Explained variance in the thermoregulatory responses to exercise: the independent roles of biophysical and fitness/fatness-related factors
Bibliographic record
Abstract
Individual variation in the thermoregulatory responses to exercise is notoriously large. Although aerobic fitness (V̇o2 max) and body fatness are traditionally considered important predictors of individual core temperature and sweating responses, recent evidence indicates potentially important and independent roles for biophysical factors. Using stepwise regression, we examined the proportion of individual variability in rectal temperature changes (ΔTre), whole body sweat loss (WBSL), and steady-state local sweat rate (LSRss) independently described by 1) biophysical factors associated with metabolic heat production (Hprod) and evaporative heat balance requirements (Ereq) relative to body size and 2) factors independently related to V̇o2 max and body fatness. In a total of 69 trials, 28 males of wide-ranging morphological traits and V̇o2 max values cycled at workloads corresponding to a range of absolute Hprod (410-898 W) and relative intensities (32.2-82.0% V̇o2 max) for 60 min in 24.8 ± 0.7°C and 33.4 ± 12.2% relative humidity. Hprod (in W/kg total body mass) alone described ∼50% of the variability in ΔTre (adjusted to r(2) = 0.496; P < 0.001), whereas surface area-to-mass ratio and body fat percentage (BF%) explained an additional 4.3 and 2.3% of variability, respectively. For WBSL, Ereq (in W) alone explained ∼71% of variance (adjusted to r(2) = 0.713, P < 0.001), and the inclusion of BF% explained an additional 1.3%. Similarly, Ereq (in W/m(2)) correlated significantly with LSRss (adjusted to r(2) = 0.603, P < 0.001), whereas %V̇o2 max described an additional ∼4% of total variance. In conclusion, biophysical parameters related to Hprod, Ereq, and body size explain 54-71% of the individual variability in ΔTre, WBSL, and LSRss, and only 1-4% of additional variance is explained by factors related to fitness or fatness.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".