Reliability of Postexercise Heart Rate Recovery
Bibliographic record
Abstract
Passive postexercise heart rate (HR) recovery is currently used in the assessment of endurance athletes to determine changes in performance or in the clinical setting as a predictor of all-cause mortality. The purpose of this investigation was to assess the reliability of HR recovery. Thirty healthy subjects performed two maximal and two submaximal treadmill exercises, followed by 5 minutes of passive recovery. HR signal was used to compute raw and Delta (exercise - recovery) HR after 1, 2, 3, and 5 minutes of exercise cessation. A mono-exponential function was fitted to the data using the least squares procedure. We found no significant bias between repeated measures. Relative reliability was lower for Delta HR when compared with raw HR (0.43 < ICC < 0.71 vs. 0.68 < ICC < 0.83, respectively). Absolute reliability was relatively constant over time for raw HR (SEM = approximately 8 %), while it decreased exponentially from the 1st (SEM = approximately 20 %) to the 5th minute of recovery (SEM = approximately 8 %) for Delta HR. The reliability of parameter estimates from exponential curve fitting was less consistent, since both ICC (0.43 to 0.88) and SEM (5.7 to 21.4 %) differed from one parameter to the other according to the intensity of exercise. We conclude that passive postexercise HR recovery reliability is heterogeneous. Raw HR is the desired method to describe it.
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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 | 0.004 | 0.014 |
| 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 | 0.001 |
| 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".