Associations between training load, heart rate variability, perceptual fatigue, sleep, and injury in endurance athletes during a 12-week training mesocycle
Bibliographic record
Abstract
This study examined associations among training load (external and internal), heart rate variability (HRV), perceptual fatigue, sleep, and injury in endurance athletes during a 12-week training mesocycle. The aims were to (i) compare these parameters between injured and non-injured weeks and (ii) assess differences between high- and low-severity injuries. Fifteen endurance athletes (80% males; median age: 36.5 years; running experience: 5.5 years) participated. Injury severity was measured using the Oslo Sports Trauma Research Centre Questionnaire on Health Problems (OSTRC-H). Training load (training stress score [TSS], Session-Rate of Perceived Exertion [sRPE]), HRV, perceptual fatigue (readiness to train and wellbeing), and sleep were collected through daily and weekly assessments over 12 weeks. The weekly injury prevalence over the 12-week period ranged from 7%–40%, with 47% classified as high severity. Injured weeks were characterised by significantly higher sleep-related impairment ( p = .004, Hedge's g = 0.704), while all other variables did not significantly differ. Athletes who sustained more severe injuries exhibited lower TSS ( p < .001, Hedge's g = 0.84), lower sRPE ( p = .029, Hedge's g = 0.705), poorer subjective wellbeing ( p = .003, Hedge's g = 0.753), and lower HRV ( p = .059, Hedge's g = 0.487) in the week preceding the injury. This exploratory study provides preliminary insights into the potential role of sleep-related impairment, training load fluctuations, and HRV in injury severity among endurance athletes. While findings suggest benefits of integrating these measures into monitoring strategies, the small sample size limits generalisability and should be interpreted with caution.
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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 | 0.001 | 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".