Effect of Early- and Adult-Life Socioeconomic Circumstances on Physical Inactivity
Bibliographic record
Abstract
PURPOSE: This study aimed to investigate the associations between early- and adult-life socioeconomic circumstances and physical inactivity (level and evolution) in aging using large-scale longitudinal data. METHODS: This study used the Survey of Health Ageing and Retirement in Europe, a 10-yr population-based cohort study with repeated measurements in five waves, every 2 yr between 2004 and 2013. Self-reported physical inactivity (waves 1, 2, 4, and 5), household income (waves 1, 2, 4, and 5), educational attainment (wave of the first measurement occasion), and early-life socioeconomic circumstance (wave 3) were collected in 22,846 individuals 50 to 95 yr of age. RESULTS: Risk of physical inactivity was increased for women with the most disadvantaged early-life socioeconomic circumstances (odds ratio [OR], 1.49; 95% confidence interval [CI], 1.20-1.86). With aging, the risk of physical inactivity increased for both sexes and was strongest for those with the most disadvantaged early-life socioeconomic circumstances (OR, 1.04 (95% CI, 1.02-1.06) for women; OR, 1.02 (95% CI, 1.00-1.05) for men), with the former effect being more robust than the latter one. The association between early-life socioeconomic circumstances and physical inactivity was mediated by adult-life socioeconomic circumstances, with education being the strongest mediator. CONCLUSIONS: Early-life socioeconomic circumstances predicted high levels of physical inactivity at older ages, but this effect was mediated by socioeconomic indicators in adult life. This finding has implications for public health policies, which should continue to promote education to reduce physical inactivity in people at older ages and to ensure optimal healthy aging trajectories, especially among women with disadvantaged early-life socioeconomic circumstances.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".