| Summary: | Objectives: To (1) characterize study paradigms used to investigate motor learning (ML) poststroke and (2) summarize the effects of different ML principles in promoting skill acquisition and retention. Our secondary objective is to evaluate the clinical utility of ML principles on stroke rehabilitation. Data Sources: Medline, Excerpta Medica Database, Allied and Complementary Medicine, Cumulative Index to Nursing and Allied Health Literature, and Cochrane Central Register of Controlled Trials were searched from inception on October 24, 2018 and repeated on June 23, 2020. Scopus was searched on January 24, 2019 and July 22, 2020 to identify additional studies. Study Selection: Our search included keywords and concepts to represent stroke and “motor learning. An iterative process was used to generate study selection criteria. Three authors independently completed title, abstract, and full-text screening. Data Extraction: Three reviewers independently completed data extraction. Data Synthesis: The Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension guidelines for scoping reviews were used to guide our synthesis. Thirty-nine studies were included. Study designs were heterogeneous, including variability in tasks practiced, acquisition parameters, and retention intervals. ML principles investigated included practice complexity, feedback, motor imagery, mental practice, action observation, implicit and explicit information, aerobic exercise, and neurostimulation. An additional 2 patient-related factors that influence ML were included: stroke characteristics and sleep. Practice complexity, feedback, and mental practice/action observation most consistently promoted ML, while provision of explicit information and more severe strokes were detrimental to ML. Other factors (ie, sleep, practice structure, aerobic exercise, neurostimulation) had a less clear influence on learning. Conclusions: Improved consistency of reporting in ML studies is needed to improve study comparability and facilitate meta-analyses to better understand the influence of ML principles on learning poststroke. Knowledge of ML principles and patient-related factors that influence ML, with clinical judgment can guide neurologic rehabilitation delivery to improve patient motor outcomes.
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