Heat-induced hypervolemia: Does the mode of acclimation matter and what are the implications for performance at Tokyo 2020?
Bibliographic record
Abstract
Tokyo 2020 will likely be the most heat stressful Olympics to date, so preparation to mitigate the effects of humid heat will be essential for performance in several of the 33 sports. One key consideration is heat acclimation (HA); the repeated exposure to heat to elicit physiological and psychophysical adaptations that improve tolerance and exercise performance in the heat. Heat can be imposed in various ways, including exercise in the heat, hot water immersion, or passive exposure to hot air (e.g., sauna). The physical requirements of each sport will determine the impact that the heat has on performance, and the adaptations required from HA to mitigate these effects. This review focuses on one key adaptation, plasma volume expansion (PVE), and how the mode of HA may affect the kinetics of adaptation. PVE constitutes a primary HA-mediated adaptation and contributes to functional adaptations (e.g., lower heart rate and increased heat loss capacity), which may be particularly important in athletes of “sub-elite” cardiorespiratory fitness (e.g., team sports), alongside athletes of prolonged endurance events. This review: i) highlights the ability of exercise in the heat, hot-water immersion, and passive hot air to expand PV, providing the first quantitative assessment of the efficacy of different heating modes; ii) discusses how this may apply to athletes at Tokyo 2020; and iii) provides recommendations regarding the protocol of HA and the prospect for achieving PVE (and the related outcomes).
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".