Goldilocks Days for Adolescent Mental Health: Gender Differences in Optimal and Sub-optimal Movement Behaviours Combinations for Well-being, Anxiety and Depression
Bibliographic record
Abstract
The purpose was to assess which combinations of movement behaviours (i.e., physical activity, screen time, sleep) would predict the best and worst mental well-being and mental ill-health in a sample of adolescents; and whether associations differed based on gender identity categories. Cross-sectional data from a 2021-2022 cohort of students attending a convenience sample of Canadian secondary schools resulted in an analytical sample consisting of 31,362 cisgender boys, 32,699 cisgender girls, and 3,187 transgender/gender-diverse (TGD) youth. Mean (SD) age of the sample was 14.8 (2.3) years. Association between self-reported compositions of sleep duration, moderate-to-vigorous physical activity (MVPA) and screen time with mental health status were assessed with regression, as were interactions between movement composition and gender identity. Predicted mental health scores were inspected graphically for dose response and classified as being in the top or bottom 5% of predictions stratified by gender. Movement behaviour composition was significantly associated with all mental health outcomes and significantly modified by gender identity. Dose response between screen time and mental health was stronger for TGD youth and cisgender girls than among cisgender boys at lower levels of exposures (≤4 hours/day). At lower levels of MVPA (≤1 hour/day) the association between MVPA and depression or anxiety among TGD youth was stronger than among cisgender boys or girls. For all groups, predictions converged towards similar “Goldilocks Days” where all outcomes were in the top 5% at approximately 11 hours/day of sleep, 3 hours/day of MVPA, and 0.8 hours/day of screen time.
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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 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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".