Individual training prescribed by heart rate variability, heart rate and well-being scores in experienced cyclists
Bibliographic record
Abstract
Optimizing the training of endurance athletes involves the nuanced balance between overload and recovery. Monitoring recovery effectively requires integrating multiple variables. This study evaluates the efficacy of training protocols guided by vagally-mediated heart rate variability (vmHRV), resting heart rate (RHR), and subjective well-being (WB) scores in improving cycling performance. It also explores the relationships between physiological and subjective measures. Twenty-eight experienced male cyclists were divided into three groups: vmHRV-only (Group 1), vmHRV + WB (Group 2), and vmHRV + WB + RHR (Group 3). Over 40 days, participants recorded daily vmHRV, RHR, and WB scores and followed customised training protocols. Pre- and post-intervention cycling tests assessed maximal power (Pmax), 1-min, 5-min, 20-min, and functional threshold power (FTP™). Daily data analysis included correlation and autocorrelation function (ACF) assessments to evaluate trends and individual variability. Across all groups, significant performance improvements were observed for 1-min, 5-min, 20-min, FTP™, and FTP™/kg. Group 3 showed the greatest improvements, particularly in 5-min and 20-min efforts (310.5 ± 60 to 337.9 ± 71 watts, and 260.9 ± 55 to 284.5 ± 64 watts, respectively). ACF revealed stress as having the highest day-to-day consistency among subjective measures. Individual correlations revealed diverse strengths of the relationships between physiological and subjective markers. Combining vmHRV, RHR, and WB offers a more nuanced assessment of athlete readiness and enhances training outcomes compared to vmHRV-only guidance. The study underscores the value of integrating physiological and subjective measures for personalising training protocols and highlights future directions for improving monitoring systems with advanced analytics.
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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) | 0.000 | 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 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".