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PTA 1601 Pathophysiology

Research Paper
L.I.R.N. Evidence Based Practice Grading Rubric

CATEGORY 4 3 2 1

Bibliography
Citations

All 5 citations are
cited correctly
according to APA and
follow instructions
requirements

4 citations are cited
correctly according
to APA, and one
citation doesn’t
follow instruction
requirements

3 citations are cited
correctly according to
APA, and 2 citations
don’t follow
instruction
requirements

2 or less citations are
cited correctly
according to APA, and 3
or more citations don’t
follow instruction
requirements

Introduction The introduction is
engaging, states the
main purpose of the
research and
previews the
structure of paper

The introduction
simply states the
purpose and
previews the
structure of the
paper

The introduction
states the main topic
but does not
adequately state the
purpose and the
structure of the
paper

There is no clear
introduction or main
purpose and the
structure of the paper
is missing

Definition,
etiology, signs and
symptoms,
diagnosis,
prognosis, what
does pathology
“look like”

Are accurately and
thoroughly described

Are accurately
described; missing
minimal
information

Are accurately
described but missing
information the
reduces
understanding of the
research

Inaccurately described

Medical
Management and
physical therapy
treatment
implications

Are accurately and
thoroughly described
Are accurately
described; missing
minimal
information
Are accurately
described but missing
information the
reduces
understanding of the
research
Inaccurately described

EBP Interventions Are accurately and
thoroughly described

Are accurately
described; missing
minimal
information
Are accurately
described but missing
information the
reduces
understanding of the
research
Inaccurately described

Progression and
Modification of
Interventions

Role of the PTA,
interactions of the
health care team

Impression is clear
and shows
knowledge and
learning

Impression not
very clear. Shows
some knowledge
and learning

Impression has little
clarity and minimal
application of
knowledge and
learning

Impression not clear,
shows no evidence of
learning

SABER College PTA Program
PTA 1601 Pathophysiology
Research Paper
L.I.R.N. Evidence Based Practice Grading Rubric

Total Points_____________________/32

Spelling/Grammar no errors 1-2 errors 3 errors more than 3 errors

THIS NEED TO BE IN THE REFERENCES

Goodman, C. C., &Fuller, K. S. (2016). Pathology for the Physical Therapy
Assistant-E-Book Elsevier Health Sciences.

American Stroke Association. / https://www.stroke.org/

SABERCollege: PTA Program

PTA 1601: Pathophysiology

Research Paper
Students will be responsible for writing a research paper on their approved musculoskeletal or

neurological pathology. The primary objective of this assignment is to research evidence-based physical

therapy treatment for the pathology. Students will share that knowledge with their classmates. The
course objectives linked to this assignment are:

17. Explain the role and benefit of physical therapy in the medical management of
musculoskeletal and neurological pathologies.

18. Discuss the interactions of the medical, therapy, and family members of the health care team in
the lifespan management of patient populations presented.

24. Complete the L.I.R.N evidence-based practice assignment by writing a review of a peer-reviewed
journal article related to physical therapy treatment of patient conditions in the body systems
covered in this course.

Students will research information on their approved pathology. Students must include at minimum the

following information:

1. Define the pathology.

2. Briefly describe the etiology, signs and symptoms, clinical manifestations, diagnosis, and

prognosis.

3. What does the pathology “look like”?

4. According to evidence-based practice, discuss 2-3 treatment interventions/physical therapy

management beneficial for patients with the pathology.

5. Discuss progression of physical therapy treatment including modifications in interventions

for effective physical therapy treatment of patients with the pathology throughout disease

process

6. Discuss medical management (including medications) of the pathology and how it may

affect physical therapy treatment

7. Discuss the role of the PTA in treating patients with the pathology, as well as the

interactions of the health care team

8. Bibliography with at least 5 references which must include 2-3 peer reviewed journals, no
more than 2 textbooks, and only 1 magazine, and information from the APTA website. All

citations must be correctly cited using APA format. SABER students may access LIRN, online
library, using the code 38119.

Research papers must be typed using Times New Roman, 12pt font, double spaced, with one inch

margins. Minimum length is 5 pages. Student should refer to this assignment’s grading rubric to further

understand the expectations of the assignment.

Topics must be approved by instructor no later than February 8 2021. Students may submit draft to

instructor for guidance and feedback no later than March 15, 2021 via hard copy/or email. Failure to

meet these deadlines will result in deduction of 3 points per missed deadline.

A Simplified Stroke Rehabilitation
Assessment of Movement Instrument

Background and Purpose. An efficient, reliable, and valid instrument for
assessing motor function in patients with stroke is needed by both clinicians
and researchers. To improve administration efficiency, we applied the multi-
dimensional Rasch model to the 30-item, 3-subscale Stroke Rehabilitation
Assessment of Movement (STREAM) instrument to produce a concise

reliable, and valid instrument (simplified STREAM [S-STREAM]) for measur-
ing motor function in patients with stroke. Subjects and Methods. The
STREAM (consisting of 3 subscales: upper-limb movements, lower-limb move-
ments, and mobility) was administered to 351 subjects with first stroke
occurrence and a median time after stroke of 12.5 months. The unidimen-
sionality of each subscale of the STREAM first was verified with unidimen-
sional Rasch analysis. Each subscale of the STREAM then was simplified by
deleting redundant items on the basis of expert opinion and the results of the
Rasch analysis. The Rasch reliability of the S-STREAM and the concurrent
validity of the S-STREAM with the STREAM were examined with multidimen-
sional Rasch analysis and the intraclass correlation coefficient (ICC), respec-
tively. Results. After deleting the items that did not fit the Rasch model, we
found that the 8-item upper-limb movement subscale, the 9-item lower-limb
movement subscale, and the 10-item mobility subscale assessed single, uni-
dimensional upper-limb movements, lower-limb movements, and mobility,
respectively. We selected 5 items from each subscale to construct the
S-STREAM and found that the reliability of each subscale of the resulting
simplified instrument was high (Rasch reliability coefficients of �.91). The
agreement between the subscale scores (Rasch estimates) of the S-STREAM
and those of the STREAM was excellent (ICC of �.99, with a lower limit for
the 95% confidence interval of �.985), indicating good concurrent validity of
the S-STREAM with the STREAM. Discussion and Conclusion. The S-STREAM
demonstrates high Rasch reliability, unidimensionality, and concurrent valid-
ity with the STREAM in patients with stroke. Furthermore, the S-STREAM is
efficient to administer, as it consists of only half the number of items in the
original STREAM. Additional studies to examine other psychometric proper-
ties (eg, predictive validity and responsiveness) of the S-STREAM or its
psychometric properties in various recovery stages after stroke are needed to
further establish its utility in both clinical and research settings. [Hsueh IP,
Wang WC, Wang CH, et al. A simplified stroke rehabilitation assessment of
movement instrument. Phys Ther. 2006;86:936 –943.]

Key Words: Motor function, Psychometrics, Rasch model, Stroke.

I-Ping Hsueh, Wen-Chung Wang, Chun-Hou Wang, Ching-Fan Sheu, Sing-Kai Lo, Jau-Hong Lin,
Ching-Lin Hsieh

936 Physical Therapy . Volume 86 . Number 7 . July 2006

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M
otor and mobility problems are very com-
mon after stroke.1 For the purposes of
treatment planning and outcome assess-
ment, it is important to reliably and accu-

rately assess motor function in patients with stroke.2
Although a number of assessment tools are available to
measure the recovery of movement after stroke, they
have rarely been used in clinical practice, partly because
of the lengthy administration time and complexity of
scoring.3 A reliable, valid, and efficient instrument for
the assessment of motor function in patients with stroke
is needed by both clinicians and researchers.

The Stroke Rehabilitation Assessment of Movement
(STREAM) instrument was designed to provide a com-
prehensive and quantitative evaluation of voluntary
movements (ie, an impairment measurement) and basic
mobility (ie, a disability measurement) in patients with
stroke.4 The STREAM consists of three 10-item sub-
scales: upper-limb movements, lower-limb movements,
and mobility. The psychometric characteristics of the
STREAM have been shown by classical test theory to be
satisfactory.2– 6 The STREAM is preferred over other
related impairment or disability measures (eg, the Box
and Block Test, the Berg Balance Scale, gait speed
testing, the Timed “Up & Go” Test, and the Barthel
Index) for monitoring recovery from a stroke at the
acute stage, as those measures appeared not to focus on
the goals of immediate therapy during this period.5
Furthermore, those measures had limited abilities to
discriminate or evaluate patients with stroke because the

Box and Block Test, the Berg Balance Scale, gait speed
testing, and the Timed “Up & Go” Test showed floor
effects in patients with severe stroke, whereas the Barthel
Index showed ceiling effects in patients with mild
stroke.5,7–9

However, the 3 subscales of the STREAM have never
been tested for unidimensionality (one type of construct
validity); such testing is required to justify the summa-
tion of scores to quantify motor function in patients with
stroke. Only items measuring the same, unique dimen-
sion (construct) should be retained in a measure. Fur-
thermore, the extremely high internal consistency of the
STREAM (ie, the Cronbach alpha value was found to be
as high as .98 for each of the subscales)3 indicates
possible redundancy among the items. These observa-
tions suggest the potential for shortening the STREAM.

Standard Rasch analysis enables the examination of
whether items from a scale constitute a unidimensional
construct10,11 so as to construct a concise scale.12 How-
ever, when an instrument consisting of more than one
subscale (eg, the STREAM) is to be calibrated, it is
inefficient to apply the standard unidimensional Rasch
model separately to each subscale. The unidimensional
approach ignores correlations between latent traits (ie,
the constructs of the subscales) and thus may yield
imprecise measurements of the construct (or character-
istic) to be measured, especially when the subscales are
short. On the other hand, the multidimensional Rasch
model simultaneously calibrates all subscales and there-

IP Hsueh, OT, MA, is Assistant Professor, School of Occupational Therapy, College of Medicine, National Taiwan University, Taipei, Taiwan.

WC Wang, PhD, is Professor, Department of Psychology, Chung-Cheng University, Chiayi, Taiwan.

CH Wang, PT, BS, is Professor, School of Physical Therapy, College of Medical Technology, Chung-Shan Medical University, and Department of
Physical Therapy, Chung-Shan Medical University Rehabilitation Hospital, Taichung, Taiwan.

CF Sheu, PhD, is Professor, Institute of Cognitive Science, National Cheng Kung University, Tainan, Taiwan.

SK Lo, PhD, is Professor, Faculty of Health and Behavioral Sciences, Deakin University, Melbourne, Australia.

JH Lin, PT, PhD, is Professor, Faculty of Physical Therapy, Kaohsiung Medical University, Kaohsiung, Taiwan.

CL Hsieh, OT, PhD, is Professor and Chair, School of Occupational Therapy, College of Medicine, National Taiwan University, and Department
of Physical Medicine and Rehabilitation, National Taiwan University Hospital, 4F, 17 Shiujou Rd, Taipei 100, Taiwan (mike26@ha.mc.ntu.edu.tw).
Address all correspondence to Dr Hsieh.

Ms Hsueh and Dr Hsieh provided concept/ideas/research design, writing, and fund procurement. Dr WC Wang provided data analysis and
facilities/equipment. Dr Hsieh provided project management. Professor CH Wang provided institutional liaisons. Dr WC Wang, Professor CH
Wang, Dr Sheu, Dr Lo, and Dr Lin provided consultation (including review of manuscript before submission).

The research protocol was approved by local institutional review boards.

This study was supported by research grants from the National Science Council (NSC 93-2314-B-002-033 and NSC 94-2314-B-002-078) and the
National Health Research Institute (NHRI-EX94-9204PP).

This article was received April 16, 2005, and was accepted January 30, 2006.

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fore uses the correlations to increase measurement
precision.13,14 Theoretically, it may be difficult to con-
ceive of constructs that are independent in the movement
domains after stroke. Therefore, the multidimensional
Rasch model takes into account the between-subscale
correlations to increase measurement precision: the
higher the correlations, the greater the measurement
precision.15,16 In other words, short subscales, if moder-
ately correlated, still can yield precise measurements
with the multidimensional approach. Because the 3
subscales of the STREAM are highly correlated with each
other,2 the multidimensional approach can be useful in
simplifying the STREAM.

To improve administration efficiency, we aimed to
shorten the 30-item, 3-subscale STREAM to produce a
simplified STREAM (S-STREAM) by using the multi-
dimensional Rasch model. We examined the psychomet-
ric properties of the S-STREAM (including Rasch reli-
ability, unidimensionality, and concurrent validity with
the STREAM) in subjects with stroke.

Method

Subjects
Subjects with a broad range of motor deficits were
recruited from the rehabilitation departments of 5 hos-
pitals in northern, central, southern, and eastern Taiwan
between October 2003 and January 2004. These rehabil-
itation departments provide both inpatient and out-
patient services (including physical therapy, occupa-
tional therapy, and speech therapy). Subjects were
included in the study if they met the following criteria:
diagnosis (International Classification of Diseases, 9th Revi-
sion, Clinical Modification [ICD-9] codes)17 of cerebral
hemorrhage (ICD-9 code 431) or cerebral infarction
(ICD-9 code 434), absence of other major diseases (eg,
tumors, arthritis) or impairments (eg, amputations, frac-
tures) that would reduce or limit a subject’s ability to
perform movements, and ability to follow 2-step instruc-
tions. Only subjects who had had their first stroke and
were able to give informed consent personally or by
proxy were included in the study.

Procedure
The STREAM, with instructions in Chinese, was admin-
istered by the same physical therapist to all of the
participants in the 5 rehabilitation departments. The
intrarater reliability of data obtained by the physical
therapist was satisfactory (intraclass correlation coeffi-
cient [ICC] of .94). Demographic characteristics and
comorbidity data for the participants were collected
from medical records.

Instrument
Items of the STREAM3 for voluntary movements of the
limbs are scored on a 3-point scale (0�unable to per-
form the test movement, 1�able to perform the test
movement only partially, and 2�able to complete the
test movement). Mobility items are scored on a 4-point
scale (0�unable to perform the test movement, 1�able
to perform the test movement only partially, 2�able to
complete the test movement with a mobility aid, and
3�able to complete the test movement without an aid).
Thus, each of the 10-item limb movement subscales was
scored out of 20 points, and the 10-item mobility sub-
scale was scored out of 30 points.

Data Analysis
The unidimensionality of the 3 subscales of the STREAM
was examined with WINSTEPS.18 The variance-
covariance matrix (and the correlation matrix) for the 3
latent traits (ie, the constructs of the 3 subscales of the
STREAM) was computed with ConQuest,19 which was
developed for the multidimensional random-coefficients
multinomial logit model (MRCMLM).13 A brief descrip-
tion of the MRCMLM is given in the Appendix.

To examine the unidimensionality of each subscale, infit
and outfit statistics were used to examine whether the
data fit the expectation of the Rasch rating scale model
(RSM). The infit mean square (MNSQ) is sensitive to
unexpected behavior affecting responses to items near
the subject’s proficiency measure (eg, motor status); the
outfit MNSQ is sensitive to unexpected behavior on
items far from the subject’s motor status. Items with infit
or outfit MNSQ values of greater than 1.4 indicate
potential misfits.20 The MNSQ can be transformed to a
standardized z value (ZSTD) which, for large samples,
follows approximately the standard normal distribution
when the items fit the expectation of the model. Items
with both infit and outfit ZSTD values beyond �2.58
(twice the tailed area of the normal curve above or below
�2.58 is 0.01) were considered to have poor fit.

In addition, when items fit the expectation of the model,
the residuals (observed scores minus expected scores)
should be distributed randomly. A principal compo-
nents analysis was conducted to determine whether any
dominant component existed among the residuals. If
dominant components were found, then the unidimen-
sionality assumption was violated.

Rasch reliability, which can be viewed as the counterpart
of classical test reliability (eg, the Cronbach alpha), was
calculated.10,20 Reliability coefficients of greater than .7
were considered good for group comparisons, whereas
those greater than .9 were considered good for individ-
ual comparisons.21

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The appropriateness of the scoring levels in each item of
the STREAM was investigated with the RSM. The RSM is
useful for polytomous items in a scale that share the
same rating scale structure (eg, all items are rated 0, 1, or
2). Estimates of the threshold difficulty between the
adjacent scoring levels can be used to examine the
appropriateness of the scoring points of a test.20 If
disorderings of the step difficulty (ie, the difficulty of a
higher step was lower than that of its adjacent lower
step) between any 2 adjacent levels were found, then the
levels of scaling of the items might be reorganized to
achieve suitable scaling.

After the unidimensionality and appropriate scoring
levels in each item of the STREAM were established, we
attempted to reduce the length of the test further while
maintaining its psychometric properties. Each of the 3
subscales of the STREAM was shortened to produce the
S-STREAM on the basis of 2 criteria: content represen-
tativeness, assessed by a panel of therapists (2 physical
therapists and 2 occupational therapists who each had
more than 10 years of experience in stroke rehabilita-
tion); and difficulty diversity, that is, even scattering of
the difficulties of the selected items over the range of the
difficulty continuum.

For each subject, the multidimensional form of the RSM
can provide estimates for the 3 subscale scores for both
the S-STREAM and the STREAM. We used the RSM
estimates for each subscale of the STREAM as the gold
standard in this study. Because the Rasch estimates for
each subscale have different score ranges, all estimates
were linearly transformed to a range of 0 to 100 to
facilitate comparisons. The relationship and agreement
among corresponding Rasch estimates for subscale
scores (ie, the concurrent validity of the S-STREAM with
the STREAM) were examined with the Pearson correla-
tion coefficient (r) and the ICC(3,1),22 respectively.
Correlation coefficients of greater than .6 indicate
acceptable concurrent validity.23

Results
A total of 351 subjects with a median time after stroke of
12.5 months met the selection criteria and agreed to
participate in the study. The participants had a wide
range of motor deficits, and their scores were found to
be scattered throughout the entire ranges of the
STREAM subscales. The clinical characteristics of the
study participants are shown in Table 1.

Unidimensional Rasch analyses of the 3 subscales, sepa-
rately, revealed that 2 items (scapular elevation and
opposition) in the upper-limb movement subscale and 1
item (hip abduction) in the lower-limb movement sub-
scale did not fit the expectation of the model (both infit
and outfit ZSTD values of beyond �2.58). These 3 items

were removed from the instrument in subsequent anal-
yses. Thereafter, the 8-item upper-limb movement sub-
scale and the 9-item lower-limb movement subscale fit
the expectation of the model (infit and outfit ZSTD
values within the range of �2.58). In addition, none of
these items had infit and outfit MNSQs of greater than
1.4. Principal components analysis revealed that no
dominant component existed among the residuals of the
Rasch-transformed scores for the 8-item upper-limb
movement subscale, the 9-item lower-limb movement
subscale, or the 10-item mobility subscale. These results
indicate that the 8-item upper-limb movement subscale,
the 9-item lower-limb movement subscale, and the
10-item mobility subscale assess single, unidimensional
upper-limb movements, lower-limb movements, and
mobility, respectively.

A multidimensional analysis with ConQuest was per-
formed for the remaining 27 items (ie, 8 items from the
upper-limb movement subscale, 9 items from the lower-
limb movement subscale, and 10 items from the mobility
subscale). Table 2 shows the correlation matrix for the

Table 1.
Clinical Characteristics of Subjects With Stroke (N�351)

Characteristica Value

Sex (no. of men/women) 222/129

Age, y, median (25th–75th percentiles) 63 (53–71)

Month after onset, median
(25th–75th percentiles)

12.5 (4–30)

Diagnosis, no. (%) of subjects
Cerebral hemorrhage 113 (32)
Cerebral infarction 238 (68)

Side of paresis, no. (%) of subjects
Right 175 (50)
Left 176 (50)

STREAM score, median (25th–75th percentiles)
Upper-limb movement raw score 9 (0–17)
Lower-limb movement raw score 7 (3–14)
Mobility raw score 15 (8–21)

S-STREAM score,b mean (SD)
Upper-limb movement score 48.8 (26.4)
Lower-limb movement score 48.3 (24.0)
Mobility score 49.2 (22.9)

a STREAM�Stroke Rehabilitation Assessment of Movement, S-STREAM�
simplified STREAM.
b Rasch-transformed score ranging from 0 to 100.

Table 2.
Correlation Matrix for the Stroke Rehabilitation Assessment of
Movement With the Multidimensional Approach

Subscale

Upper-Limb
Movement

Lower-Limb
Movement

Lower-limb movement .90
Mobility .78 .84

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STREAM, which revealed that the underlying latent
traits of the subscales of the STREAM were highly
correlated, with Pearson coefficients of between .78 and
.90. Table 3 shows that the Rasch reliability for the 3
subscales was good (reliability coefficients of �.86).20
Moreover, the 3 subscales of the STREAM showed better
reliability when the multidimensional approach was used
(reliability coefficients of �.93) than when the uni-
dimensional approach was used (reliability coefficients
of �.86).

We selected items that fit the RSM to construct the
S-STREAM. To avoid possible floor or ceiling effects, we
generally retained the most difficult and easiest items for
each subscale. The only exception was that the most
difficult item of the mobility subscale, 3 steps backward,
was not selected because it apparently cannot be classi-
fied as a daily mobility activity. The second most difficult
item, walking down 3 stairs, was selected instead. These
2 items were similar in difficulty (Tab. 4). Furthermore,
3 items with an intermediate degree of difficulty were
selected for each subscale on the basis of expert opinion
and the results of Rasch analysis. We first selected items
that were scattered evenly over the range of the difficulty
continuum. Some items of similar difficulty (eg, “making
a fist” and “moving hand to sacrum” in the upper-limb
movement subscale, “knee flexion while sitting” and
“knee flexion while standing” in the lower-limb move-
ment subscale, and “10-m walk” and “3 steps to the
affected side” in the mobility subscale) were selected by
the panel of therapists. “Making a fist” was used to
provide 2 items, in total, measuring hand function for
the upper-limb movement subscale. “Knee flexion while
standing” was not used because some subjects tended to
flex their hips simultaneously, making the rating diffi-
cult. “Three steps to the affected side” was not used
because it is obviously not a daily activity, compared with
“10-m walk.” The final 5 items used for each subscale of
the S-STREAM are shown in Table 4.

Unidimensional and multidimensional analyses were
conducted on the 15-item version of the S-STREAM.
Table 4 shows the 2 kinds of item parameter estimates
(ie, difficulty logit and standard error) for the 3 sub-
scales of the 27-item STREAM and the S-STREAM with

the multidimensional approach. These 2 versions of the
instrument had similar estimates for corresponding
items.

The threshold difficulty estimates within each subscale
were rather far apart (�2.18 logits). In addition, the
ordering of the threshold difficulty estimates was not
reversed.

Table 3 shows that the use of the multidimensional
approach with the S-STREAM resulted in high test
reliability (Rasch reliability coefficients of �.91) for the
3 subscales. These results indicate that the 3 subscales of
the S-STREAM can yield very precise estimates for indi-
vidual subjects. When the unidimensional approach was
used, the test reliability values were .85, .88, and .94 for
the upper-limb movement, lower-limb movement, and
mobility subscales of the S-STREAM, respectively.

The agreement between each pair of subscales was
excellent (transformed scores of 0 –100), with ICCs
(95% confidence intervals) of .99 (.993–.995), .99 (.989 –
.993), and .99 (.985–.990), for the upper-limb move-
ment, lower-limb movement, and mobility subscales,
respectively. Furthermore, the Pearson correlation co-
efficients for the multidimensional Rasch estimates for
the STREAM and the S-STREAM were all .99 for the 3
subscales. These results indicate that each subscale of
the S-STREAM demonstrates high concurrent validity
with the corresponding subscale of the STREAM.

Discussion
To the best of our knowledge, this study is the first to use
the multidimensional approach to produce a concise
measure of motor function for people with stroke. The
15-item S-STREAM was constructed on the basis of the
original STREAM, expert opinion, and results of Rasch
analysis. The S-STREAM contains only half the number
of items in the original STREAM and shows sound
reliability and validity.

There are 2 major advantages of using the S-STREAM.
First, it is simple and quick to use for patients with stroke
compared with the original STREAM. As the S-STREAM
contains only half the number of items in the original

Table 3.
Rasch Reliability for the 3 Subscales of the Stroke Rehabilitation Assessment of Movement (STREAM) and the Simplified STREAM (S-STREAM)
With the Unidimensional and Multidimensional Approaches

Subscale

27-Item STREAM S-STREAM

No. of
Items

Reliability No. of
Items

Reliability

Unidimensional Multidimensional Unidimensional Multidimensional

Upper-limb movement 8 .86 .93 5 .85 .91
Lower-limb movement 9 .91 .96 5 .88 .93
Mobility 10 .97 .98 5 .94 .95

940 . Hsueh et al Physical Therapy . Volume 86 . Number 7 . July 2006

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STREAM, the 15-item S-STREAM can be administered
within 10 minutes, that is, half the time required to
administer the original STREAM. Rapid assessment is a
clinically important feature of this simplified version of
the STREAM, as long tests can take a substantial amount
of time to complete and may place unreasonable
demands upon the respondents, especially in instances
in which they may be seriously unwell, as in the case of
stroke. Rapid and accurate assessment of functional
outcomes in patients with stroke therefore will provide
benefits to both clinicians and patients.

A second advantage of using the S-STREAM is that the
Rasch estimates for the 3 subscales can be viewed as
interval-level measurements.10 In contrast, most mea-

sures currently used in the assessment of patients with
stroke use ordinal-level measurements. For an ordinal
scale, a given difference in scores at one point on the
scale does not necessarily represent the same amount of
functional change as an identical difference at another
point on the scale.24 Interval scores, rather than ordinal
scores, can provide a more precise reflection and better
resolution of disease impact, differences between indi-
viduals and groups, and treatment effects.25 Further-
more, an ordinal scale precludes the use of standard
parametric statistical inferences. Because most statistical
techniques assume that the data are at least on an
interval scale, the Rasch estimates for the S-STREAM are
recommended for future applications.

Table 4.
Item Parameter Estimates for the Stroke Rehabilitation Assessment of Movement (STREAM) and the Simplified STREAM (S-STREAM) With the
Multidimensional Approach

Itema
27-Item STREAM 15-Item S-STREAM

Difficulty Logit SE Difficulty Logit SE

Upper-limb movement
Elbow extension while supine �0.77 0.30 �0.78 0.32
Raising hand to touch top of head �0.46 0.30
Scapular protraction �0.02 0.31 �0.10 0.30
Making a fist 1.21 0.32 1.05 0.31
Moving hand to sacrum while sitting 1.29 0.32
Raising arm to fullest elevation 1.50 0.32 1.31 0.31
Supination and pronation 1.78 0.33
Total extension of fingers 2.06 0.33 1.84 0.32
Threshold 1 �2.29 0.10 �2.15 0.11
Threshold 2 2.29 0.10 2.15 0.11

Lower-limb movement
Knee extension while sitting �2.12 0.27 �1.98 0.26
Hip flexion while sitting �0.83 0.26 �0.75 0.27
Bending hip and knee while supine �0.40 0.26
Knee flexion while sitting 0.87 0.27 0.84 0.27
Knee flexion while standing 0.93 0.27
Dorsiflexion while sitting 1.26 0.27
Plantar flexion while sitting 1.56 0.27 1.47 0.27
Knee extension and dorsiflexion while sitting 2.12 0.28
Dorsiflexion while standing 3.63 0.30 3.41 0.30
Threshold 1 �1.09 0.07 �1.07 0.08
Threshold 2 1.09 0.07 1.07 0.08

Mobility
Rolling �3.77 0.24 �3.84 0.27
Bridging (ie, raising hips off bed) �3.37 0.23
Moving from supine to sitting �1.49 0.22 �1.52 0.21
Standing for 20 counts by the rater �0.71 0.22
Moving from sitting to standing 0.32 0.22 0.30 0.23
Placing affected foot onto first step 0.68 0.22
10-m walk 1.32 0.22 1.28 0.22
3 steps to affected side 1.40 0.22
Walking down 3 stairs 2.16 0.22 2.11 0.22
3 steps backward 2.26 0.22
Threshold 1 �3.53 0.10 �3.47 0.13
Threshold 2 0.13 0.06 �0.13 0.06
Threshold 3 3.40 0.10 3.61 0.12

a The items selected for the S-STREAM are shown in italic type. The items are arranged in ascending order of difficulty in each subscale. Threshold indicates
difficulty between the adjacent scoring levels. Note that the items of the mobility subscale have 4 levels of scaling and thus have 3 thresholds.

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With the multidimensional approach, the between-
subscale correlations are taken into account to improve
measurement precision. Patients with the same raw
upper-extremity scores but with different lower-
extremity scores or mobility scores would have different
Rasch estimates for their upper-extremity scores. The
Rasch estimates for each subscale of the S-STREAM
derived from the multidimensional analysis cannot be
obtained by summing the raw scores and using a simple
Rasch transformation table, as in the unidimensional
analysis. Because the transformation table for the multi-
dimensional analysis of the S-STREAM is very long, we
have developed a computer program to transform the
raw scores for each subscale of the S-STREAM to the
Rasch scores. The program is easy to run on common PC
platforms. To improve the dissemination of the program
and the S-STREAM,26 the related materials can be found
at http://ccms.ntu.edu.tw/�clhsieh/s-stream/. Even if
some patients do not respond to all items, their Rasch
scores still can be estimated and compared because, with
the use of the models of the Rasch family (or item
response models in general), the estimation of a
patient’s latent traits is based on the patient’s observed
item responses.11

In this study, multidimensional Rasch analysis was shown
to be a useful tool for reducing the items of a measure
while maintaining the measurement reliability and valid-
ity (eg, the Rasch reliability coefficients of the
S-STREAM were above the preset criterion of .9, and the
subscales of the S-STREAM were highly associated with
the corresponding subscales of the STREAM). Further-
more, the multidimensional Rasch model yielded a large
number of estimates of a subject’s motor function (eg, 191
estimates for the upper-limb movement function in this
study) compared with the raw scores of the S-STREAM
(eg, 0 –10 for the upper-limb movement function).
These additional estimates of motor function are likely
to promote the psychometric properties (eg, responsive-
ness and discriminative capacity) of the S-STREAM,
although further validation is warranted.

It also should be noted that direct estimation of the
correlation among latent traits is possible only for the
multidimensional approach and not for the unidimen-
sional one.15,16 Rasch analysis can achieve even more
efficient and precise measurements when computerized
adaptive testing (CAT)27–29 is used; CAT involves the use
of a computer to administer items to respondents and
allows respondents’ levels of function to be estimated as
precisely as desired (ie, to reach a preset reliability level).
Because the impacts after stroke are multiple and a great
deal of time and effort is needed to administer the
measures that assess various impacts, it seems promising
to combine both the multidimensional approach and

CAT to simplify or elaborate on functional measure-
ments in patients with stroke.14

The appropriateness of scoring levels refers to whether
or not the motor functions of participants can be
differentiated by their responses as clearly as the levels
allow.20 Recent studies30,31 have shown that a larger
number of scoring points may not lead to a finer
differentiation of participants. The items of the subscales
of the STREAM are on a 3-point or 4-point ordinal scale,
but the appropriateness of scoring levels of the STREAM
have rarely been examined. Our study is the first to
determine the appropriateness of its scaling in subjects
with stroke. We found that the threshold difficulty
estimates within each subscale were rather far apart and
without disorderings (ie, the ordering of the threshold
difficulty of the levels was reasonable). Therefore, the
rating scales of the STREAM were supported, indicating
that they could differentiate the motor status of subjects
very well.

Any measurement tool requires an extensive psychomet-
ric examination for the purposes of understanding its
particular strengths and limitations.32 Additional studies
to examine other psychometric properties (eg, predic-
tive validity and responsiveness) of the S-STREAM are
warranted. Furthermore, patients with stroke at the
acute or subacute stage receive greater intensity of
motor rehabilitation and assessment than do those at the
chronic stage. However, more than half of the subjects in
this study had had a stroke more than 1 year before the
study; therefore, the psychometric properties of the
S-STREAM at the acute and subacute stages remain
largely unknown. Therefore, further investigations of
the psychometric properties of the S-STREAM at various
recovery stages after stroke are needed to further estab-
lish its utility in both clinical and research settings.
Direct psychometric and practical (utility) comparisons
between the S-STREAM and other related impairment
and disability measures (eg, the Fugl-Meyer Motor Test
and the Rivermead Mobility Index) also are needed for
prospective users to select a better measure based on
empirical data.

Conclusion
Our results show that the S-STREAM has high Rasch
reliability, unidimensionality, and concurrent validity
with the STREAM in patients with stroke. The
S-STREAM is efficient to administer, as it consists of only
half the number of items in the original STREAM.
Additional studies to examine the predictive validity and
responsiveness of the S-STREAM or its psychometric
properties in various recovery stages after stroke are
needed to further establish its utility.

942 . Hsueh et al Physical Therapy . Volume 86 . Number 7 . July 2006

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2 Wang CH, Hsieh CL, Dai MH, et al. Inter-rater reliability and validity
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Appendix.
Multidimensional Random-Coefficients Multinomial Logit Model
(MRCMLM)

In the MRCMLM, let subject n’s abilities on L latent traits (ie, the 3
constructs to be measured in this study: upper-limb movement, lower-
limb movement, and mobility) be denoted as � Tn � (�n1 ,…, �nL ), which is
considered to represent a random sample from a population with
multivariate normal distribution N(�, �), where � and � are the means
and variance-covariance matrices of the latent traits, respectively. The
probability of a response in scaling level j of item i for subject n is

p nij �
exp�bTij �n�a

T
ij �)

�
u�1

exp(bTiu �n�a
T
iu �)

where Ki is the number of levels in item i (in this study, Ki�3 for the items
of the upper-limb and lower-limb movement subscales and Ki�4 for the
items of the mobility subscale), � is a vector of location parameters that
describe the items, bij is a score vector given to scaling level j of item i
across L latent traits, and aij is a design vector given to scaling level j of
item i that describes the linear relationship among the elements of �.

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The Stroke Rehabilitation Assessment
of Movement (STREAM):
A Comparison With Other Measures
Used to Evaluate Effects of Stroke
and Rehabilitation

Background and Purpose. The Stroke Rehabilitation Assessment of
Movement (STREAM) is a relatively new measure of voluntary move-
ment and basic mobility. The main objectives of this study were: (1) to
examine the relationship of the STREAM to other measures of
impairment and disability and (2) to compare its usefulness for
evaluating effects of stroke and rehabilitation and for assessing change
over time with that of other measures of impairment and disability.
Subjects and Methods. The performance of 63 patients with acute
stroke on the STREAM and other measures of impairment and
disability was evaluated during the first week after stroke and 4 weeks
and 3 months later. Results. Scores on the STREAM were associated
with scores on the Box and Block test, Balance Scale, Barthel Index,
gait speed, and the Timed “Up & Go” Test (with Pearson correlation
coefficients ranging from .57 to .80) and were associated with catego-
ries of the Barthel Index and Balance Scale. The STREAM’s ability to
predict discharge destination from the acute care hospital, as well as to
predict gait speed and Barthel Index scores at 3 months poststroke, was
comparable to that of other commonly used measures. Standardized
response mean estimates provided supporting evidence for the ability
of the STREAM to reflect change over time. Discussion and Conclu-
sion. The results obtained with the STREAM, as compared with other
measures of impairment and disability in people with stroke, suggest
that it may be useful in clinical practice and research. [Ahmed S, Mayo
NE, Higgins J, et al. The Stroke Rehabilitation Assessment of Move-
ment (STREAM): a comparison with other measures used to evaluate
effects of stroke and rehabilitation. Phys Ther. 2003;83:617– 630.]

Key Words: Measurement, Motor recovery, Outcome measure, Psychometrics, Stroke.

Sara Ahmed, Nancy E Mayo, Johanne Higgins, Nancy M Salbach, Lois Finch, Sharon L Wood-Dauphinée

Physical Therapy . Volume 83 . Number 7 . July 2003 617

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T
he evaluation of motor recovery is a cornerstone
of the assessment of people with stroke. Most
measurement instruments have emerged from
theoretical frameworks developed to fit patterns

of motor recovery observed in selected samples of peo-
ple recovering from strokes. Some measures were based
on theories that have been questioned, such as the
assumption that recovery occurs in a predictable stereo-
typed pattern performed within flexor and extensor
synergies.1– 4 The theoretical basis of existing measures
may explain why, according to a 1992 Canadian survey,5,6
published instruments for motor evaluation following
stroke were used routinely in less than 5% of physical
therapy departments. Other reasons for nonuse, we
believe, may be related to practicality, including the time
to administer the test, the dependence on equipment,
and the complexity of the scoring scheme.

Given what we viewed as the limitations of many mea-
sures of motor recovery, researchers and clinicians devel-
oped what they believe is a more “user-friendly” instru-
ment called the Stroke Rehabilitation Assessment of
Movement (STREAM).7 The content and format of the
instrument were created in an effort to minimize barri-

ers to routine clinical use. The STREAM was developed
as an outcome measure that could be used to monitor
the re-emergence of voluntary movement and basic
mobility. Items in the original STREAM were based on
clinical experience of physical therapists working in
stroke rehabilitation and existing instruments. Further
content validation was carried out by 2 consensus panels
that made recommendations based on their collective
clinical experience. The first and second panels con-
sisted of 11 and 9 physical therapists, respectively, rep-
resenting all phases of stroke rehabilitation, including
acute care, inpatient and outpatient rehabilitation, and
long-term care. All therapists had more than 1 year of
clinical experience. The panels produced an intermedi-
ate test version of the STREAM made up of 43 items.
This intermediate version then underwent preliminary
reliability and internal consistency testing, and item
reduction was then carried out.7

The current version of the STREAM7,8 contains 30 items
divided among 3 subscales: 10 items for voluntary motor
ability of the upper extremity (UE), 10 items for volun-
tary motor ability of the lower extremity (LE), and 10
items for basic mobility. Examples of items on the

S Ahmed, MSc, BSc (PT), is a doctoral student in the Department of Epidemiology and Biostatistics, McGill University, Montreal, Quebec, Canada.

NE Mayo, PhD, BSc (PT), is Quebec Senior Research Scientist, Royal Victoria Hospital, Montreal, Quebec, Canada, and Associate Professor, Faculty
of Medicine, School of Physical and Occupational Therapy, McGill University. Address correspondence to Dr Mayo at Royal Victoria Hospital,
Division of Clinical Epidemiology, 687 Pine Ave W, Ross 4.29, Montreal, Quebec, Canada H3A 1A1 (nancy.mayo@mcgill.ca).

J Higgins, MSc, BSc (OT), is a doctoral student in the School of Physical and Occupational Therapy, Faculty of Medicine, McGill University.

NM Salbach, MSc, BSc (PT), is a doctoral student in the Department of Epidemiology and Biostatistics, McGill University.

L Finch, MSc, BSc (PT), is a doctoral student in the School of Physical and Occupational Therapy, Faculty of Medicine, McGill University. She was
Physical Therapist, Royal Victoria Hospital, at the time of this study.

SL Wood-Dauphinée, PhD, BSc (PT), is Professor, School of Physical and Occupational Therapy, and Professor, Faculty of Medicine, Department
of Epidemiology and Biostatistics, McGill University.

Ms Ahmed, Ms Salbach, and Ms Higgins were graduate students in the School of Physical and Occupational Therapy, Faculty of Medicine, McGill
University, when this study was completed.

Ms Ahmed, Dr Mayo, and Ms Finch provided concept/research design. Ms Ahmed and Dr Mayo provided writing and data analysis. Ms Ahmed,
Ms Higgins, and Ms Salbach provided data collection. Dr Mayo provided fund procurement, subjects, facilities/equipment, and institutional
liaisons. Dr Mayo, Ms Higgins, Ms Salbach, Ms Finch, and Dr Wood-Dauphinée provided consultation (including review of manuscript before
submission). The authors thank the research nurses Susan Anderson, Angela Andrianakis, Rosemary Hudson, and Lisa Wadup for patient
recruitment and Claudette Corrigan for assistance in running the study. They also acknowledge the assistance of Adrian Levy, Susan Scott, and
Judy Soicher in the analysis of data.

Ethical approval for this study was obtained from the ethics committees of each hospital involved and from the Institutional Review Board of McGill
University.

This research was presented, in part, in poster format at the Réseau Provincial de Recherche en Adaptaton–Réadaptation Conference; June 4 – 6,
1998; Quebec City, Quebec, Canada.

In support of this research, Ms Ahmed received a fellowship in gerontology from the Physiotherapy Foundation of Canada.

This article was submitted March 1, 2002, and was accepted March 24, 2003.

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STREAM include protraction of the scapula in a supine
position, flexion of the hip and the knee in a supine
position, and rolling onto one side from a supine
position. A 3-point ordinal scale is used for scoring
voluntary movement of the limbs, and a 4-point ordinal
scale is used for basic mobility. The extra category for
basic mobility was added to allow for one of the score
choices to be independence in the activity without the
help of an aid (eg, walking aid, splints). The quality of
movement for the UE and LE is also scored on a 3-point
scale, but it is not reflected in the final score. A total
score for each subscale is calculated, out of 20 points for
the UE and LE subscales and 30 points for basic mobility.
To allow for the possibility that occasionally an item
cannot be scored, the subscales are converted to a
percentage score out of 100 even though the scores are
not interval based, and the total score is calculated as an
average of scores obtained for the 3 subscales. The
STREAM requires approximately 15 minutes to admin-
ister.

Internal consistency of the STREAM was assessed on 26
individuals with stroke (20 subjects from a rehabilitation
center and 6 additional subjects with a low level of
voluntary movement as indicated by a score of less than
30 on the STREAM). Cronbach alphas, which reflect
how well the items of the scale relate to each other, were
.98 for the subscales and the complete STREAM. Twenty
experienced physical therapists (mean years of experi-
ence�5, SD�2.1, range�2–9) who had worked with
individuals with stroke participated in the interrater and
intrarater reliability testing of the STREAM through
videotaped assessments. For this reliability study, the
therapists were trained in a 2-hour training session (led
by K Daley, one of the original developers of the new
version of the STREAM) where the STREAM test manual
was discussed, a videotaped STREAM assessment was
scored, and the scores were discussed. Generalizability
coefficients, which indicate the extent to which a person
can generalize results to any rater, subject, or occasion,
for intrarater reliability ranged from .96 to .999 for the
subscale and total scores, and generalizability coeffi-
cients for interrater reliability ranged from .98 to .995.8

The STREAM was developed to fill what was perceived by
its developers as a need in the early 1990s, and we believe
it had good measurement properties. When used in a
clinical trial that assessed the impact of body weight
support on functional outcomes poststroke, it was able to
reflect change mediated through treadmill training with
body weight support.9 The potential of the STREAM as a
clinical evaluation tool warranted further examination
of how it relates to other stroke outcome measures
commonly used in clinical practice.

The aim of our study was to assess how the STREAM
compared with other measures of impairment and dis-
ability in people following a stroke. The objectives were:

1. To determine the degree of association between the
STREAM and other measures of impairment and
disability during the first 3 months poststroke.

2. To determine if the STREAM could be used to
differentiate different levels of performance on mea-
sures of balance and independence of activities of
daily living immediately following a stroke and at 5
weeks and 3 months poststroke.

3. To assess whether the STREAM scores obtained dur-
ing the first week poststroke could be used to predict
discharge destination and to compare this ability with
that of the Barthel Index.

4. To assess whether the STREAM and other standard
measures used to evaluate the effects of stroke and
rehabilitation during the first week poststroke could
be used to predict independence in activities of daily
living and gait speed scores 3 months poststroke.

5. To examine the extent to which scores on the
STREAM reflect change over time as compared with
other measures used to evaluate the effects of stroke
and rehabilitation.

Method

Study Design
This investigation was part of a longitudinal cohort study
designed to examine the recovery of UE and LE function
following a stroke. The methods, as well as the profile of
recovery poststroke, have been reported by Salbach et
al10 for the LEs and by Higgins ( J Higgins, unpublished
research) for the UEs. Recovery from stroke is most
rapid in the first few weeks following stroke, but contin-
ues up to 3 months following stroke.11,12 Because most
people are expected to show improvements during the
acute period after a stroke, we believe this is an impor-
tant time period during which to assess the usefulness of
the STREAM. A cohort of patients with residual physical
deficits following an acute stroke was followed over a
3-month period. Patients were evaluated during the first
week poststroke and 4 weeks and 3 months later. During
each evaluation, patients were assessed with measures of
impairment and disability.

Subjects
Consecutive patients with a first-time stroke according to
clinical and radiological criteria who had been admitted
to 1 of 5 large urban acute care university teaching
centers in Montreal, Canada, were identified. Patients

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were considered eligible for participation in the study if
they had no apparent cognitive impairment and if they
demonstrated stroke-related physical deficits of the UEs
and LEs. Patients were excluded if they had completely
recovered from the stroke, if they had severe language
deficits, or if they had comorbid conditions such as
disabling arthritis, Parkinson disease, amputation, or
severe cardiovascular disease. Each subject was required
to sign a consent form before being enrolled in the
study.

In total, 357 consecutive patients were identified for the
study. Of these, 189 patients met the inclusion criteria
and were considered eligible for participation in the
study, 78 patients were approached, and 67 patients
consented to participate. The remaining patients could
not be recruited because they were already enrolled in
other research projects or were unavailable at the time of
the first evaluation. Of the 67 consenting patients,
sufficient data were obtained for 63 subjects. Table 1
summarizes the clinical and demographic characteristics
of the final participants and nonparticipants for this
study.

Measurement
Once consent was obtained, information related to the
occurrence of the stroke, the patient’s medical history,

and sociodemographics was recorded directly from the
medical records. Subjects were classified according to
stroke severity using the Canadian Neurological Scale
(CNS).13 Three of the investigators (SA, JH, and NMS)
served as evaluators throughout the study. The evalua-
tors participated in a training session, and any difficulties
with scoring items were discussed to obtain a consensus.
In addition to the STREAM, the following instruments
were administered at each evaluation.

The CNS13 measures neurological status in patients with
stroke and is divided into 2 sections: mentation, and
motor function. Concurrent validity was evaluated by
comparing the CNS with a standard neurologic evalua-
tion, resulting in Spearman rank correlation coefficients
ranging from .574 to .775 (P�.001). The CNS has been
used to classify patients as having a mild (CNS�11),
moderate (9�CNS�11), or severe (CNS�9) stroke.13 A
Cronbach alpha of .79 was reported for the internal
consistency of the CNS among a sample of 155 individ-
uals with stroke.13 Interrater reliability testing resulted in
a Spearman correlation coefficient of .924 for the entire
measure, and kappa statistics ranged from .535 to 1.00
for the interrater reliability of individual items.13

The Barthel Index14 is a self-proxy questionnaire that is
designed to measure 3 categories of function: self-care,
continence of bowel and bladder, and mobility. It is
composed of 10 items and has a maximum score of
100.15 There is support for the reliability14,16 –18 and
validity15,19 –21 of Barthel Index scores. Wolfe et al22
reported a test-retest kappa value of .89 among 50
patients with stroke, and Roy et al18 reported a Pearson
product moment correlation coefficient of .99 for inter-
rater reliability for a sample of 25 inpatients in a
neurorehabilitation unit. Granger and colleagues15
found that patients who scored between 5 and 40 were
less likely to return home than those who scored in the
range of 41 to 60. Individuals who scored between 60
and 100 had a shorter hospital length of stay. Cutoff
scores of 60 (dependence) and 85 (independence) also
have been reported.15

The Balance Scale, developed by Berg and col-
leagues,23,24 is a measure that consists of 14 task-oriented
items, each scored on a scale from 0 to 4. An intraclass
correlation coefficient (ICC) of .97 for intrarater reli-
ability was reported for a group of 18 elderly nursing
home residents, and an ICC of .98 was reported for
interrater reliability for the total score among a group of
35 individuals with stroke.23 There is supporting evi-
dence for validity of Balance Scale scores in subjects with
stroke.25,26 Pearson product moment correlation coeffi-
cients between the Balance Scale and the Barthel Index,
the Fugl-Meyer Stroke Assessment Scale, the Timed “Up
& Go” Test (TUG), and the Tinetti Balance Scale ranged

Table 1.
Demographic and Clinical Characteristics of Study Participants and
Eligible Nonparticipantsa

Characteristic
Participants
(n�63)

Nonparticipants
(n�122) P

Age (y)
X 67 70 .561
SD 14 13
Range 25–95 34–100

Sex, n (%)
Male 39 (62) 67 (55) .363
Female 24 (38) 55 (45)

Side of lesion, n (%)b

Right 31 (49) 46 (38) .623
Left 30 (48) 38 (31)
Bilateral 2 (3) 1 (0)
Missing 0 37 (30)

Type of stroke, n (%)
Ischemic 59 (94) 66 (54) .046
Hemorrhagic 4 (6) 14 (11)
Missing 0 42 (34)

Stroke severity, n (%)
Mild 12 (19) 44 (36) .007
Moderate 33 (52) 38 (31)
Severe 18 (29) 18 (15)
Missing 0 22 (18)

a Student t test used for comparison of age; chi-square test used for all other
comparisons.
b Percentages may not add up to 100 because of rounding.

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from .67 to .94.26 Balance Scale scores have been divided
into 3 groups that roughly correspond to ambulatory
status: poor�0 –20, fair�21– 40, and good�41–56.27

Gait speed was timed over a 5-m distance. The starting
mark was placed 2 m before the test section, and the
stopping mark was placed 2 m after the test section to
allow for acceleration and deceleration. Several
researchers have estimated the reliability of gait speed
measurements obtained in a clinical setting with a
stopwatch over distances of 5 m,10 6 m,28 8 m,29 and
10 m.30 Intraclass correlation coefficients for test-retest
and interrater reliability have ranged from .89 to
1.00.29 –34 A study conducted by Salbach and col-
leagues,10 which compared comfortable and maximum
gait speeds for 5- and 10-m distances, indicated that a
5-m comfortable walking speed was the most sensitive to
change. Therefore, the 5-m walking distance at a com-
fortable pace was chosen for our study.

The TUG35 is considered to be a test of functional
mobility. The patient is seated in a chair with armrests,
and the time taken to stand up, walk forward 3 m, and
return to the seated position is measured. Test-retest
reliability and interrater reliability (ICCs of �.98) were
demonstrated in 10 elderly institutionalized people
rated by 2 therapists on 3 occasions.36 Among 12 people
with neurological disorders, coefficients for interrater
and intrarater reliability also were high (ICC�.93–.99),
and TUG measurements correlated with measurements
of walking speed (r �.71–.96).37 Although the correla-
tions were high, the numbers of subjects in these studies
were very small. Values for elderly individuals ranged
from 7 to 10 seconds.35

The Box and Block Test38 is used to measure unilateral
gross manual dexterity. This test involves the patient
moving as many blocks as possible, one by one, from one
compartment of a box to another compartment of equal
size within 60 seconds. Desrosiers and colleagues38
examined the test-retest reliability and construct validity
of measurements taken with this instrument in a group
of elderly people with UE impairment. The ICCs ranged
from .89 to .97, and correlations were demonstrated
among the Box and Block Test and an upper-limb
performance measure (ICC�.80 –.82), and a measure of
functional independence (ICC�.42–.54).38

Data Analysis
To determine the degree of association between the
STREAM and other measures of impairment and disabil-
ity, Pearson correlation coefficients were used in our
study. Scores of the subscales and the total STREAM
were correlated with scores from the Box and Block
Test,38 the Balance Scale,23 gait speed,10 the TUG,35 and
the Barthel Index.14 Correlations between 0 and .25 were

considered low, those between .25 and .5 were consid-
ered fair, those between .5 and .75 were considered
moderate, and those greater than .75 were considered
strong.39 We expected a moderate correlation between
the STREAM and other measures of impairment and
disability. All correlations were examined cross-
sectionally on data obtained at entry to the study, 4
weeks later, and 3 months poststroke.

We also wanted to assess the ability of the STREAM to be
used to differentiate among groups of individuals with
stroke on the basis of performance on measures of
balance and independence in activities of daily living
immediately after stroke and 5 weeks and 3 months after
stroke. For this analysis, subjects were grouped accord-
ing to scores on the Barthel Index and the Balance Scale.
As described in the “Measurement” section, 3 classifica-
tion groups were formed (good, fair, and poor) for each
measure based on cutoff points from the literature.27 As
these groups are based on clinical criteria, the ability of
mean scores on the STREAM to differentiate among
these groups may reflect the clinical usefulness of the
STREAM. An analysis of variance was used to test
whether the mean STREAM scores differed across the 3
groups.

To assess the ability of the STREAM to be used to predict
discharge destination from the acute care hospital, the
probability of being discharged home was examined.
Some authors40,41 have identified functional ability, as
measured with the Barthel Index, as an important pre-
dictor of discharge destination, and we therefore com-
pared the predictive ability of the Barthel Index with
that of the STREAM. For these analyses, each possible
value of the initial STREAM and Barthel Index scores,
from 0 to 100, was used as a cutoff point, and the
probability of discharge home was calculated for individ-
uals with values at or below the cutoff point. The
probability of discharge home was then plotted against
consecutive cutoff points on the STREAM and the
Barthel Index.

To test the ability of initial poststroke STREAM scores to
predict Barthel Index scores and gait speed 3 months
poststroke, multiple linear regression was used. The
predictive ability of the STREAM was compared with that
of all other measures of impairment and disability in this
study. We used standardized betas, which are interpreted
in terms of standard deviation units; for every 1–stan-
dard deviation change in the independent variable
(initial scores on the STREAM or any of the other
measures of impairment and disability), there is a mean
increase (of beta) in the dependent variable (Barthel
Index or gait speed scores at 3 months poststroke).42
Because a different scale is used for each measurement,
the standardized regression coefficient provides an esti-

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mate that is comparable between the measures. In
addition, we examined the R2 , which is the variance in
the dependent variable (Barthel Index or gait speed
scores at 3 months poststroke) explained by the initial
STREAM score (or by the initial score on one of the
other measures of impairment and disability: the Box
and Block Test, the Balance Scale, gait speed, the TUG,
and the Barthel Index) In all models, potential con-
founding variables such as age, sex, type and side of
lesion, level of cognition, and perceptual neglect were
included. Because of the large number of potential
confounding variables, this group of variables was first
modeled alone, and only the significant confounding
variables (P�.05) were retained in the final regression
model. Separate regression models were used for each
scale (STREAM, Box and Block Test, Balance Scale, gait
speed, TUG, and Barthel Index). Residual and diagnos-
tic plots were examined to assess for problems with
outliers, major deviations from normality, linearity, and
multicollinearity for the multiple linear regression and
other analyses.

To compare whether the STREAM could be used to
reflect change compared with other outcome measures
in people with strokes, we used the standardized
response mean (SRM) and the 95% confidence interval,
calculated using Liang and colleagues’ method.43 The
SRM is calculated as the mean change found in a
particular measure divided by the standard deviation of
the change score for the measure. We made compari-
sons with the entire sample as well as within each
subgroup (ie, mild, moderate, and severe) classified by
scores on the CNS. Comparisons among all of the
measures were made for the 3 time intervals: (1) from
the initial evaluation to the 5-week evaluation, (2) from
the 5-week evaluation to the 3-month evaluation, and
(3) from the initial evaluation to the 3-month evaluation.
A measure that is useful for assessing change over time
should have a small ceiling effect. The ceiling effect of
each measure used in this study was estimated by calcu-
lating the percentage of individuals who had attained
the maximum score for each evaluation.

Thirteen participants were not able to perform any of
the items on the Balance Scale during the first scheduled
visit. For these subjects, there was no information on the
Balance Scale at the subsequent visits as well. Thus, the
values for the initial evaluation were set at 0, because this
score correctly reflects the subjects’ inability to perform
any of the items. For these 13 subjects, values were
created for the second and third evaluations by giving
them the mean Balance Scale values of the subgroup of
subjects who had complete data on the Balance Scale
with a similar range of gait speed scores (because the
Balance Scale scores were highly correlated with mea-
surements of gait speed).

At the initial, 5-week, and 3-month evaluations, there
were 17, 7, and 2 individuals, respectively, who were
unable to perform the TUG because it was difficult for
them to stand from a seated position. Therefore, for
most analyses, we considered only whether or not they
could perform the test by using a variable called “TUG
ability,” which had 2 values (1 and 0) depending on
whether the person was able or not able to perform the
test. To assess the ability of the TUG to be used to assess
change, values for those participants who were unable to
perform this test were replaced with twice the maximum
score of the entire study sample at each evaluation,
because a high score on the TUG reflects worse func-
tional mobility.

To assess the impact of the created values, all analyses
were performed with and without the created scores.
The Statistical Analysis System (Windows version 6.12)*
was used.

Results
The first evaluation was performed an average of 8 days
poststroke (SD�3, range�3–14). There was a mean of
29 days (SD�5, range�19 –50) between the first and
second evaluations and a mean of 85 days (SD�17,
range�37–124) between the second and last evalua-
tions. The mean and median scores for all measures are
presented in Table 2. This table shows that mean scores
improved over time.

The correlations between the total score of the STREAM
and the scores for other measures of impairment and
disability ranged from r �.36 to r �.80 for the 3 evalua-
tions (Tab. 3). The total and subscale STREAM scores
for the 3 evaluations also were correlated with severity of
the stroke as measured by the CNS, with correlations
ranging from r �.66 to r �.77.

At the time of the initial evaluation, we could use the
STREAM to differentiate different levels of performance
on the Balance Scale (Tab. 4). The mean scores of the
STREAM for the 3 classification groups (good�41–56,
fair�21– 40, and poor�0 –20 on the g Balance Scale)
were different from each other (P�.05), with a mean
difference in the STREAM score of 13.4 between good
and fair, and 33.6 between fair and poor (Tab. 4). At 5
weeks poststroke, all means were different (good-fair
mean difference�22, P�.05) except between the partic-
ipants in the fair and poor groups (fair-poor mean
difference�7, P�.05). The sample sizes for these 2
groups at 5 weeks were much smaller due to improve-
ments in subjects’ scores, and therefore the power to
detect a difference also was reduced.39 After 3 months,
there were no patients classified as poor, and a differ-

* SAS Institute Inc, PO Box 8000, Cary, NC 27511.

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ence of 23.7 was found between those classified as fair
and good (P�.0001). For the Barthel Index classifica-
tion, the mean difference in the STREAM score between
the fair and poor groups was 28.6 and 28.9 at the initial
and 5-week evaluations, respectively (P�.05). The mean
difference in the STREAM score between the fair and
poor groups was 19.1 (P�.05) at the initial evaluation

and 4.1 (P�.05) at the 5-week evaluation. As the sample
sizes were much smaller for the fair and poor groups at
the 3-month evaluation, none of the mean differences
were different from each other.

We examined our ability to use the STREAM to predict
discharge home from the acute care hospital (Fig. 1).

Table 2.
Performance of Study Subjects (n�63) on Measures of Impairment and Disability at 3 Points in Time Poststrokea

Measure

Initial Evaluation 5-Week Evaluation 3-Month Evaluation

X SD Median Range X SD Median Range X SD Median Range

STREAM
Total 75 26.7 86 7–100 86 19.1 94 18–100 89 18.0 97 21–100
UE subscale 73 33.3 90 0–100 85 26.2 100 0–100 88 24.0 100 0–100
LE subscale 75 28.9 85 0–100 86 22.3 95 0–100 90 19.0 100 15–100
Mobility subscale 74 25.9 83 10–100 88 16.4 97 39–100 91 15.0 97 33–100

Box and Block Test
Affected UE 25 21.0 27 0–77 36 22.8 43 0–80 41 22.2 46 0–80
Unaffected UE 49 13.8 47 21–81 56 11.8 56 27–84 56 12.7 58 24–83

Barthel Index 72 27.9 85 5–100 86 20.4 100 30–100 92 14.0 100 40–100

Balance Scale
Missing values

imputed 34 21.2 39 0–56 44 15.9 52 0–56 48 10.0 52 22–56
Missing values

removed (n�50) 37 18.1 41 0–56 47 11.5 52 9–56 49 8.3 52 22–56

TUG(s)
Missing values

imputed 73 87.1 21 7–106 34 49.2 13 7–83 21 25.9 11 7–72
Missing values

removed (n�46) 21 16.5 22 8–106 12 5.3 13 7–83 12 4.0 11 7–72

Gait speed (m/s) 0.55 0.38 0.58 0–1.33 0.82 0.43 0.90 0–1.60 0.85 0.36 0.93 0.13–1.45

a STREAM�Stroke Rehabilitation Assessment of Movement, UE�upper extremity, LE�lower extremity, TUG�Timed “Up & Go” Test.

Table 3.
Pearson Correlations for the Stroke Rehabilitation Assessment of Movement (STREAM) Total and Subscale Scores With Other Measures of
Impairment and Disability at 3 Points in Time Poststroke (n�63)a

STREAM Evaluation

Box and
Block Test
(Affected UE)

Box and
Block Test
(Unaffected UE)

Barthel
Index

Balance
Scale

TUG
Ability

Gait
Speed

Total Initial .73 .36 .78 .75 .80 .74
5 weeks .77 .37 .71 .68 .64 .62
3 months .78 .44 .75 .65 .57 .73

UE Initial .78 .31 .67 .57 .69 .56
5 weeks .79 .36 .66 .61 .49 .53
3 months .76 .31 .67 .53 .60 .64

LE Initial .53 .40 .71 .73 .75 .74
5 weeks .64 .29 .59 .55 .59 .55
3 months .70 .30 .63 .55 .51 .65

Mobility Initial .66 .55 .84 .88 .85 .83
5 weeks .69 .40 .75 .71 .57 .65
3 months .66 .40 .82 .78 .62 .76

a UE�upper extremity, LE�lower extremity, TUG�Timed “Up & GO” Test. All correlations significant at the P�.0001 level except for the unaffected UE during
the Box and Block Test at all 3 evaluations (P �.025).

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For this analysis, the probability of being discharged
home versus being discharged to a rehabilitation center
was plotted against cutoff values for the STREAM. Below
a score of 63, the probability of being discharged home
was zero. As the STREAM score increased beyond 63, the
probability of discharge home increased in almost a
linear fashion. For example, 20% of the participants who
had a score of 80 or less on the STREAM at the time of
the initial evaluation were discharged home after the
acute care hospital. As shown in Figure 1, the ability of
the STREAM to predict discharge home was similar to
that for the Barthel Index.

The ability of the STREAM and all other measures
during the first week poststroke to predict gait speed and
Barthel Index scores after 3 months was assessed
(Tab. 5). The only confounding variables were age and
cognition (P�.05), and these variables were included in
each of the regression models. The parameter estimates
were significant (P�.002–.0001), and the STREAM, dur-
ing the first week poststroke, was able to be used to
predict gait speed and Barthel Index scores after 3
months. The standardized parameter estimates indi-
cated that a 1–standard deviation change on the
STREAM resulted in an 8-point increase on the Barthel
Index and a 0.22-m/s increase in gait speed at 3 months.
The Balance Scale was the strongest predictor of gait
speed at 3 months poststroke, followed by initial gait
speed measurements, the STREAM, and the Barthel
Index. Initial Barthel Index performance was the stron-
gest predictor of Barthel Index scores at 3 months

poststroke, followed by the TUG, the STREAM, and the
Balance Scale. At the time of the initial evaluation, the
STREAM could be used to explain a large proportion of
the variability in gait speed (R2�61%), and the Barthel
Index (R2�44%) at 3 months, second only to the
Balance Scale and the TUG, respectively.

All these analyses also were performed without imputing
missing values for the Balance Scale. When the scores of
individuals with missing data were removed from the
analysis, the mean of the Balance Scale scores increased
by 2 to 4 points and the standard deviation decreased by
2 to 4 points for the 3 evaluation periods. When imputed
values were left out of the analysis, the pattern of the
correlations between the 3 subscales of the STREAM and
the Balance Scale at the initial evaluation changed
slightly. In addition, our ability to use the Balance Scale
to predict gait speed and Barthel Index scores decreased
tremendously, with the standardized beta coefficients
decreasing to 4 and 0.18, respectively.

The SRMs and their confidence intervals for all mea-
sures are shown in Table 6. Over the entire 3 months of
the study, the 3 measures most able to reflect change
were gait speed (1.15), the total score on the STREAM
(0.96), and the Balance Scale (0.94). The measure least
able to reflect change was the Box and Block Test for the
affected UE (0.24). Over the first 5 weeks, the measures
that reflected the largest amount of change were the Box
and Block Test for the affected UE (1.3), followed by gait
speed (1.05) and the Balance Scale (1.0), and then the

Table 4.
Relationship Between Mean Stroke Rehabilitation Assessment of Movement (STREAM) Scores and Balance Scale and Barthel Index Score
Classifications as Good, Fair, and Poor

Classification of
Balance Scalea and
Barthel Indexb

Scores

Initial Evaluation 5-Week Evaluation 3-Month Evaluation
Balance
Scale
Barthel
Index
Balance
Scale
Barthel
Index
Balance
Scale
Barthel
Index

Good
X 91.3 88.14 93.5 91.7 94.7 91.6
95% CIc 88–95 84–92 90–97 88–95 92–97 88–95
n 30 42 45 52 49 59

Fair
X 77.9 59.5 71.5 62.8 70.1 60.8
95% CI 68–87 41–77 55–88 35–90 56–86 �35–156
n 15 8 10 7 14 3

Poor
X 44.3 40.4 64.5 58.7 47.8
95% CI 31–58 24–57 38–92 11–107
n 18 13 8 4 0 1

a Good�41–56, fair�21– 40, poor�0 –20. Significance test for the 3 Balance Scale classifications performed using analysis of variance. P �.05 except between the
fair and poor classifications at 5 weeks (P�.05). Significance was calculated using the F statistic with 2 degrees of freedom. These results were the same when the
Bonferroni correction (type I error/number of tests [0.05/3]) was used to adjust for multiple tests of comparison.
b Good�61–100, fair�41– 60, poor�0 – 40. Significance test for the 3 Barthel Index classifications performed using analysis of variance. P �.05 except for between
the fair and poor classifications at 5 weeks and all mean comparisons at 3 months (P�.05). Significance was calculated using the F statistic with 2 degrees of
freedom. These results were the same when the Bonferroni correction (type I error/number of tests [0.05/3]) was used to adjust for multiple tests of comparison.
c CI�confidence interval.

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Barthel Index (0.97) and the total score on the STREAM
(0.94). The measure that reflected the least amount of
change was the LE subscale (0.62) of the STREAM. The
SRMs were much lower between 5 weeks and 3 months
for all measures, ranging from �0.007 to 0.47. The
STREAM had a lower ceiling effect as compared with the
Barthel Index and the TUG (Tab. 7). Table 6 also shows
the effect of the substitution strategy on the estimates of
change for participants who were unable to perform the
TUG or the Balance Scale.

Figure 2 presents the SRMs of all measures of outcome
between the initial and 5-week evaluations of the study in
subjects who sustained a mild, moderate, or severe
stroke. In all severity groups, the STREAM was 1 of the 3
measures best able to be used to reflect change. The
STREAM was able to reflect the most change throughout
the 3 months of the study for individuals who were
classified as having severe stroke, compared with those
who were classified as having mild or moderate strokes.
In the subjects with mild or moderate strokes, gait speed

and Box and Block Test scores, which
are measured on true continuous scales,
showed the greatest amount of change
compared with the other measures.

Discussion and Conclusions
Patients with motor and functional def-
icits following an acute stroke are
expected to improve11,44 and thus, we
believe, provide an ideal population in
which to assess the performance of the
STREAM relative to other measures
used to evaluate effects of stroke and
rehabilitation. The STREAM showed a
moderate to high correlation with the
other measures used in this study. This
finding is expected because the ability
to perform functional activities is
dependent on a person’s motor abili-
ty.45,46 The correlations between data
obtained for the STREAM and its sub-
scales and data obtained for the Box
and Block Test, the Balance Scale, gait
speed, the TUG, and the Barthel Index
were always less than .9, indicating that
the STREAM may be related to these
scales, but is reflecting a different com-
ponent of recovery. In addition, mean
STREAM scores could be used to dis-
tinguish between different levels of per-
formance on the Barthel Index and the
Balance Scale. The cutoff criteria for
the Barthel Index were based on what
we considered clinically relevant vari-
ables, including the probability of
going home and hospital length of stay,

and the cutoff criteria for the Balance Scale taken from
the literature15,17 were based on ambulatory status. The
ability of mean STREAM scores to be used to distinguish
among the group classifications based on Balance Scale
and Barthel Index scores reflects its potential relation-
ship to these clinical variables.

The ability to predict discharge destination and func-
tional ability in individuals with stroke admitted to an
acute care hospital allows prompt discharge planning,
which may minimize hospital length of stay. The
STREAM showed a usefulness comparable to that of the
Barthel Index for predicting discharge destination from
an acute care hospital. Compared with the Box and
Block Test, the STREAM during the initial evaluation
was better able to predict gait speed and functional
ability 3 months poststroke, but its prognostic ability for
these outcomes was similar to that of the Balance Scale,
gait speed, the TUG, and the Barthel Index.

Figure 1.
The probability of being discharged home after the acute care hospital given an initial score on
the Stroke Rehabilitation Assessment of Movement (STREAM) or the Barthel Index � cutoff value
(n�63).

Physical Therapy . Volume 83 . Number 7 . July 2003 Ahmed et al . 625

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The STREAM was one of the 3 measures with scores that
changed the most over the entire 3 months of the study
(Tab. 6). Interestingly, the STREAM, the Balance Scale,
and the Barthel Index, all scored on an ordinal scale,
were able to reflect the most change throughout the first
5 weeks for individuals we classified as having severe
problems as compared with those who we classified as
having mild or moderate problems (Fig. 2). In the
subjects we classified as having mild or moderate prob-
lems, gait speed, which is measured on a continuous

scale, was found to show the greatest amount of change
compared with the other measures. It may be that in
individuals who have had a severe stroke, changes need
to occur in the components necessary for walking, which
can be assessed with the STREAM and the Balance Scale,
before recovery of gait speed is seen. These results agree
with those of Richards et al,47 who found that, in a group
of patients who were low-level performers and walked at
very slow speeds, the Balance Scale, and to a lesser extent
the Barthel Index ambulation score and the Fugl-Meyer

Table 5.
Relationship of Initial Scores on Measures of Impairment and Disability to Gait Speed and Barthel Index Scores at 3 Months Poststroke (n�63)

Measure

Gait Speeda Barthel Index

Standardized
Parameter
Estimateb (P Value) R2c

Standardized
Parameter
Estimateb (P Value) R2

STREAMd 0.22 (.0001) 61 8 (.0001) 44

Balance Scale 0.24 (.0001) 62 8 (.0001) 39

Box and Block Test (affected extremity) 0.13 (.0001) 44 5 (.002) 26

TUGe ability 0.21 (.0001) 60 9 (.0001) 49

Gait speed 0.23 (.0001) 60 7 (.002) 31

Barthel Index 0.22 (.0001) 56 10 (.0001) 50

a Measured in meters per second.
b Standardized regression coefficient��standard deviation. Adjusted for age and cognition.
c The percentage of variability in gait speed or the Barthel Index accounted for by the corresponding measures in the first column, adjusted for age and
cognition. The R2 for age and cognition was 30% for gait speed and 13% for the Barthel Index.
d STREAM�Stroke Rehabilitation Assessment of Movement.
e TUG�Timed “Up & Go” Test.

Table 6.
Standardized Response Means (95% Confidence Interval) for All Measures (n�63)a

Measure

Initial
Evaluation to
5-Week
Evaluation

5-Week
Evaluation to
3-Month
Evaluation

Initial
Evaluation to
3-Month
Evaluation

STREAM
Total score 0.94 (0.72 to 1.11) 0.37 (0.10 to 0.52) 0.96 (0.74 to 1.13)
UE score 0.72 (0.54 to 0.87) 0.22 (�0.05 to 0.48) 0.72 (0.55 to 0.87)
LE score 0.62 (0.36 to 0.80) 0.32 (0.08 to 0.55) 0.83 (0.57 to 1.02)
Mobility score 0.81 (0.59 to 0.97) �0.22 (�0.02 to 0.46) 0.85 (0.66 to 1.00)

Box and Block Test
Affected UE 1.30 (0.86 to 1.33) �0.20 (�1.93 to 1.15) 0.24 (�1.93 to 1.15)
Unaffected UE 0.89 (0.54 to 1.04) �0.007 (�0.27 to 0.24) 0.82 (0.48 to 0.98)

Barthel Index 0.97 (0.76 to 1.14) �0.42 (0.07 to 0.62) 0.91 (0.60 to 1.10)

Balance Scale
Missing values imputed 0.95 (0.73 to 1.14) �0.47 (0.23 to 0.69) 0.94 (0.71 to 1.14)
Missing values removed (n�50) 1.04 (0.64 to 1.05) �0.31 (0.03 to 0.51) 0.89 (0.54 to 0.90)

TUG (s)b

Missing values imputed �0.63 (�0.50 to �0.75) �0.37 (�0.24 to �0.50) �0.69 (�0.53 to �0.84)
Missing values removedc �0.68 (�0.20 to �0.95) �0.16 (�0.21 to 0.49) �0.61 (�0.11 to �0.89)

Gait speed (m/s) 1.05 (0.79 to 1.24) �0.17 (�0.13 to 0.43) 1.15 (0.80 to 1.43)

a STREAM�Stroke Rehabilitation Assessment of Movement, UE�upper extremity, LE�lower extremity, TUG�Timed “Up & Go” Test.
b Values are negative because a lower number reflects a better score.
c Initial evaluation to 5-week evaluation, n�46; 5-week evaluation to 3-month evaluation, n�56; initial evaluation to 3-month evaluation, n�46.

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leg subscore, were more discriminative than gait speed
for the amount of physical assistance needed to ambu-
late. In comparison, for subjects achieving 50% of nor-
mal gait speed values, these scales became less discrimi-
native and plateaued, whereas gait speed continued to
improve 6 and 12 weeks following stroke.47 In our study,
after gait speed and the Box and Block Test, the

STREAM was able to reflect the most
change in the subjects we classified as
having mild or moderate problems
during the 5 weeks poststroke.

To our knowledge, only 2 other mea-
sures, the Fugl-Meyer Stroke Assess-
ment Scale46,48 and the disability
inventory of the Chedoke-McMaster
Stroke Assessment,49,50 have been
studied for their ability to reflect
change. In one study,46 the Fugl-Meyer
scale scores were found to change
much less than Barthel Index and
Balance Scale scores. The Barthel
Index demonstrated a greater ability
to reflect a treatment effect than other
measures of neurological status, stroke
severity, and motor recovery in
patients with acute stroke.46 The Bar-
thel Index is considered by some
authors48 to be a “gold standard”
against which new instruments may be
evaluated. In our study, the correla-
tions between the STREAM scores and
the Barthel Index scores for each eval-
uation ranged from .75 to .78. In addi-

tion, the SRM of the STREAM was slightly higher than
that of the Barthel Index over the first 3 months
poststroke.

A concern with any outcome measure is that a great
proportion of individuals would be at the high end of

Figure 2.
Standardized response means for stroke outcomes among individuals with mild, moderate, and
severe strokes (initial evaluation to 5-week evaluation). STREAM�Stroke Rehabilitation Assess-
ment of Movement, TUG�Timed “Up & Go” Test.

Table 7.
Number (%) of Subjects Reaching Maximum Scores on Stroke Measuresa

Measure
Maximum
Score

Evaluation 1 Evaluation 2 Evaluation 3

No. (%) With
Maximum Score

No. (%) With
Maximum Score
No. (%) With
Maximum Score

STREAM
Total score 100 5 (8) 15 (24) 21 (33)
UE score 100 19 (30) 32 (51) 36 (57)
LE score 100 17 (27) 31 (49) 37 (58)
Mobility score 100 12 (19) 20 (32) 28 (44)

Box and Block Test (affected UE) . . .b 4 (7) 4 (7) 4 (7)

Barthel Index 100 17 (27) 32 (51) 38 (60)

Balance Scale
Missing values removed 56 4 (8) 13 (26) 15 (30)
Missing values imputed 56 18 (29) 16 (25) 18 (29)

TUG
Missing values imputed 7–10 s 8 (13) 18 (29) 21 (38)
Missing values removed 7–10 s 9 (20) 20 (44) 21 (53)

Gait speed . . .b 2 (3) 13 (21) 13 (21)

a STREAM�Stroke Rehabilitation Assessment of Movement, UE�upper extremity, LE�lower extremity, TUG�Timed “Up & Go” Test.
b Mean sex- and age-specific normal values were used as maximum scores.

Physical Therapy . Volume 83 . Number 7 . July 2003 Ahmed et al . 627

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the scale and further improvements would be difficult to
assess. The STREAM, however, was among the 3 mea-
sures with the smallest ceiling effect for the first 2
evaluations but not the third evaluation (Tab. 7). During
the acute period after a stroke, when the impact of the
disability may not yet be manifested, fewer individuals
would be expected to reach the maximum score on the
STREAM. However, by 6 weeks after a stroke, 80% of
patients would have reached their highest level of motor
recovery.51 In our study, after 3 months, less than 40% of
individuals had reached the maximum score on the total
STREAM, and less than 60% had reached the maximum
score on the UE and LE subscales. The Barthel Index
had the greatest ceiling effect at 3 months poststroke,
with 60% of the subjects reaching the maximum score.
By 3 months poststroke, the subjects may have used all
compensatory techniques for accomplishing activities of
daily living, and better function can only be achieved
with further motor recovery. The ceiling effect of the
Barthel Index has been previously documented as one of
the limitations of this measure.21,52

The results from our study have provided some indica-
tions as to when the STREAM may be preferred over
other measures to monitor recovery from a stroke. In
our study, 26% of the subjects were not able to perform
the TUG, and 21% were not able to perform the Balance
Scale at the time of the initial evaluation. When people
are unable to complete measures that require high levels
of functioning during this acute period, a measure of
voluntary movement such as the STREAM could play an
important role in monitoring recovery. In addition, for
more severe strokes, variables measured by outcomes
such as the Box and Block Test, the Balance Scale, gait
speed, the TUG, and the Barthel Index may not repre-
sent the focus of immediate therapy and, therefore, may
not be appropriate for monitoring changes in recovery
during this acute period.53 During the time immediately
after a stroke, the focus may be on restoring voluntary
movement and basic mobility. These are assessed by the
STREAM and we believe are necessary for further func-
tional recovery. Moreover, for individuals who are
unable to perform high-level functional tests, the
STREAM can be used within the first few days poststroke
to predict the probability of discharge home from an
acute care hospital and functional potential 3 months
poststroke. This is important as the length of stay in an
acute care hospital becomes shorter.

Another important aspect of selecting an outcome mea-
sure is its clinical utility. The STREAM is relatively simple
to score as compared with other instruments of motor
recovery, it requires little equipment, and it only takes 15
minutes to administer. The ease of use is comparable to
that for the other measures examined in this study.
However, because the calculation of the final score on

the STREAM requires several steps, it may be difficult to
arrive at the total score in the presence of the patient.

Our study provides information about the relationships
of the STREAM to other commonly used measures of
stroke impairment and disability, as well as the measure-
ment properties of the STREAM. Information on the
construct validity54 of the STREAM, we believe, was
demonstrated by its correlations with the other measures
of impairment and disability12,25,45– 47,51,55– 62 and its abil-
ity to differentiate levels of performance on measures of
balance and independence in activities of daily living.
The ability to use STREAM scores to predict important
outcomes of stroke, including discharge home, func-
tional independence, and gait speed, has resulted in
information about its predictive validity.63 The longitu-
dinal validity,6 which is the ability of an instrument to
reflect change, of the STREAM was demonstrated by its
capacity to monitor changes in recovery of voluntary
movement and basic mobility during the first 3 months
poststroke.

The question could be raised as to why there is a need
for yet another measure of motor recovery. When the
STREAM was developed, it filled a gap. According to a
Canada-wide survey conducted in 1992,6,64 the complex-
ity of existing measures of motor recovery was a barrier
to their use. The underlying factor that appears to be
driving use of these measures is the type of therapy being
used and the need to evaluate patients’ progress along
these therapeutic lines. The STREAM is not strongly
linked to any one theoretical framework of how recovery
occurs but rather provides a sampling of items that its
developers believe reflect the re-emergence of move-
ment and basic mobility. The STREAM, because of its
independence from a treatment philosophy, its demon-
strated measurement properties, and its ease of use,
provides therapists with an option that may emerge as a
measure of choice for the evaluation of different treat-
ment approaches.

Limitations of the Study
There were differences between the study sample and
the nonparticipants in terms of the type and severity of
stroke. The results of this study cannot be generalized to
patients who are not similar to the study sample. This
includes patients with severe cognitive impairment and
substantial comorbidities.

When testing the ability of the STREAM to predict
discharge destination, the outcome was dichotomized as
“home” versus “not home” because most patients either
went home or to rehabilitation. Testing the STREAM in
a sample of patients with stroke where discharge desti-
nation is more variable would provide more information

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with regard to its ability to discriminate between patients
on this important outcome.

Another limitation of our study was that the first evalu-
ation was done an average of 8 days poststroke. Motor
recovery can take place during the first 10 days post-
stroke; therefore, some patients may have experienced
recovery before the first evaluation. Not having evalu-
ated this early recovery may have reduced the variability
in scores for all measures used in this study and may have
underestimated their ability to predict the level of inde-
pendence in functional activities of daily living and gait
speed. In addition, this may have resulted in lower
estimates of SRMs.

The methods we used to handle the missing data were
only estimates of the true level of recovery. Some of the
results may have been overestimated or underestimated.
During the data analysis, however, care was taken to
ensure that the imputed values did not cause large
influences on the distribution of scores, and the results
were compared with and without these values. Missing
data on the TUG limited our ability to examine the
relationship of this measure to the STREAM.

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Motor Learning of a Dynamic
Balancing Task After Stroke: Implicit
Implications for Stroke Rehabilitation

Background and Purpose. After a stroke, people often attempt to
consciously control their motor actions, which, paradoxically, disrupts
optimal performance. A learning strategy that minimizes the accrual of
explicit knowledge may circumvent attempts to consciously control
motor actions, thereby resulting in better performance. The purpose
of this study was to examine the implicit learning of a dynamic
balancing task after stroke by use of 1 of 2 motor learning strategies:
learning without errors and discovery learning. Participants and Meth-
ods. Ten adults with stroke and 12 older adults practiced a dynamic
balancing task on a stabilometer under single-task (balance only) and
concurrent-task conditions. Root-mean-square error (in degrees) from
horizontal was used to measure balance performance. Results. The
balance performance of the discovery (explicit) learners after stroke
was impaired by the imposition of a concurrent cognitive task load. In
contrast, the performance of the errorless (implicit) learners (stroke
and control groups) and the discovery learning control group was not
impaired. Discussion and Conclusion. The provision of explicit infor-
mation during rehabilitation may be detrimental to the learning/
relearning and execution of motor skills in some people with stroke.
The application of implicit motor learning techniques in the rehabil-
itation setting may be beneficial. [Orrell AJ, Eves FF, Masters RSW.
Motor learning of a dynamic balancing task after stroke: implicit
implications for stroke rehabilitation. Phys Ther. 2006;86:369 –380.]

Key Words: Motor learning, Rehabilitation, Stroke.

Alison J Orrell, Frank F Eves, Rich SW Masters

Physical Therapy . Volume 86 . Number 3 . March 2006 36

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B
alance control is a fundamental motor behav-
ior in stance and gait that allows an individual
to maintain and adopt various postures, react
to external perturbances, and use automatic

postural responses that precede voluntary movements.1,

After stroke, many people find it more difficult to
perform some or all of these tasks. Thus, the learning/
relearning of balance control is a primary goal of stroke
rehabilitation.

Balance control requires the integration of visual,
somatosensory, and vestibular inputs and their adapta-
tions to changes in the environment and in the task
being performed.3 Some degree of attention is required
to maintain balance,1,4 – 6 with greater attentional
demands after stroke.7 Furthermore, higher integrative
levels have a role in balance control, because the intro-
duction of a concurrent cognitive task, such as talking,
can impair balance after stroke.8,9 Indeed, a loss of
fluency and automaticity of balance control after stroke
has been attributed to a trade-off among available cog-
nitive resources.

As a corollary to stroke, cognitive deficits can occur in
the domains of language, orientation, attention, and
memory.10 –12 Such deficits will affect the ability of peo-
ple to learn/relearn motor skills. Current rehabilitation
therapies, which are based on traditional motor learning
theories, typically involve the concurrent performance
of motor and cognitive tasks. Thus, people receive many
complex and explicit instructions on how to perform
tasks and are encouraged to evaluate performance out-
comes. The provision of many explicit instructions by
the therapist may be confusing for people because
cognitive deficits affecting memory and attention a

associated with a reduction in the speed of information
processing. Thus, many people with stroke find it very
difficult to perform concurrent tasks, such as walking
and listening, during rehabilitation. Crucially, the learn-
ing or relearning of motor skills with a concurrent
cognitive task may be diminished by the presence of
cognitive deficits after stroke, calling into question the

effectiveness of current rehabilitation strategies. A learn-
ing or relearning strategy that minimizes concurrent
cognitive tasks would be particularly advantageous for
stroke rehabilitation.

Implicit learning refers to the learning of information
without the ability to verbally describe the knowledge of
what is learned. Implicit learning is characterized as
being a relatively passive process in that people are
exposed to information and can acquire knowledge of
that information simply through exposure, such as lan-
guage learning and learning to ride a bicycle.13,14 In
contrast, explicit learning is related to the ability to
describe verbally something that is being learned, such
as tying a shoelace, and is characterized as an active
process in which people seek out the structure of any
information that is presented to them, such as solving a
geometric problem and hypothesis testing.13,*

Conventional wisdom advocates that motor skill control
progresses from explicit or conscious control in the early
stages of learning to a more implicit or automatic
control when well learned.15,16 In the early stages, rules
to avoid performance errors can be recalled consciously
or explicitly as the learner attempts to avoid errors
during succeeding performances. As learning continues,
these explicit rules are lost or “forgotten” as the process-
ing of task-relevant information becomes unconscious.
The skill then is referred to as being automated or
implicit.15,16 A limitation of explicit processing, however,
is its dependence on the cognitive resources of working

* Procedural knowledge refers to “knowing how” and underlies the performance of
actions. Declarative knowledge refers to “knowing what” and is knowledge of facts
and relationships. For the purposes of this article, the distinction between
declarative knowledge and procedural knowledge can be equated approximately
with the distinction between explicit knowledge and implicit knowledge, as
implicit knowledge, like procedural knowledge, is generally inaccessible, whereas
declarative knowledge is generally accessible and thus is explicit. Therefore,
implicit learning encompasses procedural knowledge, but the 2 terms are not
interchangeable.

AJ Orrell, PhD, is Research Fellow, Department of Health Sciences, University of York, Heslington, York, YO10 5DD, United Kingdom
(ao8@york.ac.uk). Address all correspondence to Dr Orrell.

FF Eves, PhD, is Senior Lecturer, School of Sport and Exercise Sciences, University of Birmingham, Birmingham, United Kingdom.

RSW Masters, DPhil, is Assistant Director of Research, Institute of Human Performance, The University of Hong Kong, Hong Kong.

All authors provided concept/research design. Dr Orrell provided writing and data collection and analysis. Dr Eves provided data analysis and
project management. Dr Masters provided project management and consultation (including review of manuscript before submission).

This study was approved by the South Birmingham Local Research Ethics Committee and the University of Birmingham School of Sport and
Exercise Sciences Safety and Ethics Subcommittee.

This article was received December 15, 2004, and was accepted September 6, 2005.

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memory.† More recent approaches to motor skill acqui-
sition emphasize that implicit learning occurs indepen-
dently of the influence of explicit knowledge.17 Implicit
motor learning refers to the acquisition of a motor skill
without the concurrent acquisition of explicit or verbal
knowledge about the performance of that skill.18 Implicit
processes are considered to function independently of
working memory.19 Thus, skill acquisition always involves
implicit learning and questions the assumptions of tradi-
tional motor learning models, in which skill acquisition
proceeds from an explicit state to an automated state.20,21
Recent research on the implicit acquisition of motor skills
supports this premise, as the implicit learning of complex
motor skills, such as golf putting and the topspin forehand
in table tennis, has been demonstrated in people who are
nondisabled.18,22–25

Several strategies have been developed to promote
implicit motor learning in people who are nondisabled:
learning with a concurrent task of random-letter gener-
ation to block working memory18; learning by analo-
gy,23,26 in which the biomechanical rules of a task are
disguised in the form of an image (eg, the topspin
forehand in table tennis has been successfully taught
with the analogy of bringing the bat up the hypotenuse
of a right-angled triangle23); and learning without
errors.25 These strategies are hypothesized to promote
an implicit mode of motor learning by impeding or
circumventing explicit processing and so disrupting the
accumulation of explicit knowledge relating to the
motor skill to be learned. Particularly promising for
learning/relearning after stroke is the strategy of learn-
ing without errors because it requires no additional
cognitive load.25 By reducing the number of errors made
by the learner during skill acquisition, the opportunity
for the explicit testing of hypotheses and error correc-
tion is reduced. Inhibition of the formation of explicit
knowledge of the task is hypothesized to promote an
implicit mode of learning.25 This hypothesis has been
tested by reducing the number of errors made by the
learner when learning a golf putting skill.25 Errorless
learners produced a higher level of performance during
retention than explicit learners, and their performance
was robust when a concurrent cognitive task was added.
The authors concluded that the skills acquired in an

error-free environment lessened the demand for explicit
attentional resources.25 In addition, the authors con-
cluded that learning without errors conferred an
implicit and robust mode of learning.25

Implicit motor learning of a dynamic balancing task
recently was investigated in a sample of young adults who
were nondisabled.27 Participants were required to keep a
stabilometer platform horizontal for 60 seconds in each
trial. In that study, 3 different learning conditions were
tested. Two groups learned with strategies to promote
implicit learning, that is, either analogy learning or
errorless learning, whereas the third group (explicit
learning) was required to actively discover the rules of
the task. The results showed that learning of the balanc-
ing task was implicit in character for the analogy learners
and for the errorless learners (ie, the learners accumu-
lated a minimal number of explicit rules of the task),
and the learning was durable over time and robust
under secondary task loading. Interestingly, balance
performance improved when the verbal component of
working memory was occupied with a nonbalancing task
(either a number recall task or a tone counting task).
The authors reasoned that implicit processes were the
main contributors to the learning of and performance of
the balancing task and that the use of explicit, verbal
information while performing the balancing task actu-
ally impeded optimal performance. This finding has
implications for rehabilitation. After a stroke, people
often attempt to consciously control their motor
actions,28,29 whereas people who are nondisabled seldom
use conscious control for routine movements.30 A learning
strategy that impairs the accumulation of explicit knowl-
edge may circumvent attempts to consciously control
motor action, thereby resulting in better performance.18

The application of implicit motor learning strategies
may be beneficial in stroke rehabilitation. Implicit learn-
ing confers robustness of performance with a concurrent
task and is durable over time.13 Furthermore, recent
evidence from the implicit learning literature suggests
that implicit learning processes are retained in some
people with stroke when tested with a serial reaction
time task.31–33 To date, however, no studies have investi-
gated the application of implicit motor learning tech-
niques after stroke by use of a “real-life” task. Thus, the
purpose of this study was to investigate the implicit
motor learning of a dynamic balancing task after stroke
by use of an errorless learning paradigm. People after
stroke and a control group learned a dynamic balancing
task with 1 of 2 different strategies. Thus, an errorless
learning strategy (implicit) was compared with a conven-
tional discovery learning strategy (explicit). We hypoth-
esized that learning without errors would promote learn-
ing that was implicit in character. Three criteria of
implicit learning were used to test this hypothesis: the

† Working memory is a 3-part active system that stores and manipulates informa-
tion while people perform cognitive tasks. Working memory consists of a central
executive, the phonological loop, and the visuospatial sketch pad. The central
executive is a multimodal, attentional system that supervises and coordinates a
number of subsidiary “slave” systems. The phonological loop is involved in
speech-based tasks, that is, understanding the speech that people hear and
producing speech, both aloud and subvocally. In contrast, the visuospatial sketch
pad is involved in the processing of nonverbal aspects of visual images and
movement defined by allocentric coordinates. Both the phonological loop and
the visuospatial sketch pad are limited-capacity, modality-specific storage systems
of working memory.

Physical Therapy . Volume 86 . Number 3 . March 2006 Orrell et al . 371

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accumulation of few explicit rules, the durability of
learning over time, and the robustness of performance
under a concurrent cognitive load.13,23,25 We predicted
that the errorless learners would acquire less explicit
knowledge of the kinematic mechanisms of the balanc-
ing skill than the discovery learners. In addition, we
predicted that a concurrent cognitive task would impair
performance in the discovery learners but not in the
errorless learners, on the grounds that skills learned
without explicit learning should be unaffected by the
presence of a concurrent task. Finally, we predicted that
the durability of learning would be evident in a delayed
retention test for the errorless learners but not for the
discovery learners.

Method

Participants
Twelve participants with stroke resulting in hemiparesis
and aged 28 to 69 years (X�52.17 years, SD�11.27) and
a control group of 12 adults who were neurologically
intact and aged 52 to 75 years (X�65.25 years, SD�7.48

)

volunteered to participate in the study (Tab. 1). Partic-
ipants were recruited from several stroke groups in the
West Midlands, United Kingdom, and from advertise-
ments placed in a university staff magazine and a local
newspaper. Participants with stroke fulfilled the follow-
ing inclusion criteria: diagnosis of first stroke at least 12
months before the study to reduce the potential of
spontaneous recovery confounding the data, discharge
from all rehabilitation services, ability to understand
instructions and to give informed consent, and no
obvious cognitive or perceptual problems on the Mini-
Mental State Examination.34 A score of less than 24 on
the Mini-Mental State Examination34 is indicative of
dementia. Computed topography scans confirmed that
one participant had brain damage to the right cerebel-
lum and that another participant had experienced bilat-
eral stroke. The remaining participants had stroke syn-
dromes consistent with brain lesions involving the
anterior circulation system, as classified by Bamford
et al.35 The Bamford classification of stroke is widely
applicable for community-based studies or when a nar-
row therapeutic time window exists because it is simple
and relatively easy to use (Appendix). In summary, the
participants in the stroke group had motor or sensory
deficits, or both, in at least 2 of 3 body areas (face, arm,
and leg), and one participant also had dysphasia. All
participants gave informed consent and were naive with
regard to the task.

Experimental Design
For this study, we used a mixed factorial design for
repeated measures. The study was divided into 3 distinct
phases: an acquisition phase followed by a separate test

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phase and a delayed retention test performed 1 week
after the acquisition and test phases (Tab. 2).

Instrumentation and Task
Before commencing the balancing task, all participants
were instructed to keep the stabilometer platform hori-
zontal throughout each 60-second trial. Participants in
the discovery learning groups also were instructed to
discover rules of how to perform the balancing task. In
the acquisition phase, all participants performed twenty-
four 60-second trials of the balancing task. Control
group participants had a 2-minute rest interval between
trials, whereas participants with stroke had longer rest
intervals if needed. During the rest intervals, all partici-
pants attempted a jigsaw puzzle to inhibit the formation
of explicit knowledge about the balancing task gained
from explicitly processing task-relevant information. The
acquisition phase was followed by a 15-minute rest
interval, during which participants continued with the
jigsaw puzzle.

The test phase was begun after the 15-minute rest
interval. Participants performed 2 retention tests and 2
separate transfer tests. Each transfer test followed a
retention test. During the retention tests, participants
performed two 60-second trials of the primary balancing
task. For the transfer tasks, participants performed two
60-second trials of the balancing task with a concurrent
secondary task presented during the final 30 seconds of
each trial. The first transfer task was a verbal cognitive
task that required participants to recall random 6-digit
sequences presented at a rate of 1 per second. This task
was chosen because it is similar to being told a telephone
number by another person. The number recall task was
designed to suppress the use of any verbal knowledge of
the balancing task by blocking the phonological loop of
working memory.36

The second transfer task was primarily a nonverbal
motor task that required participants to shift their center
of gravity in order to reach out and pick up and hold a
1-kg kettle with 1 hand. This task was chosen because it
imitates the everyday task of lifting a full kettle of water.
To maintain balance, participants needed to make pos-

tural adjustments. Participants with
stroke used the hand ipsilateral to the
side of the stroke to lift the kettle.
Control group participants were
matched for handedness with stroke
group participants for this task. The
participant with bilateral stroke per-
formed this task with the dominant
hand. On completion of the test phase,
the participants’ explicit knowledge of
the balancing task was assessed by use
of verbal protocols. Participants were

asked to record any “rules, methods, or techniques” that
they had thought about or used and that had enhanced
or impaired their balance performance. These verbal
protocols were scored by assessing and summing the
number of explicit rules associated with the kinematic
aspects of the balancing task. A delayed retention test
was performed 1 week after the acquisition and test
phases. This test required participants to perform two
60-second trials of the primary balancing task.

All participants performed the balancing task on a
stabilometer and were required to wear a full-body safety
harness with a rear “D” ring fall arrest attachment point
throughout the experiment to remove the fear of falling
from the stabilometer platform (Fig. 1). Fear of falling is
common among older people,37 and people with stroke
have a high risk of falling on hospital discharge and
during rehabilitation.29,38,39 The stabilometer platform
(100�67 cm) was freely mounted on a horizontal axis in
the participants’ frontal plane. A maximum range of
motion of 30 degrees of deviation from horizontal was
available. Performance data were collected with a linear
potentiometer mounted on the horizontal axis and
sampled at 500 Hz by a PC with a C.E.D. 1401 plus data
acquisition board.‡ The C.E.D. 1401 plus acquisition
board records waveform data, and the on-board proces-
sor with high-speed memory allows for real-time process-
ing. Data capture and analysis were performed with
Spike 2 version 3 software.‡

Procedure
Participants from the stroke and control groups were
randomly assigned to 1 of 4 groups: (1) errorless learn-
ing stroke group, (2) errorless learning control group,
(3) discovery learning stroke group, and (4) discovery
learning control group. At the beginning of the acquisi-
tion phase, all participants were instructed to keep the
stabilometer platform horizontal throughout each
60-second trial. The discovery learning groups also were
instructed to discover rules of how to perform the
balancing task. In the errorless learning groups, a brak-

‡ Cambridge Electronic Design Ltd, Science Park, Milton Rd, Cambridge, CB4
0FE United Kingdom.

Table 2.
Characteristics of Blocks in the Acquisition and Test Phases

Blocks Day Condition Description

1–24 1 Acquisition Balancing task only
25�26 1 Test Retention test—primary balancing task only
27�28 1 Test Primary balancing task plus number recall task
29�30 1 Test Retention test—primary balancing task only
31�32 1 Test Primary balancing task plus kettle lift task
33�34 2 Delayed retention Delayed retention test—primary balancing task only

Physical Therapy . Volume 86 . Number 3 . March 2006 Orrell et al . 373

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ing resistance of 2.5 kg was applied to the stabilometer
fulcrum to fully restrict movement of the stabilometer
platform. This resistance was progressively decreased by
0.5 kg after multiples of 4 trials such that no resistance
occurred in the final 4 trials of acquisition and during
the test and retention phases; that is, the stabilometer
platform swung freely. To minimize the possibility of a
ceiling effect on performance, that is, all participants
performing at nearly perfect levels, the movement of the
stabilometer platform was fully restricted for the first 4
acquisition trials only. Although the errorless condition
does allow some errors to occur, it is conventional to call
the substantial reduction in errors during learning
“errorless” to contrast it with conditions in which no
attempt is made to minimize errors.25 During acquisi-
tion, the stabilometer platform was placed in the hori-
zontal position for the errorless learners. For the start of
all other trials, the stabilometer platform was resting on
the left side. Data collection began when the platform
crossed horizontal.

Measures
The Berg Balance Scale40 was administered before test-
ing to assess the participants’ balance ability. No pretest
measures of performance on the balancing task were
recorded because exposure to the task before acquisi-
tion might have encouraged participants in the errorless
learning conditions to adopt a hypothesis-testing strat-
egy, thus promoting an explicit rather than an implicit
mode of learning. Root-mean-square error (RMSE) (in
degrees) about the midpoint in the vertical axis of the
stabilometer was used as a measure of balance perfor-
mance during all experimental phases.

Data Analysis
During the course of the study, 2 participants with stroke
withdrew. Thus, all statistical analyses were conducted
on the data obtained from participants who completed
the study. To determine whether the stroke and control
groups were matched for balance ability, baseline bal-
ance ability was assessed by use of a 2�2 (group [stroke,
control]�condition [errorless, discovery]) analysis of
variance (ANOVA) with pretest score on the Berg Bal-
ance Scale40 as the dependent measure.

Acquisition performance was assessed over averaged
pairs of trial blocks by use of a 2�2�11 (group [stroke,
control]�condition [errorless, discovery]�block [1, 2,
3. . .11]) multivariate analysis of variance (MANOVA)
with repeated measures for block and with RMSE as the
dependent variable. All data were normally distributed
with a skewness of �1.0, and the Fmax test for hetero-
geneity of variance between groups or conditions was
never significant (all P values were �.20). All analyses
included, when appropriate, the epsilon correction for
the degrees of freedom to counteract any violation of the
assumption of equality of covariance across repeated
measures. This correction is referred to throughout this
article as repeated-measures correction. Post hoc tests
were performed to identify the locus of interactions.
Separate analyses of the 2 learning conditions were
carried out by use of a 2�11 (group [stroke,
control]�block [1, 2, 3. . .11]) univariate ANOVA with
repeated-measures correction for block and with RMSE
as the dependent variable.

Retention and delayed retention test data were used to
reflect motor learning of the balancing task and the
durability of this learning over a period of 1 week.
Learning was assessed over averaged pairs of trial blocks
by use of a 2�2�3 (group [stroke, control]�condition
[errorless, discovery]�block [A12, R1, R3]) MANOVA
with repeated-measures correction for block and with
RMSE as the dependent measure.

Averaged pairs of trial blocks of test-phase data were
used to examine balance performance under secondary
task loading by use of a 2�2�2�2 (group [stroke,
control]�condition [errorless, discovery]�task [num-
ber recall, kettle lift]�pre-30 seconds versus post-30
seconds [balance alone during the first 30 seconds
versus balance with secondary task during the last 30
seconds]) MANOVA with repeated-measures correction
for task and pre-30 seconds versus post-30 seconds and
with RMSE as the dependent measure. Separate analyses
were run for each task by use of a 2�2�2 (group [stroke,
control]�condition [errorless, discovery]�pre-30 sec-
onds versus post-30 seconds [balance alone during the
first 30 seconds, balance with secondary task during the
last 30 seconds]) MANOVA with repeated-measures cor-

Figure 1.
Schematic diagram of a participant on the stabilometer.

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rection for pre-30 seconds versus post-30 seconds and
with RMSE as the dependent variable. Post hoc tests were
performed to identify the locus of interactions by use of
separate 1-way ANOVA and paired t tests. Verbal proto-
cols were assessed by use of a 2�2 (group [stroke,
control]�condition [errorless, discovery]) ANOVA. Post
hoc analysis with the Student-Newman-Keuls test, P�.05
test was performed to identify the locus of interactions.
The alpha level for all analyses was set at P�.05.

Results

Balance Ability
Although balance ability within the different groups
(stroke, control) was comparable on the Berg Balance
Scale40 (F�0.007; df�1,18; P�.935), as might be
expected, the balance abilities of the 2 stroke groups
were significantly poorer than those of the 2 control
groups (F�1,456.09; df�1,18; P�.001).

Acquisition Phase
Inspection of Figure 2 indicates that RMSE decreased
across blocks for the discovery learning groups during
acquisition but increased across blocks for the errorless
learning groups. When the 2 learning conditions were
considered separately, no group�block interaction was
revealed for either the errorless learning condition
(F�1.67; df�10,90; P�.20) or the discovery learning
condition (F�1.88; df�10,90; P�.16).

Retention and Delayed Retention
To demonstrate learning of the bal-
ancing task, rather than an improve-
ment in performance, no changes in
RMSE should have occurred for any
of the 4 groups over the time between
the end of acquisition (A12) and the
first retention block (R1) in the test
phase. In addition, because durability
over time is a characteristic of implicit
learning, no changes in RMSE should
have been observed over a period of 1
week (R3). There were no significant
changes in balance performance
scores across the 3 blocks (F�2.64;
df�2,17; P�.10), suggesting that
learning was retained over time for all
groups. Inspection of Figure 2 con-
firms the maintenance of learning in
the delayed retention test after 1 week
(R3). Importantly, the lack of a
group�condition�block interaction
(F�0.39; df�2,17; P�.70) suggests
that all groups had learned the bal-
ancing task equivalently and main-
tained that learning over time.

Test Phase
We predicted that balance performance would be
impaired by a concurrent task in the discovery learning
groups but not in the errorless learning groups under
secondary task loading. These findings would be
reflected by an increase in RMSE for the discovery
learning groups under secondary task loading. Figure 3
depicts the performance of the different groups in
the test phase when either number recall or kettle lift
was added to the balancing task. Initial analysis of
the secondary tasks (number recall and kettle lift)
revealed a significant group�condition�task�pre-30-
second versus post-30-second interaction (F�5.71;
df�1,18; P�.028). There was a difference in perfor-
mance under the conditions of balance only and balance
under secondary task loading for groups, learning con-
ditions, and secondary tasks. Therefore, separate analy-
ses of the number recall and kettle lift secondary tasks
were performed.

Number Recall
Inspection of Figure 3 shows the effects of the addition
of the concurrent number recall task. Balance perfor-
mance improved for the errorless learning stroke group
(F�13.52; df�1,4; P�.021) under secondary cognitive
loading and declined for the discovery learning stroke
group (F�9.75; df�1,4; P�.035). No impairment in
performance was revealed for the errorless learning

Figure 2.
Performance (mean and standard error) of the stroke and control groups in the 2 conditions over
averaged pairs of trials during acquisition, retention, and delayed retention. RMSE�root-mean-
square error, A�acquisition trial number, R1�first retention test, R3�delayed retention test.

Physical Therapy . Volume 86 . Number 3 . March 2006 Orrell et al . 375

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control group (F�0.15; df�1,5; P�.72) or the discovery
learning control group (F�0.01; df�1,5; P�.94).

Kettle Lift
There were no differences among the groups between
the first 30-second and the second 30-second period of
the trial blocks (F�0.25; df�1,18; P�.63). No decline in
performance was revealed for the errorless learning
stroke group (t4�1.44; P�.23). This result indicates that
balance performance was robust in all groups when
reaching for, lifting, and holding the kettle.

Verbal Protocols
Verbal protocol scores were established by summing the
number of explicit rules relating to kinematic aspects of
the task. The ANOVA revealed a main effect of condi-
tion (F�9.58; df�1,18; P�.007). Post hoc analysis
(Student-Newman-Keuls test, P�.05) showed that the
discovery learning stroke group (X�3.40, SD�1.34)
accumulated more explicit rules than the discovery
learning control group (X�2.67, SD�1.03), the error-
less learning control group (X�1.83, SD�0.75), and the
errorless learning stroke group (X�1.40, SD�1.14).

Discussion
The findings of this investigation support the hypothesis
that learning without errors promotes nonverbal learn-
ing and that this learning is implicit in character. This
conclusion is evidenced by the durability of learning
over time, the robustness of performance with concur-
rent cognitive task loading, and the accrual of minimal
explicit knowledge of the mechanics of the task in the

errorless learning groups. These
results support previous findings25,27
and suggest that learning without
errors promotes an implicit mode of
motor learning by inhibiting the
acquisition of explicit knowledge.

Evidence of delayed retention is con-
sidered to be an important criterion
for demonstrating learning, as learn-
ing is epitomized as a permanent
change in behavior.41 Although the
discovery learning groups demon-
strated a reduction in RMSE during
acquisition, this result may have been
an expression of improved perfor-
mance of the task rather than learn-
ing.42 To demonstrate learning of the
balancing task, rather than an
improvement in performance, no
changes in RMSE should have
occurred for any of the 4 groups over
the time between the end of acquisi-
tion (A12) and the first retention

block (R1) in the test phase. In addition, because
durability over time is a characteristic of implicit learn-
ing, no changes in RMSE should have been observed
over a period of 1 week. The absence of a
group�condition�block interaction suggests that all
groups had learned the balancing task, that there were
no differences in learning of the task among the groups,
and that this learning was retained over time.

The accumulation of minimal explicit knowledge of the
task to be learned is a characteristic of implicit motor
learning. It was predicted that participants in the error-
less learning groups would accrue significantly fewer
explicit rules of the task than participants in the discov-
ery learning groups but that no differences would be
found between the groups in the errorless learning
condition. It is possible, however, that the performance
of the errorless learners became more explicit as the
potential to make errors and thus to use hypothesis-
testing processes increased during acquisition with the
decrease in resistance on the stabilometer platform.
Verbal protocols do not support this contention, as the
number of rules reported after learning by the errorless
learners (stroke and control groups) was smaller than
the number of rules reported by the discovery learners
(stroke and control groups). This finding was expected
because the discovery learners (stroke and control
groups) were required to actively discover rules relating
to the task, and participants with stroke are more likely
to consciously try to control the execution of their motor
actions.28,29 Indeed, Maxwell et al25 suggested that initial
learning under implicit conditions confers robustness to

Figure 3.
Performance (mean and standard error) of the stroke and control groups in the 2 conditions over
averaged pairs of trial blocks during the test phases. RMSE�root-mean-square error.

376 . Orrell et al Physical Therapy . Volume 86 . Number 3 . March 2006

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performance under concurrent task conditions even
when explicit rules are subsequently accumulated.

During the acquisition, retention, and delayed retention
phases, the performance of the stroke groups was con-
sistently poorer than that of the respective control
groups. Pretest scores on the Berg Balance Scale40 clearly
demonstrated a disparity in balance ability between the
stroke and the control groups but no disparity within the
stroke groups. Although both of the stroke groups
displayed poorer balance performance throughout, the
disparity in performance between the errorless learners
and the discovery learners with stroke on the concurrent
number recall task is of interest. It was predicted that
balance performance would be impaired by a concur-
rent task in the discovery learning groups but not in the
errorless learning groups under secondary task loading.
This effect would be reflected by an increase in RMSE
for the discovery learning groups in the second
30-second period of the transfer tasks. An improvement
in balance performance for the errorless learning stroke
group when the balancing task was combined with the
number recall task contrasted with the impaired perfor-
mance for the discovery learning stroke group. Thus, a
verbal task impaired performance in the discovery learn-
ers but improved performance in those learning with the
errorless protocol. The impaired performance of discov-
ery learners would be consistent with these participants
attempting to control their motor actions consciously
after stroke28,29 but having these efforts disrupted by a
concurrent verbal task.

The improved performance of the errorless learners
with stroke when the verbal system was engaged in the
number recall task suggests that the balancing task was
performed better nonverbally. Previous studies43– 45 have
demonstrated improvements in nonverbal tasks when
the verbal system is otherwise engaged. In a series of
studies, Brandimonte and coworkers43– 45 demonstrated
that a concurrent verbal task improved the performance
of image manipulation tasks. They argued that verbal
recoding of a nonverbal stimulus impaired or degraded
long-term memory for that stimulus. Brandimonte et al43
also concluded that with a concurrent verbal task, encod-
ing of the stimuli visually rather than verbally produced
optimal performance. Similarly, Schooler and Engstler-
Schooler46 demonstrated that verbalization of nonverbal
tasks can interfere with successful performance. Thus,
the improvement in balance performance for the error-
less learning group with stroke during the number recall
task would suggest that optimal performance is inhibited
by the application of verbal information regarding this
task during the balancing task.

Although the errorless learning groups demonstrated
characteristics of implicit motor learning, the results

revealed that the balance performance of the discovery
learning control group was not impaired under second-
ary task loading. This finding is contrary to our predic-
tion that the balance performance of the discovery
learning control group would be impaired by a concur-
rent cognitive task because dual verbal tasks are hypoth-
esized to interfere with the application of explicit knowl-
edge.47 There are several possible theoretical reasons for
this finding. First, the number recall task may have been
too simple to cause interference, as complex tasks that
require more processing are associated with greater
interference in postural control than simpler tasks.4,6
This explanation, however, seems improbable, as the
presented 6-digit number recall task is difficult to per-
form successfully. As recommended by Baddeley,36 the
random 6-digit sequences were presented at a rate of
one per second in order to suppress the use of verbal
knowledge by blocking the phonological loop of work-
ing memory. Second, assuming that the discovery learn-
ing control group participants were running the balanc-
ing task explicitly, the amount of available explicit
knowledge may not have presented a large enough
processing load to saturate processing capacity during
performance of the concurrent task. Alternatively, the
similarity of the performance curves for the discovery
learning control group and the errorless learning
groups during the test phase suggests that the discovery
learning control group participants did not use their
available explicit knowledge to perform the balancing
task. As previously noted, the presence of explicit knowl-
edge does not necessarily mean that the knowledge must
be used.17

The preservation of nonverbal learning with a concur-
rent cognitive task (number recall) is consistent with
implicit processes occurring in parallel with processes
that are more dependent on the availability of explicit
knowledge.14 Gentile48 suggested that skill acquisition is
mediated by a rapid explicit process that conveys the
performer-environment relationship and a slower
implicit process that establishes the functional dynamics
of the movement. These processes therefore may be
used in parallel during performance of the skill. Thus, a
concurrent verbal task may alter the relative contribu-
tions of the implicit and explicit processes to the perfor-
mance of any nonverbal task. In our study, participants
were required to perform the balancing task for 60
seconds. In the balance-only condition, participants may
have been using explicit knowledge from the environ-
ment and from action outcomes to explicitly run the
task. When the verbal system was engaged with the
number recall task, the absence of impairment in bal-
ance performance for the errorless learning groups and
the discovery learning control group suggests that
implicit processes were the main contributors to task
performance, whereas in the balance-only condition,

Physical Therapy . Volume 86 . Number 3 . March 2006 Orrell et al . 377

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explicit processes also may have contributed to task
performance.

In contrast to the number recall task, the shifting of the
center of gravity in the kettle lift task had no significant
effect on balance performance for all groups. Although
an increase in RMSE was observed for the stroke groups
when lifting the kettle, this finding may have been a
reflection of temporal and individual differences in
regaining balance on the stabilometer. In order to
regain balance after reaching and lifting the kettle,
participants with stroke would have had to shift their
weight onto their affected limb. This process would take
longer to achieve for participants with reduced weight-
shifting abilities than for participants with intact weight-
shifting abilities.49

The findings of this study demonstrated that participants
with stroke benefited from using an errorless (implicit)
learning strategy to learn a dynamic balancing task.
Learning for the errorless learning groups was durable
over time and robust in the presence of concurrent
cognitive task loading. In comparison, learning of a
dynamic balancing task by participants with stroke and
using an explicit learning strategy resulted in durable
learning over time, as evidenced by the results of the
delayed retention test, but in less robust learning and
subsequent motor performance in the presence of con-
current cognitive task loading.

However, care must be taken in directly extending the
results of this laboratory-based study to clinical practice.
First, the small sample size suggests that caution is
appropriate at this stage for nonsignificant comparisons
between groups. Power calculations for differences
between groups or conditions suggest that there was only
36% power to detect a large effect. In contrast, the
relatively high correlation between repeated measures
(typical Pearson r value of �.80) means that there was
more than 80% power to detect differences between
repeated time points.50 Consequently, the within-subject
effects can be viewed with greater confidence. Second,
the laboratory task of balancing on a stabilometer, while
similar, is not directly comparable to real-life balance; it
is more like standing astride a seesaw. Thus, lifting a
kettle may produce an imbalance in an individual but
would not inevitably destabilize the surface on which
that individual is standing. Therefore, generalization of
the results to the general population of people with
stroke should be made with caution.

Conclusions
It appears that errorless learning strategies promote
nonverbal learning that is implicit in character. The
results of this study suggest that the application of
errorless learning strategies may be of benefit in the

rehabilitation of people with stroke. First, it appears that
verbal knowledge or attempts to control tasks with the
verbal component of working memory may be problem-
atic. Verbalization of a movement’s parameters has been
shown to exaggerate technical flaws in athletes attempt-
ing to achieve maximal performance, such as “choking”
in tennis, whereby automatic execution processing
becomes inhibited, resulting in subpar performance.18,51
Masters et al52 referred to this act of turning one’s
attention in toward the mechanics of an action as
“reinvestment.” Masters18 argued that by acquiring a
motor skill implicitly, the learner will be unable to
reinvest, as the learner will have no verbal knowledge of
the mechanics of the movement. Thus, conscious inter-
ference with the motor commands during performance
will be averted.

Concerning errorless techniques themselves, one effec-
tive strategy in non–movement-impaired participants is
to gradually progress from a very easy condition to more
difficult versions of the same task.25 The rationale
behind this approach is that a minimization of errors
should reduce the need to test hypotheses and thus the
accrual of explicit verbal knowledge about the move-
ment kinematics. This approach could be applied to the
learning/relearning of real-life sit-to-stand actions53 and
to fine coordination skills, such as turning a key in a lock
or picking up a cup.54 For example, for a sit-to-stand
action, the learner would have to reach for an object on
a table. Initially, the object would be very close at hand
so that the learner could reach it. Gradually over trials,
the distance between the object and the learner would
be increased. Thus, the learner would have to stand to
reach the object. With a progressive increase in the
distance between trials, error would be kept to a mini-
mum, a corollary being an increase in leg muscle
strength. Dean and Shepherd53 previously used this
technique but did not refer to it as errorless learning,
because they were evaluating the effectiveness of a
training program aimed at increasing distance reached
and the contributions of the affected lower leg to
support and balance.

Errorless learning strategies have been successfully
applied in the rehabilitation of people with memory
impairments.55 A further benefit of errorless learning
may originate from the effects of error minimization on
the performance of tasks that require conscious recol-
lection of a previous episode,56 that is, residual explicit
memory.57 People with stroke have a predisposition to
rely on explicit knowledge of a movement, thereby
disrupting optimal performance. Through minimization
of the amount of available explicit knowledge during
rehabilitation, subsequent motor performance may be
enhanced. From an applied perspective, the results of
our study suggest that implementation of errorless learn-

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ing strategies may be beneficial in stroke rehabilitation.
Additional studies are needed, however, to investigate
the validity of implementing this paradigm in the reha-
bilitation context.

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Appendix.
Bamford Classification of Stroke35

Classification
% of
Strokes Site of Infarcta Signs and Symptoms

Total anterior
circulation (TAC)

20 Occlusion of proximal MCA
or ICA

Volume of infarction � LAC or
PAC

Ischemia in superficial and
deep territories of MCA

ACA territory also may have
infarcts

Weakness (�/or sensory deficit) of at least 2 of 3 body areas
(face/arm/leg)

Homonymous hemianopia
Higher cerebral dysfunction (dysphasia, dyspraxia most common)

Partial anterior
circulation (PAC)

35 Occlusion of branches of
MCA

Few ACA infarcts

2 of 3 TAC criteria or restricted motor or sensory deficits (eg, 1
limb, face, and hand, or higher cerebral dysfunction alone)

Lacunar (LAC) 20 Small infarcts in basal ganglia
or pons

Pure motor—complete or incomplete weakness of 1 side, involving
the whole of 2 of 3 body areas (face/arm/leg); sensory
symptoms, including dysarthia or dysphasia

Pure sensory—sensory symptoms, signs, or both, same distribution
as motor

Sensorimotor—combination of above
Ataxic hemiparesis—hemiparesis and ipsilateral cerebellar ataxia

Posterior circulation
(POC)

25 Affects brain stem, cerebellar,
or occipital lobes

Frequently complex presentation; may include:
● Bilateral motor or sensory deficits
● Disordered conjugate eye movement
● Isolated homonymous hemianopia
● Ipsilateral cranial nerve palsy with contralateral motor or

sensory deficit
● Coma
● Disordered breathing
● Tinnitus
● Vertigo
● Horner syndrome

a MCA�middle cerebral artery, ICA�internal carotid artery, ACA�anterior cerebral artery.

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Muscle Impairments and Behavioral
Factors Mediate Functional
Limitations and Disability
Following Stroke

Background and Purpose. Stroke remains the leading cause of disability in the
United States. The purposes of this study were to examine whether quantita-
tive measures of muscle strength and power in the involved lower extremity
predict functional limitations and to evaluate the contributions of behavioral
factors to mediating disability and quality of life in people who have survived
a stroke. Subjects and Methods. A cross-sectional study design was used, and
measurements of muscle impairment, lower-body function, disability, quality
of life, and behavioral factors were obtained for 31 community-dwelling
volunteers who had experienced a single ischemic stroke in the past 6 to 24
months. Results. Stepwise regression models including impairment and behav-
ioral measures were strong predictors of function, disability, and quality of
life. Involved-extremity muscle strength and power and self-efficacy were
independently associated with function, whereas depression and self-efficacy
were strong predictors of disability and quality of life. Discussion and
Conclusion. The findings warrant future studies to determine whether inter-
ventions that address muscle strength and power, depressive symptoms, and
low self-efficacy effectively improve function, reduce disability, and enhance
quality of life in people who have survived a stroke. [LeBrasseur NK, Sayers SP,
Ouellette MM, Fielding RA. Muscle impairments and behavioral factors
mediate functional limitations and disability following stroke. Phys Ther.
2006;86:1342–1350.]

Key Words: Cerebrovascular accident, Power, Quality of life, Self-efficacy, Strength.

Nathan K LeBrasseur, Stephen P Sayers, Michelle M Ouellette, Roger A Fielding

1342 Physical Therapy . Volume 86 . Number 10 . October 2006

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S
troke presents a major public health concern in
the United States, with more than 700,000 new
or recurrent cases occurring each year.1 Despite
a noteworthy reduction in mortality in the last

century,2 stroke remains the third leading cause of
death. Moreover, morbidity in the approximately 4.8
million people who have survived a stroke is substantial,
making stroke the foremost cause of serious, long-term
disability in the United States.3

The impairments (abnormalities occurring in a specific
organ or organ system4) resulting from stroke encom-
pass motor, sensory, visual, affect, cognitive, and lan-
guage systems. Of people who have survived a stroke in
the long term, 50% demonstrate hemiparesis, 19% dem-
onstrate aphasia, and 35% demonstrate clinical depres-
sion. Stroke-related deficits are further manifested in
functional limitations (limitations in performing func-
tional tasks at the whole-body level4). Approximately
22% of people who have survived a stroke are unable to
walk without assistance, and 26% are dependent in
activities of daily living.5 The residual impairments and
functional limitations in people who have survived a
stroke in the long term represent a major cause of
disability (limitations in performing a socially defined
role in a physical or social environment4) in the popu-
lation. Therefore, gaining a more thorough understand-
ing of the relationships among impairments, functional
limitations, and disability in people who have survived a
stroke will provide a framework allowing rehabilitation
professionals to identify strategies to better assist this
population.

In previous work, various research groups investigated
the relationships among age- and disease-associated
motor impairments and limitations in physical function
(ie, ability to walk, climb stairs, and rise from a chair)
and disability. Specifically, muscle strength (maximum
force-generating capacity) was demonstrated to have a
positive association with measures of habitual walking
speed,6 stair climbing,7 and chair rising8 in older adults.
More recently, however, researchers observed that
impairments in peak skeletal muscle power (the product
of the force multiplied by the velocity of shortening)
explain more of the variability in function and disability
than does strength in older people.9 –11 Although previ-
ous studies12–15 demonstrated that lower-extremity
strength is correlated with gait quality and other mea-
sures of function following stroke, impairments in mus-
cle power and their association with function and dis-
ability have not been well described.

In chronic and complex diseases such as stroke, disabil-
ity and quality of life are related not only to physical
impairments but also to behavioral, emotional, and
psychological processes. Strategies to enable patients to
improve their outlook and self-manage their chronic
diseases so as to optimize health are fundamental.16
Self-efficacy is a psychological construct representing
confidence in one’s ability to perform a task or specific
behavior or to change a specific state, regardless of
circumstances or contexts.17 Moreover, self-efficacy
denotes the importance of an individual’s perception of
his or her ability and capability to execute and achieve
important and valued outcomes. Self-care self-efficacy

NK LeBrasseur, PT, PhD, was a doctoral candidate, Human Physiology Laboratory, Department of Health Sciences, Sargent College of Health and
Rehabilitation Sciences, Boston University, Boston, Mass, at the time of the study and is currently Assistant Professor of Medicine, Boston University
School of Medicine, 670 Albany St, Rm 218, Boston, MA 02118 (USA). Address all correspondence to Dr LeBrasseur at: nlebrass@bu.edu.

SP Sayers, PhD, is Assistant Professor, Department of Physical Therapy, School of Health Professions, University of Missouri–Columbia, Columbia,
Mo.

MM Ouellette, PT, MSPT, is Research Associate, Human Physiology Laboratory, Department of Health Sciences, Sargent College of Health and
Rehabilitation Sciences, Boston University.

RA Fielding, PhD, was Associate Professor and Director, Human Physiology Laboratory, Department of Health Sciences, Sargent College of Health
and Rehabilitation Sciences, Boston University, at the time of the study and is currently Director and Scientist I, Nutrition, Exercise Physiology and
Sarcopenia Laboratory, Jean Mayer USDA Human Nutrition Research Center on Aging, Tufts University, Boston, Mass.

Dr LeBrasseur and Dr Fielding provided concept/idea/research design. Dr LeBrasseur, Dr Sayers, and Dr Fielding provided writing. Dr LeBrasseur
and Ms Ouellette provided data collection, and Dr LeBrasseur and Dr Sayers provided data analysis.

This study was approved by the Boston University Institutional Review Board.

This work was supported by grants to Dr LeBrasseur from the Boston University Roybal Center for the Enhancement of Late-Life Function
(NIH/NIA AG11669) and to Dr Fielding from the Jacob and Valeria Langeloth Foundation. The Claude D Pepper Older Americans
Independence Center (AG08112) provided assistance in subject recruitment.

This article was received May 17, 2005, and was accepted May 18, 2006.

DOI: 10.2522/ptj.20050162

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has been shown to be highly correlated with quality-of-
life measures at both 1 and 6 months following stroke.18
Therefore, high self-efficacy for one’s physical abilities
may relate to improved function, reduced disability, and
improved quality of life in people who have survived a
stroke in the long term.

The purpose of this investigation was to quantify the
relationships among impairments in lower-extremity
strength and power, measures of lower-extremity func-
tion, and global disability following stroke. Specifically,
we examined whether quantitative measures of muscle
strength and power in the involved lower extremity
following stroke predict functional limitations and eval-
uated the contributions of behavioral factors, such as
self-efficacy and depression, to mediating disability and
quality of life.

Method

Subjects
Subjects were recruited through local newspaper adver-
tisements, volunteer databases, and local stroke support
networks. Inclusion criteria included age of 50 years or
more; 6 to 24 months following a single, unilateral,
ischemic, mild-to-moderate stroke (as classified with the
Orpington Prognostic Scale19); community dwelling;
independent ambulation with or without an assistive
device; and willingness to attend the laboratory for 2
testing sessions. Stroke history was confirmed by medical
records review. Exclusion criteria included myocardial
infarction or fracture within the past 6 months, acute or
terminal illness, symptomatic coronary artery disease or
congestive heart failure, uncontrolled hypertension
(�150/90 mm Hg), and a score 20 or less on the
Mini-Mental State Examination (MMSE).20 All subjects
provided written informed consent. All outcome mea-
sures were obtained by 2 physical therapists. All study
procedures were in accordance with institutional (Bos-
ton University) guidelines.

Thirty-one community-dwelling subjects who had expe-
rienced an ischemic stroke in the past 6 to 24 months
and who met the inclusion criteria volunteered to par-
ticipate in the study. The sample consisted of 23 men
and 8 women (74.2% white, 22.6% black, and 3.2%
American Indian). Sample and descriptive characteris-
tics are shown in Table 1.

Impairment Measures
The muscle strength and power of the involved and
uninvolved lower extremities were quantified with previ-
ously described methods21,22 that were demonstrated to
have good-to-excellent reliability in people following
stroke.23 Briefly, measurements of 1-repetition maxi-
mum (1RM) and peak power were obtained for the knee

extensors (KEs) with computer-interfaced pneumatic
resistance machines.* The 1RM is defined as the maxi-
mum load that can be moved one time only through the
full range of motion (ROM) while maintaining proper
form. An ultrasonic system measuring position and
therefore relative motion aided the examiner in estab-
lishing a subject’s full ROM during performance of the
measurement with minimal resistance. The examiner
progressively increased the resistance for each successful
repetition until the subject could no longer move the
lever arm one time through the full ROM. The maxi-
mum isometric strength of ankle plantar flexion and
dorsiflexion was captured with an isokinetic dynamom-
eter.† The 1RM and maximum isometric strength mea-
surements were obtained twice, with the second evalua-
tion occurring 3 to 7 days after the initial evaluation. The
better of the 2 measurements was recorded as the 1RM
and maximum isometric strength. In this sample, the
intraclass correlation coefficients (ICC[3,1]) of repeated
1RM measures of involved and uninvolved KEs were
both .88. For repeated maximum isometric strength
measurements of the involved and uninvolved ankles in
plantar flexion and dorsiflexion, the ICCs ranged from
.69 to .84.

Peak skeletal muscle power is the product of the force
and velocity of muscle shortening. Briefly, the power of
the KEs was evaluated at 6 relative intensities (40%, 50%,
60%, 70%, 80%, and 90% the 1RM). Beginning with
40%, subjects performed 5 lifts at each established
percentage of their 1RM (separated by 30 seconds) as
quickly as possible through the full ROM. The software
engineered for the testing equipment calculated power
(in watts) between 5% and 95% of the concentric phase.

* Keiser Sports Health Equipment Inc, 2470 S Cherry Ave, Fresno, CA 93706.
† Cybex International, 10 Trotter Dr, Medway, MA 02053.

Table 1.
Descriptive Characteristics

Characteristic X SE

Age (y) 66.2 1.5
Time after stroke (mo) 17.5 1.2
Stroke severitya 2.9 0.2
Body mass index (kg/m2) 27.4 0.6
Comorbidities (N) 4.4 0.3
Prescribed medications (N) 6.1 0.6
Depression scoreb 10.7 1.4
Cognition scorec 26.6 0.9
Self-efficacy scored 27.0 2.5
Gait speed (m/s) 0.68 0.06
Stair-climbing time (s) 14.8 2.0
Chair rising time (s) 23.6 1.7

a Orpington Prognostic Scale (mild��3.2, moderate�3.2–5.2).
b Geriatric Depression Scale (�9�depression of increasing severity).
c Mini-Mental State Examination (0 –30).
d Ewart Self-Efficacy Scale (0 –100).

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Peak power for ankle plantar flexion and dorsiflexion
was measured with the Cybex isokinetic dynamometer.
Subjects performed 5 repetitions at angular velocities of
60°, 120°, and 180°/s. For KEs and ankle plantar flexion
and dorsiflexion, the highest power achieved during the
2 testing sessions was recorded as the peak muscle
power. In this sample, the ICCs of repeated peak power
measurements of the involved and uninvolved KEs were
.86 and .87, respectively. For peak power of the involved
and uninvolved ankles in plantar flexion, the ICCs were
.83 and .87, respectively, and for peak power of the
involved and uninvolved ankles in dorsiflexion, the ICCs
were .79 and .84, respectively.

Measures of Function
Habitual gait speed, stair climbing, and chair rising were
measured as previously described.24 Briefly, habitual gait
speed was assessed over a 10-m distance with an ultra-
sonic gait speed monitor‡ and recorded as the average of
2 trials (ICC�.98). The time to climb a single flight of
stairs (10 steps, 17.7 cm per step) was determined with a
handheld timer, and the better of 2 measurements was
used for analyses (ICC�.98). The time to perform 5
sit-to-stand sequences from a standard chair was mea-
sured once. Repeated measures were not performed due
to fatigue.

Disability Measure
The limitation dimension of the Late-Life Function and
Disability Instrument (LLFDI) was used to assess disabil-
ity (inability to perform major life tasks and social roles).
The limitation dimension of the LLFDI evaluates self-
reported limitations (capabilities) in taking part in 16
major life tasks. The limitation dimension comprises 2
domains: the instrumental role and the management
role. The instrumental role domain reflects limitations
in the ability to perform activities in the home and in the
community. The management role domain reflects lim-
itations in the organization and management of socially
defined tasks that involve minimal mobility or physical
activity. The raw scores from each item response are
transformed into linear scaled scores (0 –100) and sub-
sequently summed to represent component and domain
values.25 The test-retest reliability of data for the LLFDI
domains has not been established in people following
stroke; however, test-retest reliability of data for the
LLFDI domains was previously determined in ethnically
and racially diverse adults aged 60 years and older and
was found to be moderate to high (ICC�.69 –.82).25

Quality-of-Life Measure
The shortened version of the Sickness Impact Profile
(SIP)26 was administered to evaluate 6 domains of
health-related behavior (somatic autonomy, mobility

control, psychological autonomy and communication,
social behavior, emotional stability, and mobility range),
referred to as quality of life. In people who have had
a stroke, the SIP exhibits reliability, validity, and
responsiveness.27

Behavioral Measures
The Geriatric Depression Scale was used to identify
physical and nonphysical symptoms that are related to
depression and that may have been present over the
preceding week.28 The Geriatric Depression Scale is a
reliable and valid self-rating depression screening scale
for older people and people who have had a stroke.29
Cognitive impairment was assessed with the MMSE.

Self-Efficacy Measure
The Ewart Self-Efficacy Scale measures self-perceived
ability, or confidence, to perform a number of physical
tasks (eg, walking and jogging various distances, climb-
ing stairs, lifting objects of different weights). Scores are
0 to 100, with higher scores indicating higher self-
efficacy.30 The Ewart Self-Efficacy Scale has been used
extensively in studies of people who have coronary artery
disease, but it has not been validated or reliability tested
in people who have survived a stroke.

Data Analysis
Descriptive statistics were calculated for all subjects.
Paired sample t tests were calculated for all KE and ankle
plantar-flexion and dorsiflexion muscle strength and
power measurements to determine differences between
the involved and uninvolved limbs. A Bonferroni test-
wise correction adjusted the P value to �.008 (.05/6).
Pearson correlations were calculated to examine the
relationships between potential adjustment variables
and dependent variables. Prior to regression modeling,
the normality of the dependent variables was deter-
mined with the Shapiro-Wilk test (sample size under 50).
If the Shapiro-Wilk test was significant (P�.05), then the
data were considered nonnormal and the dependent
variable was log transformed. For each regression model,
linearity was checked by adding a quadratic term to each
model. If the quadratic term was significant, then the
independent variable was log transformed to achieve
linearity and then was used in the regression model.

The relationships of impairment with function, disabil-
ity, and quality of life for both the involved and un-
involved limbs were examined by stepwise regression
modeling. The relationships of our strength and power
impairment measures (KEs and ankle plantar flexion
and dorsiflexion) with function and disability were very
similar. In this report, we have presented KE strength
and power analyses, given the fundamental role of the
involved musculature in the performance of lower-
extremity physical functions and the previously reported

‡ OCPB Electronics, G11 6 NT, Glasgow, United Kingdom.

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excellent reliability of strength and power measure-
ments.23 Thus, we fit 2 regression models for each of the
3 measures of function (habitual gait speed, chair rising
time, and stair-climbing time), the disability index (lim-
itation dimension of the LLFDI), and the quality-of-life
measure (SIP) by using the KE power of the involved
and uninvolved limbs. The covariates of cognition,
depression, and self-efficacy were chosen because of
their significant associations with dependent variables
(P�.05) and their potential effect on the relationships of
impairment with function and disability. The statistical
significance for all multivariate regression models was
accepted at P�.05. All data were analyzed with SPSS
statistical software.§

Results

Impairments in Muscle Strength and
Power
Paired sample t tests indicated signifi-
cant differences between the involved
and uninvolved limbs for all KE and
ankle plantar-flexion and dorsiflexion
strength and power measurements
(P�.004 for all measurements). Mea-
surements and comparisons of muscle
strength and power for the involved
side versus the uninvolved side are
shown in the Figure.

Relationships Between Impairments and
Function
Stair-climbing times (Shapiro-Wilk test:
P�.006) and chair rising times
(Shapiro-Wilk test: P�.001) were non-
normal, and log transformations were
performed. As shown in Table 2,
regression model 1 (strength) and
model 2 (power) were significantly
associated with habitual gait speed,
stair-climbing time, and chair rising
time (P�.001 for all measurements).
The strength of the relationships
between the 2 models and the mea-
sures of function ranged from R2�.43
to R2�.78. The KE strength and power
in the involved limb were significantly
associated with habitual gait speed
(P�.05) and explained similar degrees
of variability (R2�.13 and R2�.12,
respectively). In addition, self-efficacy
and sex were associated with habitual
gait speed in both regression model 1
and model 2 (P�.05) but explained

less of the variability than either KE strength or power in
the 2 models. Analysis of stair-climbing time revealed
that KE power in the involved limb explained more of
the variability in performance than KE strength (R2�.24
versus R2�.11). The KE power in the uninvolved limb
also was significantly associated with stair-climbing time.
The relationship between self-efficacy and stair-climbing
time also was significant and proved stronger in the
strength model (R2�.11) than in the power model
(R2�.07). In contrast to the other measures of function,
neither KE strength nor power of the involved or
uninvolved limb was associated with chair rising time
(not retained in the stepwise model; P�.05). Self-
efficacy, however, demonstrated a strong relationship
with this measure in both models (R2�.43). As shown in
Table 2, the KE strength in the uninvolved limb, cogni-
tion, and depression all failed to explain a significant

§ SPSS Inc, 233 S Wacker Dr, Chicago, IL 60606.

Figure.
Muscle strength and power are significantly impaired in the involved lower extremity of people
who have survived a stroke. Quantification of knee extension and ankle plantar-flexion and
dorsiflexion maximum strength (A) and peak power (B) in uninvolved (black bars) and involved
(hatched bars) lower extremities is shown (n�31 for all analyses; P values were �.004
[asterisks] and �.001 [daggers], respectively).

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portion of the variability in the stepwise
regression models for measures of
function (P�.05 for all measurements).

Relationships Between Impairments and
Disability and Between Impairments and
Quality of Life
Regression model 1 and model 2 were
significantly associated with the limita-
tion dimension, the instrumental role
domain, and the management role
domain (P�.001 for all measurements)
of the LLFDI. The strength of the rela-
tionships between the 2 models and the
3 dimensions of disability ranged from
R2�.43 to R2�.70. For the limitation
dimension, self-efficacy demonstrated a
strong association (R2�.55, P�.001),
and depression also was significant
(P�.05) but explained a much smaller
degree of variability (R2�.09) in both
model 1 and model 2. Self-efficacy was
the only variable associated with the
instrumental role domain (P�.001)
and explained 63% of the variability in
this measure in both models. Both self-
efficacy (P�.01) and depression
(P�.01) were significantly associated
with the management role domain and
explained similar degrees of variability
in this measure (R2�.23 and R2�.24,
respectively). Neither KE strength
(model 1) nor power (model 2) in the
involved or uninvolved limb, cognition,
or sex was significantly associated with
disability in either model (P�.05). These
relationships are shown in Table 3.

Similar to the results for disability,
regression model 1 and model 2 were
significantly associated with health-
related quality of life, as measured by
the SIP (P�.001). The strength of the
relationships of both models with qual-
ity of life was R2�.69. Depression and
self-efficacy in model 1 and model 2
were significantly associated with qual-
ity of life (P�.001). In contrast, KE
strength and KE power of both the
involved and uninvolved limbs, cogni-
tion, and sex were not associated with
quality of life (P�.05 for all measure-
ments). These relationships are shown
in Table 4.

Table 2.
Associations Among Impairments, Behavioral Factors, and Functions, as Determined by
Stepwise Regression Modelinga

Parameter � SE Partial R2 R2 P

Habitual gait
Model 1 .71 �.001

KE strength (In) 0.33 0.10 .13 .002
Self-efficacy 1.10 0.37 .10 .007
Sex 0.25 0.10 .08 .014

Model 2 .70 �.001
KE power (In) 0.21 0.06 .12 .003
Self-efficacy 1.16 0.37 .11 .005
Sex 0.21 0.09 .06 .029

Stair climbing
Model 1 .66 �.001

Ke strength (In) �0.23 0.08 .11 .006
Self-efficacy �0.95 0.32 .11 .006

Model 2 .78 �.001
KE power (In) �0.25 0.05 .24 �.001
KE power (Un) 0.00 0.00 .04 .008
Self-efficacy �0.75 0.26 .07 .005

Chair rising
Model 1 .43 �.001

Self-efficacy �0.74 0.17 .43 �.001

Model 2 .43 �.001
Self-efficacy �0.74 0.17 .43 �.001

a All stepwise models included sex, cognition, depression, self-efficacy, and either knee extensor (KE)
strength (model 1) or KE power (model 2) of both the involved (In) and the uninvolved (Un) limbs.

Table 3.
Associations Among Impairments, Behavioral Factors, and Disability, as Determined by
Stepwise Regression Modelinga

Parameter � SE Partial R2 R2 P

Limitation dimension
Model 1 .70 �.001

Self-efficacy 53.70 8.58 .55 �.001
Depression �0.42 0.17 .09 .020

Model 2 .70 �.001
Self-efficacy 53.70 8.58 .55 �.001
Depression �0.42 0.17 .09 .020

Instrumental domain
Model 1 .63 �.001

Self-efficacy 72.50 11.85 .63 �.001

Model 2 ‘ .63 �.001
Self-efficacy 72.50 11.85 .63 �.001

Management domain
Model 1 .53 �.001

Self-efficacy 36.90 11.56 .23 .004
Depression 0.74 0.23 .24 .004

Model 2 .43 �.001
Self-efficacy 36.90 11.56 .23 .004
Depression 0.74 0.23 .24 .004

a All stepwise models included sex, cognition, depression, self-efficacy, and either knee extensor (SE)
strength (model 1) or KE power (model 2) of both involved and uninvolved limbs. The limitation
dimension and instrumental and management role domains are from the Late-Life Function and
Disability Instrument.

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Discussion and Conclusions
In the United States, both stroke prevalence and survi-
vorship continue to heighten3 and have made evident
the need to attenuate stroke-related disability and opti-
mize quality of life. In this report, we examined the
disablement process in community-dwelling people 6 to
24 months following ischemic stroke. We observed a
strong association between residual impairments in skel-
etal muscle strength and power on the involved side and
performance on measures of gait speed and stair-
climbing time. Moreover, we demonstrated that behav-
ioral factors, including depression and self-efficacy,
more than physical impairments, are significantly
related to disability in socially defined life tasks and
quality of life in people who have survived a stroke.
These findings have important implications for the
design of both clinical interventions and future research
initiatives aiming to optimize rehabilitation following
stroke.

Hemiparesis is a hallmark of acute stroke and a persis-
tent burden in people who have survived a stroke in the
long term.5,31 In this study, we observed significant
deficits in lower-extremity muscle strength (�30%) and
lower-extremity muscle power (�40%) for the involved
side compared with the uninvolved side 6 to 24 months
following the onset of stroke. Residual impairments in
skeletal muscle strength in people who have survived a
stroke previously were correlated with gait capacity and
other measures of function.12–15 In this study, we con-
firmed these findings and demonstrated, for the first
time, the significant contribution of stroke-related defi-
cits in muscle power to measures of function, namely,
habitual gait speed and ability to climb a standard flight
of stairs. Muscle power was the strongest predictor of
stair climbing time and explained nearly twice the
variability as muscle strength. These observations suggest
that measures of function that require a lower percent-
age of maximum strength to perform (eg, gait speed on
level surfaces and stairs) may be more sensitive to the
velocity of movement. Thus, efforts to optimize the
power of the involved musculature may confer improve-

ments on the performance of lower-
extremity physical functions following
stroke. In contrast to gait speed and
stair-climbing time, KE muscle strength
and power failed to demonstrate an
association with chair rising time. This
finding may be attributed to the task
relying more heavily on muscle groups
(eg, core musculature), coordinated
movement patterns, balance, endur-
ance, or motor planning not assessed in
this study.

Despite the associations between motor
impairments and measures of function,

we failed to demonstrate an association between muscle
power and disability in statistical models that also
included sex, depression, and self-efficacy. However,
when these 3 variables were removed from our regres-
sion model, both KE strength and KE power of the
involved limb were significantly associated with the lim-
itation dimension of the LLFDI (P�.04 for both mea-
sures; data not shown). Thus, although various investi-
gators have demonstrated the efficacy of progressive
resistance training in improving skeletal muscle
strength23,32,33 and, more recently, power23 in the
involved lower extremity of people who have survived a
stroke in the long term, the effectiveness of this strategy
in improving function and reducing disability indepen-
dently remains ambiguous. Therefore, future studies are
warranted.

The Nagi model of physical disability outlines the pro-
gression of active pathology to impairment, impairment
to functional limitation, and functional limitation to
disability.34 Modifications of this scheme include the
addition of internal and external factors that may aug-
ment or attenuate the disablement process.4 Relevant to
stroke, we have examined the influence of cognitive
impairments and depression. At 3 months and at 1, 2,
and 3 years following stroke, the prevalences of cognitive
impairments (MMSE score of �24) have been reported
to be 39%, 35%, 30%, and 32%, respectively,35 and
associated with institutionalization 4 years following
stroke.36 The fact that we did not detect an association
between cognitive impairments and disability may be
attributable to the relatively low level of these impair-
ments in our study volunteers. In contrast, we did
observe a relatively high prevalence of depression in our
study participants. This finding is in agreement with a
recent prospective epidemiological study that reported a
high occurrence of depression in people who have
survived a stroke in the long term (odds ratio�3.5, 95%
confidence interval�1.4 – 8.3).37 In our statistical model,
depression was not associated with measures of function;
however, it was strongly correlated with disability and

Table 4.
Associations Among Impairments, Behavioral Factors, and Quality of Life, as Determined by
Stepwise Regression Modelinga

Sickness Impact
Profile � SE Partial R2 R2 P

Model 1 .69 �.001
Self-efficacy �60.70 14.20 .21 �.001
Depression 1.65 0.332 .46 .001

Model 2 .69 �.001
Self-efficacy �60.70 14.20 .21 �.001
Depression 1.65 0.332 .46 .001

a All stepwise models included sex, cognition, depression, self-efficacy, and either knee extensor (KE)
strength (model 1) or KE power (model 2) of both involved and uninvolved limbs.

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quality of life. The contribution of depression to disabil-
ity was particularly evident in tasks that involved more
socially defined roles, such as organization and manage-
ment of social activities that have little reliance on an
individual’s physical capacities. These findings corrobo-
rate those of previous investigations38,39 and underscore
the importance of early detection and treatment of
stroke-related depression in attenuating the disablement
process and improving the quality of life.

Self-efficacy, the perception of one’s ability, has been
described as an intraindividual factor modifying the
disablement process4 and recently was proposed to be a
component of the disability pathway that directly influ-
ences functional limitations.40 In this study of people
who have survived a stroke, self-efficacy, akin to muscle
power, emerged as a strong predictor of measured
functions. Moreover, self-efficacy was the only indepen-
dent variable associated with all dimensions of self-
reported disability and, in accord with a previous
report,18 was related to quality of life. These findings
strongly suggest that the perception of one’s ability may
be as important as objective physical impairments in
mediating the disablement process. Given the social-
environmental context of disability and the compensa-
tory mechanisms used by people who have survived a
stroke to cope with new challenges, strategies to improve
self-efficacy may have a direct and beneficial influence
on multiple components of the disability pathway and
quality of life.

Potential limitations of the present study also must be
considered. First, we focused on selected impairments
on the basis of our expertise and experience. Admit-
tedly, the population studied has a multitude of impair-
ments not evaluated here (eg, in sensory and language
systems) that undoubtedly further contribute to the
disablement process. Moreover, in a comparable sample
of people that had survived a stroke (3–9 months),
Nichols-Larsen et al41 determined that individual char-
acteristics (ie, age, race, and comorbidities) also may
have a significant influence on the physical domain of
health-related quality of life and are worthy of further
investigation. Second, the sample size was relatively
small, and the sample consisted of people 6 to 24 months
following mild-to-moderate stroke, a time frame that we
selected on the basis of our objective to examine residual
impairments that persist despite acute rehabilitation
efforts. Although the sample that we studied was repre-
sentative of the population that has had a stroke on the
basis of age and health status (eg, stroke severity, age,
comorbidities, medication use), a larger and more func-
tionally diverse sample would have allowed further eval-
uation of the impairment-function-disability relation-
ships across various magnitudes of the chosen measures.

Collectively, these data reflect the complexity of the
disablement process initiated by stroke. On the basis of
the work of our group and others, future studies are
warranted and necessary to determine whether interven-
tions that address residual impairments in muscle
strength and power improve function and whether strat-
egies to address depressive symptoms and low self-
efficacy help attenuate functional limitations and disabil-
ity and optimize quality of life in people who have
survived a stroke.

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