Night-to-night sleep variability in adolescent rugby players compared to non-athlete matched controls
Bibliographic record
Abstract
Despite the importance of sleep monitoring in the context of sport, few studies to date examined night-to-night sleep variability among adolescent athletes. This study compared night-to-night sleep variation between junior rugby players and age-matched non-athlete adolescents across seven consecutive nights of the in-season competitive phase. This investigation is based on data from a single centre, observational prospective study including 30 adolescents (15 junior rugby players and 15 non-athlete age-matched controls). Sleep was continuously monitored using ambulatory electroencephalogram (EEG) recordings. While the non-athlete controls catch-up on their sleep debt during the weekend by delaying their wake-up time, junior rugby players opt for an earlier bedtime to cope with sport-related travel (Fri: −00:57 h:min; p < 0.001; Sat: −01:58 h:min; p < 0.001) or early school (Mon: −00:55 h:min; p < 0.001). Night-to-night sleep examination identified greater sleep disturbances in junior rugby players the nights before and after the competition SE (Fri: −11%; p < 0.001; Sat: −9 min; p < 0.01). Junior rugby players showed higher IIV in sleep duration (CV TST : + 5.8%; P < 0.001), efficiency (CV SE : + 3.8%; p < 0.001) and staging (CV N2 : + 5.4%; p < 0.001; CV N3 : + 4.5; p < 0.01 IIV; REM: + 16.4%; p < 0.01). Higher IIV in the young athletes’ sleep outcomes could make them even more vulnerable to health and wellness concerns (i.e. overtraining, injury). The study results show the urgent need for an appropriate consideration of sleep regularity in young athletes.
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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".