Sleep Disturbance And Injuries In Collegiate Soccer And Basketball Players
Bibliographic record
Abstract
PURPOSE: (1) To assess sleep disturbance, characterized by sleep difficulty, sleep inadequacy, poor sleep quality, and evening sleep chronotype in collegiate soccer and basketball players. (2) To examine the association between sleep metrics and injury risk in players. METHODS: We conducted a cohort study including 127 collegiate soccer and basketball players [48.8% female; mean age (SD) of 20.1 (1.6) years]. Using a mobile app, we administered questionnaires to players during the preseason (2020/2021) to collect demographic, injury history, medical history, and sleep (Athlete Sleep Screening Questionnaire; ASSQ) information. Sleep difficulty was based on specific composite scores of 5-7 (mild) and > =8 (moderate/severe). Inadequate sleep was defined as <7 hours. Injury outcome was knee and/or ankle injury collected postseason using a modified version of a previously validated Oslo Sports Trauma Research Center-Patellar Tendinopathy Questionnaire through self-report. Specific sleep metrics were analyzed using descriptive statistics and sleep-injury relationships were analyzed using multivariable logistic regression models. RESULTS: Overall, 7.9% (3.8% - 14.0%) of players had a mild sleep difficulty and 4.8% (1.0% - 13.3%) had a moderate/severe (all severe) sleep difficulty. One in three players, that is, 32.5% (95% CI: 24.5% - 41.5%) had sleep inadequacy; 9.1% (95% CI: 4.4% - 16.1%) had poor sleep quality and 9.5% (95% CI: 5.0% - 15.9%) had “eveningness” sleep chronotype. The prevalence of sleep disturbance was higher in females by a range of 0.5% - 4.6% compared with males across all sleep metrics except for sleep inadequacy. Players with inadequate sleep quantity had significantly higher odds for injury (OR: 5.93, 95% CI: 9.72 - 20.47, p = 0.005). No association (OR: 1.57, 95% CI: 0.28 - 8.77, p = 0.607) was found between sleep quality and injury. CONCLUSIONS: Sleep problems are prevalent among collegiate soccer and basketball players with 1 in 3 players having sleep inadequacy and 1 in 20 players having a moderate/severe sleep difficulty. Sleep quantity and not quality predicted injury risk. These findings suggest a substantial sleep problem in collegiate soccer and basketball players and warrants that players are regularly screened, and timely interventions applied. Supported by Saint Louis University
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".