical activity
and nutrition
intervention in
community-based
participatory
research approach

Pre–post.
Intervention. 6-wk
program with 2 90-
min classes per
week.

No control

6 wk No theory Some Moderate After the intervention,
participants were more
likely to exercise regularly
(P < .001). They reported higher health-related quality of life (P < .001). Self-efficacy for diet and exercise; weight loss, waist circumference, and blood pressure were not significantly different after the intervention.

DIT indicates Dietetic Intensive Treatment; ESBA, Eating Smart Being Active; NE, nutrition education; NLP, Neurolinguistic Programming; PA, Physical Activity; RCT, ran-
domized control trials; SCM, Stage of Change Model; TRA, SCT, Social Cognitive Theory; SDT, Self-determination Theory; WIC, Special Supplemental Nutrition Program
for Women, Infants, and Children.

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Table 2. Multicomponent Nutrition Education Intervention Studies (n ¼ 13)

Authors
Study

Population Study Objective
Study Design/
Intervention
Length and
Frequency
Behavioral
Theory/
Construct
Achieved
Objectives

Risk of
Bias (Quality
Measure) Major Findings

Baruth and
Wilcox,
201336

360 African
Americans aged
$18 y

To examine extent to
which participants
in combined
physical activity
and dietary
intervention
achieved changes
in multiple health
behaviors

RCT.
Intervention: PA and

healthy eating
before, during and
after church
services

Control: delayed
intervention

15 mo SEM All Low Up to 19% indicated no
change in health
behavior, 31%
changed 1 health
behavior, 31%
changed 2 health
behaviors, 13%
changed 3 health
behaviors, and 5%
changed all 4 targeted
health behaviors.
Combinations of
multiple behavior
change included PA
and dietary behaviors,
which suggests that
both behaviors can be
changed
simultaneously.

Cullen et al,
200937

1,004 Texas
EFNEP clients in
100 classes,
mean age 35 y

To evaluate modified
curriculum for 6-
session Texas
EFNEP promoting
healthful home
food environments
and parenting skills
related to obesity
prevention

RCT.
Intervention: 6 short

videos with goal
setting, problem
solving, guided
discussion, and
handouts. Then a
weekly goal sheet
for recording was
issued Participants
monitored daily goal
attainment and
return goal sheet
the following week

Control: Traditional
EFNEP class
included brief
discussion and food
preparation

6 classes
for 6 wk

No theory Some Low There was a significant
BMI decrease at
postintervention
compared with
baseline only for the
intervention group.
This change was not
maintained at
follow-up.

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Table 2. Continued

Authors
Study
Population Study Objective
Study Design/
Intervention
Length and
Frequency
Behavioral
Theory/
Construct
Achieved
Objectives
Risk of
Bias (Quality
Measure) Major Findings

Dirige et al,
201338

673 Filipino-
American adults
aged $18

To evaluate 18-mo
nutrition and
physical activity
intervention (active
life) conducted
through culturally
specific
organizations

RCT.
Intervention:

Workshops and
activities (cooking
demonstrations,
recipe contests,
supermarket tours,
group aerobic
classes, gardening)

Control: Cancer
screening,
alternative
medicine, and
stress management

18 mo TTM Some Low Intervention participants
showed significant
increases in PA
(P < .05), adoption of a low-fat diet (P < .05), and stage of change for fruits and vegetables (P < .05), dietary fat intake (P < .01), and PA (P < .01). Intervention did not lead to increases in number of participants eating $5 servings/d of fruits and vegetables.

French et al,
201039

160 metropolitan
transit workers
aged 20–79 y

To describe and
report results from
worksite obesity
prevention
intervention that
targeted transit
employees

Group RCT (4
garages)

Intervention:
Enhancement of
PA facilities,
increased
availability of and
lower prices for
healthy vending
machine
choices, etc

Control: No
intervention

18 mo worksite
intervention

No theory Some Low Energy intake decreased
significantly and fruit
and vegetable intake
increased significantly
in intervention garages
compared with control
garages. However,
BMI and PA changes
were not significant.

Iriyama and
Murayama,
201440

57 male workers in
Japan

To evaluate effects of
new worksite
weight-control
program using
combination of
nutrition education
environmental
interventions

RCT crossover.
Intervention:

Intervention group
received 6-mo
program consisting
of nutrition
education and
provision of healthy
cafeteria meals and
nutritional
information. 1-y

6 mo TTM and
Precede-
Proceed
model

All Low Mean BMI was
significantly reduced
from baseline value of
25.6 kg/m2 to 25.3 kg/
m2 at month 6 and to
24.8 kg/m2 at year 1
(P ¼ .008) and was
significantly lower at
year 1 than at baseline
and month 6 in
multiple comparison

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follow-up
Control received

same intervention
as counterparts in
crossover design
from 6 mo after
study entry

tests in intervention
group; BMI increased
over time in control
group (P < .001).

Johansen et al,
201041

198 adults aged
25–63 y

To present effect of
intervention study
intentions to
change dietary
behavior and
changes made in
dietary intake

RCT.
Intervention:

Combination of
group sessions,
individual
counseling, and
organized exercise
groups

Control: General
advice participants
would receive from
general practitioner

6 sessions,
2 h each,
for 7 mo

TTM Some Low Differences between
intervention and control
after intervention were
significant for sugar-rich
drinks and rapeseed oil
(P < .05). Intention to reduce dietary intake of fat, sugar, and white flour, and to change type of fat and increase intake of vegetables and legumes shifted for intervention group (P < .05) from pre- action (pre- contemplation, contemplation, and preparation) stages to action stage in intervention group but not in control group. Difference between groups at follow-up was significant (P < .05). No significant differences were found for intention to increase fruit intake.

Linde et al,
201242

1,672 participants
aged 18–75 y

To influence weight
gain positively
among employees
over 2 y

RCT (worksite level).
Six worksites in US
metropolitan area
were recruited and
randomized in pairs
at worksite level to
2-y intervention or
no-contact control

Intervention: Posters
at worksites, link to
Web sites with

2 y, frequency
varied
with center

No theory
reported

None Moderate No differences between
sites in key outcome of
weight change over 2-
y study period.

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Table 2. Continued
Authors
Study
Population Study Objective
Study Design/
Intervention
Length and
Frequency
Behavioral
Theory/
Construct
Achieved
Objectives
Risk of
Bias (Quality
Measure) Major Findings

useful information
on food availability
and price, PA
promotion, scale
access, and media
enhancements

Control: No contact

Racette et al,
200943

123 participants
aged 36–54 y
with BMI
32.9 � 8.8 kg/
m2, employed at
1 of 2 selected
worksites within
a large medical
center

To evaluate
effectiveness of
worksite health
promotion program
on improving
cardiovascular
disease risk factors

Cohort randomized
trials. Intervention
included
pedometers,
healthy snack cart,
Weight Watchers
meetings, group
exercise classes,
seminars, team
competitions, and
participation
rewards

Control: Personal
health reports
containing
assessment results

Weekly
for 1 y

TTM Some Low Improvements (P < .05) were observed at both worksites for fitness, blood pressure, and total, high-density lipoprotein, and LDL-C. Additional improvements occurred in intervention group in BMI, fat mass, Framingham risk score, and prevalence of metabolic syndrome; only changes in BMI and fat mass were different between control and intervention worksites.

Rustad and
Smith,
201344

118 ethnically
diverse, low-
income women
aged 23–45 y

To assess impact of
short-term nutrition
intervention using
education on
comprehensive
array of nutrition
and health topics

Pre–post intervention.
Experiential and
interactive lectures,
activities, and
demonstrations.
Educational
sessions on
shopping and
budgeting, healthy
cooking, improving
food security by
growing foods.

No control

3 sessions
in 6 wk.

Each class
lasted
75–90 min

No theory All Moderate Postintervention
increased nutrition
knowledge and
favorable nutrition
behavior. Responses
to 7 of 11 questions in
knowledge and 9 of 11
variable set changed
significantly at
postintervention
(P < .01 for both). Women also decreased consumption of fast

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foods and processed
snacks high in sugar,
salt, and fat, and fatty
cuts of meat, and
decreased addition of
sugar, salt, and butter
to foods (P < .01).

Sarrafzadegan
et al, 201345

12,514
participated at
baseline and
9,570 in post-
intervention
survey, aged
24–54 y

To evaluate
component of
Healthy Heart
Programs to
assess feasibility
and outcomes of
program on lifestyle
behaviors and risk
factors for chronic
non-communicable
diseases

RCT.
Intervention: Public

education through
mass media,
intersectoral
cooperation and
collaboration,
professional
education and
involvement,
marketing,
organizational
development,
legislation and
policy development,
as well as research
and evaluation

Duration of
intervention
activities
varied: 3–4 y.

Precede–
Proceed
model, Social
Learning
Theory, and
innovation
diffusion
approach

Some Moderate Prevalence of abdominal
obesity, hypertension,
hypercholesterolemia,
hypertriglyceridemia,
and high LDL-C
decreased significantly
in intervention area vs
control area in both
sexes. However,
reduction in
overweight and
obesity was significant
only in females
(P < .05 for all). There were no significant changes in prevalence of diabetes mellitus.

Savoie et al,
201546

203 participants
aged $18 y

To determine whether
participation in
selected
Supplemental
Nutrition Assistance
Program–Education

lessons had an
impact on intent to
improve nutrition-
related behaviors of
participants

Retrospective post-
then-pre design

Intervention: lecture,
cooking
demonstration,
sample tasting of
food prepared in
class, handout.

No control

Not indicated TPB All Low Mean responses of
individual questions
and mean lesson
scores increased
significantly from
pretest to posttest in
the menu planning,
shopping lesson, and
the My Plate lesson
(P < .001).

Wilcox et al,
201347

74 African
Methodist
Episcopal
churches and
1,257 adult
members within
them

To report results of
intervention
targeting physical
activity and healthy
eating

RCT.
Intervention: Churches

implemented;
sharing messages
from pulpit; passing
out educational
materials (provided),
create Faith,
Activity, and

15 mo SEM Some Low There was a significant
effect favoring
intervention group in
self-reported leisure
time (d ¼ 0.18;
P ¼ .02). No group
differences were found
for self-reported fruit
and vegetable

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Table 2. Continued
Authors
Study
Population Study Objective
Study Design/
Intervention
Length and
Frequency
Behavioral
Theory/
Construct
Achieved
Objectives
Risk of
Bias (Quality
Measure) Major Findings

Nutrition Program
Control: delayed
intervention
components at end
of 15 mo

consumption,
measured blood
pressure, and self-
reported fat- and fiber-
related behaviors.

Lowe et al,
201048

96 adult
participants
(BMI
29.7 � 6.0 kg/
m2) hospital or
university
employees aged
21–65y

To evaluate nutritional
and weight
changes in
program that used
worksite cafeterias
to reduce
employees’ calorie
content of
purchased foods
and improve their
macronutrient
intake

RCT.
3 mo of baseline data

collection, then 3-
mo intervention; 6-
and 12-mo
postintervention
follow-ups.
Participants were
randomly assigned
to 1 of 2
intervention
groups:
Environmental
Change or
Environmental
Change Plus
Energy Density
Education and
Incentives.
Randomization of
participants
occurred within
each worksite

3 mo No theory Some Low There was no difference
between groups in total
energy intake over
study period.

Across groups, energy
and percentage of
energy from fat
decreased and percent
of energy from
carbohydrates
increased from baseline
tointerventionperiod(all
P < .01). Follow-up analyses, conducted by averaging baseline months 1 and 2 and comparing them with intervention month 3 as a conservative estimate of overall impact of intervention, indicated that change in energy, carbohydrate, and fat intake remained significant (P < .001).

Providing nutrition labels
and reducing energy-
density of selected foods
was associated with
improved dietary intake.

BMI indicates body mass index; EFNEP, Expanded Food and Nutrition Education Program; LDL-C, low-density lipoprotein cholesterol; PA, physical activity; RCT, random-
ized control trials; SEM, Structural Ecological Model; TPB, Theory of Planned Behavior; TTM, Trans-theoretical Model.

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Journal of Nutrition Education and Behavior � Volume 49, Number 2, 2017 Murimi et al 161

lasted longer than 5 months (n ¼ 18)
met their primary objectives.17,19,26,28,
29,31,32,36,40,43,50 For example, a study
whose objective was to evaluate the
effectiveness of a nutritional education
program that involved bimonthly
group discussions for 12 months, and
that included written and oral didactic
instructions on anthropometric, dietary,
and metabolic parameters with impa-
ired glucose tolerance, reported a
decrease in 2 risk factors related to
diabetes mellitus.29

Similarly, a study that took 1 year
and whose objective was to study the
impact of a weight management inter-
vention on theory-based psychosocial
mediators reported weight loss (–7.29%,)
and increased levels of physical activ-
ity/exercise (þ138 � 26 min/d of mod-
erateplusvigorousexercise;þ2,049� 571
steps/d) compared with control sub-
jects (P < .001).33 Finally, a study that involved a physical activity and healthy eating intervention over 15 months reported significant results and an 18% effect on the intervention group in self-reported leisure time (P ¼ .02; d ¼ 0.18).47

On the contrary, this review found
that nutrition education interventions
that lasted for a short duration were
less likely to meet their stated objec-
tives. A relevant example here is a
study whose purpose was to promote
healthy eating behavior and weight
management.27 In this study, inter-
vention involved 10 minutes of face-
to-face education on the printed
EatSmart shelf tags posted in the store.
The control group received no educa-
tion about the EatSmart shelf tags
posted in the store. Outcome measures
included purchases of total saturated
and trans fat (grams per 1,000 kcal),
fruit, vegetables, and dark green or yel-
low vegetables (servings per 1,000 kcal)
derived through a nutritional analysis
of participants’ shopping baskets. Re-
sults showed no significant differences
between the control and intervention
groups on total purchases as well as
in the choice of health eating grocery
products except in fruit and dark green
or yellow vegetables, thus indicating
minimal attainment of the study ob-
jectives.

Similarly, a nutrition education
intervention on physical activity and
nutrition35 revealed that self-efficacy
for diet and exercise, weight loss,

waist circumference, and blood pres-
sure were not significantly different
from baseline after 2-hour classes
each week for 6 weeks.

Effect of Number of Study
Objectives/Focus

In addition to the long duration, this
systematic review found that studies
with few or focused objectives were
more successful in meeting all of their
stated objectives than were interven-
tions that had several unrelated objec-
tives. For example, a 4-month nutrition
education intervention whose objec-
tive was to increase whole-grain con-
sumption among students who completed
an interactive introductory nutrition
course focusing on disease preven-
tion20 indicated that student partici-
pants increased whole-grain consumption
from 0.37 to 1.16 oz (P < .001). Simi- larly, another study22 with only 1 objective, to investigate whether die- tary education enabled a reduction in salt consumption, indicated that after 8 weeks of dietary education interven- tion there was a reduction in salt con- sumption and urinary sodium excretion.

In contrast, interventions with >3
unrelated objectives were not success-
ful in meeting all of their objectives.
For instance, a study that had >3 ob-
jectives, conducted in 3 phases with
different feeding regimes, reported
inconsistent results at the 3 phases.34

In phase 1, 88% of participants (n ¼
49) completed 12 weeks of calorie re-
striction and achieved 8% weight
loss. Phase 2 involved 5 months of
weight maintenance; participants
were divided into 2 groups each with
different feeding regimes and hence
different objectives. The 2 groups
experienced different numbers of
participant dropout, which affected
the final results. There was no differ-
ence in weight maintenance after 2
and 3 years of follow-up. In general,
it was observed that follow-up studies
did not yield many results. A lot of
follow-up studies did not yield a
significant change from the initial re-
sults. Therefore, it can be implied
that a majority of researchers could
have paid less attention to the
follow-ups. The researchers in this re-
view recommend stringent measures
in follow-up studies, just as in the

initial phase of the study, to enhance
reliability of the results of follow-ups.

Lack of Fidelity in Delivery

Fidelity in intervention ensures that
all intervention activities are executed
as planned in the methods. There
were few reported cases of lack of
intervention fidelity that could have
compromised the findings. A peer-
led nutrition education intervention
that addressed maternal and infant
health through dietary patterns re-
ported that some facilitators neglected
to follow the complete lesson plans by
omitting parts of a lesson or failing to
use the facilitator’s guide, or they did
not show enthusiasm in promoting
the desired behavior among the peer-
led cohorts. Although the interven-
tion was the same by design and
content information, results of the
study varied among cohorts. As a
result, the success of the interventions
was affected by the human factors of
the presenter.23

Theory-Based Studies

Slightly over half of the studies
(57.5%; n ¼ 23) reported being theory
based and used at least 1 theory. The
most common theories used to design
and implement nutrition education
interventions in studies selected in
this review were the Trans-theoretical
Model and Social Cognitive Theory.
The majority of the theory-based
studies (61%; n ¼ 14 of 23) were
successful in achieving their stated ob-
jectives, whereas the remaining
theory-based studies (39%; n ¼ 9)
achieved some but not all of their pri-
mary objectives.

This review considered studies that
provided information about how they
used theories in the design of the
study as a best practice, rather than
just mentioning the theory casually
in the introduction or methods: for
instance, a study conducted by Savoie
et al46 to determine whether partici-
pation in a selected Supplemental
Nutrition Assistance Program–Education
(SNAP-Ed) that clearly showed how
the constructs of the Theory of
Planned Behavior used in the design
and implementation of the study les-
sons showed an impact on the intent

162 Murimi et al Journal of Nutrition Education and Behavior � Volume 49, Number 2, 2017

to improve nutrition-related behav-
iors of participants as stated in the
study objective. Results of this study
showed that posttest scores were
significantly higher than pretest scores
related to menu planning, shopping les-
sons, and My Plate lessons (P < .001).

However, this review observed that
although some studies indicated that
they were theory based, they failed to
describe explicitlyhow thetheories guided
the studies. For example, some re-
searchers15 reported using Social Cogni-
tive Theory; others45 reported using
several theories including the Precede–
Proceed Model, Social Learning Theory,
and the Innovation Diffusion Approach.
However, the theory constructs and
how they were used or measured were
not described in either article.

Nevertheless, although 45% of
studies (n ¼ 18) were not informed
by a theory, they were equally as suc-
cessful as those that were. For instance,
a study to describe and report the re-
sults from a worksite obesity preven-
tion intervention that targeted transit
employees was not guided by a theory,
and yet it indicated success.39 The re-
sults indicated that energy intake
decreased significantly and fruit and
vegetable intake increased significantly
in intervention garages compared
with control garages. A summary of
the studies that used theories and
those that did not is provided in
Tables 1 and 2.

Environmental Interventions at
the Worksite

Worksite environmental interventions
have an integral part in modifying die-
tary and weight management behav-
iors when executed appropriately. For
example, a study to evaluate the effects
of a new worksite weight control pro-
gram using nutrition education envi-
ronmental interventions among male
adults in Japan reported significant re-
sults. At the 1-year follow-up, the inter-
vention group hadsignificantly greater
reductions in body weight, body mass
index, and alanine aminotransferase
than the control group did (P ¼ .02,
.02, and .86, respectively).40

For worksite environmental inter-
ventions to be successful, sufficient
appropriate changes must be imple-
mented at the right places. This re-
view discovered that some worksite

environmental interventions did not
ensure changes sufficient for the inter-
vention, which affected the results.
Such a failure occurred when the
worksite management and collabora-
tors resisted making sufficient envi-
ronmental changes to modify dietary
and exercise behavior in employees
at work despite promoting the
behavior.39,42 For example, a worksite
environmental intervention study
used posters at worksites, links to
Web sites with useful information
about food availability and prices,
physical activity promotion, access
to scales, and media enhancements
to promote weight gain control.42

Intervention components were food
selection, promotion of walking and
stair use, weight self-monitoring, and
health information at work. However,
the provision of a variety of healthy
options and extra time for exercise
were not offered to employees. The re-
sults of this study indicated that there
were no differences between the inter-
vention and control sites in the key
outcome of weight change over the
2-year study.

DISCUSSION

The purpose of this review was to sys-
tematically examine factors that
contribute to the efficacy of nutrition
education interventions in promoting
behavior change for good health and
well-being based on their stated objec-
tive. The main findings of this review
indicated that the efficacy of nutrition
education interventions depends on the
duration of the intervention, having few
focused objectives, the appropriate
use of theories, fidelity in interventions,
and support from policy makers and
management for the environmental
interventions. These findings are largely
congruent with the results of another
review conducted by Baird et al.4

Therefore, factors that were identi-
fied as determinants of efficacy and
which form the discussion of this
study are: (1) the types and use of de-
signs, (2) the type of intervention that
characterized the studies, (3) the dura-
tion and dosage of the interventions,
(4) the number of objectives in a
study, (5) fidelity in intervention,
and (6) the use of theories in nutrition
education interventions. Worksite
environmental interventions are also

featured in this discussion. Critical
analysis of these factors provides the
outcome of the current review.

The randomized control trial (RCT)
design has a reputation of being robust.
This design is therefore appropriate for
baseline studies intended to inform a
larger intervention project.51 This re-
view indicated that a majority (70%;
n ¼ 28) of the nutrition education in-
terventions used an RCT design. The
RCT design is robust and may be attrib-
uted to the success of the interventions
in achieving their stated objectives.

Interventions that lasted for
>5 months reported a higher level of
success. They were mainly multiple-
component interventions. Thisfinding
supported the results of a previous re-
view that reported that remarkably
more studies on nutrition education
with long-term follow-up were associ-
ated with success.51 Another study
noted that behavior change takes time
and practice.52 Therefore, it may be
argued that the length of time taken
for intervention and the frequency of
exposure are important factors for the
success of a nutrition education inter-
vention. However, another study
noted that interventions with long du-
rations are associated with a higher cost
of implementation and participants’
attrition, which constrained some in-
terventions.24

This systematic review revealed
that studies with few and succinct ob-
jectives were more successful than
were those with numerous and at
times unrelated objectives. Other re-
views acknowledged the effectiveness
of few objectives in nutrition educa-
tion interventions.51 The current re-
view noted that studies with #3
objectives that were related were suc-
cessful even when the duration of
intervention was <6 months.

It is important to report fidelity in
interventions because it allows readers
and other researchers to judge the
quality of the intervention and how
various factors may have influenced
the outcome.53 Lack of fidelity in the
delivery of a program has a counter-
productive effect on the results of an
intervention. Fidelity in intervention
is a critical element that is rarely re-
ported in many studies.54 This review
found that the few studies that re-
ported fidelity discovered that it nega-
tively affected the results: Some sites
achieved their objectives whereas

Journal of Nutrition Education and Behavior � Volume 49, Number 2, 2017 Murimi et al 163

others failed to achieve their objec-
tives despite a similar program.

However, whereas peer educators
might have experienced some profes-
sional challenges in the delivery of
educationalinterventions,otherstudies
noted that they could provide a good
form of social support associated with
successful behavior change.4 It is there-
fore important for the training of peer-
led interventions to emphasize fidelity
in the implementation in an effort to
achieve desired results.

This systematic review revealed that
although the majority of the studies
that were theory based were successful
in achieving all of their primary objec-
tives (53.8%), a good number (45%) of
well-designed, non-theory interventions
were equally successful in achieving
their primary objectives. The current
findings support a review by Baba-
tunde et al,12 who noted that overall,
an educational program developed with
a theoretical background was associ-
ated with an improvement in calcium
intake (mean increase, 556 mg dietary
calcium;P< .001),knowledge(P< .001), and self-efficacy (P < .001). This indi- cates that well-used theory is likely to make an intervention successful. This review observed that some of the studies that claimed to use a theory failed to describe explicitly how the theory was used in the study. This finding agrees with the results of a re- view on experimentally based evi- dence of the theoretical mechanisms of dietary behavior change,55 which concluded that future intervention tri- als need to focus on identifying effec- tive procedures for mediator change and adopting a more rigorous and sys- tematic approach to theory testing.

Finally, in worksite environmental
interventions, interventions without
appropriate support from collabora-
tors to support the desired behavior
were less likely to meet their objec-
tives. For instance, an intervention
did not meet the primary objective,
which was to affect weight gain posi-
tively over 2 years, owing to weak
and inconsistent implementation of
environmental changes in the work-
place.42 In contrast, an intervention
by Iriyama and Murayama40 on work-
site weight control was able to imple-
ment changes in the workplace
cafeteria and introduce healthy me-
nus. The results of this intervention
indicated a significant decrease in

mean body mass index from a base-
line value of 25.6 kg/m2 to 25.3 and
24.8 kg/m2 at 6 months and 1 year,
respectively (P < .05). These worksite environmental interventions high- light the need for effective collabora- tion among nutritionists, policy makers and stakeholders within institutions including schools and workplaces where the food environment has a big role in the success of a nutrition education intervention. These find- ings are in agreement with results of another study regarding the effects of environmental, policy, and social marketing interventions on physical activity and fat intake of middle school students. The study noted that envi- ronmental and policy interventions were effective in increasing physical activity at school. Appropriate changes are necessary for the success of work- site environmental interventions.48,56

This review has limitations. First,
only articles that were published in En-
glish were considered. Therefore, there
is a possibility that some recent and
important findings published in lan-
guages other than English were left
out. Another limitation is that there
was a potential that studies that did
notfindasignificanteffectintheirinter-
vention were not published, and there-
fore were not included in this review.

Finally, the review was limited by
articles that did not include adequate
information in their methods and re-
sults. This posed a challenge to eval-
uate the contributions of specific
components of nutrition education
interventions and their effectiveness
properly, including the use of theory
and the dosage of intervention (fre-
quency and duration). Despite the
limitations, the current review drew
its strength from the fact that the re-
searchers investigated several factors
that led to the success of various types
of interventions. This was a departure
from previous reviews that concen-
trated primarily on a single type of
intervention and the related outcome.

IMPLICATIONS FOR
RESEARCH AND
PRACTICE

The results of this review suggest that
nutrition education interventions with
longer duration have few and focused
objectives, and that those that are guided

by a theory have a higher chance of
achieving their purpose. Lack of fidel-
ity in peer-led interventions, lack of
behavior support for environmental
interventions, and too-short dura-
tions were factors that contributed to
a lack of success in some of the inter-
ventions. It was observed that various
human factors affected the effective-
ness of an intervention in cases where
peers and paraprofessionals imple-
mented the nutrition interventions.
Although the lack of fidelity during
interventions was not rampant, the
few reported incidences had profound
ramifications on the results. The studies
with control groups had better inter-
pretation of the results, which enhanced
the validity of the outcome. This im-
plies that studies that used RCTs had
a better chance of replicability, followed
by those that employed a quasi-
experimental design. The eligible studies
that were reviewed and whose inter-
ventions were considered successful
were considered largely sustainable
and could easily be replicated.

The researchers concluded that the
use of theories in designing nutri-
tional education interventions was a
common practice in which 55% of
the analyzed studies (n ¼ 22) were
informed by at least 1 theory. Many
studies that used theories indicated
success in achieving their objectives.
However, a lack of details regarding
how the behavior theories guided
the studies made it difficult to assess
the effect of the theories mentioned
in some studies. The researchers
concluded that the use of theories is
a good practice in interventions and
that worksite environmental inter-
ventions provide an important oppor-
tunity for behavioral adjustments for
better health, but that they need the
cooperation of the policy makers.

The results of this study suggest that
more focused, clearly defined, measur-
able objectives are associated with
behavior change, whereas the more
ambitious use of many objectives
may limit the effectiveness of nutrition
education by taking away from the
main message and confusing partici-
pants. The objective should have a
clear targeted behavior, followed by
adequate dosage or exposure to facili-
tate the desired behavior change. A
purposeful selection of behavior the-
ory that will guide the intervention
based on the desired behavior change.

164 Murimi et al Journal of Nutrition Education and Behavior � Volume 49, Number 2, 2017

A clear use of the theory in designing
and implementing the intervention
should be reflected in the results. For
studies that use multiple sites with
different implementers, ensuring fidel-
ity is critical to the success of the inter-
vention. Training should emphasize
the important message and targeted
behavior. Researchers should also
consider the Guide for Effective Nutri-
tion Interventions and Education as a
checklist to help design or improve
their research methods for effective in-
terventions.57

For worksite and other environ-
mental interventions, it is important
for policy makers to make healthy
choices the easy ones by allowing
time for the exercise. This can be im-
plemented in many ways depending
on the worksite environment: for
example, including markings indi-
cating the distance covered along a
path between 2 points, such as build-
ings, or by providing healthy alterna-
tives or choices at the worksite.

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165.e1 Murimi et al Journal of Nutrition Education and Behavior � Volume 49, Number 2, 2017

CONFLICT OF INTEREST

The authors have not stated any con-
flicts of interest.

• Factors Influencing Efficacy of Nutrition Education Interventions: A Systematic Review

Introduction
Methods
Literature Search
Members of the Research Team
Inclusion/Exclusion Criteria
Assessment of Study Quality/Risk of Bias
Analysis Approach
Results
Study Designs in the Reviewed Studies
Interventions in the Reviewed Studies
Effect of Duration of Intervention
Effect of Number of Study Objectives/Focus
Lack of Fidelity in Delivery
Theory-Based Studies
Environmental Interventions at the Worksite
Discussion
Implications for Research and Practice
References
Conflict of Interest

Effects of Aerobic Exercise on Cognitive Performance Among Young Adults in a
Higher Education Setting
Sebastian Ludyga , Markus Gerber , Serge Brand, Uwe Pühse, and Flora Colledge

University of Basel

ABSTRACT
Purpose: Acute benefits of aerobic exercise on executive functioning have been reported frequently
under laboratory conditions. However, to date, a beneficial effect on long-term memory has been less
well supported and no data are available regarding nonlaboratory conditions in young adults. The aim
of the current study was to investigate acute effects of aerobic exercise on cognitive functioning in a
university classroom setting. Method: Using a cross-over design, 51 participants performed a bout of
moderately intense running (RUN) and read an article while seated (CON). Afterwards, they completed
free-recall tests, followed by a Flanker task and an n-back task.

Results

: Participants in the RUN
condition compared with those in the CON condition showed shorter reaction time on the inhibition
task, F(1, 50) = 5.59, p = .022, η2 = .101, and recalled more words in the immediate- and delayed-recall
tests, F(1, 50) = 8.40, p = .006, η2 = .144. Conclusion: The present findings suggest that a moderately
intense bout of aerobic exercise benefits verbal short-term and long-term memory as well as
inhibitory control among students in a classroom setting.

ARTICLE HISTORY
Received 2 June 2017
Accepted 31 January 201

8

KEYWORDS
Cognition; free recall;
inhibitory control; working
memory

The brain has a capability for functional and structural
changes in response to internal and external demands,
thus ensuring adaptability, robustness, and diverse func-
tionality (Park & Friston, 2013). A body of human and
animal studies has shown that a period of regular exercise
promotes this plasticity (Thomas, Dennis, Bandettini,
& Johansen-Berg, 2012). While many studies have there-
fore focused on chronic effects of exercise, potential tran-
sient benefits of acute bouts of exercise for brain function
and cognition have also become prominent targets in exer-
cise psychology. Based on a meta-analytical examination of
experimental studies, Chang, Labban, Gapin, and Etnier
(2012) reported a small effect of a single exercise session on
overall cognitive performance, which was more pro-
nounced for aerobic activities compared with resistance
exercise. A closer examination of different cognitive
domains revealed that improved performance was found
only for executive function tasks both during and after
exercise.

Executive functions encompass higher-order cogni-
tive processes responsible for organizing and controlling
goal-directed behavior (Banich, 2009). Although there is
still debate on the organization of executive control,
response inhibition, working memory, and task switch-
ing are generally considered its core components
(Diamond, 2013). Combining effect sizes from

experimental studies, Verburgh, Konigs, Scherder, and
Oosterlaan (2014) reported a moderate improvement of
inhibitory control in adolescents and young adults, but
no change in working memory after a single aerobic
exercise session. According to McMorris and Hale
(2012), the intensity of exercise has been found to influ-
ence these effects in an inverted-U manner, suggesting
that moderate aerobic activities lead to greater improve-
ments in executive functions than aerobic exercise does
at low- or high-intensity levels. Focusing on moderately
intense exercise only, a recent meta-analysis showed
small improvements in reaction time and accuracy on
executive function tasks after aerobic exercise (Ludyga,
Gerber, Brand, Holsboer-Trachsler, & Pühse, 2016).
Whereas those benefits were not different between inhi-
bition, task switching, and working memory, the parti-
cipants’ age had an influence on the magnitude of the
effects. In this respect, the authors found exercise-
induced improvements to be small in young adults and
moderate in preadolescent children and older adults.
Additionally, physical fitness has been confirmed as a
moderator of the interactive relationship between exer-
cise and cognition, so highly active and physically fit
individuals seem to benefit most from a single aerobic
exercise session (Chang et al., 2012). However, Pesce
(2009) suggested that the influence of physical fitness is

CONTACT Sebastian Ludyga sebastian.ludyga@unibas.ch Department of Sport, Exercise and Health, Sport Science Section, University of Basel,
Birsstrasse 320 B, Basel, CH-4052, Switzerland.

RESEARCH QUARTERLY FOR EXERCISE AND SPORT
2018, VOL. 89, NO. 2, 164–17

2

https://doi.org/10.1080/02701367.2018.1438575

© 2018 SHAPE America

http://orcid.org/0000-0002-3905-789

4

http://orcid.org/0000-0001-6140-8948

https://crossmark.crossref.org/dialog/?doi=10.1080/02701367.2018.1438575&domain=pdf&date_stamp=2018-05-17

less pronounced when cognitive testing is performed
after exercise than during exercise.

Whereas meta-analyses have provided compelling evi-
dence for improvements in executive function after mod-
erate aerobic exercise, it is less clear whether or not those
effects can be extended to other cognitive domains, such as
verbal short-term and long-term memory. These cognitive
functions allow for the acquisition and retention of newly
acquired information, and some studies have also con-
firmed their sensitivity to aerobic exercise (Chang et al.,
2012; Lambourne & Tomporowski, 2010; Roig,
Nordbrandt, Geertsen, & Nielsen, 2013). Based on the
timing of the exercise bout, aerobic activity can have dis-
tinct effects on memory encoding and consolidation.
However, the results of a recent meta-analysis indicated
that performing aerobic exercise before exposure to infor-
mation had the greatest benefits for long-term memory
(Roig et al., 2013), probably due to the facilitation of encod-
ing processes (Labban & Etnier, 2011). In line with this
finding, previous studies have confirmed increased perfor-
mance on delayed verbal recall tasks rather than immediate
verbal recall tasks, when exposure was preceded by an
aerobic exercise session (Coles & Tomporowski, 2008;
Pesce, Crova, Cereatti, Casella, & Bellucci, 2009).

Although a strategic scheduling of exercise is promising,
the evidence for improvements in verbal short-term and
long-term memory is not as strong as it is for executive
function (Chang et al., 2012). However, these cognitive
domains are highly relevant for educational settings.
Executive control influences cognitive, social, and psycho-
logical development, and this cognitive domain is also
strongly related to academic success (Diamond, 2013).
The ability to acquire and retain information has a high
impact on the success of factual learning, so explicit mem-
ory is essential for building a pool of knowledge (Barry,
2006; Paas & Ayres, 2014). So far, exercise-induced
improvements on these cognitive functions have mainly
been investigated in the laboratory (Guiney & Machado,
2013), although studies with children and adolescents
showed that such benefits for executive control could be
replicated in a school setting (Jäger, Schmidt, Conzelmann,
& Roebers, 2014; Kubesch et al., 2009; Pirrie & Lodewyk,
2012). In contrast, it remains unclear whether or not aero-
bic exercise similarly improves young adults’ higher-order
cognitive function in a classroom setting, so the external
validity and practical relevance are still questionable
(Mitchell, 2012).

Therefore, the present study aimed to compare the effect
of antecedent aerobic exercise versus reading an academic
text on young adults’ inhibitory control, working memory,
and verbal short-term as well as long-term memory in a

classroom setting. Based on the current state of the litera-
ture, we expected that participants would show increased
performance on working-memory and inhibitory control
tasks in particular after a single moderately intense running
bout compared to a physically inactive control condition.
Additionally, we hypothesized that exercise before encod-
ing would benefit verbal short-term and long-term
memory.

Methods

Participants

As recommended by Faul, Erdfelder, Buchner, and Lang
(2009), sample size was calculated a priori using G*Power
3.1. Based on previous studies investigating acute effects of
exercise on cognitive performance in an educational setting
(Jäger et al., 2014; Kubesch et al., 2009; Pirrie & Lodewyk,
2012), a moderate effect size was expected. With an alpha
level set to .05, the initial power analysis indicated that 17
participants were required to reach 85% statistical power.

On the university campus, male and female students
with high physical activity levels and corrected-to-normal
or normal vision were recruited to attend two experimental
sessions separated by 1 week. High physical activity was
fulfilled when participants reported vigorous-intensity
activity for at least 3 days (accumulating at least 1,500 meta-
bolic equivalent [MET] minutes/week) or 7 or more days
of any combination of walking or moderate-intensity or
vigorous-intensity activities achieving a minimum of at
least 3,000 MET minutes/week on the International
Physical Activity Questionnaire (IPAQ; Craig et al.,
2003). Participants meeting one or more of the following
criteria were excluded from the study: (a) existence of an
acute or chronic disease, which is a contraindication for
exercise; (b) any injury or disease affecting the functionality
of the left or right hand; and (c) not willing or able to sign
the written and informed consent.

The recruitment and study implementation were per-
formed in two waves (first wave, October 2015, N = 18;
second wave, October 2017, N = 33). In total, 21 male and
30 female students (Mage = 21.8 ± 1.3 years; Mheight =
1.72 ± 0.08 m; Mbody mass = 68.6 ± 9.8 kg; Mbody mass index
= 23.2 ± 1.8; MIPAQ = 4,456.7 ± 1,652.8 MET minutes/
week) were deemed eligible and received information on
the testing procedures as well as information on possible
risks and benefits. Informed consent was obtained from all
participants. All procedures were in line with the
Declaration of Helsinki, and the local ethics committee
approved the study protocol as complying with the ethical
guidelines for nonclinical trials.

EXERCISE, EXECUTIVE FUNCTION, AND MEMORY 165

Design

Using a cross-over design, participants attended an
experimental session in a seminar room after a running
exercise (RUN) and after a physically inactive control
condition (CON), which involved reading an academic
text. The order of the conditions was counterbalanced
and randomly assigned across participants with gender
included as stratification factor. For optimal supervision,
the participants were divided into two groups (for each
recruitment and study implementation wave), and the
groups completed identical procedures consecutively
(see Figure 1). Following exercise or reading, partici-
pants were provided with a word list and completed
the immediate-recall test in a large seminar room.
Afterwards, they listened to a 20-min lecture on exercise
addiction. Previously, participants were told that they
would be asked to remember key words mentioned in
the lecture. Following the recall of those key words, they
completed the delayed-recall task. Additionally, all par-
ticipants performed a n-back task and a Flanker task
while sitting in front of a computer. The order of those
tests was counterbalanced across participants to elimi-
nate order effects.

Conditions

All participants were provided with a heart rate monitor
(Polar, RS 400, Espoo, Finland). Prior to the running
exercise, heart rate targets corresponding to moderate
intensity were calculated individually for each participant.
Moderately intense exercise was defined as 70% of the
maximum heart rate (Norton, Norton, & Sadgrove, 2010),
which was calculated using the equation 208 − 0.7 × Age
(Tanaka, Monahan, & Seals, 2001). Running exercise was
performed on a predefined route in the city and in small
groups up to 10 people. Environmental temperature was
13.7 ± 0.8°C during the experimental sessions. The exer-
cise protocol included a 3-min warm-up, 15 min of mod-
erate running, and a 2-min cool-down period. During
exercise, participants were required to individually adjust
their running velocity to match the predefined target
heart rate.

In the physically inactive control condition, partici-
pants were provided with an academic text, which was
related to the lecture. In a group setting, they were asked
to read the text and then identify and write down the key
statements within 20 min. During the reading task, par-
ticipants remained seated in an upright position. This
task was chosen for better comparability with previous
studies as many researchers have compared the acute
effects of exercise on cognition to the effects of reading
(Ludyga et al., 2016).

Cognitive testing

Following running exercise or reading the academic text,
participants performed computer-based cognitive tests
in a seminar room. As two groups were completing the
experimental session consecutively, assessments were
performed with up to 10 participants at a time. All
tasks were administered with E-Prime 2.0 (Psychology
Software Tools, Pittsburgh, PA), and participants were
therefore seated in front of a laptop. Brightness of the
display and viewing distance were standardized. Prior to
testing, the investigator provided instructions.
Additionally, relevant information was also presented
on the screen to make sure that participants understood
the tasks. After the instruction, participants were told to
keep silent to reduce noise to a minimum.

Inhibitory control

To assess inhibitory control as a marker of executive
function, a modified Flanker task was applied (Eriksen
& Eriksen, 1974). The task required participants to
respond to the direction of a centrally presented target
stimulus. In congruent trials, five arrows were facing
the same direction, whereas in incongruent trials, the
centrally presented target stimulus was facing in the
opposite direction of the flanking arrows. During the
task, participants were required to respond by pressing
a button corresponding to the direction of the target
stimulus. In the first block of the computer-based test,
participants completed 20 practice trials. Following the
practice round, three test blocks with 40 trials were

Flanker
task

nBack
task

Immediate
Recall

Delayed

Recall

20-min
lecture

Presentation
of word list

20-min running
(70% HRmax)

20-min reading
task

OR

Study procedures Assessment of cognitive outcomes

Figure 1. Overview of experimental procedures. Note. HRmax = maximal heart rate.

166 S. LUDYGA ET AL.

administered. The blocks were interspersed by a 10-s
resting period, and the order of the trials was rando-
mized. The congruency (congruent, incongruent) and
the directionality (left, right) of the stimuli were equi-
probable. The stimuli subtended a 1.8° visual angle in
height, and they were presented focally for 250 ms on a
white background with a response window of 1,000 ms.
The interstimulus interval varied randomly from 90

0

ms to 1,400 ms. Task performance was assessed by
calculating the mean reaction time for correct
responses as well as mean accuracy separately for con-
gruent and incongruent trials. The Flanker task has
been found to have adequate to good convergent and
discriminant validity (Zelazo et al., 2014), and it is
suitable for repeated measures, as Wöstmann et al.
(2013) confirmed accurate 4-week test–retest reliability
of performance on a Flanker task consisting of 1

20

trials (congruent stimuli, intraclass correlation coeffi-
cient [ICC] = .89; incongruent stimuli, ICC = .94).

Working memory

Working-memory performance was assessed using the
n-back task, which requires storage, manipulation, and
updating of information (Jonides et al., 1997). In the
present study, participants completed a computer-based
version of the n-back task, which was already used by
Ruiz-Contreras et al. (2013). The task involved the pre-
sentation of a sequence of letters, and participants had to
detect whether or not the current letter matched the letter
presented 1 or 2 trials earlier in the series. For each trial,
they were asked to respond by pressing a button corre-
sponding to yes or no. The participants completed the
task on two difficulty levels (one-back and two-back). For
each difficulty, there was one practice block with 20 trials,
followed by two blocks with 60 trials each. A short break
of 10 s was provided between blocks. The stimuli were
dark gray letters (vertical visual angle = 0.7°) presented on
a light gray background. Each letter in the sequence was
displayed for 500 ms, and a response window of 1,500 ms
was provided. The interstimulus interval was set at
1,000 ms. In each block, targets (letter matching the letter
presented n trials earlier) occurred with a probability of
20%. Task performance was assessed by averaging the
mean reaction time for correct responses separately in
the one-back and two-back trials. Additionally, the
adjusted hit rate was calculated by dividing the hit rate
(Number of Correct Responses for Targets / Total
Number of Targets) by the error rate (Number of Errors
for Nontargets / Total Number of Nontargets; Ruiz-
Contreras et al., 2013). Thus, the adjusted hit rate could
take on values from −1 to 1, with 1 denoting that the
participant performed correctly in all trials and −1

meaning that the participant performed incorrectly on
all the trials. Hockey and Geffen (2004) previously
showed that the n-back task is a reliable measure of work-
ing memory as performance across the difficulty levels did
not change from baseline to retest after 1 week (one-back,
r = .79; two-back, r = .72). Due to its moderate correlation
with other measures (r = .45), the n-back task has been
found to be a valid measure of working-memory function
(Shelton, Elliott, Hill, Calamia, & Gouvier, 2009).

Verbal short-term and long-term memory

The free-recall task assesses the modulation of memory
storage processes (Nielson, Radtke, & Jensen, 1996) and
has previously been used to study possible benefits of acute
exercise on verbal short-term and long-term memory
(Coles & Tomporowski, 2008; Pesce et al., 2009).
Free-recall tasks have been found to be stable and reliable
measures of verbal memory (r = .75–.77; Waters & Caplan,
2003). Following a standardized protocol (Nielson et al.,
1996), two 20-item word lists were created from the nor-
mative list of Paivio, Yuille, and Madigan (1968).
Therefore, only nouns rating 6.40 or higher on imagery
and concreteness (range = 1–7) were selected. Those words
were translated into German and were back-translated into
English by a native speaker. Nouns not matching the
original form after the back translation were excluded. A
linguist then controlled the words remaining on the list for
frequency of usage in the German language. Finally, 4
words were repeatedly matched by concreteness, imagery,
and frequency of usage and were randomly assigned to the
two word lists until 20 items were reached. For the free-
recall task, each word from the list (vertical visual angle =
1.3°) was presented for a period of 5 s, equaling a total
presentation time of 100 s. Afterwards, participants were
allowed (but not specifically encouraged) to rehearse the
word list during a 100-s consolidation period. Then a
sentence displayed on the screen asked participants to
recall and write down as many words as possible in any
order. The time limit for the immediate recall was 180 s.
Afterwards, they listened to a 20-min lecture, which served
as a distractor task administered to keep participants from
rehearsing the word list. Two lectures on the same topic
were prepared, so participants listened to Version A during
the first experimental session and Version B during the
second experimental session. Following the lecture, parti-
cipants were again asked to write down any words they
remembered to assess delayed recall. For the evaluation of
correctly recalled words, minor spelling errors and plural–
singular substitutions were ignored. Performance on the
memory task was assessed by the total number of correctly
recalled words in the immediate (short-term memory) and
delayed (long-term memory) versions of the task.

EXERCISE, EXECUTIVE FUNCTION, AND MEMORY 167

Data analysis

In advance, Gaussian distribution of the collected data
was checked by visual inspection of normality plots and
by applying the Shapiro-Wilk test. Student’s t test was
conducted to control if the mean heart rate during
exercise was different from the predefined target heart
rate. Analysis of variance (ANOVA) was used for com-
parison of cognitive performance between conditions.
The effect of exercise on inhibitory control was ana-
lyzed by applying a 2 (condition: exercise, reading) × 2
(congruency: congruent, incongruent) ANOVA for
reaction time on the Flanker task. As accuracy data
followed a non-normal distribution, Wilcoxon signed-
rank tests were used to compare accuracy between
conditions and trial types. To examine the effect of
exercise on hit rate and reaction time on the n-back
task, a 2 (condition: exercise, reading) × 2 (difficulty:
one-back, two-back) ANOVA was employed.
Moreover, within-subject differences in verbal memory
performance were analyzed using a 2 (condition: exer-
cise, reading) × 2 (time of recall: immediate, delayed)
ANOVA on the total number of correctly recalled
words. When nonsphericity was confirmed, the
Greenhouse-Geisser correction was applied. Within-
subject effects as well as interactions were reported.
For all statistical analyses, the level of significance was
set at p < .05. The statistical analysis of collected data was performed with SPSS Version 22.0 (IBM Corporation, Armonk NY) for Windows.

Results

In the exercise condition, participants’ heart rate
(135.6 ± 6.0 min−1) was not significantly different from
the prescribed target heart rate, T(50) = 0.78, p = .441.

Regarding cognitive outcomes, a main effect of condi-
tion on reaction time in the Flanker task was observed, F(1,
50) = 5.59, p = .022, η2 = .101. Participants’ reaction time to
both congruent and incongruent stimuli was lower follow-
ing exercise than after reading an academic text (see

Table 1). For incongruent trials, accuracy rates were higher
after exercise (Mdn = .95) compared with the physically
inactive control condition (Mdn = .92), Z = 1.97, p = .049,
r = .275. In contrast, accuracy rates on congruent trials
were not significantly different between conditions,
Z = 0.72, p = .469, r = .101. Furthermore, the statistical
analysis revealed a main effect of congruency on Flanker
task reaction time, F(1, 50) = 159.09, p < .001, η2 = .761, so that participants showed higher reaction time on incon- gruent trials compared with congruent trials. Additionally, statistical analysis revealed higher accuracy on congruent (Mdn = .98) relative to incongruent trials (Mdn = 0.93), Z = 5.95, p < .001, r = .833.

In contrast to the effect of exercise on Flanker task
performance, reaction time, F(1, 50) = 2.17, p = .147,
η2 = .042, and hit rate on the n-back tasks, F(1, 50) = .03,
p = .859, η2 = .001, were not influenced by condition.
However, there was a main effect of difficulty (see
Table 1) on reaction time, F(1, 50) = 66.10, p < .001, η2 = .569, and the adjusted hit rate, F(1, 50) = 81.05, p < .001, η2 = .618. In comparison with one-back trials, participants showed a lower hit rate and higher reaction time on two-back trials.

Regarding free recall, main effects of condition, F(1,
50) = 8.40, p = .006, η2 = .144, and time of recall, F(1,
50) = 18.24, p < .001, η2 = .267, on the number of correctly recalled words were found. Compared with participants in the physically inactive control condition, participants in the exercise condition recalled more words from the list (see Figure 2). Furthermore, more words were recalled in immediate recall than delayed recall.

Discussion

As the external validity of exercise-induced benefits for
cognitive performance has only been investigated in
children and adolescents (Jäger et al., 2014; Kubesch
et al., 2009; Pirrie & Lodewyk, 2012), the present study
aimed to replicate the effects of moderately intense
aerobic exercise on young adults’ executive function
and verbal memory in a classroom setting.

Table 1. Comparison of participants’ (n = 51) n-back and Flanker task performance between the antecedent running (RUN) and
control (CON) conditions.

RUN CON

Task Trials Measures M SD M SD

Flanker Congruent Reaction time (ms) 342.1* 36.4 349.7 35.4
Accuracy 0.98 0.03 0.97 0.03

Incongruent Reaction time (ms) 368.7*,# 39.7 377.1# 39.4
Accuracy 0.93*,# 0.07 0.91# 0.07

n-back One-back Reaction time (ms) 368.2 49.2 377.2 66.2
Adjusted hit rate 0.71 0.28 0.72 0.23

Two-back Reaction time (ms) 417.5# 75.9 439.4# 103.8
Adjusted hit rate 0.43# 0.23 0.42# 0.25

* p < .05 compared with CON. # p < .05 compared with congruent trials (in the Flanker task) or one-back trials (in the n-back task)

168 S. LUDYGA ET AL.

Previous meta-analyses have shown improved execu-
tive control after a single aerobic exercise session (Chang
et al., 2012; Verburgh et al., 2014). This finding was
partly supported by the present results as adjacent run-
ning compared with reading an academic text signifi-
cantly increased speed of processing and the inhibitory
aspect of executive control. A recent review combining
findings from behavioral and neuropsychological
research indicated that such improvements are partly
due to an increased allocation of attentional resources
toward the task as well as reduced stimulus classification
or evaluation time after aerobic exercise (Hillman,
Kamijo, & Scudder, 2011). Benefits on inhibition have
a high practical relevance in learning environments,
because inhibitory control involves the ability to control
one’s attention, behavior, and thoughts to override an
internal predisposition or ignore external stimuli
(Diamond, 2013). Particularly for group learning, sup-
pression of disturbing behavior and resistance to dis-
tractor interference are necessary to successfully focus
attention on relevant information. As the present study
replicated exercise-induced improvements in inhibitory
control in a classroom setting, it is very likely that such
temporary benefits may contribute to improved learning
behavior in real-life situations.

In contrast to inhibition, working-memory perfor-
mance was not different between the exercise condition
and the physically inactive control condition. This finding
is in conflict with the results of McMorris, Sproule, Turner,
and Hale (2011), who found a strong positive effect of
moderate exercise on speed of response in working-mem-
ory tasks. In a more recent meta-analysis, Ludyga et al.

(2016) found that improvements in reaction time and
accuracy on executive function tasks after moderately
intense aerobic exercise were not influenced by the com-
ponent assessed with the task. However, the majority of
findings included for quantitative synthesis were obtained
from laboratory studies, whereas the present results were
based on cognitive assessments in a classroom setting.
Therefore, the setting might have an influence on the effect
of aerobic exercise on working memory, particularly when
cognitive tests are administered in groups. Moreover, the
impact of exercise on cognitive performance is not uniform
across all individuals (Pesce, 2009), so the selection of
students with high physical activity levels might have
biased the results. In this respect, Sibley and Beilock
(2007) found that participants with the lowest working-
memory performance benefited most from a moderate
aerobic exercise session. As students with high physical
activity levels showed higher working-memory perfor-
mance than students with low physical activity levels
(Lambourne, 2006), a ceiling effect might explain why an
impact of exercise on working memory could not be
observed in the present study.

Regarding short- and long-term memory functions,
running compared with reading enhanced young adults’
performance on the free-recall task. Consequently, a short
exercise bout led to facilitation of memory functions, which
do not involve (frequent) manipulation of the acquired
information (Diamond, 2013). This result is in line with
meta-analyses that have shown acute benefits of aerobic
exercise for memory (Lambourne & Tomporowski, 2010)
and the verbal-auditory subtype in particular (Roig et al.,
2013). From a practical perspective, such exercise-induced
improvements on verbal short- and long-term memory are
interesting, because these cognitive functions are key com-
ponents of successful learning (Paas & Ayres, 2014). It is
also worth noting that running improved delayed recall,
although the group setting and the distractor task may have
affected different memory processes. However, it is possi-
ble that inhibitory control contributed to increased mem-
ory performance through an improved ability to suppress
extraneous thoughts and resist proactive interference
(Blumenfeld & Ranganath, 2007; Unsworth & Engle,
2007). This is important for encoding in particular as
maintaining focus on the items to be remembered is a
prerequisite for the acquisition of information (Diamond,
2013).

From a neurobiological perspective, exercise-induced
increases in catecholamines and neurotrophins have been
discussed as underlying mechanisms for the elicitation of
transient changes in cognition (Roig et al., 2013). Elevated
brain-derived neurotrophic factor (BDNF) levels in par-
ticular have been shown to enhance the encoding, reten-
tion, and retrieval of information (Bekinschtein,

0
2
4

6

8

10

12

14

16

18

20
RUN CON

T
o

t
a

l
n

u
m

b
e

r
o

f
w

o
r
d

s
r

e
c
a

ll
e

d
c

o
r
r
e

c
t
ly

Immediate

Delayed

**

#

Figure 2. Performance on the immediate- and delayed-recall
tasks after running (RUN) and in the physically inactive control
condition (CON). * p < .05 immediate versus delayed recall; # p < .05 RUN versus CON.

EXERCISE, EXECUTIVE FUNCTION, AND MEMORY 169

Cammarota, Izquierdo, & Medina, 2008). Although
BDNF signaling was not assessed in the present study,
the exercise dose was greater than the minimal duration
required for an increased expression (Tang, Chu, Hui,
Helmeste, & Law, 2008). Moreover, findings from animal
studies have provided evidence for increased dopamine
and noradrenaline concentrations during exercise, which
may facilitate cognitive functions in a dose-dependent
matter (McMorris, Turner, Hale,
& Sproule, 2016). Whereas a low to moderate release of
these catecholamines has been related to improvements in
inhibition and attention regulation, higher concentrations
impaired executive control due to severe neural traffic
(Arnsten, 2011; Arnsten & Li, 2005). McGaugh and
Roozendaal (2002) also supported an enhancement of
memory storage processes by facilitation of the release
of noradrenaline and the activation of β-adrenoceptors
within the basolateral amygdala. However, McMorris
et al. (2016) suggested that high exercise intensity is
necessary to increase noradrenaline to a certain level, at
which it has a beneficial effect for long-term memory. In
summary, a body of evidence has suggested that the
exercise-induced improvements in memory functions
and inhibitory control in the present study might partly
be due to elevated BDNF levels and a moderate release of
catecholamines, respectively.

As possible exercise benefits on specific cognitive
domains were investigated using field testing, some limita-
tions have to be taken into account for interpretation of the
results. First, the present study design did not allow for a
differentiation between maintenance and enhancement of
cognitive performance by antecedent exercise because
assessments were performed after the experimental condi-
tions only. Consequently, it is likely that inhibitory control
decreased due to the reading task and the subsequent
distractor task. In that case, maintenance rather than
enhancement of inhibition might explain the difference
between the RUN and CON conditions. However, Chang
et al. (2011) observed a small positive effect of reading on
measures related to working memory and inhibitory con-
trol processes in healthy young adults. This finding is an
indication that the RUN condition may have elicited
greater improvements than the CON condition in the
present study. Second, findings from laboratory studies
were replicated in a classroom setting. This replication
does not necessarily mean that all groups found in educa-
tional settings should expect cognitive benefits from an
aerobic exercise session as the present study only supports
an exercise-induced enhancement in inhibition and verbal
memory in students with high physical activity. However, a
recent meta-analysis did not show an influence of aerobic
fitness, which was associated with physical activity levels,
on exercise-induced enhancements of executive function

(Ludyga et al., 2016). It is still likely that other variables,
such as perceived stress, intelligence, or sleep had a mod-
erating role in the interaction between exercise and execu-
tive function in the present study.

Conclusions

The present study indicated that in young adults, a running
bout of moderate intensity compared with a physically
inactive condition benefited inhibitory control as well as
verbal short-term and long-term memory in a classroom
setting. Consequently, exercise-induced enhancements in
cognition were not limited to executive functioning.
Moreover, the acute effect of aerobic exercise on executive
function seemed to be selective, because working memory
was not facilitated by antecedent running. As short-term
and long-term memories as well as inhibitory control are
related to learning and academic success, benefits elicited
by exercise encourage the implementation of short running
bouts in higher education settings.

ORCID

Sebastian Ludyga http://orcid.org/0000-0002-3905-7894
Markus Gerber http://orcid.org/0000-0001-6140-8948

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• Abstract

Methods
Participants
Design
Conditions
Cognitive testing
Inhibitory control
Working memory
Verbal short-term and long-term memory
Data analysis
Results
Discussion
Conclusions
References

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Differences in Eating Behavior, Physical Activity,
and Health-related Lifestyle Choices between
Users and Nonusers of Mobile Health Apps

Alessandra Sarcona, Laura Kovacs, Josephine Wright & Christine Williams

To cite this article: Alessandra Sarcona, Laura Kovacs, Josephine Wright & Christine Williams
(2017) Differences in Eating Behavior, Physical Activity, and Health-related Lifestyle Choices
between Users and Nonusers of Mobile Health Apps, American Journal of Health Education, 48:5,
298-305, DOI: 10.1080/19325037.2017.1335630

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RESEARCH ARTICLE

Differences in Eating Behavior, Physical Activity, and Health-related Lifestyle
Choices between Users and Nonusers of Mobile Health Apps
Alessandra Sarconaa, Laura Kovacsb, Josephine Wrightc, and Christine Williamsa

aWest Chester University of Pennsylvania; bMontefiore Medical Group; cLong Island University

ABSTRACT

Background

: Weight gain and lifestyle behaviors during college may contribute to future
health problems. This population may not have sufficient self-monitoring skills to maintain
healthy lifestyle behaviors.

Purpose

: The purpose of this study was to determine the relation-
ship between usages of mobile health applications (apps) designed to track diet and physical
activity and health-related behaviors of college students.

Methods

: In a cross-sectional study,
401 university students completed a survey to assess eating behavior, physical activity, and
health-related lifestyle choices and mobile health app usage.

Results

: Mobile health app users
had significantly higher scores for eating behavior than nonusers, and the impact of using
more than one type of mobile health app significantly improved eating behavior. Most
participants also identified app use with feeling healthier, better self-monitoring of food intake
and exercise, and having more motivation to eat healthier and increase physical activity.

Discussion

: Use of mobile health apps may have a positive effect on eating behavior, and
demographic background appears to be influential with regard to health-related behaviors.

Translation to Health Education Practice

: Health Educators need to consider the use of apps
as a supplementary component of a health promotion program to assist individuals who want
to make improvements in their overall health and to prevent chronic disease.

ARTICLE HISTORY
Received 2 February 2017
Accepted 16 May 2017

Background

Young adults attending college are more vulnerable to
weight gain than the general population1 due to a decline
in exercise and unhealthy dietary habits.2 Weight gain and
lifestyle behaviors during college may contribute to over-
weight and obesity in adulthood and may increase the risk
of future health problems and future chronic disease.2 This
population may not have sufficient self-regulatory skills,
such as self-monitoring, to maintain healthy lifestyle beha-
viors in a college environment.1 With the innovation of
technology, a dramatic rise in the development of mobile-
based applications (apps) has occurred.3 An app uses a
software program operated through a browser from a
smartphone, tablet, or other mobile device. There are
many mobile apps related to health, and data show that a
little more than half of individuals who are smartphone
users have downloaded a fitness or health app that assists
them in recording, tracking, and analyzing health data.4

Given the recognized relationship between lifestyle beha-
viors and chronic disease, mobile-based tracking devices
and applications are of high interest because of their poten-
tial effect on preventative health care measures including

increased physical activity, improvement in dietary self-
monitoring, and beneficial behavioral changes.
Smartphone-based applications provide a promising
method that can be used to monitor and motivate indivi-
duals to engage in a healthy lifestyle.5 A strong interest in
the use of dietary assessment tools in mobile health apps
was reported among health care providers, especially for
patient self-monitoring and formanaging obesity, diabetes,
and heart disease.6

Because the use of mobile health apps is continuing to
increase, there needs to be more research evaluating their
effectiveness in prevention of chronic disease. The
increased prevalence of chronic disease is largely due to
several risk factors that include physical inactivity, high
blood pressure and cholesterol, use of tobacco and alcohol,
stress, and obesity.7 Most of these risk factors are modifi-
able and can be lessened by health interventions and tools
such as mobile health apps to support a healthy lifestyle
and behavior change.7 Improving risk factors for chronic
disease, such as cardiovascular disease, which is the leading
cause of death globally, can decrease morbidity and mor-
tality and improve quality of life.8 Participants in a cardiac

CONTACT Alessandra Sarcona asarcona@wcupa.edu West Chester University of PA, 303 Sturzebecker Health Science Center, 855 South New Street,
West Chester, PA 19383.
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rehabilitation center who used a mobile health app had
significantly higher adherence and completion rates than
those who did not use devices.9 Research like this that
evaluates the use of mobile health apps for health promo-
tion and disease prevention is limited but foreseen to be on
the upsurge.

Most of the popular mobile health and fitness applica-
tions available provide a focus on fitness and self-
monitoring.10 Dietary self-monitoring is consistently
associated with weight loss in behavior-based programs;
however, adherence is a recognized challenge with tradi-
tional paper-and-pencil monitoring.11 Adherence to a
self-monitoring weight management intervention was
significantly higher in a group using a smartphone app
compared to a paper diary.12 Dietary self-monitoring on a
smartphone app and the positive acceptability of compu-
ter recording methods for energy intake may lead to
increased compliance with tracking caloric consumption-
13 and demonstrate the potential for long-term weight
loss.11 It has been suggested that individuals, especially
those of younger generations, preferred the use of mobile
phones for dietary and weight loss interventions com-
pared to other web-based tools.13 Weight loss is a key
aspect in prevention of chronic disease and therefore is of
great interest in mobile health apps for assisting in this
area of health promotion and disease prevention.

To date, there is limited research that has focused on
the effects of utilizing mobile-based health and fitness
applications for the college-age population, rendering a
gap in information on the potential benefits of utilizing
this technology in a technology-savvy population.14

Approximately half of college students who completed
the 2013 National College Health Assessment indicated
a greater need for health-related information, and uni-
versity-based smartphone applications may help stu-
dents better access this information.15 Stress
management, nutrition/diet, and physical activity/fit-
ness were the most common health topics that college
students selected as most important to them.15 Young
adulthood presents an ideal time for health interven-
tions to reduce the effect of health problems and risk
factors for chronic disease in later life. Because young
adults are high users of mobile devices, interventions
that use this technology may improve engagement.

Purpose

The purpose of this study was to evaluate differences in
eating behaviors, physical activity, and health-related
lifestyle choices between users and nonusers of mobile
health apps among college students. The following
research questions were addressed: (a) “Is there an
association between gender, age, race/ethnicity, and

body mass index [BMI] with mobile health app use?”
(b) “What is the relationship between mobile health
app use and lifestyle, physical activity, and eating beha-
vior?” (c) “What is the impact of type of mobile health
app(s) on lifestyle, physical activity, and eating beha-
vior?” (d) “What is the influence of the independent
variables gender, age, and race/ethnicity on the depen-
dent variables lifestyle, physical activity, and eating
behavior?” The authors hypothesized that users of
mobile health apps have more positive eating behaviors
and health-related lifestyle choices and increased phy-
sical activity compared to nonusers.

Methods

A survey tool was used to evaluate health related beha-
viors of participants; it included the following validated
questionnaires: the 1998 Lifestyle and Habits
Questionnaire–brief version,16 which assesses lifestyle
behaviors/attitudes and tested among young adults
(18–25 years); The Godin-Shephard Leisure-Time
Physical Activity Questionnaire,17 which measures fre-
quency of physical activity; and the Eating Behavior
Inventory (EBI),18 which is designed to assess behaviors
associated with weight loss and weight management. In
addition to the questionnaires, the survey asked demo-
graphic information including age, race/ethnicity, gen-
der, and self-reported height and weight. In addition,
the following question was asked: “Within the past 12
months, have you used a mobile-based health or fitness
app?” If the response was “yes,” the following questions
were asked: “What application(s) do you use?”;
responses included MyFitness Pal, Jawbone, FitBit,
Supertracker, and/or other, please specify (more than
one response was acceptable); and “How do you feel
about using your mobile-based health or fitness app?
(Check all that apply)”; choices included a list of posi-
tive and negative possibilities.

Instruments

The Lifestyle and Habits Questionnaire includes 13
questions with responses using a Likert scale ranging
from 1 = strongly disagree to 5 = strongly agree. “I am
able to manage the stress in my life” is an example of
an item from this survey16; higher scores were asso-
ciated with more positive lifestyle habits. The Godin-
Shepard Leisure-Time Physical Activity Questionnaire
assessed self-reported physical activity for a typical 7-
day week; it included times per week individuals par-
ticipated in a list of exercises categorized as strenuous,
moderate, or mild intensity. A total weekly score using
activities in the moderate and strenuous categories

DIFFERENCES BETWEEN USERS AND NONUSERS OF MOBILE HEALTH APPS 299

was computed based on the formula devised by Godin
and Shephard.17 A final score in units, calculated
using activities in the strenuous and moderate inten-
sity categories, was advised by the authors, because
these correlate with more health benefits than the
mild intensity category.17 The EBI included 20 ques-
tions using a 5-point Likert scale as previously
described; higher scores were associated with more
positive eating behaviors. “I carefully watch the quan-
tity of food I eat” is an example of a survey item. This
survey has been shown to be consistently sensitive to
behavioral weight management interventions, but it
appears that the amount of change in EBI scores has
decreased slightly over time.18 The researchers deter-
mined that some questions in the EBI may not be
appropriate for college students; therefore, 20 gradu-
ate students tested the EBI tool for face and content
validity. Six items were changed to be more represen-
tative of college students’ eating behaviors, and 6
items were deleted by the graduate students because
they felt that these statements were not relative to
college students. According to O’Neill et al,19 the EBI
had an internal consistency with an acceptable
Cronbach’s alpha coefficient of .74. In the current
study, a similar Cronbach’s alpha coefficient of .70
was found.

Sample and recruitment

A convenience sample of individuals was recruited
from one suburban and one urban university campus
after receiving university institutional review board
study approval. For both campuses, total enrollment
for graduate and undergraduate students is 18 693
(34% male and 66% female). The researchers set up
tables in high-traffic areas of both campuses and
recruited students to complete the survey, where
healthy snacks were offered as an incentive. The stu-
dents completed the informed consent and the survey
was distributed by the researchers using Survey
Monkey (Survey Monkey Inc., San Manteo, CA;
http://www.surveymonkey.com); most participants
accessed the survey using their cell phones or on the
laptop located at the information table. Inclusion cri-
teria included participants who were students at the
university and age 18 or above; only students less
than 18 years old were excluded.

Data analysis

Version 23 of the Statistical Package for the Social
Sciences (IBM Corp.; Armonk, NY) was utilized for
all statistical analyses, and all significance levels were

set at P ≤ .05, except where noted when a more strin-
gent level of .01 was set. Preliminary assumption testing
was conducted to check for normality, linearity, uni-
variate and multivariate outliers, homogeneity of var-
iance–covariance matrices, and multicollinearity. In
addition to descriptive results, the statistical analyses
used included a chi-square to explore the relationship
between gender, age, race/ethnicity, and BMI and
mobile health app use; analysis of variance and multi-
variate analysis of variance where the dependent vari-
ables included scores from the lifestyle, physical
activity, and EBI questionnaires; and independent vari-
ables including the categorical variables app use, type of
app, gender, age, and race/ethnicity.

Results

Table 1 shows demographic data. The majority of par-
ticipants were non-Hispanic white, female, and
between the ages of 18 and 22 years of age. The
female-to-male ratio of 2.7:1 was almost consistent
with the 2:1 female-to-male ratio for the university.
Table 2 evaluates the first research question, “Is there
an association between gender, age, race/ethnicity, and
BMI with mobile health app use?” A chi-square test for

Table 1. Demographic characteristics of participants (n = 401).
Demographic Information Sample

Gender
Male 107 (27%)
Female 294 (73%)

Age (years)
18–22 277 (69%)
≥23 124 (31%)

Race/ethnicity
Non-Hispanic white or Euro American 242 (61%)
Non-white:
South Asian or Indian American 7 (2%)
Black, Afro-Caribbean or African American 72 (18%)
Middle Eastern or Arab American 9 (2%)
East Asian or Asian American 13 (3%)
Other/Hispanic 37 (9%)
I’d prefer not to answer 21 (5%)

Table 2. Demographic information related to mobile health
app use.
App user Yes No

All subjects 185 (46%) 216 (54%)
Gender
Male 41 (22.2%) 66 (30.6%)
Female 144 (77.8%) 150 (69.4%)

Age (years)
18–22 126 (68.1%) 151 (69.95)
≥23 59 (31.9%) 65 (30.1%)

Race/ethnicity
Non-Hispanic white or Euro American 119 (66.9%) 123 (60.9%)
Non-white 59 (33.1%) 79 (39.1%)

Body mass index
Normal 92 (57.5%) 106 (58.6%)
Overweight–obese 68 (42.5%) 75 (41.4%)

300 A. SARCONA ET AL.

http://www.surveymonkey.com

independence (with Yates’s continuity correction) indi-
cated no significant association between gender and
mobile health app use, χ2 (1, n = 401) = 3.17, P = .08,
phi = −0.10. There was no significant difference for age
and mobile health app use, χ2 (1, n = 401) = 0.08, P =
.78, phi = −0.02; for race/ethnicity and mobile health
app use, χ2 (1, n = 380) = 1.21, P = .27, phi = −0.06; and
for BMI and mobile health app use, χ2 (1, n = 341) =
0.008, P = .93, phi = −0.011.

Table 3 displays the perceived positive and negative
feelings in relation to mobile-based health or fitness
apps by participants with a history of or current use
of mobile health apps; each participant may have made
multiple responses. Most participants identified app use
with feeling healthier, more motivation to eat healthier
and increased physical activity, and better tracking of
exercise and food intake. There was a 4:1 positive to
negative response regarding how participants felt about
their mobile health apps.

A one-way between-groups multivariate analysis of
variance (MANOVA) was performed to investigate the
second research question, “What is the relationship
between mobile health app use and lifestyle, physical
activity, and eating behavior?” Three dependent vari-
ables were used: lifestyle scores, physical activity scores,
and eating behavior scores. The independent variable
was mobile health app use. Preliminary assumption
testing found no serious violations. There was a statis-
tically significant difference between mobile app users
and nonusers on the combined dependent variables, F
(3, 397) = 3.93, P = .009; Wilks lambda = 0.97; partial
eta squared = 0.03. When the results for the dependent
variables were considered separately, the only differ-
ence to reach statistical significance was EBI score, F
(1, 399) = 9.98. An inspection of the mean scores
indicated that mobile health app users reported higher
EBI scores than participants who did not use mobile
health apps; refer to Table 4.

A one-way between-groups analysis of variance was
conducted to explore research question 3, “What is the

impact of type of mobile health app(s) on lifestyle,
physical activity and eating behavior?” Subjects were
divided into three groups according to type of app:
group 1: My Fitness Pal (n = 53), group 2: Fit Bit (n
= 41), group 3: Other (n = 57), group 4: greater than
one app (n = 36). There was no significance noted for
lifestyle and physical activity scores. There was a statis-
tically significant main effect due to the type of mobile
health app on EBI scores, F (3, 183) = 5.3, P = .002.
There was a medium effect size of .08, calculated using
eta squared. Post hoc comparisons using Tukey’s hon-
estly significant difference test indicated that the mean
score for group 4 (65.42 ± 8.31) was significant com-
pared to group 1 (59.11 ± 7.26), P = .002, group 2
(60.02 ± 8.23), P = .019, and group 3 (59.54 ± 8.34),
P = .004; see Figure 1.

Research question 4, “What is the influence of the
independent variables gender, age, and race/ethnicity
on the dependent variables lifestyle, physical activity,
and eating behavior?” was assessed using 3
MANOVA tests. For the first MANOVA, the inde-
pendent variable was gender (male = 107; female =
294) and the 3 dependent variables were lifestyle
scores, physical activity scores, and eating behavior
scores. Preliminary assumption testing found that
physical activity and lifestyle scores violated the
equality of variances; therefore, a more stringent
alpha of .01 was used to test for significance. There
was a statistically significant difference between males
and females on the combined dependent variable, F
(3, 397) = 12.83, P < .001; Wilks lambda = 0.91; partial eta squared = 0.09, which is a moderately high effect size. When the results for the dependent variables were considered separately, they were all statistically significant: lifestyle score F (1, 399) = 22.54, partial eta squared = 0.05 (moderate effect size); physical activity score F (1, 399) = 6.61, partial eta squared = 0.02 (small effect size); EBI score F (1, 399) = 6.61, partial eta squared = 0.02 (small effect size). Males reported higher lifestyle scores and phy- sical activity scores and females reported higher EBI scores; refer to Table 5.

Table 3. Respondents’ feelings about mobile health or fitness
apps.
Response Question n

Positive It helps me keep track of my exercise and food intake 125
It helps me manage my weight better

60

It makes me feel healthier 80
It motivates me to eat healthier and increase my
physical activity

91

It helps me have a more positive body image 44
Negative It makes me feel obsessive about my exercise and food

intake
41

It creates anxiety/guilt if I do not reach my exercise or
food intake goals

34

It interferes with my daily activities and/or social life 12
It makes me neurotic about my body image 18

Table 4. Comparison of lifestyle, physical activity, and eating
behavior scores on students who use mobile health apps versus
non app users.

Lifestyle Physical Activity EBIa

Group Mean SD P Mean SD P Mean SD P

App users
(n = 185)

48.87 7.49 .095 59.91 25.72 .117 60.51 8.27 .007*

Nonusers
(n = 216)

47.55 8.15 55.71 27.53 57.88 8.33

aEBI indicates Eating Behavior Inventory.
*Significant at P ≤ .05.

DIFFERENCES BETWEEN USERS AND NONUSERS OF MOBILE HEALTH APPS 301

The second MANOVA was performed to investigate
the relationship between age and lifestyle scores, phy-
sical activity scores, and eating behavior scores. There
was a statistically significant difference between age on
the combined dependent variables, F (3, 397) = 17.432,
P < .001; Wilks lambda = 0.89, with a large effect size (partial eta squared = 0.12). When the results for the dependent variables were considered separately, age and EBI score, F (1, 399) = 30.88, partial eta squared = 0.07 (moderate effect size), and physical activity score, F (1, 399) = 7.50, partial eta squared = 0.02 (small effect size), were statistically significant. Participants who were older reported higher EBI scores than younger subjects, and participants who were younger reported higher physical activity scores than older subjects; refer to Table 5.

The third MANOVA was performed to examine
the relationship between race/ethnicity and scores for
lifestyle, physical activity, and eating behavior. See
Table 1 to view original race/ethnicity distribution.
To perform the analysis with a more even distribu-
tion, the independent variable was collapsed to form
2 groups: group 1 = white (n = 242) and group 2 =
non-white (South Asian or Indian American, Black,
Afro-Caribbean or African American, Middle Eastern

or Arab American, East Asian or Asian American,
and other; n = 138). Preliminary assumption testing
found no serious violations. There was a statistically
significant difference between group 1 and group 2
on the combined dependent variables, F (3, 376) =
3.05; Wilks lambda = 0.98; P = .003; partial eta
squared = 0.02 (small effect size). When the results
for the dependent variables were considered sepa-
rately, the only difference to reach statistical signifi-
cance was physical activity score, F (1, 378) = 8.97,
with a small effect size of 0.02. An inspection of the
mean scores indicated that participants who self-iden-
tified as white reported higher physical activity scores
than non-white participants; refer to Table 5.

Discussion

With the innovation of technology, mobile-based
health apps provide a promising new technique that
can be used to improve modern-day health interven-
tions. However, little is known about the potential
effects of utilizing this technology for the college-aged
population. The purpose of this study was to evaluate
differences in eating behaviors, physical activity, and
health-related lifestyle choices between users and

59.11

60.02

59.54

65.42

55

56

57

58

59

60

61

62

63

64

65

66

1 = My Fitness Pal 2 = Fit Bit 3 = Other 4 = > One App

Mean EBI Score

Figure 1. Eating behavior inventory scores based on type of mobile app.

Table 5. Comparison of lifestyle, physical activity, and eating behavior scores on students’ gender, age, and race/ethnicity.
Lifestyle Physical activity EBIa

Group Mean SD P Mean SD P Mean SD P

Male (n = 101) 51.17 7.74 .000* 64.14 24.61 .003* 51.17 7.74 .011*
Female (n = 294) 47.06 7.64 55.28 27.16 47.06 7.64
Younger, 18–22 years (n = 277) 47.70 8.22 .084 60.08 27.54 .000* 57.59 7.96 .006*
Older, 23+ years (n = 124) 49.17 6.95 52.22 24.16 62.45 8.41
White non-Hispanic
(n = 242) 48.61 7.68 .120 61.33 25.97 .003* 59.22 8.12 .543
Non-white (n = 138) 47.30 8.09 52.93 26.76 58.67 8.86

aEBI indicates Eating Behavior Inventory.
*Significant at P ≤ .01.

302 A. SARCONA ET AL.

nonusers of mobile health apps and to determine
whether demographic factors were associated with app
use and health behaviors.

The principal finding of this study was that mobile
health app users reported significantly higher EBI
scores or more positive eating behaviors compared to
nonusers. In addition, use of mobile health apps was
positively related to increased lifestyle scores; however,
no significance was noted. The findings of this study
correlate with the findings of Dallinga et al20 in which
app use was positively related to an overall improve-
ment in lifestyle and eating behavior. Mobile health app
use was also positively related to participants feeling
better about themselves, feeling like an athlete, and
motivating others to participate in running and losing
weight.20 Two studies have shown that the use of
mobile-based apps in a weight loss program increased
compliance and led to better health outcomes21 and
participants lost significantly more weight than the
standard group not using a mobile-based app.22

The types of mobile-based apps utilized among the
college population vary dramatically based on popular-
ity, features, etc. Therefore, mobile-based apps were
examined in reference to their effects on lifestyle beha-
vior, physical activity, and eating behavior. Results
showed that participants who used more than one
type of mobile health app had significantly higher EBI
scores or more positive eating behaviors compared to
use of individual apps. This aspect of mobile-based
technology is limited in previous studies; however, the
interaction between varying features of mobile-based
apps may be an underlying element that is key to this
type of technology-based health intervention.
Middelweerd et al evaluated college students’ prefer-
ences regarding a physical activity application for
smartphones. The majority of the students preferred
apps that coached and motivated them, included tai-
lored feedback toward personally set goals, and
involved competition with friends.23 Another study
found that college students were most influenced by
apps that were free, were easy to use, provided visual/
auditory cues, and had game-like rewards.14 Most stu-
dents used apps that coincided with their specific goals,
such as developing an exercise routine or improving
eating habits.14 Participants may benefit further from
technology interventions if mobile apps specifically
cater to their health/lifestyle needs.

This study evaluated demographics such as age,
gender, race/ethnicity, and BMI relative to mobile
health app use and health-related behaviors. The
influence of age is critical to examine because of
the notable increase in technology use seen in
younger populations. In this study, older participants

reported higher EBI scores and younger participants
reported participating in more frequency of physical
activity than older subjects; however, no significance
was found between age and mobile app use. A study
by Bhuyan et al outlined that the majority of the
population who used health-related mobile apps
were younger than 65 years old. Those who were
older were less likely to use apps for achieving health
behavior goals.24 It is noted that no participants in
our study were older than 65 and only 15% were
older than 26 years of age. In a study conducted by
Gorton et al, researchers found the greatest support
for mobile phone–delivered weight loss interventions
among younger participants (16–30 years) compared
to older age groups (31–50 years and 50–70+ years).-
25 This is also consistent with the findings of Dallinga
et al, who found that app users were significantly
younger compared to nonusers.20 These results sug-
gest that intervention success may improve with a
focus on younger populations and providing more
instruction on using apps among older populations.

Male participants in this study scored signifi-
cantly higher for lifestyle and physical activity
scores, and females scored higher for eating beha-
viors. There was no significance between gender and
mobile health app use. Research is limited with
gender-specific data collection in regard to health
behavior.14 Further research regarding mobile health
interventions that can be utilized to increase specific
types of behaviors in males and females could lead
to a large improvement in overall health.

Ethnic minority college women appear to be par-
ticularly vulnerable to high rates of overweight and
obesity26,27; therefore, race/ethnicity and BMI were
evaluated in this study. No differences in this study
were found among whites versus non-whites in
mobile health app use, and participants who self-
identified as white reported higher physical activity
scores. Rodgers et al evaluated ethnic minority col-
lege women using mobile technology to promote
healthy eating. Results revealed that adherence
decreased over the course of the study and those
with a higher BMI had lower satisfaction using the
technology, but those with higher body dissatisfac-
tion had the greatest adherence.28 This study did
not find that overweight and obese individuals
used mobile health apps more than normal-weight
persons; however, Bhuyan et al found that obese
respondents in their study were 3.2 times more
likely than normal-weight subjects to use apps for
achieving health behavior goals.24 Effective strategies
for treating high rates of obesity are vital to decrease
the prevalence of chronic diseases.

DIFFERENCES BETWEEN USERS AND NONUSERS OF MOBILE HEALTH APPS 303

Limitations

There were several strengths to this study, such as large
sample size and range of diversity of subjects. Limitations
include a wide variety of mobile health apps utilized by
participants and use of a survey versus actual measure-
ment of health-related outcomes. Additional research
exploring the evidentiary gaps discussed above is now
needed to produce effective behavior change theory–
based applications to aid in improved health. The sample
was predominately female; however, it was almost con-
sistent with the university ratio of female to male but
cannot be generalized for all universities. Our study did
not find differences in gender regarding use of mobile
health apps, but further study on types of apps and moti-
vation to use appsmay reveal different preferences among
gender. This study evaluated a psychological component
of using an app to track health behaviors. With a 4:1
positive to negative response regarding how participants
felt about their mobile based apps, future research on
specific behavioral responses may be useful. Dennison
et al reported participants’ thoughts and feelings regard-
ing their use of health and fitness apps. Accuracy, legiti-
macy, security, effort required, and how it affected their
mood were factors influencing app use.29 It is also impor-
tant to consider negative feelings that may be associated
with using an app for monitoring health. Participants in
this study noted feelings of obsession with their exercise
and food intake, anxiety/guilt when exercise or food
intake goals were not met, interference with daily activ-
ities and/or social life, and neurosis about body image.
Similar findings among participants in a study by Gowin
et al discussed negative feelings related to app use such as
guilt, avoidance, shame, or feeling stressed and becoming
obsessed, but users still had positive comments regarding
app use for health and fitness.14 Tracking health behaviors
has also been associated with eating disorder symptoma-
tology, and this is an area that requires more research.30

Translation to Health Education Practice

Mobile-based apps are a leading innovation in this new
age of technology. The benefits of using mobile apps for
health promotion programs are innumerable because up
to 92% of young adults own smart phones, as do the
majority of low-socioeconomic status and minority
populations.31 This is an ideal population when imple-
menting health promotion programs to prevent chronic
disease and reduce health care costs. Targeting these
populations when focusing on technology-based Health
Education programs is essential at reducing health dispa-
rities and striving to reach Healthy People 2020 goals.

According to the results of this study, these self-
monitoring devices may have an immeasurable effect
on behavioral interventions concerning eating beha-
vior. Most participants identified mobile health app
use with feeling healthier, feeling motivated, and
improved self-monitoring. App users were found to
have more positive eating behaviors than nonusers,
and the impact of using more than one type of
mobile-based health app significantly improved eating
behavior. Healthy eating habits and exercise are a vital
component to health promotion and chronic disease
prevention. Though social determinants have a strong
influence on one’s health, programs aimed at improv-
ing nutrition and exercise via mobile health apps may
improve quality of life and longevity. Health Education
and health promotion programs must be ingenuous,
and using technology that is accessible all hours of the
day for participants may promote adherence. Barriers
are low for using cell phones; people use smartphones
and apps everywhere and at any time. Certified Health
Education Specialists need to consider the use of apps
as a supplementary component to assist individuals
who want to make improvements in their overall health
and prevent chronic disease.

More specific randomized controlled trials utilizing dif-
ferent types of mobile apps associated with health out-
comes may help to further understand the fundamental
relationship between improved health and mobile technol-
ogy. Studies analyzing app usage adherence and app pre-
ference among target populations are necessary for
effective Health Education and promotion programming.
Leveraging mobile-based technology to improve health
offers exciting and unlimited avenues in combatting
chronic disease.

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• Abstract

Background
Purpose
Methods
Instruments
Sample and recruitment
Data analysis
Results
Discussion
Limitations
Translation to Health Education Practice
References

lable at ScienceDirect

Appetite 109 (2017) 100e107

Contents lists avai

Appetite

journal homepage: www.elsevier.com/locate/appet

Eating behaviour of university students in Germany: Dietary intake,
barriers to healthy eating and changes in eating behaviour since the
time of matriculation

Jennifer Hilger a, b, *, Adrian Loerbroks b, Katharina Diehl a

a Mannheim Institute of Public Health, Social and Preventive Medicine, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
b Institute of Occupational Medicine and Social Medicine, Centre for Health and Society, Faculty of Medicine, University of Düsseldorf, Düsseldorf, Germany

a r t i c l e i n f o

Article history:
Received 8 June 2016
Received in revised form
13 October 2016
Accepted 11 November 2016
Available online 15 November 2016

Keywords:
Nutrition
Healthy eating
University students
Barriers
Germany

* Corresponding author. Mannheim Institute of Pub
Preventive Medicine, Medical Faculty Mannheim, Hei
Ludolf-Krehl-Straße 7-11, D-68167 Mannheim, Germa

E-mail address: Jennifer.Hilger@medma.uni-heide

http://dx.doi.org/10.1016/j.appet.2016.11.016
0195-6663/

© 2016 Elsevier Ltd. All rights reserved.

a b s t r a c t

A healthy diet plays a key role in preventing obesity and non-communicable diseases such as type 2
diabetes. This is true for all age groups, including young adults. While unhealthy eating habits among
young adults, in particular university students, have been identified in former studies, this group has
been neglected in existing health promotion strategies. Our aim was to explore baseline dietary intake,
common barriers to healthy eating, and changes in eating behaviour among university students since the
time of matriculation. We used data from the quantitative part of the Nutrition and Physical Activity
Study (NuPhA), a cross-sectional online survey (data collection: 2014/10/31e2015/01/15). Students were
recruited from all over Germany. Overall, 689 university students (30.5% male; mean age: 22.69) from
more than 40 universities across Germany participated. We found that there is room for improvement
with regard to the consumption of specific food groups, for example, fruits and vegetables. The main
barriers to healthy eating were lack of time due to studies, lack of healthy meals at the university
canteen, and high prices of healthy foods. Cluster analysis revealed that barriers to healthy eating might
affect only specific subgroups, for instance freshmen. Changes in eating behaviour since matriculation
were found in the consumption of meat, fish, and regular meals. Future qualitative studies may help to
explore why university students change their eating behaviour since the time of matriculation. Such
knowledge is necessary to inform health promotion strategies in the university setting.

© 2016 Elsevier Ltd. All rights reserved.

1. Introduction

A healthy diet is widely recognized as a main factor in pre-
venting obesity and non-communicable diseases such as type 2
diabetes and cardiovascular disease (World Health Organisation,
2016). Therefore, following a healthy diet should be promoted
across all age groups. According to the WHO (2016) a healthy diet
should include, for instance, high consumption of fruits, vegetables,
and whole grains, in addition to low consumption of saturated fats,
salt, and refined carbohydrates. The transition from adolescence to
young adulthood may be a particularly important time for health
promotion strategies, including the promotion of healthy eating,
because many health behaviours are developed and established

lic Health, Social and
delberg University,
ny.
lberg.de (J. Hilger).

during this period (Nelson, Story, Larson, Neumark-Sztainer, &
Lytle, 2008; Poobalan, Aucott, Clarke, & Smith, 2014). However,
current studies indicate that poor dietary habits seem to be com-
mon among this age group. Former studies report, for example,
high levels of fast food consumption, low intake of fruits and veg-
etables, and breakfast skipping (N. Larson, Laska, Story, & Neumark-
Sztainer, 2012; Niemeier, Raynor, Lloyd-Richardson, Rogers, &
Wing, 2006). Furthermore, excessive weight gain has been
observed among young adults (Gordon-Larsen, Adair, Nelson, &
Popkin, 2004; Mensink, Schienkiewitz, Haftenberger, Lampert,
Ziese, & Scheidt-Nave, 2013; Nelson Laska, Larson, Neumark-
Sztainer, & Story, 2010), particularly university students
(Mihalopoulos, Auinger, & Klein, 2008; Racette, Deusinger, Strube,
Highstein, & Deusinger, 2005).

The transition from school to university coincides changing
living arrangements, which might also result in a reorientation of
eating behaviours (El Ansari, Stock, & Mikolajczyk, 2012). However,
only a few studies have focused on potential changes in eating

mailto:Jennifer.Hilger@medma.uni-heidelberg.de

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www.elsevier.com/locate/appet

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http://dx.doi.org/10.1016/j.appet.2016.11.016

J. Hilger et al. / Appetite 109 (2017) 100e107 101

behaviour since matriculation (Lupi, Bagordo, Stefanati, Grassi,
Piccinni, Bergamini, et al., 2015; Nelson Laska et al., 2010;
Wengreen & Moncur, 2009). In addition, little is known about the
reasons which may prevent university students from following a
healthy diet. Thus, we aimed to a) describe the baseline dietary
intake of university students, b) identify potential barriers to
healthy eating and c) explore potential changes in their eating
behaviour since the time of matriculation.

2. Material and methods

2.1. Study design and sample

The analyses are based on data from the quantitative part of the
Nutrition and Physical Activity Study (NuPhA), a cross-sectional
online survey among university students conducted across Ger-
many from October 2014 to January 2015. University students were
recruited via fliers, mailing lists, social networks, and advertising
the study during classes and lectures. Students received informa-
tion on the study aims and data security regulations; they were
informed that participation was voluntary and withdrawal from the
study possible at any point in time. Participants provided informed
consent by selecting the “agreement button” during the online
survey, which then directed them to the first question of the survey.
As an incentive we randomly awarded gift certificates to 40 stu-
dents who completed the online survey. The study was approved by
the Medical Ethics Committee of the Medical Faculty Mannheim,
Heidelberg University (2013-634N-MA).

2.2. Measures

2.2.1. Dietary assessment
Dietary intake was assessed using a brief food frequency ques-

tionnaire (FFQ). The original FFQ consisted of 17 food items and was
developed by the Max Rubner Institute (2016), the German Federal
Research Institute of Nutrition and Food. The FFQ was originally
designed as a parental questionnaire to evaluate the dietary intake
of children. We slightly adapted this FFQ to fit the population of
young adults. Our final FFQ consisted of 22 food items, and the
frequency of consumption was assessed by the following response
categories: never, less than once a week, once a week, two to three
times a week, four to five times a week, six to seven times a week, or
several times a day. For further analyses we grouped the categories
once a week and two to three times a week, as well as the categories
four to five times a week and six to seven times a week. Based on a
food pyramid developed by the German state-funded Agency for
Consumer Information (Koelsch & Brueggemann, 2012; von
Ruesten, Illner, Buijsse, Heidemann, & Boeing, 2010), the 22 food
items were grouped into six food groups: 1. Vegetables, salad; 2.
Fruits; 3. Bread, grains, side dishes; 4. Dairy products; 5. Meat,
sausages, fish, eggs; 6. Sweets and snacks.

2.2.2. Assessment of barriers to healthy eating
We used a questionnaire applied and validated in previous

research among adolescents and young adults (Andajani-Sutjahjo,
Ball, Warren, Inglis, & Crawford, 2004; Musaiger, Al-Mannai,
Tayyem, Al-Lalla, Ali, Kalam, et al., 2014; Musaiger, Al-Kandari, Al-
Mannai, Al-Faraj, Bouriki, Shehab, et al., 2013) to identify potential
barriers to healthy eating. The questionnaire was translated into
German and slightly modified by adapting and adding items to fit
the population of university students. Participants could choose
one of the following response options for each of the 22 barrier
items: “Not a barrier, a somewhat important barrier, a very
important barrier”. For further analysis, we combined the two
categories “important barrier” and “very important barrier” into

one barrier category based on sensitivity analysis.

2.2.3. Assessment of changes in eating behaviour
Changes in eating behaviour since matriculation were examined

by asking, “Has your eating behaviour changed since matricula-
tion?” (response categories: yes/no/unknown). A second question
enquired after changes in the frequency of food consumption:
“Since matriculation, do you consume: more, less, or as many as
before of the following foods”. We assessed changes in the con-
sumption of ten food items, eight of which were also included in
the FFQ and two additional ones (total calories; regular meals).
These questions were adapted from the Campbell Survey on Well-
Being in Canada (Canadian Fitness and Lifestyle Research Institute,
1988).

2.3. Data analysis

Descriptive analyses like frequency distributions and cross
classifications were performed to characterise the sample’s base-
line dietary intake, barriers to a healthy eating, and changes in
eating behaviour since matriculation. In addition, we conducted
Chi2 tests to explore if eating behaviour differs between male and
female university students. To combine similar barriers to healthy
eating into one dimension, we conducted an explorative factor
analysis using SPSS FACTOR with Varimax rotation. After excluding
eight of the primarily 22 barrier items with factor loadings <0.6, we obtained a five-factor solution (Kaiser-Meyer-Olkin-Measure: 0.685; Bartlett-Test: <0.001; supplement 1): factor 1: Personal motivation/attitudes; factor 2: Lack of knowledge/information; factor 3: Environmental barriers; factor 4: Lack of social support; factor 5: Lack of time. The five factors identified were subsequently incorporated in a hierarchical cluster analysis (Ward's method, squared Euclidian distance). The cluster analysis was conducted to group university students affected by the same barriers to healthy eating. To detect differences between the clusters identified, Chi2

tests were applied to discrete variables and Kruskal-Wallis H test to
continuous variables.

All statistical analyses were performed using IBM SPSS Statistics
22 (IBM Corporation, Armonk, USA). The pre-defined level of sig-
nificance for all tests was p < 0.05.

3. Results

The sample included 689 university students (30.5% male;
Table 1) aged 16e29 years from more than 40 universities across
Germany. Approximately 35% of them were undergraduate stu-
dents in the first to third semesters. The majority of the students
(74.2%) had left their hometown to enrol at university.

3.1. Baseline dietary intake

A minority of the students reported eating cooked vegetables
(3.2%) as well as raw vegetables and salad (3.6%) several times a
day. Fresh fruits were consumed by 26.9% of students several times
a day (Fig. 1). Brown bread was eaten by 10.3% less than once a
week. While 18.0% of the students reported that they never ate red
meat,12.6% stated that they consumed it 4e7 times per week. More
than half of the students (55.4%) ate poultry 1e3 times a week, and
43.1% consumed fish 1e3 times a week. Chocolate was eaten by
4.5% several times per day. More than half (52.5%) of the university
students reported consuming fast food less than once a week, and a
minority (1.9%) reported eating fast food frequently (4e7 times a
week).

Gender differences in the frequency of food consumption were
also identified: While females were more likely to consume cooked

Table 1
Characteristics of university students in the NuPhA Study (n ¼ 689).

Characteristics n (%)/m (SD)

Gender
Male 210 (30.5)
Female 479 (69.5)

Age 22.69 (2.73)
BMI 22.14 (2.93)
Migration background 96 (13.9)
Number of semesters studied
1e3 semesters 234 (34.9)
4e5 semesters 127 (18.9)
6e9 semesters 187 (27.9)
10 þ semesters 123 (18.3)

Subject of studies
Medicine/health care 369 (53.6)
Political sciences/social sciences 86 (12.5)
Law/business sciences 46 (6.7)
Sport sciences 42 (6.2)
Other subjects 146 (21.2)

Marital status
Married 28 (4.1)
In a partnership 360 (52.2)
Not in a partnership 301 (43.7)

Left hometown to start studies 511 (74.2)
Money available per month 766.69 (516.54)
Getting support from parents 585 (85.7)
Getting support from the German Federal Assistance Act 127 (18.6)
Having a side job 426 (62.4)

m: mean; SD: standard deviation; BMI: body mass index.

J. Hilger et al. / Appetite 109 (2017) 100e107102

vegetables (p ¼ 0.01), salad/raw vegetables, fresh fruits, and curd-/
cream cheese/yoghurt (all: p < 0.001), consumption of red meat, poultry, sausages, fish (all: p < 0.001), and hard/soft cheeses (p ¼ 0.02) was more common among male students. In addition, males ate fast food (p < 0.001) and side dishes like pasta/rice (p ¼ 0.01) and fried potatoes/chips (p < 0.001) more frequently than females. Gender differences were also seen in the frequency of chocolate consumption, with females consuming chocolate more often than males (p < 0.001). Within the total sample, 15.8% re- ported to adhere to a vegetarian diet (13.8% vegetarians, 2.0% vegans). Significantly more females (19.6%) than males (7.1%) re- ported to be vegetarians (p < 0.001).

The majority of university students (74.3%) reported regularly
eating breakfast on weekdays (4e5 times), while 8.7% stated they
seldom/never ate breakfast on weekdays. Most students (77.9%)
had breakfast on their own. Lunch was eaten by 73.6% on weekdays,
and 66.6% reported having lunch together with colleagues/friends.
More than half of the university students (51.8%) reported having
lunch at the university canteen. As the main reasons for eating
there, students mentioned eating together with fellow students/
friends (78.4%), saving time (75.1%), proximity to university (74.8%),
and wanting a warm meal (58.0%). During the week, 83.0% of stu-
dents ate dinner on 4e5 days and slightly more than half (50.5%)
reported having dinner on their own.

3.2. Barriers to healthy eating

The majority of the university students (90.9%) reported trying
to eat healthily, including 92.3% of female and 87.6% of male stu-
dents. Overall, 66.1% found it easy to follow a healthy diet, however
significantly more females (69.2%) than males (58.7%; p ¼ 0.01)
agreed on this.

The two most important barriers to healthy eating were lack of
time to prepare a healthy meal due to university commitments and
lack of healthy meals at the university canteen (Fig. 2). Gender
differences were observed for several barriers with significantly
more males (very important barrier 3.3%; important barrier 21.0%)

than females (very important barrier 2.5%; important barrier:
12.3%) reporting a lack of motivation (p ¼ 0.010). In addition, more
males (very important barrier: 5.8%; important barrier: 18.6%) than
females (very important barrier: 2.7%, important barrier: 7.4%)
stated that they did not enjoy eating healthy food (p ¼ 0.001). Fe-
males mentioned “healthy food doesn’t taste good” as a barrier to
healthy eating (very important: 2.1%; important: 8.6%) less
frequently than males did (very important: 3.8%, important: 17.7%;
p ¼ 0.001). Males reported a lack of time due to hobbies and other
interests (very important: 4.8%, important: 25.4%) as a reason for
not following a healthy diet more often than females did (very
important: 3.5%, important: 17.7%; p ¼ 0.04).

The cluster analysis resulted in five barrier clusters to healthy
eating which we defined as follows: Cluster 1: Non-supported/non-
motivated; Cluster 2: Lack of time; Cluster 3: Lack of knowledge/
information; Cluster 4: No barriers; Cluster 5: Environmental bar-
riers. We did not find gender differences among the five clusters
identified (Table 2). Students in the “Lack of knowledge/informa-
tion” cluster were slightly younger than those in the four other
clusters. More than 90% of students in clusters 2 (“Lack of time”), 4
(“No barriers”) and 5 (“Environmental barriers”) “tried to follow” a
healthy diet but this was true for only 81.8% in the “Non-supported/
non-motivated” cluster and for 79.5% in the “Lack of knowledge/
information” cluster. In addition, fewer students in the latter clus-
ters reported “finding it easy” to follow a healthy diet (41.7% and
39.7%, respectively) compared to those in the “No barriers” cluster
(91.2%). Clusters did not differ with regard to meal patterns, such as
having regular breakfast or having lunch at the university canteen.
However, fewer than 40% of students in clusters 1 (“Non-sup-
ported/non-motivated”), 2 (“Lack of time”), and 4 (“No barriers”)
mentioned “lack of cooking skills/cooking is too time consuming”
as reason for eating at the university canteen compared to 58.3% in
the “Lack of knowledge/information” cluster and 59.4% in the
”Environmental barriers” cluster. In the latter group, more than 80%
of students reported having left their hometown to start their
studies. Within this group, the majority (73.2%) mentioned that
their eating behaviour had changed since entering university.
Clusters did not differ with regard to the amount of money available
per month or the level of financial support from parents or the
German Federal Training Assistance Act. However, more than 60%
of students in the “Non-supported/non-motivated” cluster (73.5%)
and in the “Lack of knowledge/information” cluster (63.0%) re-
ported having a side job.

3.3. Changes in eating behaviour since matriculation

Most of the students (65.3%) reported that their eating behav-
iour had changed since matriculation, with more males (69.0%)
than females (63.7%; p ¼ 0.35) indicating this. In addition, more
students who had moved away from home to enrol at university
(70.6%) reported a change in eating behaviour than those who had
stayed in their hometown (50.0%; p < 0.001).

Considering changes in the consumption of specific food groups,
40.5% of the university students reported eating more vegetables
and 38.2% reported eating more fruits since matriculation (Table 3).
On the other hand students reported consuming less red meat
(53.5%), poultry (43.4%), and fish (37.3%). More than half of the
university students (55.2%) reported that they no longer eat as
many regular meals as before matriculation. Gender differences for
changes in the consumption of specific food groups since matric-
ulation were found for poultry, fish, fast food, and sugar/sweets
(Table 3).

Fig. 1. Baseline dietary intake of university students in Germany (n ¼ 689; NuPhA Study).
Reading support: The single food items are grouped into six food groups based on the German Food Pyramid developed by the German state-founded Agency for Consumer In-
formation (von Ruesten et al., 2010).

J. Hilger et al. / Appetite 109 (2017) 100e107 103

Fig. 2. The top 15 barriers to healthy eating reported by university students in Germany (n ¼ 689; NuPhA Study).
Reading support: sorted by: no barrier; * Indicates gender differences (determined by Chi2-tests; p < 0.05).

Table 2
Characteristics of the five barriers-to-healthy-eating clusters of students at German universities (NuPhA Study).

Cluster 1
“Non-supported/non-
motivated”

Cluster 2
“Lack of time”

Cluster 3
“Lack of knowledge/
information”

Cluster 4
“No barriers”

Cluster 5
“Environmental
barriers”

p value

n (%) 170 (25.5) 216 (32.4) 73 (10.9) 152 (22.8) 56 (8.4)
Female [n (%)] 112 (65.9) 160 (74.1) 51 (69.9) 106 (69.7) 35 (62.5) NS
Age [m (SD)] 22.7 (2.7) 22.6 (2.6) 21.8 (2.6) 23.2 (2.9) 22.7 (2.4) 0.012
BMI [m (SD)] 22.7 (3.2) 21.8 (2.5) 22.9 (3.2) 21.7 (2.9) 22.3 (2.9) 0.001
Number of semesters studied [m (SD)] 5.9 (3.4) 6.1 (3.6) 4.7 (3.6) 6.1 (3.6) 6.3 (3.5) 0.027
Migration background [n (%)] 25 (14.7) 33 (15.3) 9 (12.3) 12 (7.9) 13 (23.2) NS
Try to follow a healthy diet [n (%)] 139 (81.8) 209 (96.8) 58 (79.5) 147 (96.7) 51 (91.1) <0.001 Find it easy to follow a healthy diet [n (%)] 58 (41.7) 154 (73.7) 23 (39.7) 134 (91.2) 32 (62.7) <0.001 Changes in eating behaviour since matriculation [n (%)] 110 (64.7) 151 (69.9) 46 (63.0) 89 (58.1) 41 (73.2) NS Have lunch at the university canteen [n (%)] 87 (51.2) 120 (55.6) 36 (49.3) 69 (45.4) 32 (57.1) NS Have a partner [n (%)] 100 (58.8) 121 (56.0) 41 (56.2) 87 (57.2) 28 (50.0) NS Left hometown to enrol at university [n (%)] 122 (71.8) 164 (75.9) 52 (71.2) 110 (72.4) 46 (82.1) NS Money available per month [m (SD)] 756.2 (440.7) 792.3 (726.5) 680.4 (381.5) 770.1 (314.8) 756.9 (363.0) NS Receive financial support from parents [n (%)] 147 (86.5) 184 (85.2) 65 (89.0) 127 (85.8) 44 (81.5) NS Receive financial support from Federal Training Assistance Act [n (%)] 31 (18.2) 43 (19.9) 13 (17.8) 25 (16.9) 11 (20.4) NS Have a side job [n (%)] 125 (73.5) 129 (59.7) 46 (63.0) 86 (58.1) 27 (50.0) 0.006

n: number of cases; m: mean; SD: standard deviation; BMI: body mass index; Kruskal-Wallis H tests were applied to: age, BMI, number of semesters studied, money available
per month; Chi2 tests were applied to: all other variables.

J. Hilger et al. / Appetite 109 (2017) 100e107104

4. Discussion

In our nationwide NuPhA Study we found that there is room for
improvement with regard to the intake of specific food groups like

fruits and vegetables. The main barriers to healthy eating were iden-
tified as lack of time due to studies, lack of healthy food at the uni-
versitycanteen,andhighcostsofhealthyfoods.Inaddition,mostof the
students reported changes in eating behaviour since matriculation.

Table 3
Changes in food consumption since matriculation among university students at German universities (NuPhA Study).

Change in food consumption

Total sample (n ¼ 689) Males (n ¼ 210) Females (n ¼ 479) P value
Food group No change % Increased% Decreased% No change % Increased % Decreased% No change % Increase % Decrease %

Vegetables 42.7c 40.5c 16.8a 42.9c 40.5c 16.6a 42.6c 40.5c 16.9a 1.00
Fruits 44.8c 38.2c 17.0a 44.8c 35.7c 19.0a 44.6c 39.3c 16.1a 0.53
Whole grain products 46.7c 38.4c 14.8a 44.8c 44.2c 11.0a 47.6c 35.8c 16.6a 0.05
Milk/dairy products 53.1d 27.8b 19.1a 53.8d 29.5b 16.7a 52.8d 27.0b 20.1b 0.53
Red meat 40.0c 6.4a 53.6d 40.0c 9.0a 51.0d 40.0c 5.3a 54.7d 0.17
Poultry 37.9c 19.7b 42.4c 34.2b 32.9b 32.9b 39.5c 13.9a 46.6c <0.001 Fish 41.6c 21.1b 37.3c 35.4c 30.1b 34.4b 44.3c 17.1a 38.6c 0.001 Sugar/Sweets 43.6c 25.4b 31.0b 35.9c 18.2a 45.9c 47.0c 28.6b 24.4b <0.001 Fast Food 40.6c 27.2b 32.2b 34.8b 33.8b 31.4b 43.2c 24.3b 32.5b 0.02 Total calories 51.8d 22.0b 26.1b 52.2d 26.5b 21.3b 51.7d 20.0b 28.3b 0.64 Regular meals 34.2b 10.7a 55.2d 35.4c 14.4a 50.2d 33.6b 9.0a 57.4d 0.07

Gender differences were determined by Chi2 tests.
a <20.0%. b 20.0%e34.9%. c 35.0%e49.9%. d >50.0%.

J. Hilger et al. / Appetite 109 (2017) 100e107 105

According to the WHO (2016) a healthy diet includes the con-
sumption of at least five portions of fruits and vegetables a day. In
relation to this recommendation the intake of fruits and vegetables
in our sample was quite low with less than 30% of all students
reporting to eat fruit and vegetables several times a day. This
finding is in line with other studies focusing on university students
from various countries, including the USA (Yahia, Wang, Rapley, &
Dey, 2015), Spain (Moreno-Gomez, Romaguera-Bosch, Tauler-
Riera, Bennasar-Veny, Pericas-Beltran, Martinez-Andreu, et al.,
2012), Italy (Lupi, Bagordo, Stefanati, Grassi, Piccinni, Bergamini,
et al., 2015; Teleman, de Waure, Soffiani, Poscia, & Di Pietro,
2015), and Germany (Keller, Maddock, Hannover, Thyrian, &
Basler, 2008). Making fruits and vegetables more accessible and
appealing to students may be one strategy for increasing con-
sumption among students.

Furthermore, we found gender differences in the consumption
frequency of several food groups. In accordance with other studies
(Lupi et al., 2015; Mikolajczyk, El Ansari, & Maxwell, 2009), males
consumed in comparison to females fast food and meat products
more often and fruits and vegetables less often. Reasons for such
gender differences could be the generally higher health awareness
(Stock, Wille, & Kramer, 2001; Wardle & Steptoe, 1991), better
nutrition knowledge (Kresic, Kendel Jovanovic, Pavicic Zezel,
Cvijanovic, & Ivezic, 2009), and better knowledge about what
constitutes a “healthy diet” (Yahia et al., 2015) among females. A
further explanation for the gender differences might be that fe-
males in general are more concerned about their body weight than
males (Salameh, Jomaa, Issa, Farhat, Salame, Zeidan, et al., 2014;
Wardle, Haase, & Steptoe, 2006; Yahia et al., 2015). Thus, they
may follow healthier eating patterns to stay slim. However, the
females within our sample reported eating chocolate more
frequently than the males did. Previous studies also reported that
female students consumed sweet foods more frequently than their
male counterparts (El Ansari, Adetunji, & Oskrochi, 2014;
Mikolajczyk et al., 2009; Yahia et al., 2015). They found an associ-
ation between such eating habits and higher levels of perceived
stress in female students (El Ansari et al., 2014; Mikolajczyk et al.,
2009). Possibly female students may eat sweet foods, especially
chocolate, as a strategy to better cope with stress. Also in line with
earlier studies (Mikolajczyk et al., 2009; Yahia et al., 2015), we
found that female students ate fish less frequently than male stu-
dents. This may be due to the higher proportion of females in our
sample reporting to be vegetarians.

We identified the lack of time due to studies as one main barrier
to healthy eating (Musaiger et al., 2014; Pelletier & Laska, 2012).
Therefore, universities should think about strategies to overcome
this important barrier like providing time management courses
(Pelletier & Laska, 2012).

The perceived lack of healthy foods at the university canteen
was stated as another important barrier to following a healthy diet.
Intervention studies focusing on university canteens showed that
providing higher food quality and greater food variety as well as
reduced prices resulted in healthier eating habits (Davis, Cullen,
Watson, Konarik, & Radcliffe, 2009; Guagliardo, Lions, Darmon, &
Verger, 2011; Michels, Bloom, Riccardi, Rosner, & Willett, 2008).
Given that more than 50% of our study sample reported having
lunch regularly at the university canteen, offering healthy and low-
priced meals may be a promising strategy to ensure healthy eating
within the university setting.

According to our cluster analysis, most barriers to healthy eating
seem to affect only specific subgroups within our sample of uni-
versity students. Students in the “Lack of knowledge/information”
cluster, which showed the highest proportion of students reporting
difficulties in following a healthy diet, were younger and at the
beginning of their studies compared to students within the other
four clusters. An explanation for their difficulties might be their
shorter period of independence from parents and family. Previous
studies have observed an association between the decreased
involvement of children and adolescents in making family meals
and the current lack in cooking skills among young adults (Nelson
et al., 2008). Moreover, studies have found that food preparation
skills increase diet quality among young adults (Thorpe, Kestin,
Riddell, Keast, & McNaughton, 2014) but also enable healthier
eating habits during later life (Laska, Larson, Neumark-Sztainer, &
Story, 2012). Therefore, providing nutrition information and offer-
ing cooking classes within the university setting could be useful
interventions to improve the nutrition knowledge and food prep-
aration skills among university students.

The eating habits of students in the “Environmental barriers”
cluster seemed to be affected by the opening hours of nearby
grocery stores and by the lack of healthy food options in these
stores. A review revealed that better access to neighborhood gro-
cery stores was associated with better diet quality in adults (N. I.
Larson, Story, & Nelson, 2009). In addition, individuals living in
areas with a higher number of grocery stores offering healthy foods
were more likely to buy a greater variety of fruits and vegetables

J. Hilger et al. / Appetite 109 (2017) 100e107106

(Mason, Bentley, & Kavanagh, 2013). Studies among university
students indicate that on- or off-campus living seems to influence
food choices, with off-campus students having greater difficulties
in following a healthy diet (Kapinos & Yakusheva, 2011; Small,
Bailey-Davis, Morgan, & Maggs, 2013). As a useful strategy Small
et al. (2013) propose partnerships between universities and the
local communities, for example, establishing farmers’ markets next
to university campuses. In addition, they suggest providing trans-
portation to and from local grocery stores to help students with
their shopping. Although on-campus living is not common in
Germany, the proposed strategies may help German university
students overcome such environmental barriers.

Additionally, more than 80% of the individuals within the
“Environmental barriers” cluster moved away from home to enrol
at the university and 73% reported a change in eating behaviour
since matriculation. Previous studies showed that changes in eating
habits were most pronounced in students living away from home
(El Ansari et al., 2012; Lupi et al., 2015; Papadaki, Hondros, J, &
Kapsokefalou, 2007). One reason might be that these students are
entirely responsible for food shopping and meal planning purposes
for the first time in their lives (El Ansari et al., 2012; Lupi et al., 2015;
Papadaki et al., 2007). In addition, they may not know where to buy
healthy food close to their new place of residence.

Students belonging to the “No barriers” cluster seemed not to be
affected by any barriers to healthy eating. More than 90% of stu-
dents within that cluster reported finding it easy to follow a healthy
diet. As the mean age of this group was slightly higher compared to
the other clusters, this may indicate that students learn to cope
with barriers over time. Thus, interventions that aim to enable
university students to follow a healthy diet should start immedi-
ately after university enrolment.

In addition, most of the students in our sample reported changes
in eating behaviour since matriculation, which is in line with pre-
vious research (Lupi et al., 2015). Changes in dietary intake were
particularly found for meat and fish. The majority of students re-
ported a decreased consumption of these food groups. Guagliardo
et al. (2011) stated financial reasons as a possible explanation for
a lower consumption of meat, fish, fruits, and vegetables. Although
46% of our sample mentioned high prices as a barrier to healthy
eating, only a minority reported eating fewer fruits and vegetables.
A reason for this might be that fruits, vegetables, meat, and fish are
less expensive in Germany compared to other countries. Therefore,
the decreased consumption of meat and fish might at least partly
reflect the current trend in following a vegetarian or vegan diet
(Leitzmann, 2014; Vegetarierbund Deutschland, 2016).

4.1. Strengths and limitations

Our study is, to the best of our knowledge, the first study to
focus on eating behaviour and on barriers to healthy eating in a
cross-disciplinary sample of students from different universities
across Germany. Besides providing information on baseline dietary
intake, we present data on the changes in eating behaviour since
matriculation. Furthermore, the large sample size enabled us to
conduct comprehensive statistical procedures like cluster analysis.
Thus, we were able to identify specific target groups for health
promotion strategies within the whole sample of university stu-
dents. Therefore, our study provides valuable data on eating
behaviour within young adults and the potential changes in this
behaviour occurring since the time of matriculation. Nevertheless,
our study has some limitations. First of all, due to the cross-
sectional design of our study, no causal relationships can be
drawn from our data. In addition, although the NuPhA Study
recruited university students from across Germany, we cannot rule
out the possibility of a participation bias, which may limit the

generalizability of our results. Furthermore, due to the self-
reported variables, reporting bias and recall bias may have
occurred.

5. Conclusion

The results of our study indicate that changes in eating behav-
iour occur among university students since the time of matricula-
tion. However, barriers to healthy eating may differ among
university students and seem to affect only specific subgroups, for
instance freshmen. Qualitative studies may be helpful to further
explore the motives that shape the changes in eating behaviour
since the time of matriculation. Such knowledge is necessary to
inform health promotion strategies that enable healthy eating in
the university setting.

Financial support

The NuPhA Study is partially funded by the “Institut Danone
Ern€ahrung für Gesundheit e.V.”, Haar, Germany (project no: 2014/
01). The funding organisation had no role in the design, analysis
and interpretation of the data; in the writing of this manuscript;
and to submit the manuscript for publication.

Conflicts of interest

none.

Authorship

Author contributions were as follows: J.H. and K.D. defined the
conception and design of the study. J.H. conducted the statistical
analysis. J.H. and K.D. wrote the manuscript. A.L. carefully reviewed
the manuscript. All authors read and approved the final manu-
script, as well as contributing to the interpretation of the findings.

Acknowledgements

We thank all university students that participated in the quan-
titative part of the NuPhA Study.

Appendix A. Supplementary data

Supplementary data related to this article can be found at http://
dx.doi.org/10.1016/j.appet.2016.11.016.

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• Eating behaviour of university students in Germany: Dietary intake, barriers to healthy eating and changes in eating behavi …

1. Introduction
2. Material and methods
2.1. Study design and sample
2.2. Measures
2.2.1. Dietary assessment
2.2.2. Assessment of barriers to healthy eating
2.2.3. Assessment of changes in eating behaviour
2.3. Data analysis
3. Results
3.1. Baseline dietary intake
3.2. Barriers to healthy eating
3.3. Changes in eating behaviour since matriculation
4. Discussion
4.1. Strengths and limitations
5. Conclusion
Financial support
Conflicts of interest
Authorship
Acknowledgements
Appendix A. Supplementary data
References

Research Article
Exergaming Can Be a Health-Related Aerobic Physical Activity

Jacek PolechoNski ,1 MaBgorzata Dwbska ,1 and PaweB G. Dwbski

2

1Department of Tourism and Health-Oriented Physical Activity, The Jerzy Kukuczka Academy of Physical Education,
Katowice, 40-065, Poland
2Chair and Clinical Department of Psychiatry, School of Medicine with the Division of Dentistry in Zabrze,
Medical University of Silesia in Katowice, Poland

Correspondence should be addressed to Małgorzata Dębska; m.debska@awf.katowice.pl

Received 14 March 2019; Revised 10 May 2019; Accepted 20 May 2019; Published 4 June 2019

Academic Editor: Germán Vicente-Rodriguez

Copyright © 2019 Jacek Polechoński et al. This is an open access article distributed under the Creative Commons Attribution
License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly
cited.

The purpose of the study was to assess the intensity of aerobic physical activity during exergame training sessions with a moderate
(MLD) and high (HLD) level of difficulty of the interactive program “Your Shape Fitness Evolved 2012” for Xbox 360 Kinect in the
context of health benefits. The study involved 30 healthy and physically fit students. During the game, the HR of the participants
was monitored using the Polar M400 heart rate monitor. The average percentage of maximum heart rate (%HRmax) and heart rate
reserve (%HRR) during the game was calculated and referred to the criterion of intensity of aerobic physical activity of American
College of Sports Medicine and World Health Organization health recommendations. During the MLD training, the participants
achieved on average 69.6±8.7% HRmax and 57.0± 11.9% HRR (moderate intensity), while performing HLD exercises, they achieved
78.9 ± 8.1% HRmax and 70.2± 11.3% HRR (vigorous intensity). The time spent in recommended moderate-to-vigorous intensity
during 15-min exergame session was 14.6 min (97,1%) for MLD and 14.8 min (99%) for HLD. The intensity of aerobic PA during
exergame “Your Shape Fitness Evolved 2012” both medium and high level of difficulty almost all the training sessions was at the
level recommended for health benefits. Active video games, especially exergames, containing an element of physical activity, can
be used to increase the weekly dose of PA in the direction recommended for health benefits.

1. Introduction

The development of modern technology is considered one

of

the reasons for declining levels of physical activity in everyday
life of people around the world in recent decades [1]. The
lack of movement (hypokinesia) is the main cause of the
incidence of noncommunicable chronic diseases. According
to the recent statistics these diseases cause 71% of all deaths
per year [2]. Therefore, activities for health promotion are
currently focused on searching for tools popularizing phys-
ical activity among contemporary people, tailored to their
interests, physical abilities, and leisure time budgets [3].

Currently, video games are one of the most common and
dynamically developing forms of free-time activity regardless
of age. This form of recreation is declared by 58% of the
North American population [4, 5] and 53% of the European
population [6]. As a result, in the past few years, a great
development in the video game market has been observed.

Besides the traditional video games, more and more active
video games (AVGs), in which the user controls the game
by moving their whole body, are rising. Such kind of games
results in higher motor activation in players than during
typical games [7]. Among the AVGs, there are further
separated exergames, various training programs, often with
the assistance of a virtual trainer, whose goal is physical
activity (PA) [8].

In connection with the above, the research interest in
verifying the level of physical activity during differentiated
AVGs [9–12] increased, as well as the possibility of using
them to promote health behaviors, including regular physical
activity [13–18]. The results of monitoring the parameters of
physical exercise during many AVGs have shown that the
values of some of them are at the level recommended for
health by international organisations [19, 20]. At the same
time, it was noticed that the intensity and, consequently, the
caloric cost of such kind of physical activity are very diverse,

Hindawi
BioMed Research International
Volume 2019, Article ID 1890527, 7 pages
https://doi.org/10.1155/2019/1890527

http://orcid.org/0000-0002-3294-780

0

http://orcid.org/0000-0001-7019-326

4

https://creativecommons.org/licenses/by/4.0/

https://creativecommons.org/licenses/by/4.0/

https://doi.org/10.1155/2019/1890527

2 BioMed Research International

Table 1: Participants’ characteristics.

Characteristics Total (n=30)
Age [years] 23.8±1.3
Weight [kg] 76.2±9.7
Height [cm] 177.9±7.3
Body mass index [kg/m2] 24.1±2.4
Heart Rate Rest (HRrest)∗[bpm] 55.8±6.5
Maximum Heart Rate (HRmax) [bpm] 191.4±0.9
∗estimated using the formula 208 – 0.7 x age [38].

depending on the form of movement, level of involvement
of the muscle apparatus (limb vs. the whole body), level of
difficulty of the game, and experience in playing [11, 21–23].
Therefore, it seems important to observe the physiological
response to physical efforts undertaken during popular AVGs
in order to select those with a pro-health character.

The results of many studies indicate a positive effect
of physical efforts occurring during AVGs on the improve-
ment of health [24–26]. It was observed that, thanks to
the high attractiveness of games of this type, players are
able to perform longer physical activity in an interactive
form, compared to classic exercises, which may translate
into better health effects [25]. Research is also carried out
on the effectiveness of AVGs in secondary prevention [27–
29], including in patients after stroke [30], suffering from
depression [31], multiple sclerosis [32, 33], and cancers [34].

It is well known that the time spent on playing different
computer games in the population of different ages increases.
Physical effort during AVGs can raise health-enhancing PA,
so this form of recreation could be a supplement to the
daily dose of recommended physical activity. In addition, the
high level of pleasure experienced during AVGs increases the
attractiveness of physical activity undertaken in its course
and consequently may more effectively motivate to take it
systematically [35, 36].

Today, there are many consoles that allow practising
exergames. The most popular ones include Nintendo Wii,
Play Station (Move), and Xbox (Kinect). The last of the
mentioned devices is particularly noteworthy. The Xbox
Kinect gaming console (Microsoft Corp) consists of the Xbox
video game console and a self-adjustable camera, which acts
as a sensor to detect whole body movements. This gaming
system provides a controller-free type of gaming in which
the individual controls the games using his or her body
movements. Overall, exergames focusing on the lower body
and the whole body expend more energy than those focusing
on the upper body alone [17].

The aim of the study was to assess the level of intensity
of aerobic PA of selected training sessions with a moderate
(MLD) and high (HLD) level of difficulty of the interactive
program “Your Shape Fitness Evolved 2012” for Xbox 360
Kinect for the possibility of achieving health benefits. The
results obtained in the course of the research were referred to
the World Health Organization and the American College of
Sports Medicine health recommendations. Both the average
intensity levels of PA during MLD and HLD were assessed, as
well as the time spent in moderate-to-vigorous PA (MVPA)

during 15-minute bouts of exergaming with the Xbox Kinect
game console.

2. Materials and Methods

2.1. Participants. The study involved 30 healthy and physi-
cally fit male students of the Academy of Physical Education
in Katowice. The characteristics of the group are shown
in Table 1. Participants were excluded if they were taking
any medications affecting heart rate or had any physical
limitations affecting exercise (pregnancy, injury, etc.). They
had no history of seizures or epilepsy, and they were informed
of the product safety information. All subjects were famil-
iarized with the aim of monitoring of physical activity and
conducting measurements and forms of use of their results.
They did not have any previous experience with the exergame
used in this study.

2.2. Procedures

2.2.1. Your Shape Fitness Evolved 2012. Among the many
popular exergames for Xbox Kinect, such as Zumba Fitness,
EA Sports Active, or UFC Personal Trainer, Ubisoft’s Your
Shape Fitness Evolved 2012 is positively distinguished, which
not only accurately transfers the movements of exercising
people to the screen, but also offers quite a substantial and
varied set of exercises and activities. The Player Projection
system helps in this. It is a tool that can very accurately
reproduce the shape of the player’s body and display it on
the screen. Then, it follows the player’s movements during the
exercise and keeps them informed about any mistakes.

2.2.2. Experimental Trial. The students participated in two
15-minute exergaming sessions included in the “Workouts
Cardio” section of Your Shape Fitness Evolved 2012 for Xbox
360 Kinect. The task of the respondents was to imitate the
movements of the virtual trainer (Figure 1).

The study was conducted on two levels of difficulty:
medium (MLD, “Break a Sweat E” training) and high (HLD,
“Break a Sweat G” training). Between the training sessions,
there was a 20-minute rest break. Previously, the students
were instructed in the use of the application. The Break a
Sweat version E consisted of 6 sets of 3 exercises each; version
G consisted of 2 sets of 9 exercises (Table 2).

The research was carried out before noon in a quiet lab-
oratory room equipped with a multimedia projector, a 118-
inch screen, and Xbox 360 Kinect console from Microsoft.

BioMed Research International 3

Table 2: Description of Break a Sweat exercises and repetitions.

Version E Version G
Exercises Repetitions Exercises Repetitions
Power Jog 16 Flying Jog 1

6

Squat Punch 16 Oblique Swing 24
Slide Knee Cross 16 Oblique Swing 24
Sumo Pulse 8 Jab Knee-up

8

Knee up Cross 16 Triple Run Punch 16
Sumo Pulse 8 Slide Jump 8
X-Jog 16 Punch Side-Leap 16
Shuffle Cross Punch 8 Plyo Leg Curl 8
Jumping Jack Punch 8 Shuffle Cross Punch 8
Power Jog 16
Squat Punch 16
Slide Knee Cross 16
Sumo Pulse 8
Knee up Cross 16
Sumo Pulse 8
X-Jog 16
Shuffle Cross Punch 8
Jumping Jack Punch 8

Figure 1: Test place: the distance between the subject and the Xbox
Kinect controller, 2,5 m. Source: our own elaboration.

The test subjects were located approximately 4.5 m from the
screen. They had a space allowing for the free movement
of the whole body and limbs (about 25 m2). The subjects
exercised in loose sports outfits. The tests were performed
individually, without the presence of people other than the
researcher (minimisation of factors that could affect the heart
rate, HR). The students proceeded to gaming without a prior
warm-up to avoid any possible impact on the measurement
results.

During the game, the HR of the study participants
was monitored using the Polar M400 heart rate monitor
cooperating with the transmitter (H7) placed with a flexible
chest strap. The intensity of physical exercise during the
game was determined on the basis of the average per-
centage of maximum heart rate (%HRmax) and heart rate
reserve (%HRR) of the participants. The level of intensity
was estimated according to the classification of PA-intensity
proposed by the American Heart Association, according to
which an intensity of ≥ 50% HRmax or ≥45% HRR was
considered moderate and≥70% HRmax or≥60% HRR was
vigorous [37]. The HRmax values were calculated from the
Tanaka formula [38] while the HRR values were calculated
by using the Karvonen formula [39]. The data obtained in
this way was referred to the criterion of intensity of aerobic
physical activity recommended by the American College of
Sports Medicine and the World Health Organization. It was
assumed that moderate and vigorous intensity efforts are
beneficial to health [19, 20].

The exercise intensity was also categorised into HR zones,
using the Polar Flow training analysis tool. Both the absolute
(in minutes) and the relative (in percent) times of HR spent in
each of the six zones were estimated:<50% HRmax, 50-59% HRmax, 60-69% HRmax, 70-79% HRmax, 80-89% HRmax, and ≥90% HRmax. The calculations were made for each of the selected training programs (MLD, HLD), which allowed comparing them in the context of the intensity.

2.3. Ethics. The study procedures were reviewed and ap-
proved by institutional review board. It was conducted in
accordance with the Declaration of Helsinki, and the pro-
cedures were approved by the Research Ethics Committee
of the Jerzy Kukuczka Academy of Physical Education in

4 BioMed Research International

100.0

MLD

HLD

90.0

80.0

70.0

60.0

50.0

40.0

30.0

20.0

Vigorous-
intensity

Moderate-
intensity

Low-
intensity

p<0.001

69.6
78.9

In
te

ns
ity

[%
H
２

Ｇ
；
Ｒ
]

(a)

MLD
HLD
p<0.001 100.0 90.0 80.0 70.0 60.0 50.0 40.0 30.0 20.0 In te ns ity [% H ２

R
]

57.0
70.2

Vigorous-
intensity
Moderate-
intensity
Low-
intensity

(b)

Figure 2: Physical activity intensity during exergaming shown in %HRmax. (a) %HRR (b). MLD: moderate level of difficulty; HLD: high level
of difficulty; HRmax: maximum heart rate. Error bars represent SD (n=30).

Katowice. All participants took part in the study voluntarily
and could discontinue their participation at any time. They
have provided written consent for the use of information
collected during examination.

2.4. Statistical Analysis. For the statistical analysis, the Sta-
tistica v. 13 software (TIBCO Software Inc.) was used.
Arithmetic means, standard deviations, and the differences
between the mean values were calculated. The normality of
data distribution was assessed with the Shapiro-Wilk test. The
statistical significance of the differences between the results
was determined by Student’s t-test.

3. Results

The mean exercise heart rate during the HLD program (150.9
± 15.5 bpm) was 17.8 bpm higher compared to MLD (133.1
± 16.6 bpm). The demonstrated difference turned out to be
statistically significant (p <0.001). The average PA intensity expressed by % HRmax and % HRR during both training programs was at the level recommended for health in all participants. In the case of MLD training, the participants achieved on average 69.6 ± 8.7% HRmax and 57.0 ± 11.9% HRR (moderate intensity), while, performing HLD exercises, they achieved 78.9 ± 8.1% HRmax and 70.2 ± 11.3% HRR (vigorous intensity). Differences in results recorded during both trainings amounted to 9.3% HRmax and 13.2% HRR and were statistically significant (p<0.001) (Figure 2).

Assessing the health-enhancing nature of PA, it is worth
determining not only the average level of its intensity, but
also the time for which the person doing the exercises stays
in the range of training loads recommended for health. The
participants spent in MVPA (above 50% HRmax) 14.6 min
(97,1%) during MLD and 14.8 min (99%) during HLD out
of 15-min training session. Analyzing the time spent in each

of the heart rate zones, the biggest difference between the
intensity during MLD and HLD was found in areas: 60-69%
HR max and above 80% HRmax (Figure 3).

4. Discussion

The aim of the work was to verify the PA intensity of the
aerobic type of selected training units of the interactive
program “Your Shape Fitness Evolved 2012” for Xbox 360
Kinect in relation to the recommended values for health
benefits by WHO [20] and ACSM [19]. The results of our own
research showed that the intensity of aerobic PA during both
MLD and HLD exercise programs expressed by %HRmax and
%HRR was usually moderate or vigorous. The participants
spent in MVPA during a 15-minute MLD training session
14.6 min (97.1%) and during HLD one – 14.8 min (99%). It
can, therefore, be said that exergaming “Your Shape Fitness
Evolved 2012” on both tested levels of difficulty (MLD –
“Break a Sweat E” and HLD training “Break a Sweat G”)
guarantees PA of an intensity considered as prohealth.

However, not all AVGs that work with the Xbox Kinect
console are intense enough to let the player perform physical
effort that is recommended for health. The results of research
aimed at verifying PA parameters during River Rush showed
that the intensity of the effort was too low for health benefits
to occur [9]. In this case, it was probably conditioned by
the rather static plot of the game, which stimulated users
mainly to body balancing and sporadic jumps. The low PA
intensity could also result from the short duration of the
gaming experience (5 minutes), and/or the selected game,
which was focused on adventure.

Also, studies verifying the intensity of various exergames
on another popular console enabling participation in AVGs,
the Nintendo Wii, show varied results in terms of their pro-
health character. Some exergames require less physical effort

BioMed Research International 5

10

p<0.001 p<0.001 p<0.001 p<0.001

H２Ｇ；Ｒ

p<0.05

4
6
8
2
0

2.0

0.2
0.50.4

2.1
2.6

<50% 50-59% 60-69% 70-79% 80-89% ≥90%

A
bs

ol
ut

e
tim

e
[m

in
]

5.0

0.8

4.3 4.0

2.5

5.6

MLD
HLD

Figure 3: Absolute time spent in different heart rate (HR) zones during exergaming. MLD: moderate level of difficulty; HLD: high level of
difficulty; HRmax: maximum heart rate. Error bars represent SD (n=30).

than that recommended by the WHO for health benefits (<3 METs) [7, 40], while others may be considered to have a health-enhancing intensity level [41–44]. For example, the average intensity of aerobic training (expressed in% HRR) demonstrated in the study of students engaged in Wii Fit aerobics activity by Mullins et al. [22] was almost two times lower (30% HRR) than during Your Shape Fitness Evolved 2012 AVG on moderate difficulty (57.0± 11.9% HRR) and two and a half times lower than estimated during the game on a high level of difficulty (70.2 ± 11.3% HRR), thus allowing health benefits to be obtained only by people with low levels of physical fitness, leading a typically sedentary lifestyle [19]. In the case of our study results, however, it was in the range of intensity commonly recommended for health benefits.

It should be remembered that, in addition to the cri-
terion of health-oriented aerobic PA intensity, WHO and
ACSM defined a minimum single bout of that type of effort
(uninterrupted 10 minutes) and its weekly volume (150 min
MPA or 75 min VPA or equivalent) [20], whereas ACSM
recommends a specific frequency (5 times x 30 min MPA
or 3 times x 25 min VPA or equivalent) [19], on the basis
of which we can determine the pro-health character of this
type of activity. In view of the above, wishing to fill in the
above recommendation criteria, a one-time exergame effort
should last at least 10 minutes, which is possible, among
others, thanks to “Workouts Cardio” exercise units of Your
Shape Fitness Evolved 2012 lasting on average for 15 minutes.
The option of lengthening the training session also allows
achieving the ACSM’s recommended volume of PA (30 min
MPA, 25 min VPA or equivalent). In the case of trainings
tested in own research, for example, a 25-minute game on
HLD three times a week allows meeting the above criteria.

International organizations related to public health rec-
ommend reducing the use of on-screen media, which in
the general opinion is associated with passive spending of
free time. However, active video games, especially exergames,

containing an element of physical activity, can be used to
increase the weekly dose of PA in the direction recommended
for health benefits. Exergaming can be a particularly impor-
tant tool for increasing health-oriented PA among children
and adolescents, among whom the percentage of those with
adequate PA level is low [7, 45, 46].

Among the most frequently declared reasons for the lack
of regular participation in physical activity by various social
groups, the most important are the following: not having
enough time to exercise, finding it inconvenient to exercise,
lack of self-motivation and finding exercises boring [47].
Therefore, it is necessary to look for forms of PA that will
be attractive to the practitioners and will influence their
motivation to act. There is scientific evidence that those
who practice exergames find it satisfying [7, 17, 41]. Part
of the research indicates an even greater level of enjoyment
and increase in positive emotions during different forms of
PA during AVGs compared to traditional ones [10, 23, 48].
Graves et al. 2014 [7] showed a high level of enjoyment
during exergame despite a relatively high intensity of physical
exercise during the game. The research conducted by Oh et al.
2016 [49] shows that exergaming activities are psychologically
enjoyable pursuits for college-aged individuals that can help
increase their physical health and quality of life. Therefore,
it seems to be an effective tool to overcome the above-
mentioned barriers to PA, contributing to the popularization
of this healthy behavior and, consequently, to improving
public health.

The limitation of our research is the small study group.
The research was conducted in the group of healthy and
physically fit students and tested a selected AVG. We are
aware that its results do not give a complete answer to
the question asked in the manuscript title. In order to
get a full answer further comprehensive research in this
area is needed. The findings of this manuscript should be
investigated in different population groups (e.g., children,
elderly, and disabled) and various active video games.

6 BioMed Research International

5. Conclusions

The study results indicate that the intensity of aerobic PA
during Xbox Kinect exergame “Your Shape Fitness Evolved
2012” with both a medium and high level of difficulty
was at the level recommended by WHO and ACSM for
health benefits (moderate-to-vigorous). During MLD play,
participants achieved on average 69.6 ± 8.7% HRmax and
57.0± 11.9% HRR (moderate intensity), while. during HLD,
they achieved 78.9 ± 8.1% HRmax and 70.2 ± 11.3% HRR
(vigorous intensity). Almost all the 15-minute exergaming
session, the intensity of the effort remained moderate-to-
vigorous, 14.6 min (97%) for MLD and 14.2 min (98.9%) for
HLD.

The findings suggest that active video games, especially
exergames, containing an element of physical activity, can
be used to increase the weekly dose of PA in the direction
recommended for health benefits. However, the availability of
various types of exergames and consoles on the market leads
to further research aimed at identifying those devices and
software that will enable users to participate in PA compliant
with health recommendations.

Data Availability

The data used to support the findings of this study are
available from the corresponding author upon request.

Conflicts of Interest

The authors declare that there is no conflict of interest
regarding the publication of this paper.

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CUES TO HEALTHY DECISION-MAKING AMONG COLLEGE
STUDENTS: RESULTS FROM A PILOT STUDY

Satya P. R ao, PhD , MCHES*
Department o f Public Health Sciences

New Mexico State University

V alerie Lozano, MPH
School o f Nursing

New Mexico State University

M urad Taani, B SN
Department o f Public Health Sciences

New Mexico State University

Problem Almost 60% o f college students consume less than two serv­
ings o f fruits and vegetables a day while 40-50% either exercise irreg­
ularly or do not exercise. When surveyed in another study, half o f the
students responded that they had not received any information about
healthy eating and behaviors from their colleges while nearly 60% said
they would be interested in receiving such information. Therefore col­
lege environments provide opportunities for implementing interven­
tions that encourage healthier lifestyles including on healthy nutrition
and physical activity.

Purpose and Method The purpose o f the current pilot study was to
implement and evaluate a campus-wide campaign o f cues to healthy
decision-making to improve students’ knowledge and awareness about
healthier lifestyle choices and to serve as reminders towards positive
changes in health-related behaviors and actions.

Results Based on analysis, 86% (48) o f the participants indicated that
they had learned something from reading the information on the table
toppers and 75% (42) agreed that the information had made them more
aware and reminded them o f healthier lifestyle choices.

Conclusions The need for such communication-based campaigns
using cues to healthy decision-making show promise among college
students.

Key Words: Cues, Obesity, Nutrition, Physical Activity, Diabetes

697

698 / College Student Journal

Introduction

Nearly one-third o f American adults aged
20 years or older are overweight and 33.8%
are obese, triggering obesity-related condi­
tions, such as heart disease, stroke, and Type II
Diabetes (Ogden & Carroll, 2010). Research
suggests that the above conditions may be
largely linked to a person’s choices, behaviors,
and lifestyle (Kees 2011; Nyaga 2000). Kees
(2011) suggests that consumers make poor
health decisions because of a low tendency to
consider the potential costs o f their behaviors
as noted particularly among college students.
These health decisions by college students are
further negatively impacted by stress and time
constraints related to managing and balancing
coursework, employment, relationships, and
families, making eating healthy meals and
being physical active problematic and low on
the priority list (Kees, 2011).

Choices and decision-making

College years present a different set o f
nutritional priorities including with eating
habits. Many factors play a role in obesity
among college students such as unhealthy
dietary choices and physical inactivity. Most
student diets disproportionately include fast
foods that are high in calories, fat, and sodi­
um content (Cluskey & Grobe, 2009; Kelly,
Mazzeo, & Bean, 2013). Existing evidence
shows that negative changes can occur in
several health behaviors during young adults’
progression from high school to college
(Racette, Deusinger, Strube, Highstein, &
Deusinger, 2005).

On an average day, college students con­
sume one serving o f fruit, 1.5 servings o f
vegetables, half a serving o f low-fat dairy,
and 1.4 servings o f whole grain daily (Kelly,
Mazzeo, & Bean, 2013). These numbers are
considerably lower in some men and non­
white students, are severely lower than the
recommended dietary intake, and continue

to decrease during the course of students’
first year o f college (Kelly, Mazzeo, & Bean,
2013). According to the Healthy Campus
2010 Report, 5.4% college students reported
eating zero servings o f fruits and vegetables
a day and 58.3% consumed 1-2 servings o f
fruits and vegetables daily (ACHA, 2011). In
addition, nearly half o f the 104,826 participat­
ing U.S college students, when asked if they
had ever received information on nutrition
from their colleges, responded that they had
not. In addition, nearly 60% said they would
be interested in receiving such information
(ACHA, 2011). Therefore college environ­
ments provide opportunities for implement­
ing interventions that encourage healthier
lifestyles and enhancement o f self-efficacy
and healthy decision-making.

Existing data indicates that college stu­
dents favor processed foods that cost less than
healthy foods. These dietary decisions are
linked to inadequate access to healthy foods,
easy access to fast foods, the high cost of
healthier alternatives, and limited peer support
for healthier food choices (Cluskey & Grobe,
2009). The diminished health and increased
weight gain o f college students are associated
with inadequate nutritional habits, sedentary
lifestyle and decreased physical activity, and
a heightened stress level. In addition, weight
gain among undergraduate college students is
distinctively related to stress levels (Jackson,
Berry, & Kennedy, 2009). Often, demanding
schedules and difficulties balancing various
priorities leave students little time to shop,
prepare meals, and engage in physical activ­
ity. Therefore, physical activity is sacrificed
and healthier foods are replaced by cheaper,
less nutritious and more convenient fast food
options (Meltzer, Fontaine, Colbert, Creadore,
& Cuoco, 2007).

Grace (1997) found that students who
live away from campus had increased health
risks: such as a Body Mass Index (BMI) at
the overweight and obesity levels, smoking,

Cues to Healthy Decisions / 699

consumption of alcohol, and consumption
of fewer and less variety of fruits, vegeta­
bles, and dairy products. Since smoking
and alcohol consumption are generally not
permitted in students’ residence halls, many
students choose to live outside the campus
accounting for their dietary choices and
increased health risks of obesity and over­
weight (Brunt & Rhee, 2008).

An additional concern to students’ di­
etary behaviors is their lack of or limited
engagement in physical activity. Studies
with undergraduate, graduate, and medical
students suggest that between 40-50% of
the student participants either exercised ir­
regularly or did not exercise at all (Nelson,
Story, Larson, Neumark-Sztainer, & Lytle,
2008; Racette, Deusinger, Strube, Highstein,
& Deusinger, 2005).

Cues to Healthy Decision-making

Due to the negative health implications of
college students’ unhealthy eating habits and
sedentary lifestyle, it is vital to investigate
the dynamics that predict and adequately ad­
dress them. The Health Belief Model (HBM)
explicates and predicts health behaviors and
suggests that changes in behaviors are esti­
mated by perceived susceptibility, severity,
benefits, and barriers, cues to action, and
self-efficacy. Park (2011) examined HBM
constructs and found cues to action to be a
significant predictor of behavioral intention
of weight reduction. A cue to action is a mo­
tive to readiness and refers to influences of
social environment, such as family, friends
and mass media. Cues to action are important
in serving as specific motivators necessary to
prompt appropriate health behaviors and can
be internal or external.

Consequently, the framing and communi­
cations of cues to healthy decision-making
has the potential to improve students’ knowl­
edge and awareness of healthy lifestyles.
Additionally, when such cues are framed

and communicated in creative, simple,
goal-oriented, and straightforward ways
that include the ‘problem(s)’ and highlight
the positive and/or the negative aspects of
specific behaviors and tasks involved they
have the potential to influence students to
adopt more healthy lifestyles (McCormick &
McElroy, 2009). The purpose of the current
pilot study was to implement and evaluate
a campus-wide communications campaign
of cues to healthy decision-making to im­
prove students’ knowledge and awareness
about healthier lifestyle choices and serve
as reminders towards positive changes in
health-related behaviors and actions. Few
studies have evaluated interventions that
have used cues to actions to improve college
students’ unhealthy eating and sedentary
lifestyles. Therefore, the findings from the
current pilot study have the potential to add
to the existing scant literature.

Methods

The “Good Choices Make Damn Good
Looking Aggies” campaign was implement­
ed at a college campus in Southwest United
States in November 2010 before the holiday
season and final exams.

Study design

The study and its post-test only protocol
were approved by the university Institution­
al Review Board (IRB). Two sets of table
toppers were developed with various health
and lifestyle facts to serve as cues to healthy
decision-making. Figure 1 shows the content
included in the two table toppers developed
for the campaign. The content was printed on
colorful paper, folded into a tent, and rein­
forced with tape to be able to stand vertically
on tables. Framing of cues in the form of “did
you know” statements expressing the costs
of not adopting a healthy behavior versus the
benefits of adopting healthy habits were in­
cluded in each of the table toppers. In addition

700 / College Student Journal

the title of the campaign was prominently
displayed along with the incentives included
in a random drawing for participants who
completed the evaluation survey and provided
their first names and e-mail addresses sepa­
rately. Instructions to complete the evaluation
survey were also included.

Protocol

Before the start of the campaign an an­
nouncement was sent out through the campus
website encouraging participation. Each of
the two table toppers were displaced for one
whole week starting on a Monday morning
by placing them on tables at three eating
establishments on campus that were housed
in the same building. The table toppers were
checked every day to make sure that they were
in place for the diners to notice and read. Any
discarded or displaced table toppers were re­
placed. Evaluation surveys were placed next
to sealed survey drop boxes strategically and
conveniently located close to the cash regis­
ters and were emptied out daily. The survey
was limited to enrolled students and excluded
staff, faculty, and other university personnel
and employees.

Table Toppers

The first table topper included the fol­
lowing nutritional cues 1.) Frozen food is as
good for you as fresh food 2.) There are 10
packets of sugar in one can of soda, and 3.)
Drinking 8 glasses of water a day may help
you lose about 8 pounds in one year. They
also included the following healthy lifestyle
cues: 1) Adults need between 7.5-9 hours of
sleep per night to function at their best 2.)
Exercise can improve appearance, and 3.)
Twenty five minutes of exercise is all it takes
to improve mood and ward off depression.
The second table topper included a different
set of nutritional cues as follows: 1.) Whole
fruit has more fiber and fewer calories than
juice 2.) The acid found in strawberries

fights tartar from teeth 3.) College students
don’t get enough calcium, and 4.) If you’re
thirsty you are already dehydrated. Healthy
lifestyle cues included in this table topper
consisted of the following statements: 1.)
High levels of stress decrease your immune
system function making it easier for you to
get sick 2.) Our skin starts to age when we
are bom, 3.) Breakfast eaters are champions
of good health, and 4.) Walking 6-9 miles a
week helps improve memory.

Evaluation Survey

The evaluation survey consisted of four
questions, two dichotomous and two qualita­
tive open ended questions, taking about 5-10
minutes to complete. The following ques­
tions were included in the survey: 1.) Did you
learn anything from reading the information
on the table topper tents? 2.) Did the infor­
mation make you more aware of healthier
lifestyle choices for yourself? 3.) How else
was the information useful to you? 4.) What
else about the campaign caught your atten­
tion? At the end of two weeks there were a
total of 56 participants who completed the
evaluation survey and dropped them in the
boxes provided.

Incentives

Incentives in the form of gift certificates
donated by local business were included in a
drawing that was conducted in February 2011
and the winners notified via the e-mail they
provided. The gift certificates included two
fifteen dollar vouchers to health conscious
restaurants, two forty dollar vouchers for
body massages, and two vouchers for a body
and fitness assessment. In addition, six t-shirts
with the campaign logo served as additional
incentives. Participants’ names were ran­
domly drawn from those who participated in
the survey and provided their first name and
email address.

Cues to Healthy Decisions / 701

Results

A total o f 56 student participants completed
the evaluation survey. Based on descriptive
analysis 86% (48) o f the participants indicated
that they had learned something from reading
the information on the table toppers and 75%
(42) agreed that the information had made them
more aware and reminded them o f healthier
lifestyle decisions. In addition, when asked if
the information was useful to them, 45% (25)
o f the participants indicated that the informa­
tion was either new and/or useful to them.
Fifty-six percent o f them (n=14) highlighted
the utility o f ‘healthy nutrition’ information,
24% specified the information and tips about
exercise, and the remaining 20% mentioned
lifestyle-related information such as about
sleep and alcohol use. The remaining thirty one
participants (55%) provided specifics about the
cues they received that highlighted their cur­
rent choices, or reminded, and/or empowered
them to make healthier choices as compiled in
table 1. These cues fell into three categories:
adding to existing participants’ knowledge;
reminding participants o f what they already
knew and could use in their lives; and empow­
ering them to make changes and take action
towards a healthier lifestyle.

The last survey question inquired how the
campaign caught participants’ attention. Thirty
two percent (18) stated that the slogan was the
most ‘noticeable attention grabber;’ 25% (14)
m entioned the design and layout o f the table
toppers was attractive; 13% (7) stated that the
nutrition and life-style cues caught their atten­
tion; while only 5% (3) noticed the incentives
such as massages included in the drawing.

Discussion
Findings

The cues used in the cam paign highlighted
the costs o f not adopting healthier behaviors
versus the benefits o f adopting such behav­
iors. They were designed not to undermine
w hat students already knew and were doing

b ut to provide them w ith sim ple and practical
strategies that could be incorporated into their
current everyday living.

The cues used in the campaign were not
about judgm ent or preaching but to remind
students o f what they already knew, add to
their current knowledge, and empower them
to incorporate their awareness into a healthier
lifestyle. According to the Healthy Campus
2010 (ACFLA, 2011), 46% o f college students
responded that that they never received informa­
tion about nutrition and healthy lifestyles from
their institution o f higher learning – which is why
it is critical to implement such campus-based
interventions not only on nutrition and physical
activity, but on other healthy lifestyle topics
benefitting students. Ongoing and recurring
campus interventions that incorporate cues to
healthy decision-making on a variety o f topics
can fill the information gap and when timed
carefully can remind and empower students o f
healthy decision-making. They also have the
potential to augment existing cognitive and in-
formation-based efforts through their emotional
appeal, contextual and cultural relevance.

Study limitations

The current pilot cam paign had limitations
including a small sample size. Since there was
no estimate o f the total num ber o f student din­
ers during the two weeks o f the cam paign it
was difficult to estim ate how representative
the study participants were in com parison to
other student diners. A nother lim itation was
our reliance on participants’ responses in the
evaluation survey. Further, the completion
o f the surveys w as n ot m onitored and no
research assistant w as available to answer
questions that participants m ay have had o f
the survey. Results reported here need to be
replicated with larger sample sizes, in other
college settings, and through the use o f reli­
able evaluation tools that docum ent the short
and long-term benefits o f cues to healthy de­
cision-m aking am ong this population.

702 / College Student Journal

Conclusion

Existing evidence highlights the rise o f
contributing unhealthy choices and behaviors
leading to obesity and obesity related condi­
tions among college students. The current pa­
per highlighted a campaign based on cues to
healthy decision-making to improve college
students’ knowledge and awareness and em­
power them to make healthier lifestyle choic­
es. Evaluation results reported here suggest
that health communication campaigns based
on cues to healthy decision-making show
promise and need to be further investigated.
Few studies have reported findings evaluating
interventions that have used cues to actions
specifically targeting college students and
unhealthy eating and sedentary lifestyles.
Therefore, the findings from the current pilot
study have the potential to add to the existing
scant literature.

References
American College Health Assessment [ACHA] (2011).

National college health assessment II: Reference
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Brunt, A. R., & Rhee, Y. S. (2008). Obesity and lifestyle
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ments. Appetite,51(3), 615-621.

Cluskey, M., & Grobe, D. (2009). College weight gain
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Grace, T. W. (1997). Health problems o f college stu­
dents. Journal o f American College Health, 45(6),
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Jackson, R.A., Berry, T.R., & Kennedy, M.D. (2009). The
relationship between lifestyle and campus eating be­
haviours in male and female university students. Col­
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Kees, J. (2011). Advertising framing effects and con­
sideration of future consequences. The Journal o f
Consumer Affairs, 45(1), 7-32.

Kelly, N.R., Mazzeo, S.E., & Bean, M.K. (2013). Sys­
temic review o f dietary interventions with college
students: Directions for firture research and practice.
Journal o f Nutrition Education and Behavior, 45,
304-313.

McCormick, M., & McElroy, T. (2009). Healthy choices
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Meltzer, K., Fonatine, E., Colbert, D., Creadore, J.,
& Cuoco, L. (2007). Communication campaign:
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Persuasion and Influence Class. Retrieved April
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Nayga, R.M. (2000). Nutritional knowledge, gender, and
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Nelson, M.C., Story, M., Larson, N.I., Neumark-Sz-
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Ogden, C.L., & Carroll, M.D. (2010). Prevalence of
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tion. Division of Health and Nutrition Examination
Surveys, 1-6.

Cues to Healthy Decisions / 703

Table 1: Cues to Healthy Decision-Making (N=31)

Statements and comments by study participants in response to evaluation survey question #3: How else was
the information useful to you?

Cues that added to existing participants’ knowledge

1. “I do not like fruits and vegetables but I realize I need them.”

2. ‘Frozen fruits and vegetables are just as good. Great!”

3. “It showed me that I need knowledge to make better choices.”

4. “The nutrition facts — I needed to know them and be reminded.”

5. “The toppers focused on things that I did not want to work on.”

6. “Gave me facts that I did not know such as the ‘thirst’ fact that I could use. The campaign was very useful.”

7. “Made me aware o f eating breakfast. I skip it all the time.”

8. “Healthy nutrition can be easy but has to be a priority.”

9. “Learning things about health I did not know but need to.”

10. “I became aware o f the unexpected benefits o f exercise.”

11. “I got cool facts about nutrition I can now use.”

12. “I learned all about sugar in cola. I do not want to drink it anymore.”

13. “It showed me what kinds o f things I was putting in my body.”

14. “Wow! 25 minutes o f exercise prevents depression and improves my mood.”

Cues that reminded participants of what they already knew and could use in their lives

15. “It was reminder to me about what I was putting in my body.”

16. “Reminded me o f how to become a healthier person.”

17. “Reminded me to relax and take some time for myself.”

18. “Reminded me o f the packets o f sugar in soda.”

19. “It showed to be healthier and not drink so much.”

20. “It made me realize how ‘really unhealthy’ I was.”

21. “It was about my awareness and what I can do for myself.”

22. “It was a good reminder o f how ‘lax’ I have been. I will not be drinking as much soda”

23. “It made me realize that I was not getting enough sleep.”

Cues that empowered participants to make changes towards a healthier lifestyle

24. “Helps me make better health choices.”

25. “Helped me make more nutritious and healthy choices for the future.”

26. “The campaign gave me tips for making better choices.”

27. “It helped me lead towards a healthier life and better choices.”

28. “I realized that I could make tad bit healthier choices.”

29. “It reminded me about better choices and lifestyle I can choose.”

30. “It convinced me that I could make the right choice for me.”

31. “Helps me become more involved about my health.”

7 0 4 / C o lleg e S tu d en t Journal

Park, D.Y. (2011). Utilizing the HBM to predicting fe­
male middle school students behaviors intention o f
weight reduction/status. Nutrition Research & Prac­
tice, 5(4), 337-348.

Racettc, S.B., Deusingcr, S.S., Strube, M.J., Highstein,
G.R., & Deusinger, R.H. (2005). Weight changes,
exercise, and dietary pattemssuring freshman and
sophomore years in college. Journal o f American
College Health, 53(6), 245-251.

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Universal Journal of Educational Research 6(7): 1424-1430, 2018 http://www.hrpub.org
DOI: 10.13189/ujer.2018.060702

The Effects of Eating Habits, Physical Activity, Nutrition
Knowledge and Self-efficacy Levels on Obesityi

Nevzat Demirci1,*, Pervin To ta Demirci2, Erdal Demirci3

1High School of Physical Education and Sport, Mersin University, Turkey
2Erdemli Department of Tourism Animation, Mersin University, Turkey

3High School of Physical Education and Sport, Kafkas University, Turkey

Copyright©2018 by authors, all rights reserved. Authors agree that this article remains permanently open access under
the terms of the Creative Commons Attribution License 4.0 International License

Abstract The aim of this study was to investigate the
effects of eating habits, physical activity, nutrition
knowledge and self-efficacy levels on obesity. The
participants of the research were the students of Kafkas
University Physical Education and Sports College and
Sar kam ocational School. Research includes eating
habits, physical activity (PA), nutrition knowledge and
self-efficacy questionnaire. The cases were divided into
normal weight (NW) and overweight – obese (OW) groups
based on age, gender, and body mass index percentages.
The obtained data were analyzed using SPSS. According to
the findings; approximately 35.5% of participants were
identified as overweight or obese. Significant differences
were observed between the OW and NW groups in terms of
gender, weight control (P <0.01). OW group women were found to exhibit less desirable behaviors compared to NW. In comparison between OW group and NW group, it was determined that women participated in less physical activity than men. There was no significant difference in nutritional information between OW and NW groups. In particular, the self-efficacy level of the PA was significantly lower in the OW group than in the NW group (P <0.01). Conclusion: this study reveals eating habits, PA and self-efficacy differences among university students. It should focus on improving the self-efficacy of university students, changing eating habits and increasing PA levels by organizing programs to combat obesity.

Keywords Student, Obesity, Weight Control, Physical
Activity,

Nutrition Knowledge

1. Introduction
According to the World Health Organization (WHO)

report, the prevalence of obesity doubled worldwide
between 1980 and 2014 [30]. Many countries have
embraced and implemented various national policies to
prevent obesity and to reduce obesity and socio-economic
burden, since obesity has been shown to be a risk factor for

chronic diseases such as cardiovascular disease, type II
diabetes and some cancers [10]. Throughout the period
corresponding to early adulthood in college, social and
emotional development is complemented by physical
maturity and one’s eating habits are determined [35].

However, the increasing risk of chronic illness due to
changes in nutrition habits by university students is not
adequately considered. Obesity in college students is seen
as the first indication of the risk of future chronic diseases
of obesity [15]. For this reason, it is very important to
actively control obesity from the first years of university.
Although the cause of obesity is complicated, nutrition
habits or lifestyle play an important role in the
development of obese conditions [13, 19]. Physical activity
deficiency and inadequate nutrition of university students
are considered as an important public health problem.
Physical activity may continue during adolescence and
during adulthood [5, 23]. It is emphasized that especially
after the students enter and graduate from college, they
have experienced a significant decrease in their physical
activities [20]. There are reports that 50% of university
students are not at the recommended level of physical
activity [13, 22].

Physical inactivity is among the most important causes
of the increase in the number of obese people. In addition,
there is a close relationship between obesity and
cardiovascular diseases, diabetes, osteoporosis, some types
of cancer, mental problems, and many health problems in
studies conducted [16, 17]. Increasing physical activity has
a positive effect on obesity, and therefore it is suggested
that there are many studies emphasizing the effect of
treatment with the preventive effect on the above
mentioned diseases [28]. Factors related to eating or
physical activity should also be defined in order to help
students adopt healthy behaviors [26]. Knowledge of
eating or physical activity is necessary to make the
behavior, but it needs to be combined with the skills.
Self-efficacy represents perceived ability to perform
behavior and is known to be important in describing health
behaviors such as eating and physical activity [15, 13]. The
aim of this study is to examine the eating habits, physical

Universal Journal of Educational Research 6(7): 1424-1430, 2018 1425

activities, nutrition knowledge and self-sufficiency of
university students and to investigate whether these
characteristics differ according to obesity status.

2. Materials and Methods

2.1. Subject and Participants

This study was planned to examine the factors related to
eating habits, physical activities and nutrition knowledge
of students of Kafkas University. Inclusion criteria: does
not having musculoskeletal problems that could affect
chronic disease and physical activity, being older than 18.
The participants of the research were the students of
Kafkas University Physical Education and Sports College
and Sar kam ocational School. The researchers
explained the work to the school principals or teachers and
asked every student to participate in the study. A written
consent was obtained for the students to participate in the
study. 220 male and female healthy university students
participated in the study. Participants were divided into two
groups, normal weight (NW) and overweight obese (OW),
according to their age, gender and body mass index
percentages. The study was conducted in accordance with
the Helsinki declaration [33].

2.2. Procedures

The study questionnaire was based on university
students’ eating habits, physical activity and nutrition
knowledge, and literature review to determine self-efficacy
levels [30, 8, 14]. General features include the items of age,
gender, height, weight, body mass index (BMI). The body
mass index was calculated based on the weight and the dye
reported. Participants’ height measurements were measured
by the millimetric height scale and body weight
measurements by electronic scales. Body weight and
height measurements were formulated by adding them to
personal information forms. BMI = Body Weight (kg) /
Boy2 (m). BMI values were obtained by dividing the body
length by body weight after taking the length of the body
length. Overweight-obese (OW) with BMI 25 and BMI
18.5

2.4. Eating Habits

Eating habits included diverse foods, regular meals, size
of food, frequency of breakfast meals, eating and snacks,
behavior during meals, unbalanced diet and unfavorable
food [25, 9]. These variables were measured using 5-point
scales or by asking them to record the frequency of their
behavior or to check the categories.

2.5. Physical Activity

Physical activity is measured based on seven factors: the
frequency of physical activity for at least 30 minutes per

day, the frequency of walking or cycling, the frequency of
exercise, weekday or weekend walking times, weekday or
weekly moving time, by the number of activities they have
performed [12,7]. The time spent walking was measured
using four categories: “less than 30 minutes a day” or
“more than 2 hours a day.” The inactive time spent was
measured using the categories “from less than one hour a
day” to “no more than 4 hours a day”.

2.6. Nutrition Knowledge

Nutrition knowledge was measured on 10 items,
including general nutrition (six items) and information
about obesity (four items) [25, 9], information about
obesity, definition of obesity, adequate weight control, fruit
and energy and the effects of regular exercise. For each
nutritional information item, the number and percentage of
correct answers of the subjects were examined. The total
score of the nutrition knowledge was the total score of the
correct answers for 10 nutrition knowledge items.

2.7.

Self-efficacy

Self-efficacy obesity status in eating or physical activity
was assessed using 10 items [25,18,14]. Self-efficacy in
physical activity was measured using four items. They
regularly participate in sports exercises, perceived efficacy
on tired or bad weather conditions, driving at short
distances, exercising at lunch or in the malls. Each item
was measured on a 4-item scale between ‘very difficult’ (1)
and ‘very easy’ (4). The total score for self-efficacy was
calculated as a total of 10 item points.

2.8. Statistical Analyses

SPSS (PASW Statistics 18.0; SPSS Inc., Chicago, IL,
USA) was used for statistical analyses. Descriptive
statistics including frequency, percentages, mean and
standard deviation were calculated. Body weight and
height measurements were formulated by adding them to
personal information forms. BMI = Body Weight (kg) /
Boy2 (m). BMI values were obtained by dividing the body
length by body length after taking the body length. In this
study T-test was used for parametric variables to examine
the differences between the eating habits, physical activity,
nutrition knowledge and self-efficacy according to obesity
status. Chi-square analyses were conducted for
non-parametric variables. Statistical significance was
examined at P <0.05.

3. Results
Participants were found to have an average age of 21.97

and approximately 64.5% (142) of them were in the normal
weight (NW) group and 35.5% (78) of them were in the
overweight – obesity (OW). Gender is significantly
different according to obesity status; In the OW group, the

1426 The Effects of Eating Habits, Physical Activity, Nutrition Knowledge and Self-efficacy Levels on Obesity

rate of famale (64.1%) was higher than that of the NW
group (56.3%, P <0.01) (Table 1).

Table 1. General Descriptive Characteristics of University Students

Obesity status1

ariables

Normal

(n = 142)
Overweight &

Obesity (n = 78)
Total

(n=220)
Age 22.0±1.7 21.95±2.22) 21.97±1.95

Weight (kg)
Height (cm)

BMI
Male

66.4 ± 14.8
175.0 ± 10.3

22.6 ± 4.1
62 (43.7)

78.5 ± 11.8
169.0 ± 10.1

28.2 ± 3.4
28 (35.9)

72.45±13.3
172±10.2
25.4±3.7
90 (39.8)

Famale 80 (56.3) 50 (64.1) 3)** 130 (60.2)
** P < 0.01, 1) BK 25 overweight - obesity (OW) ve BK 18.5 < BK < 25 normal weight (NW), 2) Mean ± SD, 3) n (%)

The average rate of having breakfast was 5.0 ± 1.6. The
frequency of breakfast both sexes in the AO group was
lower compared to the NA group (P <0.05). While the frequency of eating outside did not differ according to obesity status in men, AO women NA was found to eat less than women (P <0.01). The mean prevalence of snacks in

both sexes was 1.6 ± 1.3 fold in the NA group and 1.2 ± 0.8
** fold in the AO group (P <0.01).Approximately 27% of respondents indicated that they did not eat a variety of foods or a wide variety of foods, while 43% of them reported that they ate various foods or ate a wide variety of foods very frequently. Approximately 34% of participants were fed with irregular food, while the rate of regular eating was about 35%. The proportion of those who responded as 'small' or 'very small' according to the size of the meal ratio was significantly higher in the AO group (P <0.001) compared to the NA group both in boys and girls. With respect to the eating behavior, 50.5% of the participants were chatting with family members. Approximately 39.1% of the participants were fed an unbalanced diet (Table 2). The proportion of women fed an unbalanced diet was 41.2% lower in the AO group (36.0%) than in the NA group (P <0.001). No significant difference was observed between the participants regarding the unfavorable foods (Table 2).

Table 2. Eating habits according to obesity status in university students

Variables

Male (n=90 ) Female (n=130 )

Normal
(n= 62)

Overweight & Obesity
(n =28)

Normal
(n= 80 )

Overweight & Obesity
(n =50)

Total
(n=220)

Breakfast frequency (times/week) 5.6 ± 1.4 4.5 ± 1.63)* 5.7 ± 1.6 4.4 ± 1.7* 5.0 ± 1.6
Frequency of eating out (times/week) 1.3 ± 1.1 1.1 ± 0.8 1.4 ± 0.7 1.1 ± 0.8** 1.2 ± 0.8

Frequency of eating snacks
(times/day) 1.6 ± 1.3 1.2 ± 0.8** 1.6 ± 1.2 1.2 ± 0.8** 1.4 ± 1.0

Variety of foods
Do not eat a variety of foods at all 7 (11.3) 3 (10.7)4)* 11 (13.7) 4 (8.0)* 25(11.4)

Do not eat a variety of foods 8 (12.9) 5 (17.9) 13 (16.3) 9 (18.0) 35(15.9)
Average 17 (27.4) 7 (25.0) 26 (32.5) 14 (28.0) 64(29.0)

Eat a variety of foods 16 (25.8) 8 (28.5) 18 (22.5) 13 (26.0) 55(25.0)
Eat a variety of foods very often 14 (22.6) 5 (17.9) 12 (15.0) 10 (20.0) 41(18.7)

Regular meals
ery irregular 6 (9.7) 4 (14.3) 9 (11.2) 8 (16.0)** 27(12.3)
Irregular 12 (19.3) 6 (21.5) 18 (22.5) 12 (24.0) 48(21.8

Neither irregular nor regular 17 (27.4) 8 (28.5) 25 (31.3) 16 (32.0) 64(29.0)
Regular 18 (29.0) 7 (25.0) 20 (25.0) 9 (18.0) 54(24.5)
ery regular 9 (14.6) 3 (10.7) 8 (10.0) 5 (10.0) 25(11.4)

Size of meals
ery small/ small 18 (29.0) 5 (17.9)** 15 (18.7) 17 (34.0)** 55(25.0)

Adequate 24 (38.7) 16 (57.1) 43 (53.8) 23 (46.0) 106(48.1)
Large/very large 20 (32.3) 7 (25.0) 22 (27.5) 10 (20.0) 59(26.9)

Behavior during meals
Just eating 15 (24.1) 7 (25.0) 12 (15.0) 7 (14.0) 41(18.6)

Conversation with family members 25 (40.3) 13 (46.4) 48 (60.0) 25 (50.0) 111(50.5)
Playing games or watching T 13 (20.9) 5 (17.9) 13 (16.3) 10 (20.0) 41(18.6)

Reading a book or others 9 (14.7) 3 (10.7) 7 (8.7) 8 (16.0) 27(12.3)
Unbalanced diet

Yes 25 (40.3) 10 (35.7) 33 (41.2) 18 (36.0)** 86(39.1)
No 37 (59.7) 18 (64.3) 47 (58.8) 32 (64.0) 134(60.9)

Foods that they dislike1)
Grains and starches 11(17.7)5) 8 (28.4) 16 (20.0) 12 (24.0) 47(21.4)

Meat 7 (11.3) 3 (10.7) 11 (13.7) 8 (16.0) 29(13.2)
Fish 3 (4.8) 2 (7.2) 5 (6.3) 4 (8.0) 14(6.3)
Eggs 6 (9.7) 2 (7.2) 8 (10.0) 5 (10.0) 21(9.5)
Beans 11 (17.7) 3 (10.7) 13 (16.3) 7(14.0) 34(15.5)

egetables 8 (12.9) 2 (7.2) 7 (8.7) 5 (10.0) 22(10.0
Fruits 6 (9.7) 3 (10.7) 6 (7.5) 4 (8.0) 19(8.6)

Dairy products 4 (6.5) 3 (10.7) 5 (6.3) 3 (6.0) 15(6.9)
Seaweeds 0 0 0 0 0
Others2) 6 (9.7) 2 (7.2) 9 (11.2) 2 (4.0) 19(8.6)

* P < 0.05, ** P < 0.01, 1) Multiple answers, 2) Shellfish, soy bean paste, greasy foods, spicy foods, etc. 3) Mean ± SD 4) n (%), 5) The number in parentheses is the percentage of total subjects in each group.

Universal Journal of Educational Research 6(7): 1424-1430, 2018 1427

Physical activity variables in women and men were
significantly different between OW and NW groups. The
percentage of those who stated they did not walk or bike on
weekends was higher in males and females of the OW
groups (P <0.05). In the OW group 71.5% of males and 76% of females were less than 3 hours per day during the weekend, 28.7% of NW females performed more than 3 hours at the weekend (P <0.01). Approximately 30% of OW women participated in physical activity for at least 30

minutes a day. The proportion of OW women exercising
three or more times per week was lower than NW women
(P <0.01). Approximately 90% of OW women walked less than an hour during weekdays or weekends, which was significantly higher than NW men (weekday and weekend p <0.01). Participants spent 29.5% and 28.7%, respectively, 3 hours or more per day on sedentary activity. About 72% and 71.3% of the OW women spent 3 hours or less on weekdays and weekends (P <0.01) (Table 3).

Table 3. The level of physical activity according to obesity status of university students

ariables
Male (n=90 ) Female (n=130 )
Normal
(n= 62)

Overweight &
Obesity
(n =28)

Normal
(n= 80 )

Overweight &
Obesity
(n =50)

Total
(n=220)

At least 30 minutes of physical activity per day
(days/week)

No 13(20.9) 6 (21.5) 15(18.7) 13 (26.0)** 47(21.4)

1-2 18(29.0) 8 (28.5) 25(31.3) 11 (22.0) 62(28.2)

3-4 17(27.4) 6 (21.5) 18(22.5) 16 (32.0) 57(25.9)

5-6 8(12.9) 5 (17.9) 14(17.5) 6 (12.0) 33(15.0)

7 6(9.7) 3 (10.7) 8(10.0) 4 (8.0) 21(9.5)

Walking or riding a bicycle (days/week)

No 9(14.6) 11 (39.2)* 24(30.0) 14 (28.0)* 58(26.4)

1-2 10(16.2) 4 (14.3) 10(12.5) 7 (14.0) 31(14.1)

3-4 15(24.1 3 (10.7) 9(11.2) 8 (16.0) 35(16.0)

5-6 17(27.4) 5 (17.9) 20(25.0) 11 (22.0) 53(24.0)

7 11(17.7) 5 (17.9) 17(21.3) 10 (20.0) 43(19.5)

Time spent walking during weekdays (hours/day)

< 30 min 12(19.3) 6 (21.5) 19(23.7) 15 (30.0)** 52(23.6)

30 min < 1 hour 27(43.6) 14 (50.0) 24(30.0) 20 (40.0) 85(38.6)

1 hour < 2 hours 10(16.2) 6 (21.5) 20(25.0) 8 (16.0) 44(20.0)

2 hours 13(20.9) 2 (7.0) 17(21.3) 7 (14.0) 39(17.8)

Time spent walking during the weekend (hours/day)

< 30 min 11(17.7) 7 (25.1) 18(22.5) 16 (32.0)** 52(23.7)

30 min < 1 hour 28(45.2) 15 (53.5) 25(31.3) 19 (38.0) 87(39.5)

1 hour < 2 hours 10(16.2) 5 (17.9) 21(26.2) 9 (18.0) 44(20.0)

2 hours 13(20.9) 1 (3.5) 17(21.3) 6 (12.0) 37(16.8)

Sedentary activity during weekdays (hours/day)

< 3 42(67.7) 22 (78.5)** 55(68.7) 36(72.0)** 155(70.5)

3 20(32.3) 6 (21.5) 25(31.3) 14(28.0) 65(29.5)

Sedentary activity during the weekend (hours/day)

< 3 46(74.1) 20 (71.5)** 53(66.2) 38 (76.0)** 157(71.3)

3 16(25.9) 8 (28.5) 27(33.8) 12 (24.0) 63 (28.7)

Number of days for exercise (times/week)

No 8(12.9) 3 (10.7) 12(15.0) 8 (16.0)** 31(14.1)

1 14(22.6) 6 (21.5) 18(22.5) 11 (22.0) 49(22.3)

2 18(29.0) 9 (32.1) 24(30.0) 15 (30.0) 66(30.0)

3 22(35.5) 10 (35.7) 26(32.5) 16 (32.0) 74(33.6)

* P < 0.05, ** P < 0.01, 1) n (%)

1428 The Effects of Eating Habits, Physical Activity, Nutrition Knowledge and Self-efficacy Levels on Obesity

Table 4. Nutritional knowledge and self-efficacy levels according to obesity status in university students

ariables
Male (n=90 ) Female (n=130 )

Normal
(n= 62)
Overweight & Obesity
(n =28)
Normal
(n= 80 )
Overweight & Obesity
(n =50)
Total
(n=220)
Nutrition Knowledge

General nutrition knowledge score1) 4.2 ± 0.7 3.8 ± 0.6 4.2 ± 0.7 3.8 ± 0.6 4.0 ± 0.7

Obesity knowledge score 3.2 ± 0.7 3.1 ± 0.6 3.4 ± 0.8 3.3 ± 0.7 3.3 ± 0.7

Nutrition knowledge total score 7.8 ± 1.4 7,9 ± 1.2 7.8 ± 1.4 7.9 ± 1.2 7.9 ±1.3

Self-efficacy

Eating self-efficacy score 17.2 ± 2.8 18.2 ± 2.9 18.4 ±3.0 19.1 ± 3.1 18.2±2.9

Physical activity self-efficacy score 12.8 ± 2.1 11.2 ± 2.7** 12.7 ±2.2 11.1 ± 2.7** 11.9±2.4

Self-efficacy total score 31.7 ± 4.1 31.7 ± 4.2 31.8 ±4.1 30.7 ± 3.7** 31.4±4.0
Mean ± SD, ** P < 0.01

There was no significant difference between the OW and
NW groups in both genders regarding nutrition knowledge.
OW was found to have a total self-efficacy score (P <0.01) and a physical activity self-efficacy score (P <0.01) in women. OW women had significantly lower physical activity self-efficacy scores than NW women (P <0.01). However, there was no significant difference in eating habit between self-efficacy score between OW and NW groups in both genders (Table 4).

4. Discussion
The aim of this study is to examine the eating habits,

physical activities, nutrition knowledge and
self-sufficiency of university students and to investigate
whether these characteristics differ according to obesity
status. Participants were found to have an average age of
21.97 and approximately 64.5% (142) of them were in the
normal weight (NA) group and 35.5% (78) of them were in
overweight – obesity (OW). Gender is significantly
different according to obesity status; In the OW group, the
rate of female (64.1%) was higher than that of the NW
group (56.3%). In a study conducted (Yahia et al., 2008),
the majority of university students had normal weight.
Normal weight women (76.8%) and men (49%) are
overweight and obese than males. In the United States, 35%
of the college students are reported to be overweight or
obese (BMI 25) [22].

According to our research results, eating habits
according to obesity status of university students were
lower than the NW group of both sexes. While eating out
does not differ from obesity in men, OW consumes less
women than women. The eating rate was significantly
higher in the OW group than in the NW group both in
boys and girls. It was determined that 50.5% of the
participants talked about eating behavior with family
members, about 39.1% of them were fed with an
unbalanced. In a study [29], it was found that the
proportion of individuals with regular eating patterns in
young Japanese was low. Skipping breakfast is associated

with low nutritional status and the risk of cardiovascular
disease. It has been reported that adequate breakfast habits
may contribute to the development and further
development of obesity [27]. These findings support our
findings. Another study reported that approximately 40%
of male students (527 males, 462 females) and 23% of
female students of Crete University reported BMI> 25 kg
/ m2 [4]. A cross-sectional survey of 300 male students in
the United Arab Emirates reported that the prevalence of
obesity in men was 35.7%, which is higher than in women
[24]. These findings are different from our findings.

This study shows that; physical activity variables in
women and men were significantly different between OW
and NW groups. The percentage of those who said that
they did not use hiking or cycling on weekends was
higher in male OW and female OW groups. On weekends,
men and women participated in physical activity less than
3 hours a day, and on weekends NW group participated in
physical activity for at least 30 minutes a day. The rate of
OW women exercising three or more times a week is
lower than NW. OW women walked less than an hour on
weekdays or weekends. OW women spent 3 hours or less
on weekdays and weekends. In a study conducted, the
nutrition and physical activity habits and obesity cases of
the university students were investigated, only 8.5% of
girl students and only 28.1% of male students had
sufficient physical activity level [2]. Similarly, in the
previous study [31] obese children were reported to have
negative attitudes and are less likely to participate in
physical activity than normal weight children. Baek [3]
reported that obese children often exercise, but do not
exercise vigorously or prefer to sit and have fun.
Obviously, obesity is the result of modern life styles such
as irregular physical activity and sedanter.

This study revealed that there was no significant
difference between the OW and NW groups in both
genders regarding nutrition knowledge. The OW women
had significantly lower physical activity self-efficacy
scores than NW women. However, there was no significant
difference in eating habit between self-efficacy score
between OW and NW groups in both genders. This finding

Universal Journal of Educational Research 6(7): 1424-1430, 2018 1429

suggests the importance of self-sufficiency that explains
obesity or healthy behavior. Studies of self-efficacy in
obese children [1, 11] have found that children have
difficulties with psychosocial adaptation and that they are
able to perform or perceive their physical activity more
negatively with increasing obesity. In a study examining
health-related physical fitness, [19] it was found that as
children increased in their obesity levels, their physical
fitness for health decreased. Contrary to our expectation,
perceived confidence in eating behavior was not
significantly different from obesity in boys or girls. This
was finding unlike a previous study of self-efficacy for
nutritional behavior [21].

As a result, it was determined that OW group students
less often participated in physical activity than NW
students in this study. Healthy eating habits such as having
breakfast and the size of an adequate meal seemed to be
less preferred in OW group students and especially in
women. Nutritional information does not show any
significant difference between OW and NW groups, while
physical activity self-efficacy is lower in OW group than
NW group. For this reason, physical education programs
for the prevention of obesity in children should attach
importance to increasing the confidence in performing
exercise or physical activity. Physical education programs
should focus on providing practical tips for increasing
physical activity and changing eating behavior. In addition,
they should include adequate methods of body image, body
satisfaction and weight control. In addition, university
students are at risk because of the lack of nutrition
knowledge, psycho-social and economic reasons, T and
peer interaction and similar reasons. In this context it is
important to give information to young people, families
and trainers about this issue and to raise awareness.

Conflicts of Interest
There isn’t any conflict of interest to be declared

regarding the manuscript.

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i *This research International Scientific Congress for applied sport
science 1-2 December, 2017, Sofia, Bulgaria presented as oral
presentation.