Heat Acclimation By Warm-water Immersion Enhances Whole-body Heat Loss In Older Adults
Bibliographic record
Abstract
Whole-body heat loss declines with increasing age (~3-4% per decade). Consequently, relative to their younger counterparts, older adults experience greater increases in core temperature and cardiovascular strain during heat stress. A recent report showed that heat acclimation induced by 90-min of continuous moderate-intensity exercise performed over 7-days in the heat increased whole-body heat loss in physically active older adults. However, such a protocol may not be tenable for many older adults who are sedentary or not engaged in regular physical activity. Heat acclimation via warm-water immersion has been shown to enhance heat loss and reduce core temperature in young adults during heat stress. Therefore, such an intervention may be an effective alternate approach to exercise to improve heat dissipation in older adults. PURPOSE: To determine if heat acclimation by warm-water immersion over 7-days would induce an increase in whole-body heat loss in older adults. METHODS: Twelve habitually active older males (mean (SD): 69 (6) years) completed 7-days of warm-water immersion (~40 °C) for 60-minutes at a fixed core temperature of 38.5 °C. Before (Day 0) and after (Day 8) the heat acclimation protocol, whole-body dry and evaporative heat loss was measured by direct calorimetry during three bouts of incremental exercise, consisting of 30-minutes of cycling at increasing fixed rates of metabolic heat production of 150, 200, and 250 W/m2, each separated by 15-minutes of recovery in the heat (40 °C, 15% relative humidity). Core (rectal) temperature and heart rate were measured continuously. Data in the last five minutes of the final exercise bout (250 W/m2) were analyzed via a paired t-test. RESULTS: Relative to Day 0, whole-body heat loss was elevated by 29 W/m2 [95% Confidence Interval: 19, 39] on Day 8 (P < 0.001). This was paralleled by a decrease in body heat storage (-82 [-116, -48] kJ) and rectal temperature (-0.3 [-0.5, -0.2] °C; both P < 0.01), as well as a reduction in cardiovascular strain evidenced by a decrease in heart rate (-12 [-7, -17] beats/min) on Day 8 (P < 0.0001). CONCLUSION: We showed that a brief 7-day warm-water immersion heat acclimation protocol improved heat loss capacity in older males leading to a reduction in thermal and cardiovascular strain during exercise heat stress. Natural Sciences and Engineering Research Council of Canada and Mitacs.
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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".