Objectively measured and self-reported sedentary time in older Canadians
Bibliographic record
Abstract
OBJECTIVE: The aim of this study was to examine objectively measured total and self-reported leisure sedentary time among older Canadians by work status. METHODS: The analysis was based on 1729 older adults (60-79 years) from the 2007/09 and 2010/11 Canadian Health Measures Survey. Work status, functional limitations, smoking, and perceived health were assessed by self-report and waist circumference (WC) was measured. Total sedentary time (ST) and physical activity (PA) were objectively measured by accelerometer and leisure sedentary activities were assessed by questionnaire. RESULTS: 93.6% of individuals were sedentary for 8 or more hours per day. Measured ST did not differ by work status, while self-reported leisure ST was higher in those not working compared to those working (239 vs. 207 minutes/day, p < 0.05). Correlates of measured ST were fair/poor perceived health (β: 28.76, p < 0.01), smoking (β: 17.12, p < 0.05), high-risk WC (β: 13.14, p < 0.05), and not meeting PA guidelines (β: 35.67, p < 0.001). For self-reported leisure ST, working status (β: 33.80, p < 0.001) and functional limitations (β: 16.31, p < 0.05) were significant correlates. CONCLUSIONS: Older adults accumulate substantial ST regardless of their working status and ST is correlated with indicators of health risk. Older adults are an important target population for interventions to reduce ST.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".