Heart rate variability and nonlinear analysis of heart rate dynamics following single and multiple Wingate bouts
Bibliographic record
Abstract
Sprint interval training involves short bouts of high-intensity exercise and has produced training responses similar to those of endurance training. The effects of multiple supramaximal exercise bouts on neurocardiac modulation have not been examined. Therefore, we investigated the recovery of heart rate (HR) variability and nonlinear HR dynamics in 10 young (20.1 +/- 1.2 years) healthy males following single (1) and multiple (4) Wingate tests. HR variability was assessed with time and frequency domain measures, whereas nonlinear HR dynamics were determined by assessing the complexity (sample entropy) and fractal nature (detrended fluctuation analysis) of the HR time series. Responses were determined at pre-exercise baseline and at 3 time points during recovery from exercise: Post1 (5-20 min), Post2 (45-60 min), and Post3 (105-120 min). Following a single Wingate test, all temporal and spectral HR measures had returned to baseline by 1 h of recovery. In contrast, these measures were different from baseline at 2 h following multiple Wingate tests. Fractal HR properties were altered (p < 0.05) at Post1 following a single Wingate test and at Post1 and Post2 following multiple Wingate tests. HR complexity was reduced (p < 0.001) throughout the 2-h recovery following both exercise conditions. In conclusion, Wingate tests result in alterations in cardiac autonomic control, with multiple Wingate tests resulting in larger, more prolonged alterations. Based on the results of the single Wingate test, nonlinear measures, such as HR complexity, may be more sensitive in detecting subtle alterations in neurocardiac behaviour, compared with traditional measures of HR variability.
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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".