Understanding the sleep of ultra-marathon swimmers: Guidance for coaches and swimmers
Bibliographic record
Abstract
Open water swimming ultra-marathon events ≥10 km have become increasingly popular amongst master athletes. However, very little is known about the timing of training sessions and the impact on sleep. This study aimed to examine sleep behaviours, sleep problems and disorders and the relationship with training timings. This study used a longitudinal observational design for 42 nights with 24 masters' swimmers (n = 13 females), aged 39 ± 11 years, body mass index of 26 ± 3 kg/m 2 during a training squad for an ocean ultra-swim (19.7 km) in Western Australia. Objective measures of sleep were obtained from a wrist-activity monitor, the Readiband™ (Fatigue Science Inc., Canada). Swimmers completed a survey instrument related to sleep problems, disorders, chronotype, anthropometric and demographic information. Generalised linear mixed models were fitted to examine relationships between predictor variables and sleep responses. Body mass index was associated with a decline in Total Sleep Time (TST), each one-unit increase in BMI was associated with 5 min less TST (p = 0.04). Swimmers with a “high risk” of sleep apnea had 21 min more wake time (p = 0.04) and 5% lower Sleep Efficiency (p = 0.04). Sleep Offset on the morning of a morning training session was earlier by 48 min (p < 0.001) resulting in less TST by 39 min (p < 0.001). This study provides evidence that coaches need to consider sleep behaviours and problems before designing training schedules. Swimmers need to plan and allocate an adequate sleep opportunity and those who have a suspected sleep disorder or problem should seek the support of a sleep physician.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".