Taking up physical activity in later life and healthy ageing: the English longitudinal study of ageing
Bibliographic record
Abstract
BACKGROUND: Physical activity is associated with improved overall health in those people who survive to older ages, otherwise conceptualised as healthy ageing. Previous studies have examined the effects of mid-life physical activity on healthy ageing, but not the effects of taking up activity later in life. We examined the association between physical activity and healthy ageing over 8 years of follow-up. METHODS: Participants were 3454 initially disease-free men and women (aged 63.7 ± 8.9 years at baseline) from the English Longitudinal Study of Ageing, a prospective study of community dwelling older adults. Self-reported physical activity was assessed at baseline (2002-2003) and through follow-up. Healthy ageing, assessed at 8 years of follow-up (2010-2011), was defined as those participants who survived without developing major chronic disease, depressive symptoms, physical or cognitive impairment. RESULTS: At follow-up, 19.3% of the sample was defined as healthy ageing. In comparison with inactive participants, moderate (OR, 2.67, 95% CI 1.95 to 3.64), or vigorous activity (3.53, 2.54 to 4.89) at least once a week was associated with healthy ageing, after adjustment for age, sex, smoking, alcohol, marital status and wealth. Becoming active (multivariate adjusted, 3.37, 1.67 to 6.78) or remaining active (7.68, 4.18 to 14.09) was associated with healthy ageing in comparison with remaining inactive over follow-up. CONCLUSIONS: Sustained physical activity in older age is associated with improved overall health. Significant health benefits were even seen among participants who became physically active relatively late in life.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".