Physical demands at work and physical activity are associated with frailty in retirement
Bibliographic record
Abstract
BACKGROUND: The relationship between occupational physical activity and frailty is complex and understudied. OBJECTIVE: We explore whether moderate-vigorous physical activity (MVPA) in retirement and main lifetime occupation physical demands (OPD) are associated with frailty in retirement. METHODS: Retired adults aged 50 + who participated in waves 3-4 of the Survey of Health, Ageing and Retirement in Europe were included. We constructed a 65-item frailty index (FI; Wave 4). Linear regressions tested the independent associations between OPD (Wave 3) and retirement MVPA (Wave 4) with FI (B: 95% CI) controlling for occupation characteristics (Wave 3) and demographics (Wave 4). These models were repeated across country groups (Nordic; Mediterranean; Continental) and sexes. RESULTS: We included 8,411 adults (51.1% male) aged 72.4 years (SD 8.0). Frequent MVPA was consistently associated with lower FI (-0.09 : 0.10--0.08, p < .001) while OPD was associated with higher FI (0.02 : 0.01-0.03, p < .001). The MVPA*OPD interaction (-0.02: -0.04--0.00, p = .043) was weakly associated with FI, but did not explain additional model variance or was significant among any country group or sex. CONCLUSIONS: For a sample of European community-dwelling retirees, a physically demanding main lifetime occupation independently predicts worse frailty, even in individuals who are physically active in retirement.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".