Determinants of Change in Stroke-Specific Quality of
Life After Distributed Constraint-Induced Therapy
Yan-Hua Huang, Ching-Yi Wu, Keh-Chung Lin, Yu-Wei Hsieh,
Wilaiwan M. Snow, Tien-Ni Wang
MeSH TERMS
outcome assessment (health care)
quality of life
restraint, physical
stroke
OBJECTIVE. We identified the predictive factors of change in quality of life (QOL) after a distributed form of
constraint-induced therapy (dCIT) among stroke survivors.
METHOD. Seventy-four participants were treated with dCIT. We identified eight potential determinants of
change: age, gender, side of lesion, time since stroke, cognitive status, motor impairment of the upper extremity, activities of daily living (ADLs), and instrumental ADLs (IADLs). The Stroke-Specific Quality of Life
Scale (SS–QOL) was used to assess QOL.
RESULTS. Right-sided lesion and onset >17 mo earlier determined greater improvement in the SS–QOL
Energy domain. Onset >10 mo earlier, poorer IADL performance, and age >68 yr predicted improvement in
the Family Role, Mobility, and Mood domains, respectively.
CONCLUSION. Side of lesion, time since stroke, IADL performance, and age were the most important
determinants of QOL in patients receiving stroke motor rehabilitation.
Yan-Hua Huang, PhD, OTR/L, is Associate Professor,
Department of Occupational Therapy, School of Health and
Human Services, College of Professional Studies,
California State University, Dominguez Hills.
Ching-Yi Wu, ScD, OTR, is Professor and Chair,
Department of Occupational Therapy and Graduate
Institute of Behavioral Science, College of Medicine,
Chang Gung University, Taoyuan, Taiwan.
Keh-Chung Lin, ScD, OTR, is Professor and Chair,
School of Occupational Therapy, College of Medicine,
National Taiwan University, 17, F4, Xu Zhou Road, Taipei,
Taiwan 100, and Director, Division of Occupational
Therapy, Department of Physical Medicine and
Rehabilitation, National Taiwan University Hospital, Taipei;
kehchunglin@ntu.edu.tw
Yu-Wei Hsieh, PhD, is Assistant Professor, Department
of Occupational Therapy and Graduate Institute of
Behavioral Science, College of Medicine, Chang Gung
University, Taoyuan, Taiwan.
Wilaiwan M. Snow, PhD, is Assistant Professor,
Department of Occupational Therapy and Faculty of
Associated Medical Sciences, Chiang Mai University,
Chiang Mai, Thailand.
Tien-Ni Wang, PhD, is Assistant Professor, School of
Occupational Therapy, College of Medicine, National
Taiwan University, Taipei.
54
Huang, Y.-H., Wu, C.-Y., Lin, K.-C., Hsieh, Y.-W., Snow, W. M., & Wang, T.-N. (2013). Determinants of change in strokespecific quality of life after distributed constraint-induced therapy. American Journal of Occupational Therapy, 67,
54–63. http://dx.doi.org/10.5014/ajot.2013.004820
A
goal of stroke rehabilitation is to improve health-related quality of life
(HRQOL) for stroke survivors. The importance of HRQOL has been
recognized in many stroke clinical trials. Identifying determinants of changes in
quality of life (QOL) after rehabilitation can help rehabilitation professionals set
relevant rehabilitation targets to improve HRQOL for stroke survivors (Algurén,
Fridlund, Cieza, Sunnerhagen, & Christensson, 2012).
Constraint-induced therapy (CIT) and its distributed form (dCIT) have
been advocated for use in stroke rehabilitation to improve a multitude of
functional outcomes (Boake et al., 2007; Dettmers et al., 2005; Lin, Huang,
Hsieh, & Wu, 2009; Lin, Wu, Liu, Chen, & Hsu, 2009; Lin et al., 2007;
Rowe, Blanton, & Wolf, 2009; Sunderland & Tuke, 2005; Wu, Chen, Chen,
Lin, & Yeh, 2012; Wu, Chen, Tsai, Lin, & Chou, 2007). CIT involves constraint
of the unaffected upper extremity (UE), allowing the use of only the affected
arm daily for 2 wk (Sunderland & Tuke, 2005). CIT has been modified to
dCIT, a less intensive form that involves 2–3 hr of training of the affected arm
combined with 6–9 hr of daily restraint of the unaffected arm for 2–4 wk
(Boake et al., 2007; Dettmers et al., 2005; Lin et al., 2007; Lin, Huang, et al.,
2009; Wu et al., 2007). Several randomized controlled trials have demonstrated
that CIT and dCIT are effective in improving QOL for stroke patients (Lin,
Chang, Wu, & Chen, 2009; Lin, Wu, et al., 2009; Lin et al., 2007; Rowe et al.,
2009; Wu et al., 2007).
The Stroke Impact Scale (SIS) and the Stroke-Specific Quality of Life (SS–
QOL) Scale, which are often used in CIT studies (Kissela, 2006; Lin et al.,
January/February 2013, Volume 67, Number 1
2010; Lin, Wu, et al., 2009; Williams, Weinberger,
Harris, & Biller, 1999), are the most comprehensive
stroke-specific QOL assessments that measure QOL
among stroke survivors (Salter, Moses, Foley, & Teasell,
2008). The SS–QOL is more comprehensive than the SIS
and includes more domains, among them Personality,
Energy, Vision, Thinking, Work–Productivity, Family
Roles, and Social Roles. These domains are important
because they may significantly influence a stroke survivor’s QOL. The SS–QOL has been widely used internationally to assess the HRQOL outcome of stroke
survivors (Ewert & Stucki, 2007; Hilari, Byng, Lamping,
& Smith, 2003; Lima, Teixeira-Salmela, Magalhães, &
Gomes-Neto, 2008; Muus, Williams, & Ringsberg,
2007; Schmid et al., 2009; Teixeira-Salmela, Neto,
Magalhães, Lima, & Faria, 2009; Vrdoljak & Rumboldt,
2008).
Although several studies have reported that motor
ability of the distal part of the arm, time since stroke, age,
and gender can predict improvement in motor performance after CIT (Lin, Huang, et al., 2009; Rijntjes et al.,
2005), the potential predictors of HRQOL improvement
after CIT have received scant attention. Only one previous study has examined the predictors of HRQOL as
measured with the SIS after dCIT. The seven variables of
age, gender, side of lesion, time since stroke, cognitive
status, motor impairment, and activities of daily living
(ADLs) were used as potential predictors of SIS scores
(Huang, Wu, Hsieh, & Lin, 2010). FIM (Uniform
Data System for Medical Rehabilitation, 19971) scores
for basic ADLs are predictive of SIS scores. However,
basic ADLs are not comprehensive enough for the range
of activities relevant to function and to explain HRQOL
(Coster, Haley, Jette, Tao, & Siebens, 2007; HartmanMaeir, Soroker, Ring, Avni, & Katz, 2007). The inclusion of an instrumental ADL (IADL) measure may
improve understanding of the person’s independence in
extended ADLs and the ability to possibly predict
HRQOL after dCIT.
IADLs include shopping, cooking, housekeeping, use
of transportation, money management, care of others,
child rearing, communication management, community
mobility, health management and maintenance, home
establishment and management, religious observance,
safety, and emergency maintenance (American Occupational Therapy Association, 2008; Garcia & McCarthy,
2000). The literature has suggested that IADLs are related
to QOL for stroke patients (Hartman-Maeir et al., 2007;
1
FIM is a trademark of the Uniform Data System for Medical Rehabilitation,
a division of UB Foundation Activities, Inc.
The American Journal of Occupational Therapy
Sveen, Thommessen, Bautz-Holter, Wyller, & Laake,
2004), and the IADL leisure domain is associated with
well-being and life satisfaction after stroke (Sveen et al.,
2004). Improved IADL ability after training appeared to
accelerate recovery in some HRQOL domains (HartmanMaeir et al., 2007). We therefore included an assessment
of IADL performance as a possible predictor for change
in QOL after dCIT, which to our knowledge has not
been investigated.
The x2 automatic interaction detector (CHAID) is
a data-mining approach that uses categorical statistics to
provide detailed information about interactions among
predictor variables and outcome measures. One advantage of CHAID is that it allows all predictors measured at
nominal, interval, and ordinal levels (Kass, 1980). Unlike
traditional statistical modeling systems, such as regression
analysis, which evaluates relational statistics or estimates
linear parameters for statistical prediction, CHAID uses
categorical statistics (x2, analysis of variance) to devise
a decision tree that identifies variables that differentiate
the sample into subgroups and separate individuals
within subgroups by their respective outcomes (Skidmore,
Rogers, Chandler, & Holm, 2006; SPSS Training Department, 2001). Use of the CHAID approach is appropriate in the exploratory study of predictors of
patient-reported outcomes after rehabilitation (Chan,
Cheing, Chan, Rosenthal, & Chronister, 2006; Huang
et al., 2010).
To date, no study has used the SS–QOL to investigate
the predictors of HRQOL outcome after CIT or dCIT.
The purpose of this study was to identify the determinant
factors contributing to change in stroke-specific QOL
measured by the SS–QOL after dCIT among stroke
survivors. The study results may facilitate the understanding of the relationship between client factors (i.e.,
age, side of lesion, ADLs) and HRQOL and assist in
outcome predictions and treatment planning.
Method
Research Design
The research design involved decision analyses (i.e.,
CHAID) for one group, before and after the dCIT intervention. The research ethics committees of the participating sites approved the study, and all participants
gave informed consent.
Participants
We obtained data from 74 participants enrolled in dCIT
studies investigating the effects of UE motor rehabilitation
55
therapy (Lin, Chang, et al., 2009; Wu et al., 2007). These
patients, who were consecutively screened and recruited
from four stroke rehabilitation units, completed the SS–
QOL before and after the dCIT intervention.
The inclusion criteria were as follows: hemiplegia, first
stroke and ³1 mo after stroke onset, no cognitive
deficiency (Mini-Mental State Examination [MMSE;
Folstein, Folstein, & McHugh, 1975] score ³23) and
able to understand the questionnaires, voluntary movement (Brunnstrom stage >3) for the proximal part of the
affected UE, and no excessive spasticity in the joints of
the affected UE (Modified Ashworth Scale [Bohannon &
Smith, 1987] score £ 2).
Instruments
We measured QOL outcomes with the SS–QOL (Williams,
Weinberger, Harris, & Biller, 1999), a self-report assessment that includes 12 stroke-specific subscales with 49
items. The SS–QOL attempts to capture the domains of
stroke-specific QOL that are insufficiently assessed with
generic QOL measures. The 12 subscales, which are
unidimensional, are Energy, Family Role, Language,
Mobility, Mood, Personality, Self-Care, Social Roles,
Thinking, Upper Extremity Function, Vision, and
Work–Productivity. Participants responded to each item
on a 5-point Likert scale. Domain scores are the averages
of the item scores, and the total score is the average of the
domain scores. All summary scores therefore range from
1 to 5. Higher scores indicate better function.
Reliability was established for each domain, with
Cronbach’s as ranging from .73 to .89. Construct validity was established by correlating the SS–QOL scores to
scores on the Short Form 36-Item Health Survey (Ware
& Sherbourne, 1992), Beck Depression Inventory (Beck,
Ward, Mendelson, Mock, & Erbaugh, 1961), National
Institutes of Health Stroke Scale (Brott et al., 1989), and
Barthel Index (Mahoney & Barthel, 1965) (r2s 5 .3–.5;
Williams, Weinberger, Harris, Clark, & Biller, 1999).
Validity (rs 5 .25–.88) and test–retest reliability (intraclass correlations [ICCs] 5 .53–.96) of the SS–QOL have
been established in many cultures (Boosman, Passier,
Visser-Meily, Rinkel, & Post, 2010; Ewert & Stucki,
2007; Lima et al., 2008; Lin et al., 2010; Muus et al.,
2007).
Procedures
Occupational therapists screened patients, recruited eligible participants, and delivered the intervention to
patients. Screening tests included a demographic survey
and the MMSE. All clinical measures were administered
to study participants by a blinded rater before and
56
immediately after the intervention. After pretesting,
the therapists provided a 3-wk intervention for each
participant.
Predictors. We identified eight potential determinants:
age, gender, side of lesion, time since stroke, cognitive
status (MMSE), motor impairment of the UE (FuglMeyer Assessment [FMA; Fugl-Meyer, Jääskö, Leyman,
Olsson, & Steglind, 1975]), ADLs (FIM), and IADLs
(Nottingham Extended Activities of Daily Living
[NEADL] Scale; Green, Forster, & Young, 2001). We
selected these predictors because the findings of previous
studies and expert opinions indicated that they were
important predictors of outcomes after rehabilitation or
that they were highly related to QOL as described in the
aforementioned research. We used the MMSE, which has
good reliability and validity, to evaluate cognitive status
(Elhan et al., 2005; Folstein et al., 1975). The FMA
measured UE motor function. The FIM was used to
evaluate basic ADL capabilities (Hamilton et al., 1994;
Hamilton, Klein, Opat, & Timmins, 1987). The
NEADL has 22 questions assessing Mobility, Kitchen,
Domestic, and Leisure domains (Lincoln & Gladman,
1992) and is designed to measure IADL after stroke. It is
suitable for studies evaluating outcome after stroke rehabilitation (Lincoln & Gladman, 1992).
Intervention. After the pretest assessment, participants
received the dCIT intervention in occupational therapy
sessions for 2 hr/day, 5 days/wk, for 3 wk. Five licensed
occupational therapists trained in the study procedures
and intervention protocols provided the treatments. The
treatment activities involved functional tasks such as
brushing hair or picking up a cup. The unaffected arm was
restricted in a mitt 6 hr/day, 5 days/wk, for 3 wk.
Data Collection
Within 1 wk before and 1 wk after the 3-wk dCIT intervention, three blinded raters administered the four
standardized measurements (FMA, FIM, NEADL, and
SS–QOL). Approximately 1 hr was needed to finish these
measurements. The raters were trained under the supervision of senior occupational therapists and followed the
standardized written instructions that were provided
by the study investigators. The interrater reliability
between the raters of each SS–QOL domain was high
(ICCs ³ .84).
Data Analysis
The sample used in this study included the participants
who received the dCIT intervention. We used SPSS
software (Version 19.0; IBM SPSS Statistics Inc., Chicago) for data entry and generation of descriptive statistics.
January/February 2013, Volume 67, Number 1
CHAID analysis was used to identify the predictors that
were most strongly linked with each SS–QOL domain.
The advantage of using CHAID is that it offers concrete
information about the predictors. Therefore, we also used
CHAID to further identify the best specific scores of the
predictors that distinguished the SS–QOL domains. The
a level for all statistical tests was .05.
Results
Table 1 presents the demographic and clinical characteristics of the 74 patients enrolled in this study (30%
women, 70% men). Participants’ average age was 57 yr,
and the average time since stroke was 19 mo. The
number of participants with left and right hemisphere
lesions was about equal (49% and 51%, respectively).
Table 2 presents the numbers of patients who improved,
did not change, or deteriorated after dCIT in each SIS
domain. Using CHAID analysis, we identified the factors
predictive of outcome for 4 of the 12 SS–QOL domains.
These 4 domains and their predictors are summarized
next.
Table 1. Participants’ Demographics and Clinical Characteristics
(N 5 74)
Characteristics
Age, yr (mean ± SD)
Value
57.15 ± 11.72
Gender, n (%)
Female
22 (29.7)
Male
52 (70.3)
Side of stroke lesion, n (%)
Left
Right
Months since stroke, mean (range)
36 (48.6)
38 (51.4)
18.85 (1.5–88)
Mini-Mental State Examination, mean ± SD
27.47 ± 2.28
Fugl-Meyer Assessment, mean ± SD
44.35 ± 12.67
FIM, mean ± SD
NEADL scale, mean ± SD
SS–QOL domain, mean ± SD
Energy
112.18 ± 21.56
28.81 ± 13.22
3.37 ± 1.32
Family Role
2.93 ± 1.06
Language
4.12 ± 0.97
Mobility
4.34 ± 0.79
Mood
3.89 ± 1.34
Personality
3.01 ± 1.12
Self-Care
4.14 ± 0.75
Social Roles
Thinking
2.76 ± 1.22
3.79 ± 1.10
Upper Extremity Function
3.99 ± 0.73
Vision
4.74 ± 0.52
Work–Productivity
3.50 ± 0.87
Overall SS–QOL, mean ± SD
3.03 ± 0.39
Note. NEADL 5 Nottingham Extended Activities of Daily Living Scale; SD 5
standard deviation; SS–QOL 5 Stroke-Specific Quality of Life Scale.
The American Journal of Occupational Therapy
Table 2. Number of Participants Who Improved, Did Not
Change, or Deteriorated After Intervention in Each Domain
of the SS–QOL Scale
Participant Outcome, n
SS–QOL Domain
Improved
No Change
Deteriorated
Energy
28
17
29
Family Role
25
21
28
Language
33
26
15
Mobility
28
25
21
Mood
27
14
33
Personality
Self-Care
38
31
17
22
19
21
Social Roles
35
10
29
Thinking
27
24
23
Upper-Extremity Function
33
13
28
Vision
15
50
9
Work–Productivity
25
21
28
Overall
47
2
25
Note. SS–QOL 5 Stroke-Specific Quality of Life Scale.
Energy Domain
Side of lesion was the indicator most strongly associated
with Energy outcomes, separating the sample into two
groups (Figure 1A). SS–QOL Energy domain scores
improved significantly more among participants with a
right-sided lesion than among those with a left-sided lesion. The subgroup with a right-sided lesion was further
differentiated into two groups by the time since stroke:
those who were >17 mo poststroke had more improvement than those who were <17 mo poststroke.
The misclassification risk estimate of the original
sample on the SS–QOL Energy domain scores was .176.
The average misclassification risk estimate of the validation samples was .240.
Family Roles Domain
Time since stroke was the indicator most strongly associated with Family Roles outcomes, separating the sample
into two groups (Figure 1B). Family Roles domain scores
improved significantly more among participants who
were >10 mo poststroke than among participants who
were £10 mo poststroke.
The misclassification risk estimate of the original
sample on the SS–QOL Family Role scores was .139. The
average misclassification risk estimate of the validation
samples was .177.
Mobility Domain
NEADL score was the indicator most strongly associated
with mobility outcomes in the SS–QOL Mobility domain scores, separating the sample into two groups
57
Figure 1. Chi-square automatic interaction detector diagram showing the factors predictive of outcomes for four domains of the StrokeSpecific Quality of Life Scale (A) Energy; (B) Family Roles; (C) Mobility, according to the NEADL scale; and (D) Mood, according
to the NEADL scale.
Note. The means and standard deviations in the nodes indicate relative change scores of the respective sample. NEADL 5 Nottingham Extended Activities of Daily
Living Scale; SD 5 standard deviation.
(Figure 1C). Participants with NEADL scores of £11
improved significantly more than those whose scores
were >11.
The misclassification risk estimate of the original
sample on the SS–QOL Mobility scores was .106. The
average misclassification risk estimate of the validation
samples was .139.
Mood Domain
Age was the indicator most strongly associated with Mood
domain outcomes, separating the sample into two groups
58
(Figure 1D). Participants >68 yr old showed significantly
greater improvement in SS–QOL Mood domain scores
than did participants £68 yr old. Those £68 yr old were
further differentiated by their NEADL scores into
two subgroups. Patients whose NEADL scores were >20
had a better outcome than those who whose NEADL
scores were £20.
The misclassification risk estimate of the original
sample on the SS–QOL Mood scores was .065. The
average misclassification risk estimate of the validation
samples was .095.
January/February 2013, Volume 67, Number 1
Discussion
This study is the first to determine predictors of SS–QOL
outcomes for participants receiving dCIT. A previous
study found only one predictor of SIS outcome (Huang
et al., 2010), whereas we found four predictors of SS–
QOL outcome after dCIT. The SS–QOL is a comprehensive and multidimensional HRQOL scale that
focuses less on the physical dimensions by assessing additional multidimensional domains, such as Energy and
Family Role, that are not covered by the SIS scale. This
more comprehensive range of QOL domains could explain why more predictors were found in our study than
in the previous study. The four predictors of SS–QOL
outcome after dCIT determined by this study are side of
lesion, time since stroke, IADL performance, and age.
Effect of Side of Stroke on the SS–QOL
Energy Domain
SS–QOL Energy domain scores improved significantly
more among participants with a right-sided brain lesion
(left hemiparesis) than among those with a left-sided
brain lesion (right hemiparesis). This result is similar
to those of Byl et al. (2003), who found that stroke
survivors with right-sided brain damage had advantages
after functional independence training. Because 70 participants in our study were right-handed, those with a
right-sided brain lesion exhibited deficits in the nondominant (left) hand. After dCIT, the left hand could
assist the right dominant hand, and participants performed tasks without a strong feeling of being tired.
People with a left-sided brain lesion, however, may
require the affected right-dominant hand, which is primarily responsible for activity performance, to expend
great effort in doing complicated activities. Therefore,
even though the affected right-dominant hand showed
some improvement after dCIT, stroke survivors still felt
exhausted and frustrated when engaging in activities using the affected right-dominant hand.
Participants in the right-sided lesion subgroups were
further differentiated into two groups by time since stroke.
Patients who were >17 mo poststroke experienced more
improvement in the SS–QOL Energy domain than those
who were <17 mo poststroke. One study showed that
52% of patients with disabling ischemic stroke recovered
within 18 mo (Hankey et al., 2007). Stroke patients with
onset >17 mo ago may have recovered maximally and did
not need to spend a lot of time in bed, nor did they feel
tired in performing daily life activities. These changes
in recovery as a result of dCIT were reflected in selfperceived energy consumption for task performance.
The American Journal of Occupational Therapy
Influence of Time Since Stroke on the SS–QOL Family
Role Domain
Family Role domain scores improved significantly more
among participants who were >10 mo poststroke than
among those £10 mo poststroke. Patients with onset >10
mo ago have usually received a period of standard rehabilitation treatment, and the opportunity for spontaneous recovery to occur is attenuated (Wolf et al., 2006).
Neurologic status and motor reorganization might tend
to be more stable after 10 mo. Participants with onset
>10 mo ago might have better capacity after receiving
dCIT to apply these learned motor skills to daily life and
family life than patients with onset <10 mo ago; therefore, they will have greater opportunities to engage in a
family role.
Effect of IADLs on the SS–QOL Mobility Domain
Participants with NEADL scores of £11 of the total 22
points improved significantly more in SS–QOL Mobility
domain scores than those with NEADL scores >11
points. This finding suggests that stroke patients with
lower capabilities in extended ADL performance before
treatment had more room to improve than those who had
higher capabilities in extended ADL performance before
treatment. The four NEADL domains are Mobility,
Kitchen, Domestic, and Leisure. The NEADL Mobility
domain, along with some other NEADL domains, is
related to the SS–QOL Mobility domain, resulting in the
NEADL being a significant predictor for the Mobility
outcome on the SS–QOL.
Evidence Regarding Age and IADLs on the SS–QOL
Mood Domain
SS–QOL Mood domain scores improved significantly
more among participants who were >68 yr old than
among those £68 yr old. Older age is related to increasing
subjective well-being among stroke patients (Wyller,
Holmen, Laake, & Laake, 1998). People who are >68 yr
old may have fewer expectations for potential improvement from dCIT; if true, they may be more easily satisfied with improvement after dCIT than younger people.
However, people who were <68 yr old and had lower
IADL abilities (NEADL scores <20) were located in the
unfavorable outcome end of the SS–QOL Mood domain,
indicating that these two risk factors deteriorated mood
outcome after the intervention. The explanation might
be that stroke survivors who are <68 yr old might have
more desire and expectations for performing IADLs.
Poor IADL ability (NEADL score <20) may have impeded them from achieving their expectations, even after
59
training, thus negatively affecting their mood outcome.
This study lends support to the findings of previous
studies that IADL is related to HRQOL and life satisfaction (Hartman-Maeir et al., 2007; Sveen et al., 2004).
Gender, cognitive status (as measured by the MMSE),
motor ability (as measured by the FMA), and daily
functional ability (as measured by the FIM) did not appear
to be predictors of SS–QOL scores in this study sample.
Our findings were consistent with those of a previous
study (Jönsson, Lindgren, Hallström, Norrving, &
Lindgren, 2005) that showed that gender and levels of
cognitive and motor function were not determinants of
any changes in the HRQOL domains. An unexpected
finding was that change in FIM scores was not significantly related to change in SS–QOL scores. Use of different potential predictors and measurement tools for
HRQOL outcome (e.g., the SS–QOL and SIS) in the
research literature might account for this discrepancy.
Unlike the SIS, the SS–QOL involves Energy, Family Role,
Social Role, and Work–Productivity domains, which may
be closely related to IADL and which the FIM is not sensitive in detecting. The SIS and SS–QOL both contain the
ADL–IADL domain; however, this domain in the SIS includes some items, such as bladder and bowel control, that
are more specific and basic than items on the SS–QOL.
The FIM might better predict the basic ADL items on the
SIS than the general and complex ones on the SS–QOL.
Even though the SS–QOL scores improved after
treatment, the eight predictors that we tested only predicted 4 of the 12 domains. No specific predictors
were identified in the SS–QOL domains of Language,
Personality, Self-Care, Social Roles, Thinking, Upper
Extremity Function, Vision, and Work–Productivity.
Contrary to our intuition, we found no significant predictors for the Upper Extremity Function, Self-Care, and
Work–Productivity domains. These three domains involve increasingly complicated physical activities that require finer motor control and bilateral arm movement. A
close examination of the items in the SS–QOL Upper
Extremity Function domain suggested that the questions
related to daily functional tasks, such as “Did you have
trouble putting on socks?” or “Did you have trouble
opening a jar?” may require bilateral arm function. The
dCIT regimen focuses on mass practice of the affected
arm, and the possible benefits of motor ability after dCIT
may not have translated into self-perceived improvement
in functional tasks performed with both arms. In addition, participants were close to age 60, and returning to
work may not have been a primary goal for rehabilitation,
resulting in no notable improvement in the Work–
Productivity domain.
60
Implications for Occupational
Therapy Practice
This study provides information for health professionals
about which client factors are relevant for predicting
HRQOL outcomes after dCIT. Because dCIT is an intensive treatment that requires careful patient selection,
knowing the factors predictive of how well a patient might
benefit from dCIT is important. If confirmed, this study’s
findings may also inform referral criteria and insurance
reimbursement. The findings can be used to plan interventions and set treatment goals for stroke survivors. For
example, an occupational therapist who knows that a
patient meets the criteria for admission to dCIT and has
NEADL scores <11 may expect that the patient holds
promise for improvement in mobility after dCIT, because
the findings of this exploratory study showed that the SS–
QOL Mobility domain scores improved significantly
more among stroke patients with NEADL scores of <11
than among stoke patients with NEADL scores >11.
Moreover, occupational therapists may expect greater
improvement in the Energy domain after dCIT among
patients with right-side lesions and onset >17 mo earlier.
Onset >10 mo ago and age >68 yr predicted improvement in the Family Role and Mood domains, respectively. Given the exploratory nature of this CHAID
analysis, a need exists for further occupational therapy
research to test for robustness of the predictive models.
In summary, this research has the following implications for occupational therapy practice and research:
• Side of lesion, time since stroke, IADL performance,
and age are important determinants of change in
HRQOL. This study showed that these four predictors
of SS–QOL warrant consideration when implementing
stroke rehabilitation programs such as dCIT.
• CHAID analysis is a useful method for exploring predictors of change in HRQOL after stroke rehabilitation.
Limitations and Future Research
This study has two limitations that warrant consideration.
First, the results were obtained from higher level stroke
survivors receiving dCIT. The findings may not be generalizable beyond the context of this study. Second, not all
potentially relevant determinants, such as caregiver factors,
were studied in this research. Awareness of the importance
of caregivers for the long-term care of stroke survivors has
been growing (Bugge, Alexander, & Hagen, 1999; Haley
et al., 2009; Han & Haley, 1999; Jönsson et al., 2005;
McCullagh, Brigstocke, Donaldson, & Kalra, 2005).
Stroke studies have emphasized the need to consider the
January/February 2013, Volume 67, Number 1
patient’s QOL as well as that of the caregiver (Jönsson
et al., 2005; McCullagh et al., 2005). More expanded
research is recommended to include caregiver factors,
such as age, strain, and QOL, as potential determinants
of stroke survivors’ HRQOL.
Conclusion
The findings inform occupational therapists about the factors
that may predict how well a participant will respond to dCIT
and can also be used to create suitable treatment plans and
goals for stroke patients. Future research may include factors
such as caregiver characteristics, family support, and premorbid lifestyle as potential predictors of QOL outcomes after
dCIT and after other regimens of stroke rehabilitation. s
Acknowledgments
This project was supported in part by the National Science
Council (NSC 99-2314-B-182-014-MY3, NSC 1002314-B-002-008-MY3, and NSC 100-2811-B-002-131),
the National Health Research Institutes (NHRI-EX10010010PI and NHRI-EX100-9920PI), and the Healthy
Aging Research Center at Chang Gung University
(EMRPD1A0891), Taoyuan, Taiwan.
References
Algurén, B., Fridlund, B., Cieza, A., Sunnerhagen, K. S., &
Christensson, L. (2012). Factors associated with healthrelated quality of life after stroke: A 1-year prospective
cohort study. Neurorehabilitation and Neural Repair, 26,
266–274. http://dx.doi.org/10.1177/1545968311414204
American Occupational Therapy Association. (2008). Occupational therapy practice framework: Domain and process
(2nd ed.). American Journal of Occupational Therapy, 62,
625–683. http://dx.doi.org/10.5014/ajot.62.6.625
Beck, A. T., Ward, C. H., Mendelson, M., Mock, J., & Erbaugh,
J. (1961). An inventory for measuring depression. Archives of
General Psychiatry, 4, 561–571. http://dx.doi.org/10.1001%
2Farchpsyc.1961.01710120031004
Boake, C., Noser, E. A., Ro, T., Baraniuk, S., Gaber, M.,
Johnson, R., . . . Levin, H. S. (2007). Constraint-induced
movement therapy during early stroke rehabilitation. Neurorehabilitation and Neural Repair, 21, 14–24. http://dx.
doi.org/10.1177/1545968306291858
Bohannon, R., & Smith, M. (1987). Interrater reliability
of a modified Ashworth Scale of muscle spasticity.
Physical Therapy, 67, 206–207. http://dx.doi.org/10.1080/
02699050903200548
Boosman, H., Passier, P. E., Visser-Meily, J. M., Rinkel, G. J.,
& Post, M. W. (2010). Validation of the Stroke Specific
Quality of Life scale in patients with aneurysmal subarachnoid haemorrhage. Journal of Neurology, Neurosurgery, and
Psychiatry, 81, 485–489. http://dx.doi.org/10.1136/jnnp.
2009.184960
The American Journal of Occupational Therapy
Brott, T., Adams, H. P., Jr., Olinger, C. P., Marler, J. R.,
Barsan, W. G., Biller, J., . . . Walker, M. (1989). Measurement of acute cerebral infarction: A clinical examination
scale. Stroke, 20, 864–870. http://dx.doi.org/10.1161%
2F01.STR.20.7.864
Bugge, C., Alexander, H., & Hagen, S. (1999). Stroke patients’
informal caregivers: Patient, caregiver, and service factors
that affect caregiver strain. Stroke, 30, 1517–1523. http://
dx.doi.org/10.1161/01.STR.30.8.1517
Byl, N., Roderick, J., Mohamed, O., Hanny, M., Kotler, J.,
Smith, A., . . . Abrams, G. (2003). Effectiveness of sensory
and motor rehabilitation of the upper limb following the
principles of neuroplasticity: Patients stable poststroke.
Neurorehabilitation and Neural Repair, 17, 176–191. http://
dx.doi.org/10.1177/0888439003257137
Chan, F., Cheing, G., Chan, J. Y., Rosenthal, D. A., & Chronister,
J. (2006). Predicting employment outcomes of rehabilitation
clients with orthopedic disabilities: A CHAID analysis.
Disability and Rehabilitation, 28, 257–270. http://dx.doi.
org/10.1080/09638280500158307
Coster, W., Haley, S. M., Jette, A., Tao, W., & Siebens, H.
(2007). Predictors of basic and instrumental activities of
daily living performance in persons receiving rehabilitation
services. Archives of Physical Medicine and Rehabilitation,
88, 928–935. http://dx.doi.org/10.1016/j.apmr.2007.03.037
Dettmers, C., Teske, U., Hamzei, F., Uswatte, G., Taub, E., &
Weiller, C. (2005). Distributed form of constraint-induced
movement therapy improves functional outcome and quality of life after stroke. Archives of Physical Medicine and
Rehabilitation, 86, 204–209. http://dx.doi.org/10.1016/j.
apmr.2004.05.007
Elhan, A. H., Kutlay, S., Küçükdeveci, A. A., Cotuk, C.,
Oztürk, G., Tesio, L., & Tennant, A. (2005). Psychometric
properties of the Mini-Mental State Examination in patients
with acquired brain injury in Turkey. Journal of Rehabilitation Medicine, 37, 306–311. http://dx.doi.org/10.1080/
16501970510037573
Ewert, T., & Stucki, G. (2007). Validity of the SS–QOL in
Germany and in survivors of hemorrhagic or ischemic
stroke. Neurorehabilitation and Neural Repair, 21, 161–
168. http://dx.doi.org/10.1177/1545968306292255
Folstein, M. F., Folstein, S. E., & McHugh, P. R. (1975).
“Mini-Mental State”: A practical method for grading the
cognitive state of patients for the clinician. Journal of Psychiatric Research, 12, 189–198. http://dx.doi.org/10.1016/
0022-3956(75)90026-6
Fugl-Meyer, A. R., Jääskö, L., Leyman, I., Olsson, S., &
Steglind, S. (1975). The post-stroke hemiplegic patient:
1. A method for evaluation of physical performance. Scandinavian Journal of Rehabilitation Medicine, 7, 13–31.
Garcia, P., & McCarthy, M. (2000). Measuring health: A step
in the development of city health profiles. Copenhagen:
World Health Organization, Regional Office for Europe.
Green, J., Forster, A., & Young, J. (2001). A test–retest
reliability study of the Barthel Index, the Rivermead Mobility Index, the Nottingham Extended Activities of Daily
Living Scale and the Frenchay Activities Index in stroke
patients. Disability and Rehabilitation, 23, 670–676. http://
dx.doi.org/10.1080/09638280110045382
61
Haley, W. E., Allen, J. Y., Grant, J. S., Clay, O. J., Perkins, M.,
& Roth, D. L. (2009). Problems and benefits reported by
stroke family caregivers: Results from a prospective epidemiological study. Stroke, 40, 2129–2133. http://dx.doi.
org/10.1161/STROKEAHA.108.545269
Hamilton, W. A., Butler, P. D., Baker, S. M., Smith, G. S.,
Hayter, J. B., Magid, L. J., & Pynn, R. (1994). Shear
induced hexagonal ordering observed in an ionic viscoelastic
fluid in flow past a surface. Physical Review Letters, 72,
2219–2222. http://dx.doi.org/10.1103/PhysRevLett.72.2219
Hamilton, W. A., Klein, A. G., Opat, G. I., & Timmins, P. A.
(1987). Neutron diffraction by surface acoustic waves.
Physical Review Letters, 58, 2770–2773. http://dx.doi.org/
10.1103/PhysRevLett.58.2770
Han, B., & Haley, W. E. (1999). Family caregiving for patients
with stroke: Review and analysis. Stroke, 30, 1478–1485.
http://dx.doi.org/10.1161/01.STR.30.7.1478
Hankey, G. J., Spiesser, J., Hakimi, Z., Bego, G., Carita, P., &
Gabriel, S. (2007). Rate, degree, and predictors of
recovery from disability following ischemic stroke. Neurology, 68, 1583–1587. http://dx.doi.org/10.1212/01.wnl.
0000260967.77422.97
Hartman-Maeir, A., Soroker, N., Ring, H., Avni, N., & Katz,
N. (2007). Activities, participation and satisfaction oneyear post stroke. Disability and Rehabilitation, 29, 559–
566. http://dx.doi.org/10.1080/09638280600924996
Hilari, K., Byng, S., Lamping, D. L., & Smith, S. C.
(2003). Stroke and Aphasia Quality of Life Scale–39
(SAQOL–39): Evaluation of acceptability, reliability,
and validity. Stroke, 34, 1944–1950. http://dx.doi.
org/10.1161/01.STR.0000081987.46660.ED
Huang, Y.-H., Wu, C.-Y., Hsieh, Y.-W., & Lin, K.-C. (2010).
Predictors of change in quality of life after distributed
constraint-induced therapy in patients with chronic stroke.
Neurorehabilitation and Neural Repair, 24, 559–566. http://
dx.doi.org/10.1177/1545968309358074
Jönsson, A. C., Lindgren, I., Hallström, B., Norrving, B., &
Lindgren, A. (2005). Determinants of quality of life in stroke
survivors and their informal caregivers. Stroke, 36, 803–808.
http://dx.doi.org/10.1161/01.STR.0000160873.32791.20
Kass, G. (1980). An exploratory technique for investigating
large quantities of categorical data. Applied Statistics, 29,
119–127. http://dx.doi.org/10.2307/2986296
Kissela, B. (2006). The value of quality of life research in
stroke. Stroke, 37, 1958–1959. http://dx.doi.org/10.1161/
01.STR.0000234047.57744.d9
Lima, R. C. M., Teixeira-Salmela, L. F., Magalhães, L. C., &
Gomes-Neto, M. (2008). Psychometric properties of the
Brazilian version of the Stroke-Specific Quality of Life
scale: Application of the Rasch model. Brazilian Journal
of Physical Therapy, 12, 149–156.
Lin, K.-C., Chang, Y.-F., Wu, C.-Y., & Chen, Y.-A. (2009).
Effects of constraint-induced therapy versus bilateral
arm training on motor performance, daily functions, and
quality of life in stroke survivors. Neurorehabilitation and
Neural Repair, 23, 441–448. http://dx.doi.org/10.1177/
1545968308328719
Lin, K.-C., Fu, T., Wu, C.-Y., Hsieh, Y.-W., Chen, C.-L., &
Lee, P.-C. (2010). Psychometric comparisons of the
62
Stroke Impact Scale 3.0 and Stroke-Specific Quality of
Life Scale. Quality of Life Research, 19, 435–443. http://
dx.doi.org/10.1007/s11136-010-9597-5
Lin, K.-C., Huang, Y.-H., Hsieh, Y.-W., & Wu, C.-Y. (2009).
Potential predictors of motor and functional outcomes
after distributed constraint-induced therapy for patients
with stroke. Neurorehabilitation and Neural Repair, 23,
336–342. http://dx.doi.org/10.1177/1545968308321773
Lin, K.-C., Wu, C.-Y., Liu, J.-S., Chen, Y.-T., & Hsu, C.-J.
(2009). Constraint-induced therapy versus dose-matched
control intervention to improve motor ability, basic/
extended daily functions, and quality of life in stroke.
Neurorehabilitation and Neural Repair, 23, 160–165.
http://dx.doi.org/10.1177/1545968308320642
Lin, K.-C., Wu, C.-Y., Wei, T.-H., Gung, C., Lee, C.-Y., &
Liu, J.-S. (2007). Effects of modified constraint-induced
movement therapy on reach-to-grasp movements and functional performance after chronic stroke: A randomized controlled study. Clinical Rehabilitation, 21, 1075–1086. http:
//dx.doi.org/10.1177/0269215507079843
Lincoln, N. B., & Gladman, J. R. (1992). The Extended
Activities of Daily Living Scale: A further validation. Disability and Rehabilitation, 14, 41–43. http://dx.doi.org/
10.3109/09638289209166426
Mahoney, F. D., & Barthel, D. W. (1965). Functional evaluation: The Barthel Index. Maryland State Medical Journal,
14, 61–63.
McCullagh, E., Brigstocke, G., Donaldson, N., & Kalra, L.
(2005). Determinants of caregiving burden and quality of
life in caregivers of stroke patients. Stroke, 36, 2181–2186.
http://dx.doi.org/10.1161/01.STR.0000181755.23914.53
Muus, I., Williams, L. S., & Ringsberg, K. C. (2007). Validation
of the Stroke-Specific Quality of Life Scale (SS–QOL): Test
of reliability and validity of the Danish version (SS–QOL–
DK). Clinical Rehabilitation, 21, 620–627. http://dx.doi.
org/10.1177/0269215507075504
Rijntjes, M., Hobbeling, V., Hamzei, F., Dohse, S., Ketels, G.,
Liepert, J., & Weiller, C. (2005). Individual factors in
constraint-induced movement therapy after stroke. Neurorehabilitation and Neural Repair, 19, 238–249. http://dx.
doi.org/10.1177/1545968305279205
Rowe, V. T., Blanton, S., & Wolf, S. L. (2009). Long-term
follow-up after constraint-induced therapy: A case report of
a chronic stroke survivor. American Journal of Occupational
Therapy, 63, 317–322. http://dx.doi:10.5014/ajot.63.3.317
Salter, K. L., Moses, M. B., Foley, N. C., & Teasell, R. W.
(2008). Health-related quality of life after stroke: What are
we measuring? International Journal of Rehabilitation Research, 31, 111–117. http://dx.doi.org/10.1097/MRR.
0b013e3282fc0f33
Schmid, A. A., Acuff, M., Doster, K., Gwaltney-Duiser, A.,
Whitaker, A., Damash, T., . . . Hendrie, H. (2009). Poststroke fear of falling in the hospital setting. Topics in Stroke
Rehabilitation, 16, 357–366. http://dx.doi.org/10.1310/
tsr1605-357
Skidmore, E. R., Rogers, J. C., Chandler, L. S., & Holm,
M. B. (2006). Dynamic interactions between impairment
and activity after stroke: Examining the utility of decision
January/February 2013, Volume 67, Number 1
analysis methods. Clinical Rehabilitation, 20, 523–535.
http://dx.doi.org/10.1191/0269215506cr980oa
SPSS Training Department. (2001). SPSS Answer Tree 3.0
training manual. Chicago: SPSS Inc.
Sunderland, A., & Tuke, A. (2005). Neuroplasticity, learning
and recovery after stroke: A critical evaluation of constraintinduced therapy. Neuropsychological Rehabilitation, 15, 81–
96. http://dx.doi.org/10.1080/09602010443000047
Sveen, U., Thommessen, B., Bautz-Holter, E., Wyller, T. B.,
& Laake, K. (2004). Well-being and instrumental activities of daily living after stroke. Clinical Rehabilitation, 18,
267–274. http://dx.doi.org/10.1191/0269215504cr719oa
Teixeira-Salmela, L. F., Neto, M. G., Magalhães, L. C., Lima,
R. C., & Faria, C. D. C. M. (2009). Content comparisons
of stroke-specific quality of life based upon the International Classification of Functioning, Disability and Health.
Quality of Life Research, 18, 765–773. http://dx.doi.org/
10.1007/s11136-009-9488-9
Uniform Data System for Medical Rehabilitation. (1997).Guide
for the Uniform Data Set for Medical Rehabilitation (including
the FIM instrument), version 5.1. Buffalo, NY: Author.
Vrdoljak, D., & Rumboldt, M. (2008). Quality of life after stroke
in Croatian patients. Collegium Antropologicum, 32, 355–359.
Ware, J. E., & Sherbourne, C. D. (1992). The MOS 36-item
short form health survey (SF–36), I: Conceptual framework
and item selection. Medical Care, 30, 472–483. http://dx.
doi.org/10.1097%2F00005650-199206000-00002
The American Journal of Occupational Therapy
Williams, L. S., Weinberger, M., Harris, L. E., & Biller, J.
(1999). Measuring quality of life in a way that is meaningful to stroke patients. Neurology, 53, 1839–1843. http://
dx.doi.org/10.1212/WNL.53.8.1839
Williams, L. S., Weinberger, M., Harris, L. E., Clark, D. O.,
& Biller, J. (1999). Development of a stroke-specific quality of life scale. Stroke, 30, 1362–1369. http://dx.doi.
org/10.1161/01.STR.30.7.1362
Wolf, S. L., Winstein, C. J., Miller, J. P., Taub, E., Uswatte,
G., Morris, D., . . . Nichols-Larsen, D.; EXCITE Investigators. (2006). Effect of constraint-induced movement
therapy on upper extremity function 3 to 9 months after
stroke: The EXCITE randomized clinical trial. JAMA, 296,
2095–2104. http://dx.doi.org/10.1001/jama.296.17.2095
Wu, C. Y., Chen, Y. A., Chen, H. C., Lin, K. C., & Yeh, I. L.
(2012). Pilot trial of distributed constraint-induced therapy
with trunk restraint to improve poststroke reach to grasp and
trunk kinematics. Neurorehabilitation and Neural Repair, 26,
247–255. http://dx.doi.org/10.1177/1545968311415862
Wu, C. Y., Chen, C. L., Tsai, W. C., Lin, K. C., & Chou,
S. H. (2007). A randomized, controlled trial of modified
constraint-induced movement therapy for elderly stroke survivors: Changes in motor impairment, daily functioning, and
quality of life. Archives of Physical Medicine and Rehabilitation,
88, 273–278. http://dx.doi.org/10.1016/j.apmr.2006.11.021
Wyller, T. B., Holmen, J., Laake, P., & Laake, K. (1998).
Correlates of subjective well-being in stroke patients. Stroke,
29, 363–367. http://dx.doi.org/10.1161/01.STR.29.2.363
63