Steady‐state sweating during exercise is determined by the evaporative requirement for heat balance independently of absolute core and skin temperatures
Bibliographic record
Abstract
Key points When exercise was prescribed to elicit a fixed evaporative heat balance requirement (Ereq), no differences in steady‐state sweat rates were observed with different absolute oesophageal and/or skin temperatures, secondary to differences in time of the day (i.e. morning (AM) vs. afternoon (PM)) and ambient temperature (i.e. 23°C vs. 33°C). Exercise at a fixed metabolic heat production (Hprod), but a different Ereq (due to differences in air temperature), yielded higher steady‐state sweat rates with a higher Ereq, irrespective of absolute oesophageal temperature. Circadian rhythm did not alter the change in core temperature prior to the onset for sudomotor activation, nor the thermosensitivity, resulting in similar cumulative whole‐body sweat rates irrespective of time of day at a fixed Ereq. Collectively, these data indicate that during exercise in a compensable environment, steady‐state sudomotor responses are influenced by Ereq rather than absolute core and skin temperatures, or Hprod. Abstract The present study sought to determine whether absolute core temperature (modified via diurnal variation) and absolute skin temperature (modified by different air temperatures (Ta)) alters the steady‐state sweating response to exercise at a fixed evaporative heat balance requirement (Ereq). Ten males exercised for 60 min on six occasions. Three Ta/heat production (Hprod) combinations (23°C/525 W, 33°C/400 W, 33˚C/525 W) were completed in the morning (08.00 h, AM) and afternoon (16.00 h, PM), to yield: (1) the same Ereq (200 or 275 W·m−2) with different absolute core temperatures (AM vs. PM); (2) the same Ereq (200 W·m−2) with different skin temperatures (Ta: 23˚C vs. 33˚C); (3) the same heat production (525 W) with different Ereq (200 vs. 275 W·m−2). Oesophageal temperature (Toes), local sweat rate (LSR) on the arm and upper‐back, and whole‐body sweat rate (WBSR) were measured. Steady‐state Toes was always higher in PM versus AM at an Ereq of 200 W·m−2 (23°C, P = 0.001; 33°C, P = 0.004) and 275 W·m−2, (33°C, P = 0.001). However steady‐state mean LSR (200 W·m−2/23°C: P = 0.25; 200 W·m−2/33°C: P = 0.86; 275 W·m−2/33°C: P = 0.53) and WBSR (200 W·m−2/23°C: P = 0.79; 200 W·m−2/33°C: P = 0.48; 275W·m−2/33°C: P = 0.32) were similar. When Ereq was matched (200 W·m−2) with different Ta (23°C vs. 33°C), steady‐state LSR (P > 0.17) and WBSR (P > 0.93) were similar despite different skin temperatures. For the same Hprod (525 W) but different Ereq (200 vs. 275 W·m−2), mean LSR (P < 0.001), and WBSR (P < 0.001) were higher with a greater Ereq. Collectively, steady‐state sweating during exercise is altered by Ereq but not Toes, skin temperature, or Hprod.
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) | 0.000 | 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".