The Effect of Age and Gender on Heart Rate Variability after Endurance Training
Bibliographic record
Abstract
PURPOSE: This research investigated the age and gender differences in cardiovascular adaptation to a standardized/quantified endurance-training program that included two taper periods. METHODS: The latter was analyzed from spectral analysis of electrocardiogram records of heart rate variability (HRV) at rest in groups of young (19-21 yr) and middle aged (40-45 yr), mixed gender groups (6 males and 6 females), pre- and poststandardized training. All subjects were recreational runners who completed the same 12-wk running program. Before, and subsequent to training, HRV was measured during supine rest and submaximal cycling. RESULTS: There was a significant decrease in heart rate both at rest (2.7 +/- 0.45 beats x min-1) and during submaximal exercise (8.1 +/- 0.67 beats x min-1) in both age groups after training. After training, total spectral power increased (560.7 +/- 308.9 ms2), as well as high-frequency power (362.3 +/- 405.5 ms2), in both age groups at rest. The young group showed a greater increase in total power (849.0 +/- 308.7 ms2) after the training program. CONCLUSION: It is concluded that a well-designed 12-wk endurance-training program will decrease resting and submaximal heart rate in both younger and older adults. The significant increase in HRV, total power, and high-frequency power in all groups after endurance training indicates that HRV measurement appears to provide an effective, noninvasive assessment of cardiovascular adaptation to aerobic training.
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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) | 0.002 | 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".