Physical activity mediates the effect of education on mental health trajectories in older age
Bibliographic record
Abstract
OBJECTIVE: Why people with lower levels of educational attainment have poorer mental health than people with higher levels can partly be explained by financial circumstances. However, whether behavioral factors can further explain this association remains unclear. Here, we examined the extent to which physical activity mediates the effect of education on mental health trajectories in later life. METHODS: Data from 54,818 adults 50 years of age or older (55 % women) included in the Survey of Health, Aging and Retirement in Europe (SHARE) were analyzed using longitudinal mediation and growth curve models to estimate the mediating role of physical activity (baseline and change) in the association between education and mental health trajectories. Education and physical activity were self-reported. Mental health was derived from depressive symptoms and well-being, which were measured by validated scales. RESULTS: Lower education was associated with lower levels and steeper declines in physical activity over time, which predicted greater increases in depressive symptoms and greater decreases in well-being. In other words, education affected mental health through both levels and trajectories of physical activity. Physical activity explained 26.8 % of the variance in depressive symptoms and 24.4 % in well-being, controlling for the socioeconomic path (i.e., wealth and occupation). CONCLUSIONS: These results suggest that physical activity is an important factor in explaining the association between low educational attainment and poor mental health trajectories in adults aged 50 years and older.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".