Visuospatial Skills Explain Differences in the Ability to Use Propulsion Biofeedback Post-stroke

Abstract

Background and Purpose: Visual biofeedback can be used to help people post-stroke reduce biomechanical gait impairments. Using visual biofeedback engages an explicit, cognitively demanding motor learning process. Participants with better overall cognitive function are better able to use visual biofeedback to promote locomotor learning; however, which specific cognitive domains are responsible for this effect are unknown. We aimed to understand which cognitive domains were associated with performance during acquisition and immediate retention when using visual biofeedback to increase paretic propulsion in individuals post-stroke. Methods: Participants post-stroke completed cognitive testing, which provided scores for different cognitive domains, including executive function, immediate memory, visuospatial/constructional skills, language, attention, and delayed memory. Next, participants completed a single session of paretic propulsion biofeedback training, where we collected treadmill-walking data for 20 min with biofeedback and 2 min without biofeedback. We fit separate regression models to determine if cognitive domain scores, motor impairment (measured with the lower-extremity Fugl-Meyer), and gait speed could explain propulsion error and variability during biofeedback use and recall error during immediate retention. Results: Visuospatial/constructional skills and motor impairment best-explained propulsion error during biofeedback use (adjusted R2 = 0.56, P = 0.0008), and attention best-explained performance variability (adjusted R2 = 0.17, P = 0.048). Language skills best-explained recall error during immediate retention (adjusted R2 = 0.37, P = 0.02). Discussion and Conclusions: These results demonstrate that specific cognitive domain impairments explain variability in locomotor learning outcomes in individuals with chronic stroke. This suggests that with further investigation, specific cognitive impairment information may be useful to predict responsiveness to interventions and personalize training parameters to facilitate locomotor learning.