Evidence for age‐related differences in heat acclimatisation responsiveness
Bibliographic record
Abstract
NEW FINDINGS: What is the central question of this study? Repeated heat exposure during the summer months can enhance heat loss in humans (seasonal heat acclimatisation), but does the magnitude of that enhancement differ between young and older adults when assessed during passive heat exposure? What is the main finding and its importance? While seasonal heat acclimatisation enhanced evaporative heat loss (i.e. sweating) in both young and older adults, those improvements led to a greater reduction in body heat storage in older adults. These outcomes indicate that heat acclimatisation may confer greater thermoregulatory benefits with increasing age. ABSTRACT: Repeated heat exposure throughout summer can enhance heat loss in humans (seasonal heat acclimatisation), although the effect of ageing on those improvements remains unclear. We therefore sought to assess thermoregulatory function in young and older adults during environmental heat exposure prior to and following seasonal heat acclimatisation, hypothesizing that the magnitude of adaptation would be greater in older relative to young adults. To achieve this, 14 young (19-27 years) and 10 older adults (55-72 years), who resided in a temperate humid-continental climate, completed a 3 h resting heat exposure (44°C, ∼30% relative humidity) in the winter-spring months as part of a larger investigation (pre-acclimatisation), before being re-evaluated using the same heat stress test following the summer months (post-acclimatisation). Whole-body dry and evaporative heat exchange, and metabolic rate were measured throughout using direct and indirect calorimetry (respectively), and used to quantify body heat storage (metabolic rate + dry heat gain - evaporative heat loss). Evaporative heat loss increased in both groups following acclimatisation, but those improvements led to a decrease in body heat storage in older (mean difference (95% CI); 213 (295, 131) kJ; P < 0.001), but not young adults (-25 (-94, 44) kJ; P = 0.458). Thus, body heat storage was greater in older compared to young adults before (222 (123, 314) kJ; P < 0.001), but not following acclimatisation (34 (-55, 123) kJ; P = 0.433). Although there is a need for larger and more controlled confirmatory studies, our findings indicate that seasonal heat acclimatisation may induce greater thermoregulatory adaptation in older compared to young adults.
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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 | 0.004 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".