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Updated: Jan 30, 2026

Author Spotlight: Rehabilitation of Stroke Patients With a Digital Occupational Training System
Published on: December 29, 2023
Executive functioning as a predictor of stroke rehabilitation outcomes
N Beckett Shea-Shumsky1, Stefanie Schoeneberger2, Jim Grigsby3
1a Colorado College , Colorado Springs , CO , USA.
Abstract:
Objective: Stroke is a common cause of death and adult chronic neurologic disability. Although factors such as cardiovascular disease affect the incidence of stroke, less is known about factors influencing longitudinal stroke outcomes. The purpose of this research was to assess the contribution of executive functioning (EF) at discharge to the prediction of functional status at several timepoints between discharge from a stroke rehabilitation unit and 12 months, in comparison with depression, mental status, comorbidity, and pain at discharge, and daily functioning prior to admission. Methods: The sample comprised 246 inpatients aged 65 and older who were on inpatient rehabilitation services following acute hospitalization for a stroke. Patients (or proxies) were interviewed in person at discharge about their ability to engage in activities of daily living (ADL), and by telephone at follow-ups 3, 6, 9, and 12 months after discharge. Functional outcomes included independence in bathing, dressing, walking, use of the toilet, and chair/bed transfers. Hypotheses were tested concerning the relative contribution of EF, depression, mental status, comorbidity, and several other demographic and clinical variables to ADL performance. Results: Executive functioning, depression, and pre-admission ADL functioning were strong predictors of outcome at all five timepoints, while neither comorbidity nor mental status were retained in any regression models. Pain at discharge was a significant predictor at discharge and 6 month follow-up. Conclusions: Executive functioning and depression are robust predictors of functional status following stroke rehabilitation. Although not consistently a significant predictor, pain might also be a useful addition to predictive models.
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