Teenage night owls or early birds? Chronotype and the mental health of adolescents
Bibliographic record
Abstract
Evidence suggests that the timing of sleep (chronotype) impacts mental health in young people, but previous studies have not accounted for sleep duration or school start time in this association, or examined a broad range of mental outcomes. In this study, we investigated the association between chronotype and mental health in a representative sample of adolescents from the 2014 Canadian Health Behaviour in School-Aged Children survey (29,635 students, 362 schools). We examined positive and negative aspects of mental health, using scores for emotional problems (range 0-33), emotional well-being (0-22), behavioural problems (0-28) and prosocial behaviours (0-25). We estimated chronotype using the time of mid-sleep on weekends and examined the associations using multilevel regressions, adjusted for sleep duration, school start time, individual, family and geographic characteristics. The average time of mid-sleep (chronotype) was 04:11 hr. An hour delay in mid-sleep time was associated with more emotional problems (0.34 [95% confidence interval 0.23, 0.45] point higher score), more behavioural problems (2.0% [95% confidence interval 1.4%, 2.6%] higher score), less emotional well-being (0.19 [95% confidence interval 0.09, 0.20] point lower score), and fewer prosocial behaviours (0.18 [95% confidence interval 0.08, 0.29] point lower score). A later chronotype was associated with poorer mental health, independent of sleep duration and school start time, and across internalizing and externalizing mental health domains. Further research is needed to clarify the mechanisms underlying this association. The timing of sleep, and not just its duration, may be an additional consideration for youth mental health.
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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 | 0.000 | 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) | 0.002 | 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".