An exploratory investigation of heart rate and heart rate variability responses to daylong heat exposure in young and older adults
Bibliographic record
Abstract
Heart rate variability (HRV) has shown potential as a tool for monitoring thermal strain, but there is limited data to support its efficacy in older adults during prolonged heat exposures. We compared HRV between young (19–31 years, n = 20) and older (61–78 years, n = 39) adults during 9 h of heat exposure (40 °C, 9% RH). We also explored whether heart rate (HR) and/or HRV could be used to distinguish older adults who achieved elevated thermal strain, defined as either (1) an increase in core temperature >1.0 °C (occurring in 39%, 15/39) or (2) a reduction in systolic blood pressure >10 mm Hg (occurring in 67%, 26/39). Percentage of age-predicted maximal HR and percentage of heart rate reserve (HRR) were higher, whereas standard deviation of normal RR intervals, the square root of the mean of squared differences between successive RR intervals (RMSSD), high frequency power (HF), and cardiac vagal index (CVI) were lower in older compared to young adults during heat exposure ( P ≤ 0.004). In older adults, increases in core temperature were correlated with percentage of age-predicted maximal HR, percentage of HRR, RMSSD, and CVI ( P ≤ 0.031), whereas changes in systolic blood pressure were not significantly associated with HR or HRV indices ( P ≥ 0.327). Receiver operating characteristic curve analysis indicated that HR and HRV indices had generally poor ability to identify older adults with elevated thermal strain (area under the curve ≤0.65). Age-related differences in HRV, consistent with vagal withdrawal among older adults, remained during daylong heat exposure, but marked heterogeneity of response likely contributed to HRV providing limited discriminatory value in identifying changes in core temperature or blood pressure in older 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 | 0.000 | 0.001 |
| 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".