In Physical therapy
BACKGROUND : Constraint-induced movement therapy (CI therapy) produces, on average, large and clinically meaningful improvements in the daily use of a more affected upper extremity in individuals with hemiparesis. However, individual responses vary widely.
OBJECTIVE : The study objective was to investigate the extent to which individual characteristics before treatment predict improved use of the more affected arm following CI therapy.
DESIGN : This study was a retrospective analysis of 47 people who had chronic (>6 months) mild to moderate upper extremity hemiparesis and were consecutively enrolled in 2 CI therapy randomized controlled trials.
METHODS : An enhanced probabilistic neural network model predicted whether individuals showed a low, medium, or high response to CI therapy, as measured with the Motor Activity Log, on the basis of the following baseline assessments: Wolf Motor Function Test, Semmes-Weinstein Monofilament Test of touch threshold, Motor Activity Log, and Montreal Cognitive Assessment. Then, a neural dynamic classification algorithm was applied to improve prognostic accuracy using the most accurate combination obtained in the previous step.
RESULTS : Motor ability and tactile sense predicted improvement in arm use for daily activities following intensive upper extremity rehabilitation with an accuracy of nearly 100%. Complex patterns of interaction among these predictors were observed.
LIMITATIONS : The fact that this study was a retrospective analysis with a moderate sample size was a limitation.
CONCLUSIONS : Advanced machine learning/classification algorithms produce more accurate personalized predictions of rehabilitation outcomes than commonly used general linear models.
Rafiei Mohammad H, Kelly Kristina M, Borstad Alexandra L, Adeli Hojjat, Gauthier Lynne V