The Influence of Neighbourhoods and the Social Environment on Sedentary Behaviour in Older Adults in Three Prospective Cohorts
Bibliographic record
Abstract
Sedentary behaviour is an emerging risk factor for poor health. This study aimed to identify ecological determinants of sedentary behaviour, for which evidence is currently scarce. The study participants were community dwelling adults from, respectively, the Lothian Birth Cohort 1936 (n = 271, mean age 79) and the 1930s (n = 119, mean age 83) and 1950s (n = 310, mean age 64) cohorts of the West of Scotland Twenty-07 study. The outcome measure, percentage of waking time spent sedentary (sedentary time), was measured using an activPAL activity monitor worn continuously for seven days. Potential determinants included objective and subjective neighbourhood measures such as natural space, crime, social cohesion and fear of crime. Other determinants included measures of social participation such as social support, social group membership and providing care. Results from multivariable regression analyses indicated that providing care was associated with reduced sedentary time in retired participants in all cohorts. Fear of crime and perceived absence of services were associated with increased sedentary time for retired 1950s cohort members. Higher crime rates were associated with increased sedentary time in all cohorts but this was not significant after adjustment for socio-demographic characteristics. Most other neighbourhood and social participation measures showed no association with sedentary time.
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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 | 0.002 |
| 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 | 0.000 |
| 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".