Myths and methodologies: Reliability of non‐invasive estimates of cardiac autonomic modulation during whole‐body passive heating
Bibliographic record
Abstract
Observed individual variability in cardiac baroreflex sensitivity (cBRS) and heart rate variability (HRV) is extensive, especially during exposure to stressors such as heat. A large part of the observed variation may be related to the reliability (consistency) of the measurement. We therefore examined the test-retest reliability of cBRS and HRV measurements on three separate occasions in 14 young men (age: 24 (SD 5) years), at rest and during whole-body heating (water-perfused suit) to raise and clamp oesophageal temperature 0.6°C, 1.2°C and 1.8°C above baseline. Beat-to-beat measurements of RR interval and systolic blood pressure (BP) were obtained for deriving HRV (from RR), and cBRS calculated via (i) the spontaneous method, α coefficients and transfer function analysis at each level of heat strain, and (ii) during forced oscillations via squat-stand manoeuvres (0.1 Hz) before and after heating. Absolute values and changes in all cBRS estimates were variable but generally consistent with reductions in parasympathetic activity. cBRS estimates demonstrated poor absolute reliability (coefficient of variation ≥25%), but relative reliability (intraclass correlation coefficient; ICC) of some frequency estimates was acceptable (ICC ≥0.70) during low-heat strain (ICC: 0.56-0.74). After heating, forced oscillations in BP demonstrated more favourable responses than spontaneous oscillations (better reliability, lower minimum detectable change). Absolute reliability of HRV estimates were poor, but relative reliability estimates were often acceptable (≥0.70). Our findings illustrate how measurement consistency of cardiac autonomic modulation estimates are altered during heat stress, and we demonstrate the possible implications on research design and data interpretation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.146 | 0.269 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.024 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.004 | 0.006 |
| 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".