Prospective association between 24-hour movement behaviors and mental health among overweight/obese college students: a compositional data analysis approach
Bibliographic record
Abstract
Background: 24-h movement behaviors, including light physical activity (LPA), moderate-to-vigorous physical activity (MVPA), screen-based sedentary behavior (SSB), non-screen-based sedentary behavior (NSB) and sleep are crucial components affecting mental health. This study aimed to examine the associations of movement behaviors with mental health outcomes among overweight/obese college students using a compositional data analysis approach. Methods: Using a prospective design, 437 Chinese college students (20.1 ± 1.7 years, 51.7% female) completed a two-wave online data collection, where demographics and movement behaviors (LPA, MVPA, SSB, NSB, sleep) were collected at baseline, while depression, anxiety and stress were measured at the 2-month follow-up (Apr-Jul 2022). Compositional data analyses were implemented using R. Results: < 0.001). Reallocating 15 min to MVPA from LPA, SSB and NSB predicted improvements in depression (LPA: -0.234 unit; SSB: -0.375 unit; NSB: -0.249 unit), anxiety (LPA: -0.092 unit; SSB: -0.284 unit; NSB: -0.165 unit), and stress (LPA: -0.083 unit; SSB: -0.312 unit; NSB: -0.191 unit). For dose-response relationships of 5-55 min isotemporal substitution, when time was reallocated to MVPA from LPA, NSB, and SSB, the estimated detriments to mental health were larger in magnitude than the estimated benefits of time reallocation from MVPA to LPA, NSB, and SSB. Conclusion: The findings emphasize the importance of participating in MVPA to improve mental health in overweight/obese college students during the post-COVID-19 era. The compositional analysis produced clear targets for the time allocation of these behaviors for future interventions and policymaking.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| 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".