Seven days of hot water heat acclimation does not modulate the change in heart rate variability during passive heat exposure
Bibliographic record
Abstract
We examined if the change in heart rate variability during passive heat exposure is modified by hot water heat acclimation (HA). Sixteen healthy adults (28 ± 5 years, 5 females/11 males) underwent heat exposure in a water-perfused suit, before and after 7 days of HA (60 min at rectal temperature ≥38.6 °C). During passive heat exposure, heart rate, the standard deviation of NN intervals (SDNN), the square root of the mean squared differences of successive NN intervals (RMSSD), and the power in the high-frequency range (HF) were measured. No difference in heart rate (P = 0.22), SDNN (P = 0.87), RMSSD (P = 0.79), and HF (P = 0.23) was observed at baseline. The increase in HR (pre-HA, 43 ± 10; post-HA, 42 ± 9 bpm; P = 0.57) and the decrease of SDNN (pre-HA, −54.1 ± 41.0; post-HA, −52.2 ± 36.8 ms; P = 0.85), RMSSD (pre-HA, −70.8 ± 49.5; post-HA, −72.7 ± 50.4 ms; P = 0.91) and HF (pre-HA, −28.0% ± 14.5; post-HA, −23.2% ± 17.1%; P = 0.27) were not different between experimental visits at fixed increases in esophageal temperature. These results suggest that 7 consecutive days of hot water HA does not modify the change in heart rate variability indices during passive heat exposure. Novelty: It remains unclear if HA alters the change in heart rate variability that occurs during passive heat exposure. At matched levels of thermal strain, 7 consecutive days of hot water immersion did not modulate the change in indices of heart rate variability during passive heat exposure.
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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".