Discrepancies in the Change in Body Heat Content and Core Temperature Responses During Exercise Performed Under the Threshold Limit Values Exposure Guidelines
Bibliographic record
Abstract
During exercise, especially in hot conditions, heat will be continuously stored within the body if the body's capacity to dissipate heat is insufficient to offset the rate of metabolic and environmental heat gain, causing tissue temperature to rise to potentially dangerous levels. For this reason, multiple guidelines have been developed to limit thermal strain during exercise in the heat. One such guideline is the American Conference of Governmental and Industrial Hygiene's (ACGIH) Threshold Limit Values (TLVs), which consist of work‐rest allocations that consider environmental conditions (expressed as wet‐bulb globe temperature [WBGT]) and work intensity (metabolic rate) with the goal of preventing core temperature from exceeding predefined thresholds (38.0°C in non‐heat acclimated workers). However, thermometric measurements (core and skin temperatures), on which the TLV core temperature thresholds are based, underestimate the change in body heat content (ΔH b ) during exercise. Therefore, we evaluated heat strain through thermometry and ΔH b via direct calorimetry during work performed in accordance with the TLVs. Nine young males (21±3 years) cycled at a fixed rate of heat production of 360 W (considered the onset of a moderate work demand by ACGIH) under four combinations of work‐rest ratios (WR) and environmental conditions (WGBT). Each protocol was 120 min in duration. The first protocol consisted of 120‐min of continuous cycling at a WBGT of 28.0°C (CON[28.0°C]). In the remaining three protocols, intermittent work bouts were performed under increasing WBGT requiring longer recovery periods: i) WR3:1[29.0°C], WR of 3:1, WBGT of 29.0°C; ii) WR1:1[30.0°C], WR of 1:1, WBGT of 30.0 and iii) WR1:3[31.5°C], WR of 1:3, WBGT of 31.5. ΔH b was quantified as the cumulative sum of heat production (indirect calorimetry) and whole‐body heat loss (direct calorimetry). Mean body temperature was subsequently calculated from ΔH b and compared to the mean body temperature response estimated with thermometry. ΔH b was similar between conditions (CON[28.0°C], 285±121 kJ; WR3:1[28.0°C], 323±82 kJ; WR1:1[30.0°C], 279±107 kJ; WR1:1[31.5°C], 352±48 kJ; P=0.70). However, when responses were compared, a greater increase in mean body temperature was observed with direct calorimetry relative to thermometry in WR3:1[28.0°C] (calorimetry (C): 1.19±0.33°C, thermometry (T): 0.67±0.22°C; P=0.03), WR1:1[30.0°C] (C: 0.99±0.33°C, T: 0.47±0.11°C; P=0.02) and WR1:3[31.5°C] (C: 1.29±0.18°C, T: 0.43±0.12°C; P<0.01) but not CON[28.0°C] (C: 1.05±0.44°C, T: 0.86±0.44°C; P=0.32). We show that work performed under the TLVs resulted in the same ΔH b in various environmental conditions; however, these responses differed from those observed with thermometry, on which the TLV exposure limits are based. Support or Funding Information Natural Sciences and Engineering Research Council of Canada and the Deep Mining Research Consortium (funds held by Dr. Glen P. Kenny).
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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 | 0.001 | 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".