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Updated: Jul 27, 2026

Kinematic Analysis Using 3D Motion Capture of Drinking Task in People With and Without Upper-extremity Impairments
Published on: March 28, 2018
Tracking bimanual recovery after stroke: Grasp function and stroke severity predict 1-year performance
Annick Van Gils1,2, Yue Zou1, Sarah Meyer1,3
1Department of Rehabilitation Sciences, KU Leuven - University of Leuven, Leuven, Belgium.
Abstract:
ObjectiveTo characterize the trajectory and identify early predictors of bimanual performance at 6 and 12 months post-stroke.DesignProspective longitudinal study with assessments at admission, 6, and 12 months post-stroke.SettingInpatient rehabilitation centre.ParticipantsFirst-ever stroke survivors with unilateral upper limb motor impairment.Main MeasuresBimanual performance was assessed using the Adult Assisting Hand Assessment Stroke. Potential predictors were collected at admission through comprehensive clinical and functional assessments. We used multivariate linear regression to identify key predictors and a Chi-square Automatic Interaction Detection analysis to derive a clinical decision tree.ResultsNinety-two participants (mean age 67 ± 12 years; 22 ± 8 days post-stroke) participated. Bimanual performance improved over time (p < 0.001), with median scores increasing from 8 (interquartile range 0-52) at admission to 48 (8-70) at 6 months, and 48 (8-75) at 12 months. At 6 months, admission grasp function and stroke severity (64% and 9%) jointly explained 73% of the variance in bimanual performance scores. At 12 months, admission stroke severity, grasp function, and Barthel Index (66%, 10%, and 2%) accounted for 78% of the variance. Decision tree analysis confirmed stroke severity and grasp function as the primary predictors and generated a clinically interpretable model.ConclusionBimanual performance improves most within the first 6 months post-stroke. Grasp function and stroke severity at admission strongly predict long-term bimanual outcomes. The decision tree derived from the Chi-square Automatic Interaction Detection analysis may support stratified rehabilitation and realistic goal-setting for daily bimanual use.

