The effect of cooling prior to and during exercise on exercise performance and capacity in the heat: a meta-analysis
Bibliographic record
Abstract
Exercise is impaired in hot, compared with moderate, conditions. The development of hyperthermia is strongly linked to the impairment and as a result various strategies have been investigated to combat this condition. This meta-analysis focused on the most popular strategy: cooling. Precooling has received the most attention but recently cooling applied during the bout of exercise has been investigated and both were reviewed. We conducted a literature search and retrieved 28 articles which investigated the effect of cooling administered either prior to (n=23) or during (n=5) an exercise test in hot (wet bulb globe temperature >26°C) conditions. Mean and weighted effect size (Cohen's d) were calculated. Overall, precooling has a moderate (d=0.73) effect on subsequent performance but the magnitude of the effect is dependent on the nature of the test. Sprint performance is impaired (d=-0.26) but intermittent performance and prolonged exercise are both improved following cooling (d=0.47 and d=1.91, respectively). Cooling during exercise has a positive effect on performance and capacity (d=0.76). Improvements were observed in studies with and without cooling-induced physiological alterations, and the literature supports the suggestion of a dose-response relationship among cooling, thermal strain and improvements in performance and capacity. In summary, precooling can improve subsequent intermittent and prolonged exercise performance and capacity in a hot environment but sprint performance is impaired. Cooling during exercise also has a positive effect on exercise performance and capacity in a hot environment.
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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 | 0.009 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.043 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".