Determinants of Sleep and Morning Alertness in Elite Short-Track Speed Skaters During the Preseason
Bibliographic record
Abstract
PURPOSE: Sleep in elite athletes could be compromised by stressors such as high training volumes and mediated by factors related to individual characteristics and training schedule. However, longitudinal real-world data capturing the factors influencing sleep in elite athletes remain scarce. We aimed to determine what predicts sleep quantity, sleep quality, and morning cognitive readiness in elite speed skaters of the Canadian short-track World Cup team. METHODS: In the beginning of the preseason period, 16 athletes (7 women) completed the Sleep Health Index, the Insomnia Severity Index questionnaire, and the Caen Chronotype Questionnaire. For 14 consecutive days, athletes filled in detailed logs, performed morning psychomotor vigilance tasks, and wore an actigraphic device. RESULTS: Athletes overestimated their sleep by 9:42 ± 21:34 min:s, sleeping on average 07:56 ± 00:59 hours. Mean psychomotor vigilance-task reaction time was 275 ± 34.5 milliseconds. Sex, chronotype, and nap and training duration explained 34% of the variance in sleep (P < .001). Sleep quality was mainly predicted by a combination of sex, sleep, sleep credit, and chronotype (Akaike information criterion = 49.44). Psychomotor vigilance-task performance was mainly explained by sex, with men being 12.4% faster than women (P < .001). Evening chronotypes were associated with lower sleep duration, whereas men possibly experienced faster recovery. CONCLUSIONS: These preliminary results provide insights into factors influencing sleep in speed skaters, such as chronotype, training load, and sex differences, which could be leveraged to optimize training and recovery.
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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 | 0.001 | 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) | 0.002 | 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".