A practical guide to improve sleep and performance in athletes
Bibliographic record
Abstract
Sleep is vital for optimal mental and physical health. For athletes, optimizing sleep is becoming a popular strategy to enhance athletic performance. Athletes often complain of sleep problems including insufficient sleep and insomnia symptoms and are also at a higher risk for sleep disordered breathing. Sleep disorders and insufficient sleep can contribute to excessive sleepiness, daytime dysfunction, and performance problems. In contrast, better sleep provides benefits for physical health and athletic performance. For athletes, multiple factors can contribute to insufficient sleep. Sport-specific factors include frequent travel across time zones, competition and training schedules, high training loads, and sleeping in an unfamiliar environment. Non-sport-related factors include work, social, and family commitments; attitudes and sleeping beliefs; individual characteristics, such as chronotype or preference for morning or evening; and lifestyle choices. Fortunately, there are strategies that can be implemented to improve sleep in athletes including (a) education and emphasis on the importance of sleep; (b) sleep screening; providing extra sleep opportunities like (c) banking sleep and (d) napping; improving sleep hygiene like (e) proper light exposure; (f) a good pre-sleep routine; (g) a conducive sleep environment; (h) a strategy for supplementation; (i) utilizing circadian timing adjustments; and (j) jet lag management. Increased recognition of the importance of sleep from sport professionals and screening for sleep disorders and disturbances will be key for future athlete health, well-being, and performance.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.228 | 0.160 |
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".