The Sweating and Core Temperature Response to Compensable and Uncompensable Heat Stress Following Heat Acclimation
Bibliographic record
Abstract
While a greater maximal sweat rate following heat acclimation has been suggested to mitigate the rise in core temperature during uncompensable heat stress, it remains unclear whether heat acclimation will alter the core temperature response to compensable heat stress wherein heat balance is attainable. Thus, the primary aim of the present study was to evaluate the influence of complete heat acclimation on sweating and core temperature response to exercise in a compensable and uncompensable environment. A total of 8 (6 males, 2 females) unacclimated individuals were recruited. On separate days, participants exercised at a fixed rate of heat production (450 W) for 45‐mins of compensable heat stress (CHS; 37°C, 30% RH) and 60‐min of uncompressible heat stress (UCHS; 37°C, 60% RH) before and after heat acclimation. Core temperature [esophageal (T es ) and rectal (T re )], local sweat rate of the arm (LSR arm ) and back (LSR back ), and whole body sweat loss (WBSL) were measured during experimental trials. Heat acclimation included an 8‐wk aerobic training intervention followed by 10 consecutive days of up to 90 minutes of treadmill walking in hot and humid conditions (38°C, 65% RH). Prior to CHS or UCHS, resting absolute T es and T re were lower following heat acclimation (T es : 36.6±0.2°C; T re : 36.8±0.1°C) relative to unacclimated (T es : 36.9±0.2°C, P=0.002; T re : 37.1±0.2°C, P=0.003). During CHS, the change in T es and T re were similar following heat acclimation (ΔT es : 0.4±0.1°C; ΔT re : 0.7±0.1°C) compared to unacclimated (ΔT es : 0.4±0.2°C, P=0.72; ΔT re : 0.7±0.1°C, P=0.61). Cumulative WBSL during CHS was marginally greater with acclimation (557±40 g) relative to unacclimated (494±59 g, P=0.01). Despite no difference in LSR back following 45‐min of CHS with or without heat acclimation (P=0.94), stead‐state LSR arm was slightly higher with acclimation (0.75±0.16 mg/cm 2 /min) compared to unacclimated (0.61±0.15 mg/cm 2 /min, P<0.001). In contrast, the change in T es and T re during UCHS was significantly smaller following heat acclimation (ΔT es : 0.7±0.2°C; ΔT re : 0.9±0.2°C) relative to an unacclimated state (ΔT es : 1.1±0.3°C, P=0.04; ΔT re : 1.1±0.2°C, P<0.001). WBSL was far greater during UCHS post‐heat acclimation (913±126 g) in comparison to prior to acclimation (671±83 g, P<0.001). Further, end‐exercise LSR back and LSR arm were higher post‐heat acclimation (LSR back : 1.48±0.28 mg/cm 2 /min; LSR arm : 1.20±0.33 mg/cm 2 /min) relative to pre‐acclimation (LSR back : 1.21±0.26, P<0.001; LSR arm : 0.91±0.26 mg/cm 2 /min, P<0.001) during UCHS. Taken together, the core temperature response to compensable heat stress is similar irrespective of acclimation status, despite marginal greater whole‐body sweat rates. However, the large increases local and whole body sweat rate following heat acclimation clearly mitigated the rise in core temperature during uncompensable heat stress. Support or Funding Information This research was supported by a Discovery Grant from the Natural Sciences and Engineering Research Council (NSERC) of Canada (#386143‐2010, held by O.J.). N.R. was supported by an NSERC Postgraduate Scholarship‐Doctoral and a University of Ottawa Excellence Scholarship. This abstract is from the Experimental Biology 2018 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 | 0.000 | 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) | 0.001 | 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".