Twenty-Year Trajectories of Physical Activity Types from Midlife to Old Age
Bibliographic record
Abstract
PURPOSE: Correlates of physical activity (PA) vary according to type. However, predictors of long-term patterns of PA types into old age are unknown. This study aimed to identify 20-yr trajectories of PA types into old age and their predictors. METHODS: Seven thousand seven hundred thirty-five men (age, 40-59 yr) recruited from UK towns in 1978 to 1980 were followed up after 12, 16, and 20 yr. Men reported participation in sport/exercise, recreational activity and walking, health status, lifestyle behaviors and socio-demographic characteristics. Group-based trajectory modeling identified the trajectories of PA types and associations with time-stable and time-varying covariates. RESULTS: Men with ≥3 measures of sport/exercise (n = 5116), recreational activity (n = 5085) and walking (n = 5106) respectively were included in analyses. Three trajectory groups were identified for sport/exercise, four for recreational activity and three for walking. Poor health, obesity and smoking were associated with reduced odds of following a more favorable trajectory for all PA types. A range of socioeconomic, regional and lifestyle factors were also associated with PA trajectories but the magnitude and direction were specific to PA type. For example, men with manual occupations were less likely to follow a favorable sport/exercise trajectory but more likely to follow an increasing walking trajectory compared to men with nonmanual occupations. Retirement was associated with increased PA but this was largely due to increased sport/exercise participation. CONCLUSIONS: Physical activity trajectories from middle to old age vary by activity type. The predictors of these trajectories and effects of major life events, such as retirement, are also specific to the type of PA.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".