Sex differences in linear and nonlinear heart rate variability during early recovery from supramaximal exercise
Bibliographic record
Abstract
Women demonstrate greater RR interval variability than men of similar age. Enhanced parasympathetic input into cardiac regulation appears to be not only greater in women, but also protective during periods of cardiac stress. Even though women may have a more favorable autonomic profile after exercise, little research has been conducted on this issue. This study was designed to examine the cardiac autonomic response, in both male and female participants, during the early recovery from supramaximal exercise. Twenty-five individuals, aged 20 to 33 years (13 males and 12 females), performed a 30-s Wingate test. Beat-to-beat RR series were recorded before and 5 min after exercise, with the participants in the supine position and under paced breathing. Linear (spectral analysis) and nonlinear analyses (detrended fluctuation analysis (DFA)) were performed on the same RR series. At rest, women presented lower raw low frequency (LF) power and higher normalized high frequency (HF) power. Under these conditions, the LF/HF ratio of women was also lower than that of men (p<0.05), but there were no differences in the short-term scaling exponent (α1). Even though both sexes showed a significant modification in linear and nonlinear measures of heart rate variability (HRV) (p<0.05), women had a greater change in LF/HF ratio and α1 than men from rest to recovery. This study demonstrates that the cardiac autonomic function of women is more affected by supramaximal exercise than that of men. Additionally, DFA did not provide additional information about sexual dimorphisms, compared with conventional spectral HRV techniques.
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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".