Sleep hygiene education for improving sleep in ultra-marathon swimmers: Guidance for coaches and swimmers
Bibliographic record
Abstract
Sleep for recovery is an essential factor for performance in athletes. One such group is recreational ultra-marathon swimmers (>10km). We aimed to compare measures of sleep before and after a sleep hygiene education intervention during a 16-week training programme. Using a prospective study design, the experiment was conducted in two phases (pre- and post-intervention), whereby pre- and post-intervention data were collected for 42 nights after the sleep hygiene education. This study had 24 masters’ swimmers (n = 13 females), aged 39 ± 11 years, and body mass index (BMI) of 26 ± 3 kg/m 2 during a training squad for an ocean ultra-marathon swimmer (19.7 km) in Perth, Western Australia. Objective measures of sleep were obtained from a wrist activity monitor, the Readiband™ (Fatigue Science Inc., Canada). Participants underwent a 2-hour sleep hygiene education session. Generalised linear mixed models were fitted to examine relationships between predictor variables and sleep responses. Sleep onset and offset increased by 12 minutes post-intervention ( p < 0.001). For nights before morning training, sleep onset increased by 12 minutes and offset by 24 minutes post-intervention. Females increased sleep onset by 18 minutes and delayed sleep offset by 12 minutes sleep ( p < 0.05) post-intervention. The sleep hygiene education was insufficient in making meaningful improvements to measures of sleep. Individual sleep hygiene education and continuous reinforcement of sleep for recovery during a training programme may be required to observe improvements. Coaches should aim to design training schedules to minimise the impact on swimmer’s sleep opportunity and swimmers need to involve family in the planning of rest periods during a training programme.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".