Movement Behaviors and Mental Wellbeing: A Cross-Sectional Isotemporal Substitution Analysis of Canadian Adolescents
Bibliographic record
Abstract
Background: Studies have shown reallocating screen time for healthy movement behaviors such as physical activity and sleep can provide important benefits for mental health. However, the focus on positive aspects of mental health such as wellbeing has received limited attention, particularly among adolescents. The purpose of this study was to examine the effects of reallocating physical activity, screen time, and sleep on mental wellbeing in adolescents. Methods: This study involved cross-sectional analysis of data from Wave 1 of the ADAPT study. A total of 1,118 Canadian adolescents enrolled in grade 11 classes (MAGE = 15.92; 54.5% female) self-reported their movement behaviors using the International Physical Activity Questionnaire – Short Form to assess moderate-to-vigorous physical activity and daily recall questionnaires to assess recreational screen time and sleep. Participants also completed three measures of mental wellbeing: the Flourishing Scale, Rosenberg Self-Esteem Scale, and a brief Resiliency scale from the Canadian Campus Wellbeing Survey. Results: Isotemporal substitution analysis revealed replacing 60 min of screen time with either moderate-to-vigorous physical activity or sleep has significant benefits for mental wellbeing. Comparatively, reallocating 60 min between moderate-to-vigorous physical activity and sleep does not impact mental wellbeing. Discussion: Findings suggest healthy movement behaviors confer similar beneficial effects for adolescent’s mental wellbeing. Health promotion campaigns targeted toward adolescents should consider highlighting that reallocation of screen time to either sleep or moderate-to-vigorous physical activity may provide important benefits for mental wellbeing.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".