Reliability of heart rate measures used to assess post‐exercise parasympathetic reactivation
Bibliographic record
Abstract
PURPOSE: Postexercise HRR (heart rate recovery) and HRV (heart rate variability) are commonly used to asses non-invasive cardiac autonomic regulation and more particularly reactivation parasympathetic function. Unfortunately, the reliability of postexercise HRR and HRV remains poorly quantified and is still lacking. The aim of this study was to examine absolute and relative reliability of HRR and HRV indices used to assess postexercise cardiac parasympathetic reactivation. METHODS: We studied 30 healthy men, who underwent 10-minute heart rate recording after cessation of maximal and submaximal intensity exercises. Each condition of testing was repeated twice within 5 ± 2 days after the first one. Standard indexes of HRR and HRV were computed from heart rate and RR intervals. RESULTS: We found no significant bias between repeated measures. Relative reliability was assessed with the intraclass coefficient correlation (ICC) and absolute reliability with the standard error measurement (SEM) and coefficient of variation (CV). A large range for ICC was observed for both indexes of HRR and HRV (0.12 <ICC<0.87 and 0.14 <ICC<0.97, respectively). The same heterogeneity was observed for absolute reliability (5% <CV<72% for HRR parameters and 24% <CV<141% for HRV parameters). CONCLUSION: According to our results, ∆60 (the absolute difference between heart rate immediately at the end of exercise and after 60 s) and HFnu (High Frequency expressed in normalized unit; that is, in a percentage of LF+HF) represent the most reliable parameters. In conclusion, we found that the measures used to asses cardiac parasympathetic reactivation were characterized by large random variations and their reliability remains moderate.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".