Independent and Joint Associations of Physical Activity and Sleep on Mental Health Among a Global Sample of 200,743 Adults
Bibliographic record
Abstract
BACKGROUND: Previous research has demonstrated that both sleep and physical activity (PA) are independently associated with various indicators of mental health among adults. However, their joint contribution to mental health has received limited attention. The present study used cross-sectional data from the Mental Health Million Project to examine the independent and joint effects of sleep and PA on mental health among a global sample of adults, and whether these effects differ among individuals receiving mental health treatment. METHOD: The sample included 200,743 participants (33.1% young adults, 45.6% middle-aged adults, 21.3% older adults; 57.6% females, 0.9% other) from 213 countries, territories, and archipelagos worldwide that completed a comprehensive 47-item assessment of mental health including both problems (i.e., ill-being) and assets (i.e., well-being): the Mental Health Quotient. Participants also reported their weekly frequency of PA and adequate sleep, and mental health treatment status. A series of generalized linear mixed models were computed. RESULTS: Independent dose-response associations were observed, whereby greater amounts of PA and adequate sleep were each associated with better mental health. In addition, a synergistic interaction was observed in which the positive correlation of PA with mental health was strengthened with greater frequency of adequate sleep. These benefits were less pronounced among adults receiving mental health treatment. CONCLUSION: While findings suggest sleep can help to offset the negative influence of a physically inactive lifestyle (and vice versa), our results point to a "more is better" approach for both behaviors when it comes to promoting mental health.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".