Prevention of frailty in relation with social out-of-home activities in older adults: results from the Survey of Health, Ageing, and Retirement in Europe
Bibliographic record
Abstract
Out-of-home mobility and social participation have been identified as resources to postpone frailty. We aim to examine the mediating role and specific contribution of social out-of-home activities in frailty prevention. Data from the Survey of Health, Ageing, and Retirement in Europe (SHARE) waves six (w6), seven (w7), and eight (w8) were used. Frailty was measured with the SHARE version of the Edmonton Frail Scale (EFS) with frailty states fit, pre-fail and frail. First, a mediation model with 13,456 fit participants aged ≥ 50 years in w6 was specified with social network size, loneliness (UCLA 3-Item Loneliness Scale), and lack of motivation (EURO-D) as predictors and number of performed social out-of-home activities in w7 as mediator variable on EFS-scores in w8. Age, education, gender, cohabitation, widowhood, urban environment, and country served as covariates. Second, we investigated the association of increasing social out-of-home activities from w6 to w7 with change in EFS-score from w6 to w8 using a linear mixed model with 17,439 participants in all frailty states. Direct effects of loneliness (w6) and lack of motivation (w6) on EFS-scores (w8) were partially mediated by social out-of-home activities (loneliness ß = 0.005; 95% CI = 0.003-0.008) and (lack of motivation ß = 0.014; 95% CI = 0.009-0.019). The linear mixed model revealed a significant effect of increasing social out-of-home activities (w6-w7) on reduction of EFS-scores (w8) (ß = - 0.21; 95% CI = - 0.29-0.04; p < 0.001). Social out-of-home activities appear to play a crucial role in frailty prevention, which could be used for future interventions.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".