Experimentally induced fatigue and motor learning: A scoping review
Bibliographic record
Abstract
Abstract The literature on the effect of fatigue on motor learning is limited and marked by inconsistent findings. This scoping review aimed to explore the available knowledge on the effects of fatigue induced by physical and cognitive exertion on motor learning, and to compile and understand how it is studied. A comprehensive search strategy using relevant index terms and keywords was conducted across MEDLINE, EMBASE, SPORTDiscus, Web of Science, PsycINFO, CINAHL, ERIC, and Dissertations & Theses Global. Twenty-five studies met the inclusion criteria. The findings revealed considerable inconsistencies in how fatigue and motor learning were defined and measured. None of the studies examined the effect of fatigue induced by combined physical and cognitive exertion, and only 7 studies investigated fatigue induced by cognitive exertion. Acuity tasks were the most frequently used to assess motor learning, employed in 14 studies. Notably, all participants were between 16.5 and 31 years of age, and reporting of key demographic and physiological characteristics such as sex, gender, physical activity level, and body mass index was inconsistent or absent. This review highlights the need for comprehensive definitions of both fatigue and motor learning to improve consistency and reproducibility across studies. Given the limited research on the effects of fatigue induced by cognitive and combined physical and cognitive exertion, future studies should prioritize using these experimental manipulations. Also, future studies should diversify the motor learning tasks used in research to allow both direct and conceptual replication. Additionally, broader age ranges and comprehensive participant profiling should be prioritized.
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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 | 0.017 | 0.084 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.016 | 0.014 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".