Consecutive non‐training days over a weekend for assessing cardiac parasympathetic variation in response to accumulated exercise stress
Bibliographic record
Abstract
ABSTRACT Purpose: To examine the association between day‐to‐day resting cardiac parasympathetic variability over consecutive non‐training days (i.e. weekend) and accumulated exercise stress when quantified using indices of cardiovascular strain. Methods: Twelve international calibre female field hockey players training as part of a national team were participants over a four‐week mesocycle prior to a 2016 Olympic qualifying tournament. On‐field exercise stress was examined using heart rate (HR) dynamics and quantified as; (1) training load and (2) time (min) spent above anaerobic threshold. The square root of the mean squared differences of successive cardiac cycles (R–R intervals) recorded on Saturday and Sunday were individually calculated and log‐transformed prior to being averaged (Ln rMSSDweekend). Day‐to‐day variation in Ln rMSSD over the weekend was expressed using the coefficient of variation (Ln rMSSD CV ). Non‐linear regression analysis examined the association between accumulated exercise stress and Ln rMSSD CV . Results: A quadratic association between each index of exercise stress and Ln rMSSD CV was identified. After converting the coefficient of determination into a correlation coefficient (90% CL), the respective association between Ln rMSSD CV and training load (AU); r = 0.40 (0.16:0.59) and time above threshold; r = 0.35 (0.06:0.59) were observed. Conclusion: Ln rMSSD CV derived over consecutive non‐training days displayed a moderate, yet significant association between accumulated exercise stress when expressed as global or high‐intensity indices of cardiovascular strain. Weekend assessments may offer a practical and appropriate juncture between microcycles to assess the magnitude of perturbation in cardiac autonomic homeostasis prior to entering subsequent training periods.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".