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PHYS THER
Vol. 87, No. 2, February 2007, pp. 170-178
DOI: 10.2522/ptj.20060101
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Research Reports

Participant Perception of Recovery as Criterion to Establish Importance of Improvement for Constraint-Induced Movement Therapy Outcome Measures: A Preliminary Study

Stacy L Fritz, Steven Z George, Steven L Wolf and Kathye E Light

SL Fritz, PT, PhD, is Clinical Assistant Professor, Physical Therapy Program, Department of Exercise Science, University of South Carolina, 1300 Wheat St, Blatt PE Bldg, Columbia, SC 29208 (USA)
SZ George, PT, PhD, is Assistant Professor, Department of Physical Therapy, College of Public Health and Health Professions, Brooks Center for Rehabilitation Studies, University of Florida, Gainesville, Fla
SL Wolf, PT, PhD, FAPTA, is Professor, Center for Rehabilitation Medicine, Emory University School of Medicine, Atlanta, Ga
KE Light, PT, PhD, is Associate Professor, Department of Physical Therapy, College of Public Health and Health Professions, University of Florida

Address all correspondence to Dr Fritz at: sfritz{at}gwm.sc.edu


Submitted March 29, 2006; Accepted September 25, 2006


   Abstract
 
Background and Purpose: Changes in function following constraint-induced movement therapy (CIMT) are characterized primarily by improvements in performance; however, the importance of these outcome measures to the participant may be unclear. The primary purpose of this study was to determine whether either change scores or raw follow-up scores for the Motor Activity Log amount scale (MALa) and the Wolf Motor Function Test (WMFT) predicted participants' self-reports of recovery of upper-extremity function at 4 to 6 months after starting CIMT.

Subjects and Methods: This study was a secondary analysis of a cohort of subjects (N=46) who participated in CIMT trials. Subjects completed measures at baseline and 4 to 6 months later. Hierarchical regression models determined whether change scores or raw follow-up scores of CIMT outcome measures were predictive of perceived recovery. Receiver operating characteristic (ROC) curves determined cutoff scores for measures that significantly contributed to participants' reports of perceived recovery.

Results: The regression models indicated that raw follow-up MALa scores (ß=0.80, P=.024) and WMFT scores (ß=–0.37, P=.03) contributed to perceived recovery. Proposed cutoff scores for the MALa scores were less than 1.15 (negative likelihood ratio [LR]=0.17) for predicting less than 50% recovery and greater than 2.50 (positive LR=2.75) for predicting 50% or greater recovery. Proposed cutoff scores for follow-up WMFT scores were greater than 34.0 seconds (negative LR=0.24) for predicting less than 50% recovery and less than 11.0 seconds (positive LR=5.96) for predicting 50% or greater recovery.

Discussion and Conclusion: Raw follow-up scores for the MALa and WMFT were better predictors of self-report of recovery in comparison with change scores. These data also serve as a starting point for developing cutoff scores that accurately predict self-report of recovery.


   Introduction
Top
 Abstract
 Introduction
Method
 Results
 Discussion
 Conclusions
 References
 
The number of people who have survived a stroke has almost doubled over the last 25 years1,2 and is predicted to double again over the next 50 years.3 Currently, stroke is the leading cause of disability in the United States3 and costs the American public more than $43 billion per year.4,5 More than half4 of the 4.7 million people who have survived a stroke3 have residual motor disability, and of these, 30% to 66% have a nonfunctional paretic arm.5 Undeniably, rehabilitative strategies aimed at reducing stroke-related disabilities are important to this growing population. Currently, few traditional rehabilitation methods have been proven effective for the treatment of the upper-extremity (UE) in people who have survived a stroke.6

Constraint-induced movement therapy (CIMT), however, is reported to significantly improve functional use of the UE in 20% to 25% of people with chronic stroke disability.1 The goal of CIMT is to overcome learned nonuse by increasing the functional use of the neurologically weaker UE through massed practice, while restraining the lesser-involved UE.1 Originally tested in an animal model, the results of CIMT studies have demonstrated significant and lasting improvements of UE movement function.1,2,6–14

Changes in function following CIMT are characterized primarily by changes in performance or perceived changes on assessment instruments. These mean change scores may demonstrate statistically significant results, especially when larger samples are used. The importance of statistically significant findings, however, can be unclear. One approach to examine the meaningfulness of scores is by comparing outcomes with an established external criterion15,16 such as validated outcome questionnaires or health care practitioner perceptions of recovery.15,17–22 Another external criterion to consider is the patient's perspective, which is important because the patient's perspective may differ from the practitioner's perspective or from a questionnaire. The patient has been described as the final arbiter of how well or how poorly a particular intervention is working.23 This "patient-centered" paradigm has been advocated for in physical therapist practice guidelines24 and utilized in pain studies25–27; however, this topic has not been extensively investigated in stroke rehabilitation. Given that recovery of UE function following stroke is an individual process, there would appear to be value in determining whether commonly used CIMT outcome measures are associated with patient self-reports of UE recovery.

Outcomes following CIMT have been assessed with multiple outcome measures. Standardized assessment tools such as the Wolf Motor Function Test (WMFT)28,29 and the Motor Activity Log (MAL)30,31 quantify changes following CIMT. They have not been compared, however, with an external criterion based on patient perception of recovery. The purposes of this study were: (1) to determine whether change scores or raw follow-up scores on the MAL and WMFT predicted perceived recovery of the UE as measured by the perceived recovery section of the Stroke Impact Scale and (2) to calculate cutoff values that predicted perceived recovery of the UE for the appropriate MAL and WMFT measures.


   Method
Top
 Abstract
 Introduction
 Method
Results
 Discussion
 Conclusions
 References
 
CIMT Data Collection

This secondary analysis is from a convenience sample of 46 participants who participated in 2 local CIMT projects. These participants met specific inclusion and exclusion criteria (Tab. 1).32 First, the participants signed an institutional review board–approved informed consent statement. After a pretest, participants received 2 weeks (10 consecutive weekdays) of supervised task practice using their affected hand and arm for 6 hours per day. For this period, the unaffected hand was immobilized in a padded mitt for a goal of 90% of their waking hours. An activity log was kept by the trainer to chronicle what tasks had been attempted and how the tasks were progressed during training. The CIMT consisted of a set of tasks such as picking up pencils, moving beans between containers, and stacking items. The treatment was focused on frequent movement repetitions while performing functional activities. To remain challenging, as performance improved, tasks were increased in complexity and difficulty. This was accomplished by adding a time component, increasing the degrees of freedom, incorporating multijoint tasks, increasing the height or distance in which the task was performed, or increasing the number of choices or the pattern complexity.


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  		Table 1. Inclusion and Exclusion Criteria for Participation

 
Throughout the 2 weeks, the participants were strongly encouraged to continue to use their weaker hand during activities throughout the day and while at home. After the 6 hours of intensive therapy, the participants returned home and maintained a diary documenting activities and mitt time use. During the weekends, there were no assigned tasks, but the participants were instructed to continue to wear their mitt and maintain a home diary. The 10 days of training were followed by an immediate posttest and a 4- to 6-month follow-up posttest (referred to as "follow-up test").

Instrumentation

Predictors
Two main outcome measures, commonly reported in CIMT studies, were used for this study: (1) the amount component of the MAL (MALa), a test of perceived use, and (2) the WMFT, a test of movement capability. The MAL, a commonly used CIMT outcome measure,1,9–11,33 is a 30-question, structured interview in which the participants respond with a number corresponding to a given amount of use or perception of how well they have used their affected arm when away from the laboratory environment. For example, a participant would respond to the question "How much do you use your more affected arm to turn on a light switch?" by choosing the appropriate response from the MALa (Tab. 2). Only the mean of the "amount" section of the MAL was used as an outcome measure for this analysis. The internal consistency of the MAL is relatively stable, and the reproducibility of the MAL is sufficient to detect an individual change of less than 1.0 on the scale.31


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  		Table 2. Motor Activity Log Amount Scale Scoring33

 
The WMFT also is a commonly reported outcome measure in CIMT studies.1,9–11 It evaluates movement capability through a series of 15 timed tasks and 2 strength tasks. Only the timed tasks were used in this analysis. The WMFT tasks progress from joint-specific to multijoint movements.9,10 The reliability of WMFT scores has been reported, with interrater reliability (r) established at .96.29 The WMFT outcome measure is reported as a mean of the times for the affected hand and arm minus the mean of the times for the unaffected hand and arm. If a participant was unable to complete a task in 120 seconds, 120 seconds was used as the score for that item.

Covariates
The following variables previously were found to be predictive of follow-up outcomes for CIMT34–36 and, therefore, were included as covariates in the model to account for individual differences of participants prior to the intervention. The UE motor component of the Fugl-Meyer Sensorimotor Assessment was used as a covariate in the MALa model.36 It is a measure of the percentage of recovery of a person following a stroke that provides a quantifiable measurement of motor function and was designed primarily for use in rehabilitation settings. The UE motor component of the Fugl-Meyer test is a measure of a person's ability to move in and out of synergy, reflexes, wrist stability, grasping ability, and coordination.37

For the WMFT model, the ability to open the involved hand was used as a covariate.34 This covariate was defined as the ability to actively release a mass flexion grasp as defined by Fugl-Meyer assessment. This 0 to 2 scale is graded as follows: a score of 0 was given if the individual was unable to release the grasp, a score of 1 was given if the participant could release the mass flexion grasp, and a score of 2 was given if the participant could fully extend the fingers from the starting grasp position.38

External Criterion

The external criterion used for this study was the perceived recovery section of the Stroke Impact Scale39 taken at the follow-up test. The participants rated their perceived recovery of their more affected hand and arm since the stroke. This is rated by the individual on a scale of 0 to 100, with 100 corresponding to full recovery and 0 corresponding to no recovery. The use of a participant-based questionnaire as an external criterion has not been widely reported in stroke rehabilitation, but this method is similar to that implemented in other rehabilitation populations in which the respondent's perception of recovery was used as an external criterion.15,17–20,40 For the purposes of this study, we dichotomized patients into ≥50% (50% or greater perception of recovery in more-affected UE), and <50% (less than 50% perception of recovery in more-affected UE) groups.

Data Analysis

All data analyses were performed with the SPSS software program, version 12.0.* First, descriptive statistics were generated for each measure. Kolmonorov-Smirnov tests were used to assess whether the data for each measure approximated a normal distribution and whether transformations of the measures of interest were necessary before being included in the regression models. The following data analyses then were performed to address the specific purposes of this study.

Prediction of outcome (purpose 1)
Simultaneous regression models determined whether MALa and WMFT scores contributed to perceived recovery ratings 4 to 6 months after starting CIMT. The first regression model included age, Fugl-Meyer UE motor component score, change in MALa scores over 4 to 6 months, and raw follow-up MALa score. The rationale for including age and the Fugl-Meyer UE motor component score was that these variables were previously found to be predictive of follow-up MALa scores for CIMT.35 The second regression model included the ability to open the involved hand (Fugl-Meyer test item), follow-up (4–6 months) change in WMFT score, and raw follow-up WMFT score. The rationale for including the ability to open the involved hand was that this variable was previously found to be predictive of WMFT scores for CIMT.34

The change and raw MALa and WMFT scores were simultaneously entered into the regression model to determine which measure was most appropriate for determining a clinically meaningful outcome at follow-up. Variance inflation factor (VIF) was reported to assess whether excessive collinearity was a concern by having change and raw scores for the predictor variables in the regression model. The VIF expresses the degree to which collinearity among the predictors degrades the precision of the regression estimate. Theoretically, VIF estimates range from 1 to infinite, with larger numbers being more indicative of collinearity. Practically, a VIF value greater than 10 indicates that the predictor variable has a strong linear association (r>.95) with other predictor variables included in the regression model.41

Calculation of cutoff scores (purpose 2)
Cutoff scores were calculated only for individual measures that contributed to perceived recovery ratings at P<.10. We used a liberal criterion for selecting measures for cutoff scores because we wanted to avoid eliminating potentially useful measures at this stage of the analysis. First, receiver operating characteristic (ROC) curves assessed whether the measure accurately predicted subjects with self-reports of 50% or greater perceived recovery since the time of their stroke.42,43 The ROC curve was generated by considering each individual point of the selected measure as a potential cutoff for distinguishing between subjects who reported 50% or greater perceived recovery and those who did not. The predictive performance of each individual point is then plotted on a graph with the y-axis representing the true positive rate and the x-axis representing the false positive rate. The ROC curve is generated when the individual points are connected, giving an overall estimate of the predictive accuracy of the selected measure.

A common way to summarize findings from an ROC curve is to report the amount of area under the ROC curve (AUC). The expected range of AUC scores is from 0.5 (no better than chance identification of outcome of interest) to 1.0 (perfect identification of outcome of interest).44,45 In this study, the AUC (and corresponding 95% confidence interval [CI]) was calculated with a nonparametric method that did not require normal distribution of perceived recovery ratings.46 The AUC for this study can be interpreted as the probability the selected measure has in identifying CIMT participants who rated perceived recovery at 50% or greater at follow-up.

After the ROC curves were generated, cutoff scores were calculated for each measure that explained more than 50% AUC based on the lower bound of 95% CI. This criterion was selected because it indicated that the measure likely predicted improved status at better than chance rates. Individual points were considered for cutoff scores by visually inspecting the ROC curve to identify points associated with maximal true positive and minimal false positive rates. These candidate points were further investigated by calculating sensitivity (Sn), specificity (Sp), and likelihood ratios (LR) for each potential cutoff score. For each point, Sn was determined by calculating the true positive rate, Sp was determined by calculating the false positive rate, the positive LR was calculated by dividing the Sn by (1 – Sp), and the negative LR was calculated by dividing (1 – Sn) by the Sp.47

The decision on which specific cutoff score was reported in the manuscript was based on the following criteria. First, we wanted to generate a cutoff score that accurately identified participants rating their perception of UE recovery at less than 50%. This cutoff score was determined by selected the individual point that minimized the negative LR, or the ratio of false negatives to true negatives. Therefore, the negative LR that resulted in the smallest value was selected as a cutoff score that could potentially identify participants rating their perception of UE recovery at less than 50%. Second, we wanted to generate a cutoff score that accurately identified participants rating their perception of UE recovery at 50% or greater. This cutoff score was determined by selecting the individual point that maximized the positive LR, or the ratio of true positives to false positives. Therefore, the positive LR that resulted in the largest value was selected as a cutoff score that could potentially identify participants rating their perception of UE recover at 50% or greater. Although these analytical techniques have not been widely reported in the stroke literature, similar techniques have been reported previously to determine cutoff scores in the rehabilitation literature.21,22


   Results
Top
 Abstract
 Introduction
 Method
 Results
Discussion
 Conclusions
 References
 
The descriptive statistics are reported in Table 3. The distribution of each CIMT outcome measure (change and follow-up scores) used in the regression analyses approximated a normal distribution by Kolmonorov-Smirnov tests (P>.05). Therefore, no data transformations were performed. The simultaneous regression model for the MALa contribution to perceived recovery is reported in Table 4. The follow-up MALa score was the only contributor of perceived recovery (ß=0.80, P=.024) considered for cutoff score calculation. The regression model for the WMFT is reported in Table 5. The follow-up WMFT was the only predictor of perceived recovery (ß=–0.37, P=.030) considered for cutoff score calculation. Excessive collinearity did not appear to be a concern with the regression models because the VIF did not exceed 10.0 for any individual predictor variable.41


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  		Table 3. Descriptive Statistics of Sample (N=46)a

 

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  		Table 4. Multivariate Prediction of Follow-up Self-report of Recovery With Motor Activity Loga

 

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  		Table 5. Multivariate Prediction of Follow-up Self-report of Recovery With Wolf Motor Function Testa

 
The follow-up MALa score explained 0.746 AUC (95% CI=0.602–0.890) for perceived UE recovery. The curve is depicted in the Figure (graph A), and the associated Sn, Sp, and LR scores are reported in Table 6. A follow-up MALa score of less than 1.15 was predictive of an individual rating less than 50% recovered (negative LR=0.17). A follow-up MALa score of greater than 2.50 was predictive of an individual rating greater than 50% on the perceived recovery scale (positive LR=2.75). The raw follow-up WMFT score explained 0.761 AUC (95% CI=0.618–0.904) for perceived UE recovery. The curve is depicted in the Figure (graph B), and the associated Sn, Sp, and LR scores are reported in Table 6. A follow-up WMFT score of less than 11.0 seconds was predictive of an individual rating greater than 50% on the perceived recovery scale (positive LR=5.96). A follow-up WMFT score of greater than 34.0 seconds was predictive of an individual rating less than 50% recovered (negative LR=0.24).


Figure 1
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  		Figure. Receiver operating characteristic curves for prediction of perceived upper-extremity recovery rating with follow-up Motor Activity Log amount scale (MALa) scores (graph A) and with follow-up Wolf Motor Function Test (WMFT) scores (graph B). Y-axis represents the true positive rate, and x-axis represents the false positive rate. Perceived upper-extremity recovery variable was dichotomized into ≥50% (50% or greater perception of recovery in more affected upper extremity) and <50% (less than 50% perception of recovery in more affected upper extremity) groups. Diagonal segments are produced by ties.

 

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  		Table 6. Proposed Cutoff Scores for Motor Activity Log and Wolf Motor Function Testa

 

   Discussion
Top
 Abstract
 Introduction
 Method
 Results
 Discussion
Conclusions
 References
 
The goal of this study was to provide meaning to changes in CIMT outcome measures by calculating15 cutoff scores that were related to participants' perceptions of UE recovery.17,18 This approach complements other methods that attempt to determine the meaning of change by investigating numerical change, statistical significance, or clinical expertise of describing whether the observed change was meaningful.

Our results show that if participants scored less than 1.15 on the MALa or more than 34.0 seconds on the WMFT on their follow-up posttest, they were more likely to indicate less than 50% recovery from their stroke. In contrast, if they scored greater than 2.50 on the MALa and less than an average of 11 seconds on the WMFT tasks, they were more likely to have rated 50% or more UE recovery from their stroke. Thus, these data give us a potential indication of the relationship between participants' perceived recovery and outcome measures used for CIMT. While the original intent of this project was to determine a minimal clinically important difference (MCID) for outcome measures following CIMT, our data supported the use of raw scores rather than change scores for predicting perceived recovery. When both the change and follow-up scores were simultaneously entered into the regression model, the follow-up scores were statistically more appropriate. This is a preliminary indication that a threshold score, and not a change score, is an appropriate measurement of improvement when the external criterion is participants' perceived recovery of UE function.

This methodology has not been widely utilized in the stroke literature, so comparisons to other studies are limited. For example, van der Lee et al48 have proposed an MCID for the MAL that was 10% of the total range of the scale (0.5 change in MAL). This MCID was based on clinical expertise and reports of similar outcome measures used in manual therapy interventions for the spine.49 While this proposed MCID for the MAL may have been a good starting ground to demonstrate improvement, measurement properties may have been neglected, as our data suggest that a threshold value may be more appropriate. Furthermore, the MCID proposed by van der Lee et al was not based on an external criterion. More research is needed to determine the meanings of changes in CIMT outcome measures and whether threshold or change scores predict outcome.

Other authors50 have indicated that a threshold score based on the outcome measure's scoring definitions may be more appropriate. Preliminary results from the Extremity Constraint-Induced Therapy Evaluation (ExCITE) trial51 suggest that a numerical change in MAL may indicate an increase in amount or function of the affected hand and arm, but this change may not result in substantial or clinical improvement if the individual still cannot use the hand functionally. A suggested threshold of a 3.0 on the MAL, indicating that individuals are able to use their impaired hand independently, may be an appropriate threshold to determine functional significance.50 That said, while possibly being an appropriate parameter to determine success, this approach does not offer a statistically calculated threshold that is linked to an external criterion based on the patient's perception of overall recovery.

Measuring and interpreting "clinically important change" have some limitations. The magnitude of this score can be influenced by prior functional level. Individuals who have lower initial functional levels may need smaller changes in function to achieve a "clinically important change." Individuals with higher initial function may need a greater change to render it clinically important.15,18 We attempted to control for initial function by including covariates in the regression models. The extent of change that is considered to be minimally important also may differ depending on whether the external criterion is determined by clinician, the caregiver, payer, or the patient.15,17,18 Using the participants' perception of recovery was core to our study. Clinically meaningful progress can be defined by many respondents following an intervention as what is "worthwhile."40 In our study, the participants were the judge of what was a worthwhile improvement, consistent with a patient-centered model.24–26 In such a model, the perception of recovery from stroke is the criterion by which the other CIMT outcome measures are judged and the determination of what is a meaningful change ultimately lies with the participant.

The potential limitations of this preliminary study should be considered when interpreting these results. First, this study was performed with individuals who met strict inclusion and exclusion criteria. While more diverse than most CIMT studies, this sample is not representative of the entire stroke population. Second, individuals were not randomly selected from the community, but were respondents to inquiries for participants.

Third, the external criterion for this study was based strictly on participant perspective. Although we have stated the importance of using the patient's perspective to determine what is a meaningful change, this approach can potentially introduce variability that may confound the responses. For instance, the participants' perceptions could have been influenced by depression, medication, or other behavior changes that we could not account for in our regression models.

Fourth, the reliability of MAL scores has been questioned in recent reports,30,31 and although we used a different version of the MAL, our approach could have affected the study. A shortened version of the MAL (MAL-14) has shown improved reliability and validity over the original measure,30 but that scale was not available at the time of the present study. No matter what form of the MAL is used, the effect that readministering the MAL within 2 weeks of its first application could have resulted in patient bias due to familiarity. Although this possibility has not been systematically studied, repeat baseline MAL testing within 2 weeks has not resulted in improved scores.52


   Conclusions
Top
 Abstract
 Introduction
 Method
 Results
 Discussion
 Conclusions
References
 
This study supports evidence for follow-up scores for MALa and the WMFT being predictive of perception of UE recovery, not change scores. Cutoff scores were proposed, but more research is needed to verify the accuracy of these scores.


   Footnotes
 
Dr Fritz provided concept/idea/research design. Dr Fritz, Dr George, and Dr Wolf provided writing and data analysis. Dr Fritz and Dr Light provided data collection and project management. Dr Light provided fund procurement, subjects, facilities/equipment, institutional liaisons, and clerical support. Dr Wolf and Dr Light provided consultation (including review of manuscript before submission). The authors acknowledge all of the therapists, physicians, and trainers who participated in subject recruitment, evaluation, and training. A special thanks to Andrea L Behrman, PT, PhD, Sandra B Davis, PT, and Stephen Nadeau, MD, for their guidance and participation. The authors also thank Shannon N Clifford, PT, for her suggestions that guided the design of the manuscript.

This study was supported, in part, by the Office of Research and Development Rehabilitation R&D Service, Brain Rehabilitation Research Center, Department of Veterans Affairs, Gainesville, Fla, and Florida Biomedical Grant BM042 (Principal Investigator: Dr Light).

* SPSS Inc, 233 S Wacker Dr, Chicago, IL 60606. Back


   References
Top
 Abstract
 Introduction
 Method
 Results
 Discussion
 Conclusions
 References
 

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