On the effects of constant and variable work of equivalent average intensity on whole‐body heat exchange
Bibliographic record
Abstract
Currently recommended heat exposure guidelines prescribe work‐rest allocations based on the level of heat stress originating from the environment and estimated work intensity with the primary goal of maintaining body core temperature within safe limits. Due to the nature of most occupations, these parameters are rarely, if ever, constant. For this reason, ambient conditions (e.g., temperature and humidity) and metabolic rate (endogenous heat production) are quantified as a time‐weighted average over a given work period. What is unknown, however, is whether time‐dependent alterations in work intensity influence whole‐body heat exchange and cardiovascular strain during work eliciting an equivalent time‐weighted average heat production. On four occasions, six young (27 years [SD 4]) males performed 90‐min of semi‐recumbent cycling at a time‐weighted average rate of heat production (~200 W·m −2 ) in dry heat (40°C, ~20% relative humidity) elicited via cycling at a constant external work rate of 40 W·m −2 (CON) or variable intensity cycling performed in 10‐min cycles: 1) 5 min at an external work rate of 15 W·m −2 followed by 5‐min at 60 W·m −2 (VAR‐LOW); 2) 6 min at 15 W·m −2 followed by 4 min at 70 W·m −2 (VAR‐MOD); and 3) 7 min at 15 W·m −2 followed by 3 min at 80 W·m −2 (VAR‐HIGH). Heat production was measured via indirect calorimetry while whole‐body evaporative heat loss and dry heat gain were evaluated with direct calorimetry. Body heat storage was calculated as the temporal‐summation of heat production and net heat exchange (evaporative heat loss minus dry heat gain). Calorimetric data were expressed as a time‐weighted average (W·m −2 ) except for heat storage, which was presented as a cumulative value (kJ). Rectal temperature and heart rate were monitored continuously and reported as an average of the final 10‐min of exercise (i.e., final cycle) as well as a peak 30‐s average during this period. Across conditions, there were no differences in evaporative heat loss (241 [20] W·m −2 , P=0.20 ), dry heat gain (59 [13] W·m −2 , P=0.52 ), net heat exchange (281 [10] W·m −2 , P=0.54 ) or body heat storage (224 [55] kJ, P=0.87) . Likewise, rectal temperature was not influenced by varying work intensity (average: 37.79 [0.23]°C; peak: 37.84 [0.22]°C; both P≥0.65 ). However, compared to CON (115 [8] beats·min −1 ), average heart rate over the final 10 min of exercise was elevated in VAR‐LOW (124 [12] beats·min −1 ; P=0.01 ), VAR‐MOD (122 [10] beats·min −1 ; P=0.04 ) and VAR‐HIGH (124 [11] beats·min −1 ; P=0.01 ), but similar between the variable work conditions (all P≥0.17 ). Similarly, peak heart rate was elevated in each of the variable work conditions compared to CON (120 [10] beats·min −1 ; all P<0.01 ) and in VAR‐HIGH (154 [11] beats·min −1 ) compared to both VAR‐LOW (140 [12] beats·min −1 ; P<0.01 ) and VAR‐MOD (143 [12] beats·min −1 ; P=0.01 ). These preliminary data suggest that time‐dependent alterations in work intensity eliciting equivalent time‐weighted rates of metabolic heat production do not influence whole‐body heat exchange or thermal strain, but exacerbate cardiovascular strain compared to constant‐intensity work. Support or Funding Information This project was funded by the Government of Ontario and the Natural Sciences and Engineering Research Council of Canada. This abstract is from the Experimental Biology 2019 Meeting. There is no full text article associated with this abstract published in The FASEB Journal .
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".