Physical activity motives have a direct effect on mental health
Bibliographic record
Abstract
Physical activity (PA) motives are associated with both moderate‐to‐vigorous intensity PA (MVPA) and mental health. Studies examining whether PA motives relate directly to mental health or indirectly through MVPA are lacking. This study examined the direct effect of five PA motives (i.e., enjoyment, competence, fitness, social, appearance) on mental health and their indirect effects through MVPA in adolescents. A total of 424 participants (57.1% females) ages 14–15 years from the longitudinal MATCH study were included. Mediation analyses, based on the counterfactual framework, assessed the natural direct effect of PA motives on mental health, and the natural indirect effects through MVPA. Separate models were conducted for each PA motive. Natural direct effects were observed for enjoyment ([95%CI] = 2.12 [0.34, 3.90]), competence ([95%CI] = 1.58[0.28, 2.88]), fitness ([95%CI] = 1.42[0.04, 2.80]), and social ([95%CI] = 2.32[1.03, 3.60]) motives. No natural direct effects were observed for appearance motives. A natural indirect effect through MVPA was observed for fitness motives, and no other natural indirect effects were found. Interventions and public health strategies in adolescents need to acknowledge the importance of enjoyment, competence social and fitness motives in PA to promote mental health, and integrate specific recommendations on the importance of the reasons why adolescents participate in PA.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".