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Descriptive Characteristics as Potential Predictors of Outcomes Following Constraint-Induced Movement Therapy for People After Stroke

SL Fritz, PT, PhD, is Clinical Assistant Professor, Department of Exercise Science, University of South Carolina, 1300 Wheat St, Blatt PE Bldg, Columbia, SC 29208 (USA) The data were collected while Dr Fritz was a graduate student at Department of Physical Therapy, College of Public Health and Health Professions, University of Florida, Gainesville, Fla, and a predoctoral fellow at VA Brain Rehabilitation Research Center, Malcolm Randall VA Medical Center, Gainesville, Fla.
KE Light, PT, PhD, is Associate Professor, Department of Physical Therapy, College of Public Health and Health Professions, University of Florida
SN Clifford, PT, MPT, is a graduate student at Department of Physical Therapy, University of Pittsburgh, Pittsburgh, Pa, and Assistant Professor, Department of Physical Therapy, Chatham College, Pittsburgh, Pa
TS Patterson, MEd, is a graduate student in rehabilitation sciences at Department of Physical Therapy, College of Public Health and Health Professions, University of Florida
AL Behrman, PT, PhD, is Associate Professor, Department of Physical Therapy, College of Public Health and Health Professions, University of Florida, and Research Investigator, VA Brain Rehabilitation Research Center, Malcolm Randall VA Medical Center, Gainesville, Fla
SB Davis, PT, is Research Physical Therapist, VA Brain Rehabilitation Research Center, Malcolm Randall VA Medical Center

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

Background and Purpose. Limited evidence exists regarding the characteristics of people who benefit most from constraint-induced movement therapy (CIMT). This study’s purpose was to investigate 6 potential descriptors in predicting CIMT outcomes. Subjects. The participants were a convenience sample (N=55) of people who were more than 6 months poststroke. Methods. The Wolf Motor Function Test (WMFT) and the Motor Activity Log amount scale (MALa) were used to assess outcomes for CIMT. The potential predictors (side of stroke, time since stroke, hand dominance, age, sex, and ambulatory status) were entered into a linear regression model using stepwise entry, with simultaneous entry of the dependent variables’ pretest scores as the covariate. Results. Age was the only significant predictor of the 6 potential predictors in the model and was predictive only of MALa scores. None of the independent variables showed a predictive relationship with the WMFT. Discussion and Conclusion. Although age was the only significant predictor, an equally strong finding in this study was that side of stroke, chronicity, hand dominance, sex, and ambulatory status were not found to be predictors at the follow-up session. This finding emphasizes the importance of not excluding people from CIMT based on these predictors. [Fritz SL, Light KE, Clifford SN, et al. Descriptive characteristics as potential predictors of outcomes following constraint-induced movement therapy for people after stroke.

Key Words: Constraint-induced movement therapy • Hemiplegia • Physical therapy • Stroke

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