Influence of Sex on Whole‐Body Heat Exchange Following Cold Beverage Ingestion During Work–Rest Cycles in the Heat
Bibliographic record
Abstract
ABSTRACT Background Consuming cold beverages during work in the heat can reduce sweat output in males. However, responses in females may differ given sex‐related differences in whole‐body heat exchange during work in the heat. We therefore assessed whether sex influences whole‐body heat exchange following cold beverage ingestion during intermittent work in hot conditions. Methods Twenty young adults (ten females, mean ± SD: 23 ± 3 years) performed four 15‐min bouts of moderate‐intensity cycling at a fixed rate of metabolic heat production (200 W·m−2), each interspersed by 15‐min rest periods in dry heat (40°C, ~12% relative humidity). On separate days, participants ingested either ice‐slurry (~0°C), standardized to provide a heat transfer capacity of 75 kJ·m−2, or an identical mass of warm fluid (37.5°C) prior to the first and third exercise bouts. Dry and evaporative heat exchange (direct calorimetry), as well as metabolic heat production (indirect calorimetry), were measured continuously and used to determine cumulative heat storage (summation of heat loss and heat gain) over the entire protocol. Results Dry and evaporative heat exchange were unaffected by beverage condition or sex (all p > 0.05). Relative to warm fluid, ice‐slurry ingestion reduced cumulative heat storage in females (86 ± 120 vs. 167 ± 103 kJ, p = 0.01) and males (69 ± 181 vs. 216 ± 94 kJ) but responses did not differ between sexes (p = 0.70). Conclusion Whole‐body heat exchange was unaffected by beverage temperature, albeit the heat transfer to the ingested ice‐slurry reduced cumulative heat storage in both sexes during intermittent work in dry heat.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".