A time compositional analysis of the association between movement behaviors and indicators of mental health in young adults
Bibliographic record
Abstract
BACKGROUND: Movement behaviors (i.e., physical activity [PA], sedentary behaviors [SB], sleep) relate to mental health. Although movement behaviors are often analyzed as distinct entities, they are in fact highly inter-dependent (e.g., if an individual increases sleep, then PA and/or SB must be reduced) and these dependencies should be accounted for in the analysis. We tested whether perceptions of time spent in movement behaviors (i.e., moderate-to-vigorous intensity PA [MVPA], light physical activity [LPA], SB, and sleep) related to depressive symptoms and self-report mental health in young adults using a compositional analysis. We then estimated change in depressive symptoms with reallocation of time across movement behaviors using compositional time-reallocation models. METHODS: = 20.3, 55% females). RESULTS: The proportion of time spent in MVPA relative to other movement behaviors related to depressive symptoms non-significantly and to mental health significantly. Reallocating 15 min from MVPA to SB resulted in a significant (0.46 unit) increase in depressive symptoms, and reallocating 15 min of MVPA to LPA was associated with a (0.57) increase in depressive symptoms. CONCLUSION: These results indicate the importance of relative time spent in each movement behavior to mental health. Further research should examine these associations over time.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".