Ice Slurry Ingestion Leads to a Lower Net Heat Loss during Exercise in the Heat
Bibliographic record
Abstract
PURPOSE: To compare the reductions in evaporative heat loss from the skin (Esk) to internal heat loss (Hfluid) induced by ice slurry (ICE) ingestion relative to 37 °C fluid and the accompanying body temperature and local thermoeffector responses during exercise in warm, dry conditions (33.5 °C ± 1.4 °C; 23.7% ± 2.6% relative humidity [RH]). METHODS: Nine men cycled at approximately 55% VO2peak for 75 min and ingested 3.2 mL · kg(-1) aliquots of 37 °C fluid or ICE after 15, 30, and 45 min of exercise. Metabolic heat production (M-W), rectal temperature (Tre), mean skin temperature (Tsk), whole-body sweat loss (WBSL), local sweat rate (LSR), and skin blood flow (SkBF) were measured throughout. Net heat loss (HLnet) and heat storage (S) were estimated using partitional calorimetry. RESULTS: Relative to the 37 °C trial, M-W was similar (P = 0.81) with ICE ingestion; however, the 200 ± 20 kJ greater Hfluid (P < 0.001) with ICE ingestion was overcompensated by a 381 ± 199-kJ lower Esk (P < 0.001). Net heat loss (HLnet) was consequently 131 ± 120 kJ lower (P = 0.01) and S was greater (P = 0.05) with ICE ingestion compared with 37 °C fluid ingestion. Concurrently, LSR and WBSL were lower by 0.16 ± 0.14 mg · min(-1) · cm(-2) (P < 0.01) and 191 ± 122 g (P < 0.001), respectively, and SkBF tended to be lower (P = 0.06) by 5.4%maxAU ± 13.4%maxAU in the ICE trial. Changes in Tre and Tsk were similar throughout exercise with ICE compared to 37 °C fluid ingestion. CONCLUSIONS: Relative to 37 °C, ICE ingestion caused disproportionately greater reductions in Esk relative to Hfluid, resulting in a lower HLnet and greater S. Mechanistically, LSR and possibly SkBF were suppressed independently of Tre or Tsk, reaffirming the concept of human abdominal thermoreception. From a heat balance perspective, recommendations for ICE ingestion during exercise in warm, dry conditions should be reconsidered.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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) | 0.002 | 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".