A symptom cluster-based approach to studying the association between physical activity and depressive symptoms
Bibliographic record
Abstract
Background: Several studies indicate that physical activity (PA) is inversely associated with depressive symptoms across the lifespan. In most of these studies, depressive symptoms were treated as a unidimensional construct; however, depression is a multidimensional construct consisting of affective, somatic, cognitive, and behavioural symptoms. In this cross-sectional study, we examined which clusters – depressed affect, somatic symptoms, interpersonal problems, and lack of positive affect – are associated with light PA (LPA) and moderate-to-vigorous PA (MVPA) in university students. Methods: Participants were 738 undergraduate students (mean age = 19.6 years; 76.9% female) who completed an online questionnaire. Four depressive symptom cluster scores were computed from responses on the 20-item Center for Epidemiologic Studies Depression Scale, and PA scores were computed from responses on the Leisure Time Exercise Questionnaire. Results: In separate linear regression analyses (controlling for age, sex, number of exams/assignments, and target grade point average), MVPA was significantly associated with lack of positive affect (beta = -.12, p < .05), but not somatic symptoms, interpersonal problems, or negative affect. LPA was not significantly associated with any of the four depressive symptoms clusters. Conclusions: Our results suggest that the association between MVPA and overall depressive symptoms among university students may be driven primarily by the positive affect cluster. As such, researchers should seek to tease apart the positive affective symptoms of depression from the cognitive, negative affective, and interpersonal symptoms in future studies.
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| 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".