The utility of heart rate and heart rate variability to identify limits of tolerance to moderate-intensity work in the heat: a secondary analysis
Bibliographic record
Abstract
We investigated the utility of heart rate (HR) and heart rate variability (HRV) for identifying individuals who may terminate work early due to excessive heat strain. Forty-eight men and women (median = 36 years; Q1 = 20 years; Q3 = 54 years) attempted 180 min of moderate-intensity work at a fixed metabolic rate (∼200 W/m2; ∼3.5 METs) in a hot environment (wet-bulb globe temperature: 32 °C). Receiver operating characteristics (ROC) curves were used to identify the ability of indices of HR (absolute HR, percentage of maximum HR, percentage of HR reserve) and HRV (root-mean-square of successive differences (RMSSD), high-frequency power, and detrended fluctuation analysis component alpha 1 (DFA α1)) to discriminate between participants who completed the 180 min work bout or terminated prematurely. Participants who terminated work prematurely ( n = 26) exhibited higher HR and percentage of HR measures, as well as reduced RMSSD and DFA α1 after the first hour of work compared to participants who completed the bout. The discriminative utility of HR and HRV indices was strongest within the first hour of work, with percentage of HR reserve demonstrating excellent discriminative power (ROC area under curve (AUC) of 0.8). Stratifying participants by age and sex improved ROC AUC point estimates for most indices, particularly in female participants. The study provides preliminary evidence supporting the use of noninvasive cardiac monitoring for predicting work tolerance in healthy individuals exposed to occupational heat stress. HR and percentage of HR reserve were suggested to discriminate work termination most effectively. Further investigations are warranted to explore the influence of individual factors and refine the discriminative thresholds for early identification of excessive occupational heat strain.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".