Aging impairs heat loss, but when does it matter?
Bibliographic record
Abstract
Aging is associated with an attenuated physiological ability to dissipate heat. However, it remains unclear if age-related impairments in heat dissipation only occur above a certain level of heat stress and whether this response is altered by aerobic fitness. Therefore, we examined changes in whole body evaporative heat loss (HE) as determined using whole body direct calorimetry in young (n = 10; 21 ± 1 yr), untrained middle-aged (n = 10; 48 ± 5 yr), and older (n = 10; 65 ± 3 yr) males matched for body surface area. We also studied a group of trained middle-aged males (n = 10; 49 ± 5 yr) matched for body surface area with all groups and for aerobic fitness with the young group. Participants performed intermittent aerobic exercise (30-min exercise bouts separated by 15-min rest) in the heat (40°C and 15% relative humidity) at progressively greater fixed rates of heat production equal to 300 (Ex1), 400 (Ex2), and 500 (Ex3) W. Results showed that HE was significantly lower in middle-aged untrained (Ex2: 426 ± 34; and Ex3: 497 ± 17 W) and older (Ex2: 424 ± 38; and Ex3: 485 ± 44 W) compared with young (Ex2: 472 ± 42; and Ex3: 558 ± 51 W) and middle-aged trained (474 ± 21; Ex3: 552 ± 23 W) males at the end of Ex2 and Ex3 (P < 0.05). No differences among groups were observed during recovery. We conclude that impairments in HE in older and middle-aged untrained males occur at exercise-induced heat loads of ≥400 W when performed in a hot environment. These impairments in untrained middle-aged males can be minimized through regular aerobic exercise training.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".