Association Between Variations in Training Load, Sleep, and the Well-Being of Professional Hockey Players
Bibliographic record
Abstract
PURPOSE: To evaluate whether sleep quantity and quality of professional hockey players is affected by external training load (TL), their perception of well-being, and contextual factors associated with match participation. METHODS: Fifty male athletes were monitored daily during the 28 weeks of the regular season using well-being and sleep surveys. On-ice external TL was monitored using portable inertial measurement units during practices and matches. Linear mixed-effects models were applied to evaluate whether well-being perception (ie, pain, nutrition, stress, and rest) and external TL may affect sleep quality and quantity. RESULTS: High levels of well-being positively affected sleep duration and quality (P < .001), whereas high-intensity TL had a detrimental impact on sleep duration (P = .007). Moreover, away and evening matches had a detrimental effect on sleep quantity and quality (P < .001). Finally, a high match on-ice load per minute had a negative association with sleep quality (P = .04). CONCLUSIONS: Findings indicate that well-being and high-intensity trainings can impair sleep duration and quality. In addition, high-intensity match load, away matches, and evening matches can impair postmatch sleep. Therefore, monitoring well-being in conjunction with TL is essential to understand sleep disturbances in athletes. Practitioners should also implement sleep hygiene strategies that facilitate longer time in bed after high-intensity, away, and evening matches to help athletes recover.
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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".