Motivation Matters: Understanding the Antidepressant Mechanism of Physical Activity among Young Adults
Bibliographic record
Abstract
International Journal of Exercise Science 17(5): 861-873, 2024. A negative association between physical activity and depressive symptoms is consistently reported within scientific literature and physical self-concept has been suggested to mediate this pathway. However, for whom these associations are strongest remains poorly understood, and little is known about how other psychosocial factors might be implicated. Consequently, we examined how various exercise motivations, specifically appearance, physical health, and mental health, might moderate the indirect effect of physical activity on depressive symptoms through physical self-concept. Canadian young adults (N = 496, Mage = 20.36, SD = 1.87) completed an online questionnaire. Mediation and moderated-mediation models were tested using PROCESS macro in RStudio. A significant indirect effect (ß = -0.18, CI [-0.005, -0.003]) of physical activity on depressive symptoms through physical self-concept was found. Exercise motivations moderated the association between physical activity and physical self-concept, such that the association was stronger when individuals were motivated by physical health. Thus, the effect of physical activity on depressive symptoms varied according to physical self-concept and physical health-exercise motivations. We conclude that motivation should be considered when developing and delivering physical activity prevention efforts for depressive symptoms.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".