Temperature of Ingested Water during Exercise Does Not Affect Body Heat Storage
Bibliographic record
Abstract
PURPOSE: The objective of this study was to examine the effect of ingested water temperature on heat balance during exercise as assessed by direct calorimetry. METHODS: Ten healthy males (25 ± 4 yr) cycled at 50% V˙O2peak (equivalent rate of metabolic heat production (M-W) of 523 ± 84 W) for 75 min under thermocomfortable conditions (25°C, 25% relative humidity) while consuming either hot (50°C) or cold (1.5°C) water. Four 3.2 mL·kg⁻¹ boluses of hot or cold water were consumed 5 min before and at 15, 30, and 45 min after the onset of exercise. Total heat loss (HL = evaporative heat loss (HE) ± dry heat exchange (HD)) and M-W were measured by direct and indirect calorimetry, respectively. Change in body heat content (ΔHb) was calculated as the temporal summation of M-W and HL and adjusted for changes in heat transfer from the ingested fluid (Hfluid). RESULTS: The absolute difference for HL (209 ± 81 kJ) was similar to the absolute difference of Hfluid (204 ± 36 kJ) between conditions (P = 0.785). Furthermore, the difference in HL was primarily explained by the corresponding changes in HE (hot: 1538 ± 393 kJ; cold: 1358 ± 330 kJ) because HD was found to be similar between conditions (P = 0.220). Consequently, no difference in ΔHb was observed between the hot (364 ± 152 kJ) and cold (363 ± 134 kJ) conditions (P = 0.971) during exercise. CONCLUSION: We show that ingestion of hot water elicits a greater HL relative to cold water ingestion during exercise. However, this response was only compensated for the heat of the ingested fluid as evidenced by similar ΔHb between conditions. Therefore, our findings indicate that relative to cold water ingestion, consuming hot water does not provide a thermoregulatory advantage. Both hot and cold water ingestion results in the same amount of heat stored during prolonged moderate-intensity exercise.
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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) | 0.001 | 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".