Effects of a Sleep Hygiene Strategy on Parameters of Sleep Quality and Quantity in Youth Elite Rugby Union Players
Bibliographic record
Abstract
PURPOSE: To assess the effects of a sleep hygiene strategy on parameters of sleep quality and quantity in youth elite rugby union players. METHOD: Eleven male players (age: 19.0 [1.4] y) undertook a sleep hygiene strategy composed of 2 theoretical sessions and 3 practical sessions over a 4-week period. Sleeping time, time in bed, total sleep time, sleep latency (SL), sleep efficiency (SE), wake after sleep onset, and wake bouts were recorded with an actigraphic device during the 4-week sleep hygiene strategy (baseline) and during 4 weeks after the last intervention (postintervention). RESULTS: At baseline, the overall group reported poor sleep quantity (total sleep time = 6:27 [0:30] min), but sleep quality was considered acceptable (SL = 0:18 [0:08] min and SE = 77.8% [5.8%]). Postintervention, the overall group showed a small improvement in SL (d = -0.23 [-0.42 to -0.04], P = .003) and SE (d = 0.30 [0.03 to 0.57], P = .0004). For individual responses, sleeping time, time in bed, and total sleep time were positively influenced in only 4, 3, and 5 players, respectively. For parameters of sleep quality, SL and SE were positively influenced in a majority of players (n = 7 and 8, respectively). The magnitude of difference between baseline and postintervention was strongly associated with baseline values in SE (r = -.86; P = .0005) and wake after sleep onset (r = -.87; P = .0007). CONCLUSION: A sleep hygiene strategy is efficient to improve sleep quality but not sleep quantity in young rugby union players. The strategy was more efficient in players with lower initial sleep quality and should be implemented prior to a high cumulative fatigue period.
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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) | 0.001 | 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".