The effect of chronic exercise training and acute exercise on power spectral analysis of heart rate variability
Bibliographic record
Abstract
Moderate to vigorous physical activity performed regularly is cardioprotective and reduces all-cause mortality, concomitant with increased resting heart rate variability (HRV). However, there are contradictory reports regarding the effects of chronic and acute exercise on nocturnal HRV in those performing exercise well-beyond physical activity guidelines. Therefore, the purpose of this study was to compare the power spectral analysis components of HRV in middle-aged endurance athletes (EA) and recreationally active individuals (REC) and explore acute exercise effects in EA. A total of 119 EA (52, 49–57 years) and 32 REC (56, 52–60 years) were recruited to complete 24 h Holter monitoring (GE SEER 1000) in the absence of exercise. Fifty one EA (52, 49–57 years) then underwent 24 h Holter monitoring following an intense bout of endurance exercise. Power spectral HRV analysis was completed hourly and averaged to quantify morning (1000–1200 h), evening (1900–2100 h), and nocturnal (0200–0400 h) HRV. EA had greater very low frequency (VLF) and low frequency (LF) (both p < 0.001) compared to REC. LF/high frequency (HF) was greater in EA at 0200–0400 h ( p = 0.04). Among all participants, the change in HR and HF from 1000–1200 to 0200–0400 h was negatively correlated ( r = −0.47, p < 0.001). Following acute exercise in EA, only nocturnal HRV was assessed. VLF ( p < 0.001) and HF ( p = 0.008) decreased, while LF/HF increased ( p = 0.02). These results suggest that in EA, both long-term and acute exercises increase nocturnal sympathovagal activity through an increase in LF and decrease in HF, respectively. Further work is required to understand the mechanism underlying reduced nocturnal HRV in middle-aged EA and the long-term health implications.
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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".