Heart Rate Variability is Correlated with Perceived Physical Fitness in Elite Soccer Players
Bibliographic record
Abstract
Heart Rate Variability (HRV) has been typically used to monitor athletes' physical fitness readiness. The supine position maximizes parasympathetic tone, which is important for monitoring in continuous aerobic sports, however, this is not the case of team sports that rely on anaerobic intermittent bouts, thus increasing sympathetic activation and vagal withdrawal. We hypothesized that HRV during sympathetic activation and vagal withdrawal would be a useful marker to evaluate perceived physical fitness in team sports. HRV was measured in both supine and standing positions during the mornings of 4 match days in 14 professional players. The supine Root Mean Square of the Successive Differences (RMSSD), as well as spectral analysis indices were recorded. Perceived physical fitness was assessed after each match by means of a visual analogue scale (VAS). Supine RMSSD was moderately correlated with perceived physical fitness (rho = 0.416), however, larger correlations were observed for supine and standing spectral indices (rho > 0.5). Correlation between RMSSD and Total Power was very large, thus questioning the usual interpretation of RMSSD (rho > 0.7). Standing Spectral HRV analyses may be a useful method for evaluating perceived physical fitness in the context of team sports. RMSSD may reflect the overall variability of HR and not only the parasympathetic influence, as observed in the current study.
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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 | 0.003 |
| 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".