Correlates of change in accelerometer-assessed total sedentary time and prolonged sedentary bouts among older English adults: results from five-year follow-up in the EPIC-Norfolk cohort
Bibliographic record
Abstract
BACKGROUND: Development of effective strategies to reduce sedentary time among older adults necessitates understanding of its determinants but longitudinal studies of this utilising objective measures are scarce. METHODS: Among 1536 older adults (≥60 years) in the EPIC-Norfolk study, sedentary time was assessed for seven days at two time-points using accelerometers. We assessed associations of change in total and prolonged bouts of sedentary time (≥ 30 minutes) with change in demographic and behavioural factors using multi-level regression. RESULTS: Over follow-up (5.3±1.9 years), greater increases in total sedentary time were associated with older age, being male, higher rate of increase in BMI, lower rate of increase in gardening (0.5 min/day/yr greater sedentary time per hour/week/yr less gardening, 95% CI 0.1, 1.0), a lower rate of increase in walking (0.2 min/day/yr greater sedentary time per hour/week/yr less walking, 95% CI 0.1, 0.3) and a higher rate of increase in television viewing. Correlates of change in prolonged sedentary bouts were similar. CONCLUSION: Individuals in specific sub-groups (older, male, higher BMI) and who differentially participate in certain behaviours (less gardening, less walking and more television viewing) but not others increase their sedentary time at a higher rate than others; utilising this information could inform successful intervention content and targeting.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".