0236 Team-Based Athletes Sleep Less Than Individual Athletes, But Do Not Report More Insomnia or Fatigue
Bibliographic record
Abstract
Abstract Introduction Collegiate student-athletes face challenges balancing academics and athletics, and getting an adequate amount of sleep is one factor that can assist in sustaining an elite level of play. Team-based sports may present with systematically different sets of demands. Methods Data were obtained at the start of the academic semester from N=189 NCAA Division-1 athletes from a wide range of sports. The sample was 46% female. Individuals were classified as playing in a team sport (e.g., football, basketball, baseball, softball, volleyball) or an individual sport (e.g., swimming, track, golf). Sleep-related outcomes included self-reported sleep duration and sleep latency, frequency of sleeping pill use (Never, Rarely, Sometimes, Often), Insomnia Severity Index score, and Fatigue Severity Scale score. Regression analyses were adjusted for age and sex. Results In adjusted analyses, team-based athletes reported 22.4 minutes less sleep than individual athletes (95%CI -42.8,-1.9; p<0.05). They also reported 5.6 less minutes of sleep latency (95%CI -10.8,-0.3; p<0.05). More frequent sleeping pill use was also reported (oOR=0.96; 95%CI: 0.26,1.67; p=0.007). They did not report any differences in insomnia or daytime fatigue levels. Conclusion These results suggest that even though team-based athletes may not report more sleep complaints or daytime complaints, they may be at increased risk for less sleep and more sleep medication. Further work is needed to identify the sources of these differences to guide interventions. Support The REST study was funded by an NCAA Innovations grant. Dr. Grandner is supported by R01MD011600
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".