The effects of fatigue on perceptual-cognitive performance among open-skill sport athletes: A scoping review
Bibliographic record
Abstract
Perceptual-cognitive performance is fundamental for the anticipation and decision-making demands of open-skill sports but may be disrupted by fatigue. This scoping review aimed to describe what is known about the effects of fatigue on perceptual-cognitive performance among open-skill sport athletes. Six databases were systematically searched. Articles were included if they involved open-skill sport athletes, a perceptual-cognitive task assessed on two or more occasions, and induction of acute fatigue confirmed by a manipulation check. Sixty-seven studies, chapters, and reviews were included. In 51% of studies, fatigue was induced through physical exertion, with the rest by mental exertion (27%), or a combination of physical and mental exertion (22%). Only 35% of studies involved sport-specific exertion to induce fatigue, and 29% included measures of participants’ subjective ratings that confirmed the presence of fatigue. Forty-seven percent of perceptual-cognitive tasks were sport-specific, and just 19% assessed perceptual-cognitive performance simultaneous to the fatigue-inducing exertion. Negative, positive, and no effects of fatigue on perceptual-cognitive performance were reported, and these equivocal findings may be attributable to methodological discrepancies between studies. Future research should include more sport-specific designs, as well as stressors other than fatigue, such as environmental and psychosocial stressors.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".