Physical exercise and motor learning: A scoping review
Bibliographic record
Abstract
Abstract Physical exercise can enhance motor learning by inducing neurophysiological changes that facilitate this process. This preregistered scoping review aimed to map current knowledge on the effects of physical exercise on motor learning. Experimental studies and review articles examining any form of physical exercise in healthy or clinical populations were included, with outcomes assessing motor skill acquisition and/or retention. A systematic search across eight databases (e.g., MEDLINE, EMBASE, PsychINFO) identified 66 sources, including 62 experimental studies and 4 reviews. Two researchers independently reviewed articles and extracted data using a pretested data extraction table; disagreements at the study selection or data extraction stages were resolved through discussion with a third researcher. Most studies involved healthy populations (83%), with clinical populations underrepresented (17%). Aerobic exercise was most commonly investigated, particularly lower-limb cycling (73%), while resistance exercise was rarely examined (0.6%). Exercise intensity was predominantly high, although 6% of studies reported intensity not reflecting the prescribed method. Motor learning outcomes varied: 55% assessed both skill acquisition and retention, while 45% relied on retention tests alone at short-term or delayed time points, or both. Infrequent use of long-term retention tests limits understanding of lasting effects. Overall, this review highlights gaps in the literature, including the underrepresentation of clinical populations, inconsistent reporting of exercise intensity, scarce research on resistance exercise, and limited assessment of long-term retention, which may affect interpretation of exercise’s impact on motor learning. Highlights Clinical populations were underrepresented in the exercise and motor learning field. High-intensity lower-limb cycling was the most common exercise studied. Resistance exercise was underrepresented in the field. Accurate prescription of exercise intensity is essential to ensure reliable results. Future research should more frequently use longer-term retention tests.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.043 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.019 | 0.019 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".