Abstract

BackgroundStroke can lead to lasting sensorimotor deficits of the upper limb (UL) persisting into the chronic phase despite intensive rehabilitation. A major impairment of reaching after stroke is a decreased range of active elbow extension, which in turn leads to the use of compensatory movements. Retraining movement patterns relies on cognition and motor learning principles. Implicit learning may lead to better outcomes than explicit learning. Error augmentation (EA) is a feedback modality based on implicit learning resulting in improved precision and speed of UL reaching movements in people with stroke. However, accompanying changes in UL joint movement patterns have not been investigated. The objective of this study is to determine the capacity for implicit motor learning in people with chronic stroke and how this capacity is affected by post-stroke cognitive impairments. MethodsFifty-two subjects who have chronic stroke will practice reaching movements 3×/wk. for 9 wk. in a virtual reality environment. Participants will be randomly allocated to 1 of 2 groups to train with or without EA feedback. Outcome measures (pre-, post- and follow-up) will be: endpoint precision, speed, smoothness, and straightness and joint (UL and trunk) kinematics during a functional reaching task. The degree of cognitive impairment, lesion profile, and integrity of descending white matter tracts will be related to training outcomes. ConclusionsThe results will inform us which patients can best benefit from training programs that rely on motor learning and utilize enhanced feedback.Trial status: Ethical approval for this study was finalized in May 2022. Recruitment and data collection is actively in progress and is planned to finish in 2026. Data analysis and evaluation will occur subsequently, and the final results will be published.

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