0345 The Moderating Effect of Screen Time on the Relationship Between Physical Activity and Sleep in Young Athletes
Bibliographic record
Abstract
Abstract Introduction Studies have emphasized the significance of sleep in performance and well-being of young athletes. Physical activity has been shown to improve various aspects of sleep in adolescents, including sleep quality. Yet, excessive screen time has been found to have a negative impact on sleep among adolescents, possibly dampening the beneficial effects of physical activity. Hence, our research aimed to investigate the moderating effect of screen time on the relationship between physical activity and sleep quality in young athletes. Methods 211 young elite athletes (M=14.9±1.6 years old; 60.4% females) completed online questionnaires, including the Pittsburgh Sleep Quality Index (PSQI) and a homemade sports and lifestyle habits questionnaire. A moderation analysis was conducted using PROCESS 4.2 to examine the moderating effect of screen time (the average number of hours per day spent on screens) on the relationship between physical activity (the average number of training hours per week) and sleep quality (subscale #1 of the PSQI). Age, BMI, and sex were added as covariates, since they were correlated with the physical activity and sleep quality variables. Results The moderation model was significant (F(6,203)=8.14, p<.001) and accounted for 16.2% of the variance. Results indicate a significant main effect of screen time on sleep quality (b=.294, p=.004) and a significant interaction of screen time and physical activity on sleep quality (b=.005, p=.021). Physical activity was associated with sleep quality when screen time was at one SD below the mean (b=-.025, p<.018) but not at the mean (b=-.006, p<.433) nor above the mean (b=.014, p<.236). The simple plot analysis revealed that when young athletes had low screen time, more physical activity was related to better sleep quality, while lower training hours were associated with poorer sleep quality. However, in athletes with high and average screen time, their level of physical activity was not related to their sleep quality. Conclusion This study highlights the possible mitigating effect of screen time on the potential beneficial association between physical activity and sleep in young athletes. This underscores the importance of promoting healthy lifestyle habits and appropriate sleep hygiene among athletes, who are also a population at greater risk of sleep disturbances. Support (if any)
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".