Passive heating in sport: context-specific benefits, detriments, and considerations
Bibliographic record
Abstract
Exercise and passive heating share some acute physiological responses. These include increases in body temperature, sweat rate, blood flow, heart rate, and redistribution of plasma and blood volume. These responses can vary depending on the heating modality or dose (e.g., temperature, duration, body coverage) and are beneficial to athletes in specific scenarios. These scenarios include being applied to increase muscle or force production, induce rapid weight loss, stimulate thermoregulatory or cardiovascular adaptation, or to accelerate recovery. The rationale being to tailor the specific passive heating protocol to target the desired physiological response. However, some acute responses to passive heating may also be detrimental to sporting outcomes, such as exercising in the heat, having unintended residual negative effects on performance or perceptions of fatigue, or even resulting in hospitalisation if implemented inappropriately. Accordingly, the effects of passive heating should be carefully considered prior to implementation by athletes, coaches, and support staff. Therefore, the purpose of this review is to evaluate the physiological responses to different modes and doses of passive heating and explore the various sport contexts where these effects may either benefit or hinder athletes. Understanding these responses can aid the implementation of passive heating in sport and identify potential recommended heating protocols in each given scenario.
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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 | 0.001 | 0.002 |
| 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 | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".