0210 Chronotype, Sleep Difficulties and Lifestyle Habits in Adolescent Athletes During the COVID-19 Pandemic
Bibliographic record
Abstract
Abstract Introduction It is increasingly known that elite athletes have a high prevalence of sleep difficulties and that chronotype may influence this relationship. Although adolescents are also at risk for sleep disturbances, including a phase delay, few studies have characterized the sleep of adolescent athletes. The aim of this study was to investigate sleep difficulties and lifestyle habits in young athletes of different chronotypes. Methods 208 young elite athletes (M=14.9y) completed the following online questionnaires during the first wave of the pandemic: Pittsburgh Sleep Quality Index (PSQI), Insomnia Severity Index (ISI), and homemade questions on electronic and caffeine usage. Training quantity during, and before the pandemic (retrospectively) was also assessed. One-way ANOVAs were computed with chronotypes on 1) sleep duration and 2) social jetlag calculated from the PSQI, 3) PSQI total score, 4) ISI total score, 5) average number of time they use electronic during the hour before bedtime/week, and 6) average number of caffeinate product consume/day. A repeated measures ANOVA was also computed with the chronotypes on the training quantity before and during the pandemic. Results Results reveal significant differences between chronotypes in sleep duration (F[2,206]=20.12, p<.001), social jetlag (F[2,206]=6.77, p=.001), PSQI total score (F[2,206]=9.73, p<.001), and ISI total score (F[2,206]=11.19, p<.001). Post-hoc analyses show that in all cases, evening types had significantly more sleep disturbances than neutral types and morning types, while they were no differences between neutral and morning types. The same pattern was also found in usage of electronic devices less than an hour before bed (F[2,206]=10.04, p<.001), where evening types used electronic devices before bed significantly more than neutral types and morning types. No differences were found between neutral and morning types. No differences were found in caffeine usage (p=.06). Finally, results show that athletes of all chronotypes significantly reduced their amount of training hours during the pandemic (F[2,206]=123.47, p<.001). Conclusion This study raises important questions about challenges associated with sleep difficulties and evening chronotypes in young athletes. Given the very early onset of these difficulties, the implementation of prevention strategies and sleep hygiene awareness programs in young adolescents should be included and prioritized in national athlete development programs. Support (if any)
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".