The Effect of Rapid and Slow Heat Acquisition on Body Weight and Blood Glucose Levels
Bibliographic record
Abstract
Body water loss due to thermoregulation during exercise in a hot environment may cause a significant decrease in body mass, affecting blood plasma volume and consequently parameters such as blood glucose (BG) concentration. It is not known if the increased rate of thermal acquisition that occurs as a result of exercise in a microclimate such as personal protective equipment impacts BG concentrations differently than a slower rate of thermal acquisition. PURPOSE: The purpose of this study was to determine if rapid heat acquisition impacts body mass, urine specific gravity (USG) and BG concentration differently than slow heat acquisition during exercise. METHODS: Fourteen healthy male subjects (mean age, 33.6 + 12.1 years) performed an incremental exercise test to a termination criterion in a control session (CON) and an experimental session (PPE). Body mass, USG and BG were measured before and after each trial. RESULTS: Rate of thermal acquisition was significantly different (p<0.001) between CON (0.02±0.04 °/min) and PPE (0.04±0.19 °/min). Time to termination (TTT) was also significantly different between CON (77.3 ± 22.8 min) and PPE (50.3 ± 12.4 min) and subjects also showed a lower HR throughout CON (pre = 76.8 ± 8.6 bpm; post = 161.1 ± 20.7 bpm) when compared to PPE (pre = 86.5 ± 9.3 bpm; post = 179.6 ± 11.7 bpm). Both conditions resulted in an identical and significant loss of total body mass (1.45 ± 0.62 kg; p<0.05), with a corresponding increase in USG (p<0.01). Despite body water loss, no significant change in blood glucose concentration occurred pre- to post-exercise in either condition (BGCON= -0.04±853 mmol.L-1; BGPPE = 0.34±93 mmol.L-). CONCLUSION: This data suggests that constant levels of blood glucose concentration are maintained regardless of rate of heat acquisition and despite body water loss that would affect plasma concentration.
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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 | 0.001 |
| 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".