Practicing Sport in Cold Environments: Practical Recommendations to Improve Sport Performance and Reduce Negative Health Outcomes
Bibliographic record
Abstract
Although not a barrier to perform sport, cold weather environments (low ambient temperature, high wind speeds, and increased precipitation, i.e., rain/water/snow) may influence sport performance. Despite the obvious requirement for practical recommendations and guidelines to better facilitate training and competition in such cold environments, the current scientific evidence-base is lacking. Nonetheless, this review summarizes the current available knowledge specifically related to the physiological impact of cold exposure, in an attempt to provide practitioners and coaches alike with practical recommendations to minimize any potential negative performance effects, mitigate health issues, and best optimize athlete preparation across various sporting disciplines. Herein, the review is split into sections which explore some of the key physiological effects of cold exposure on performance (i.e., endurance exercise capacity and explosive athletic power), potential health issues (short-term and long-term), and what is currently known with regard to best preparation or mitigation strategies considered to negate the potential negative effects of cold on performance. Specific focus is given to "winter" sports that are usually completed in cold environments and practical recommendations for physical preparation.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".