Heart rate variability and recovery following maximal exercise in endurance athletes and physically active individuals
Bibliographic record
Abstract
The purpose of this study was to determine potential adverse cardiac effects of chronic endurance training by comparing sympathovagal modulation via heart rate variability (HRV) and heart rate recovery (HRR) in middle-aged endurance athletes (EA) and physically active individuals (PA) following maximal exercise. Thirty-six (age, 53 ± 5 years) EA and 19 (age, 56 ± 5 years) PA were recruited to complete a 2-week exercise diary and graded exercise to exhaustion. Time domain and power spectral HRV analyses were completed on recorded R-R intervals. EA had a greater HRR slope following exercise (95% confidence interval, 0.0134–0.0138 vs. 0.0101–0.0104 beats/s; p < 0.001). While EA had greater HRR at 1–5 min after exercise (all p < 0.01), PA and EA did not differ when expressed as a percentage of baseline heart rate (130 ± 19 vs. 139 ± 19; p = 0.2). Root mean square of successive differences in R-R intervals (rest and immediately after exercise) were elevated in EA (p < 0.05). Low-frequency (LF) and high-frequency (HF) spectral components were nonsignificantly elevated after exercise (p = 0.045–0.147) in EA while LF/HF was not different (p = 0.529–0.986). This data suggests greater HRR in EA may arise in part due to a lower resting HR. While nonsignificant elevations in HF and LF in EA produces a LF/HF similar to PA, absolute spectral component modulation differed. These observations require further exploration. Novelty Acute effects of exercise on HRV in EA compared with a relevant control group, PA, are unknown. EA had greater HRR and nonsignificant elevations in LF and HF compared with PA, yet LF/HF was not different. Future work should explore the implications of this observation.
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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 | 0.001 |
| 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".