Sex Does Not Modify Fluid Balance During Work In The Heat When Drinking Ad Libitum
Bibliographic record
Abstract
Occupational hydration recommendations are based on the premise that voluntarily fluid intake is insufficient during physical work in the heat, thereby increasing the likelihood of dehydration. Nearly all studies informing these recommendations have been performed in men. Ovarian sex hormones may reduce urine output and promote fluid intake. However, it is unknown if fluid balance during occupationally relevant physical work in the heat differs between men and women across menstrual cycle. PURPOSE: Test the hypothesis that when fluids are freely available during physical work in the heat men will be more dehydrated than women, independent of menstrual cycle phase. METHODS: Twelve healthy men (25 ± 5 y) and twelve healthy eumenorrheic women (26 ± 5 y) completed 4 h exposures to 33.7 ± 0.3 °C, 54 ± 1% relative humidity. Each hour participants walked for 30 min at an intensity evoking a rate of metabolic heat production of 5.2 ± 0.7 W/kg body mass, which did not differ between men and women (p ≥ 0.34). Participants drank a cool (12 ± 1 °C) flavor-preferred, non-caloric sport drink ad libitum. Men completed the exposure once. Women completed one exposure during the early follicular (EF), late follicular (LF) and mid-luteal (ML) phases of the menstrual cycle in a random order. Each phase was independently compared to the men. Data are presented as mean ± SD. RESULTS: The increase in rectal temperature during EF (+0.4 ± 0.2 °C, p = 0.16) and LF (+0.4 ± 0.2 °C, p = 0.31) did not differ from the men (+0.5 ± 0.3 °C) but was lower than the men in ML (0.3 ± 0.2, p = 0.05). Fluid intake (p ≥ 0.09) and sweat loss (p ≥ 0.06) did not differ from the men in EF, LF, or ML. Urine output during EF (833 ± 546 mL, p = 0.09) did not differ from men (525 ± 253 mL) but was higher than the men in LF (905 ± 593 mL, p = 0.05) and ML (940 ± 438 mL, p < 0.01). Changes in plasma volume did not differ from the men (+145 ± 157 mL) in EF (+175 ± 281 mL, p = 0.77), LF (+183 ± 263 mL, p = 0.69) or ML (+145 ± 263 mL, p = 0.77). Percent changes in body mass did not differ from men (-0.5 ± 0.5%) in EF (-0.5 ± 0.9%, p = 0.97), LF (-0.3 ± 0.9%, p = 0.44) or ML (-0.3 ± 0.7%, p = 0.33). CONCLUSIONS: When fluids are readily available, fluid balance does not differ between men and women across the menstrual cycle during physical work in the heat. Supported by the ACSM Foundation Carl V. Gisolfi Memorial Fund & R01OH011528
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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 | 0.001 |
| 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".