A Review of Elite Athlete Evidence-Based Knowledge and Preparation for Competing in the Heat
Bibliographic record
Abstract
Abstract Elite athletes will compete in extreme heat more frequently as global land and sea temperatures increase, alongside more intense, frequent and longer duration heatwaves. Best practices to protect athlete health and performance during competition include heat acclimation/acclimatisation [(HA); i.e., long-term pre-competition preparation] complemented by pre-planned and practised cooling and hydration strategies (i.e., short-term interventions immediately before or during competition). This review explores elite athletes’ current behaviours and practices when preparing for competition in the heat and assesses the level of knowledge that has been exhibited by athletes and their practitioners in this space. Recommendations for future research, discussions of current best practices, and methods to improve translation of research into practice are provided. Available research focuses on small samples of elite endurance athletes during a selection of World Championship/Olympic/Paralympic events (~6% of competing athletes). While generally an increase in the adoption of evidence-based HA is seen chronologically from 2015 onwards, universal adoption is not seen. HA adoption is lowest in those who live/train in cold/temperate environments with cost and access to facilities/equipment being the most commonly reported barriers. Further research is required across the sporting landscape to fully characterise elite athlete behaviours and practices in these spaces. International federations and national governing bodies should continue their efforts to educate athletes and focus on regularly updated and reinvigorated release of evidence-based guidelines (in multiple germane languages) for competing in the heat, to increase the adoption of HA and other heat related best practice.
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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 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".