Cooling during exercise enhances performances, but the cooled body areas matter: A systematic review with meta‐analyses
Bibliographic record
Abstract
INTRODUCTION: Hyperthermia during exercise induces central and peripheral fatigue and impairs physical performance. To facilitate heat loss and optimize performance, athletes can hasten body cooling prior (pre-cooling) or during (per-cooling) exercise. However, it is unclear whether per-cooling effect is the same on 'aerobic' and 'anaerobic' types of exercise (duration <75 and >76 seconds, respectively, according to Gastin [Sports Med 2001;31:725-741]) and whether the body area that is cooled makes a difference. METHODS: A literature search led to the identification of 1582 potential studies. Included studies had to include physical exercise with sufficient details on the type, duration, intensity, and provide valid performance measures and a cooling intervention administered during exercise with sufficient details on the type and site of application. RESULTS: Forty-five studies were included. Per-cooling provides a performance benefit during 'aerobic' (standardized mean difference (SMD) of 0.60, P < .001) and 'anaerobic' exercises (SMD = 0.27, P < .02). The effects were greater during aerobic compared to anaerobic exercises (P < .01). Internal cooling (cold fluid ingestion such as cold water and ice slurry/menthol beverage) and external cooling (face, neck, and torso) provide the greatest performance benefit for 'aerobic' performance with a moderate to large effect (0.46 < SMD < 1.24). For 'anaerobic' exercises, wearing a whole-body cooling garment is the best way to enhance exercise performance (SMD = 0.39, P < .01). CONCLUSION: Per-cooling improves 'aerobic' and 'anaerobic' exercise performance with a greater benefit for 'aerobic' exercise. The magnitude of the effect depends on the type and site of the cooling application.
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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 | 0.013 | 0.041 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.016 | 0.028 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".