Oxygen Consumption and Substrate Oxidation Rates Following Passive Acclimation to a Hot Environment
Bibliographic record
Abstract
Introduction Acclimation to a hot environment via prolonged or repeated, daily exposure to a heat stress results in adaptations of the human thermoregulatory responses. In this study, it was investigated whether metabolic responses to passive heat acclimation (HA) adapted similarly to thermoregulatory heat loss responses including following a 10‐day, 2 h·day −1 passive heat acclimation (HA) to 50°C and 20 % RH. This study was approved by the Simon Fraser University Office of Research Ethics. Purpose It was hypothesized that, similar to that seen during active heat acclimation with exercise, there would be an elevation of oxygen consumption following a passive heat acclimation. It was also hypothesized that following heat acclimation there would be increased lipid oxidation relative to the pre‐acclimation state. Methods Nine male participants performed two immersions in a 40°C bath separated by a 10‐day passive heat acclimation. Heat acclimation was performed in a climatic chamber controlled at 50°C and 20 % RH where participants remained seated at rest for 2 hr·day −1 with their rectal temperature maintained between 38.5 – 39.0°C. A two‐way repeated measures ANOVA with factors of Acclimation State (Pre and Post) and delta T ES (0 to 1.4°C above rest) was employed with post hoc analysis completed with two‐tailed, paired t‐tests. The significance level was p<0.05. Results HA was confirmed by a significant decrease in resting esophageal temperature (T ES ) from 37.60 ± 0.19 (mean ± SD) to 37.31 ± 0.11°C (p=0.001). During the 40°C immersions significant decreases in the T ES thresholds for temple cutaneous blood velocity from 37.34 ± 0.12 to 37.14 ± 0.16°C (p=0.008) and for forehead eccrine sweating from 37.61 ± 0.21 to 37.24 ± 0.24°C (p=0.007) also confirmed heat acclimation. HA did not influence oxygen consumption from 0.21 ± 0.07 to 0.22 ± 0.07 L/min (p=0.15). Carbohydrate oxidation rate increased from 0.21 ± 0.21 to 0.28 ± 0.26 g·min −1 (p=0.002). Lipid oxidation rate decreased from 1.16 ± 0.22 to 1.09 ± 0.24 g·min −1 (p<0.001). Conclusion Following a passive heat acclimation the evidence reports no change in oxygen consumption, and contrary to that hypothesized, there was a decrease in lipid oxidation rate and an increase in carbohydrate oxidation rate. Support or Funding Information Funding Source This project was supported by Natural Sciences and Engineering Research Council of Canada and the Canada Foundation for Innovation.
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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) | 0.003 | 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".