300 The Use of Sleep Aids in Young Athletes and Non-Athletes: An Exploratory Study
Bibliographic record
Abstract
Abstract Introduction Studies have shown that sleep in adolescents is characterized by sleep disturbances. Many teens resort to prescribed or nonprescribed medication to alleviate their sleep difficulties. Research suggests that sport and physical activity may be protective factors regarding sleep. The aims of the present study were to investigate the use of sleep aids among young athletes and non-athletes, and to identify possible factors associated with prescribed and nonprescribed sleep aids Methods 35 young athletes (14.6±0.7 years old; 54.3 % males) and 30 young non-athletes (15.1±0.7 years old; 16.7% males) completed questions on sleep aids, the Academic Motivation Scale, the anxiety and depression scales of the Beck Youth Inventory-II, and the Multidimensional Self-Esteem Questionnaire, at the beginning, middle, and end of the school year. Mean scores for the school year were computed for amotivation in school, intrinsic academic motivation of accomplishment, self-esteem, anxiety symptoms, and depressive symptoms. Teens were each categorized as user or non-user if they had or had not used sleep aids during the school year. First, comparison of sleep aids usage between groups were done using a Chi-square test. Then, both groups of athletes and non-athletes were combined. Amotivation, intrinsic motivation, self-esteem, and anxiety and depressive symptoms were compared between users and non-users using paired t-tests Results Results show that young non-athletes report using sleep aids more often than young athletes (X2(1,N=65)=5.205, p=.023). Indeed, 65.2% non-athletes compared to 34.8% athletes reported using sleep aids during school year. Users represent 35.4% of the total sample. T-tests showed that users have a significantly higher amotivation score (t(65)=-2.010, p=.049), more anxiety symptoms (t(65)=-2,480, p=.016), and more depressive symptoms (t(65)=-2,126, p=.037) than non-users. Conclusion These results show a high prevalence of prescribed and nonprescribed sleep aids usage in teens. Our results also suggest that sleep aids in young adolescents is associated with mental health problems and academic motivation issues. On the other hand, our results support that sport and physical activity may have a protective role regarding sleep. This highlights the importance to promote sport participation among adolescents. Support (if any) n/a
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".