Association of objective sedentary behaviour and self-rated health in English older adults
Bibliographic record
Abstract
OBJECTIVE: Reducing sedentary behaviour (SB) might improve the health of older adults. However, we know little about how objectively measured SB impacts on self-rated health in older adults. We aimed to explore the associations between objectively measured SB and self-rated health in English older adults. RESULTS: A random sub-sample of older adults (≥ 65 years old) from the 2008 Health Survey for England wore an ActiGraph GT1M accelerometer for 7 days. Self-rated health was measured using an item from the General Health Questionnaire. Linear regression and analysis of covariance were used to test the associations between percentage time spent in SB and mean daily minutes in SB and self-rated health (very good/good; fair; bad/very bad), adjusting for covariates. Valid accelerometry datasets were returned by 578 individuals. Significant negative associations between percentage time and mean daily minutes in SB and self-rated health were found. In particular, individuals spending reduced percentages of time being sedentary had higher self-rated health. In conclusion, SB appears to be associated with self-rated health in older people independently from MVPA. If longitudinal research could determine how changes in SB influence self-rated health as individuals' age, this might be an important lifestyle variable to target for health improvement.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.002 | 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".