Muscle-Strengthening Exercise and Depression in Chinese Young Adults
Bibliographic record
Abstract
Abstract Studies based on western population have indicated that muscle-strengthening exercise (MSE) has positive roles against mental disorders, but little is known about that in Chinese adults. This study, thus, aimed to explore the association between MSE and depression in Chinese university students (aged 18-24 years). A convenient sample of 1794 university students (mean age: 20.67 years) were recruited into this study. A self-reported questionnaire was used to collect information on participants’ sociodemographic information (e.g., sex, body mass index) and MSE. Physical activity and sleep were measured using the International Physical Activity Questionnaire-Short Form and Pittsburgh Sleep Quality Index. Study participants’ depression severity was assessed using the Patient Health Questionnaire-9. A multilevel linear regression was performed to examine the association between MSE and depression. Only 24.87% of study participants met the World Health Organization MSE guidelines of more than 2 days/week. The mean score of depression was 6.80 (± 5.19). More days for MSE (0-7 days) was negatively associated with depression (beta = -0.17, 95%CI: -0.31 ─ -0.03, p = 0.015). Students who did not meet MSE guidelines were more likely to have higher risks for depression (beta = 0.63, 95%CI: 0.09-0.19, p = 0.027). The results indicate that engaging in MSE could be related to decreased depression in Chinese young adults. Interventions aiming at reducing depression could incorporate MSE as a strategic component.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".