Adolescent Movement Behaviour Profiles are Associated with Indicators of Mental Wellbeing
Bibliographic record
Abstract
Recent work has demonstrated the collective impact of daily movement behaviours on mental health outcomes, however, positive aspects of mental health have received much less attention. The purpose of this study was to identify unique adolescent movement behaviour profiles and determine whether profile membership is associated with differences in mental wellbeing. This study used data from the baseline assessment of the ADAPT study. A total of 1166 Canadian adolescents enrolled in grade 11 classes (Mage = 15.91 ± 0.48; 54% female) self-reported their movement behaviours – moderate-to-vigorous physical activity (MVPA), recreational screen time (ST) and sleep – and completed three measures of mental wellbeing: flourishing, self-esteem and resiliency. Latent profile analysis with distal outcomes comparisons were conducted. Four distinct profiles were identified: one healthy profile (high MVPA/low ST), two mixed behavioural profiles (low MVPA/low ST and high MVPA/high ST), and one profile considered to be the least healthy (low MVPA and high ST). Sleep patterns were similar across the profiles. The healthiest profile was consistently associated with better mental wellbeing, followed by the mixed behaviour profiles, and the least healthy profile had the poorest scores for mental wellbeing. These findings highlight the additive benefits of engaging in a full complement of healthy movement behaviours. Moving forward, behavioural interventionists should consider adopting an integrated approach to promoting mental wellbeing through targeting each of the movement behaviours concurrently.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".