Device-worn measures of sedentary time and physical activity in South Asian adults at high risk for type 2 diabetes in Metro-Vancouver, Canada
Bibliographic record
Abstract
BACKGROUND: South Asians have high incidence of chronic disease. Physical activity (PA) and sedentary time are modifiable risk factors for chronic disease but their assessment in South Asians has been primarily based on self-report. This study presents directly-measured PA and sedentary time in South Asian adults in Canada. METHODS: A subset of 100 South Asian participants from a larger study who were identified at being at a higher risk for type 2 diabetes wore Actical accelerometers for 7 days. Anthropometric measures were taken and socio-demographic factors including age, income, education level, years since immigration, presence of children under the age of 12 years in the household and employment status were self-reported. RESULTS: Ninety-one participants (mean age 65.6 years) provided valid accelerometer data. Participants accumulated mean 673.5 (95% CI: 656.6, 691.0) min/day sedentary time, 130.5 (95% CI: 117.3, 145.3) min/day light PA (LPA) and 2.3 (95% CI: 1.3, 4.2) min/day moderate-to-vigorous PA (MVPA). For sedentary time and LPA, sex and BMI explained 51% of variability. For MVPA, BMI, season of assessment and employment status explained 23% variability with those who were employed accumulating significantly higher mean min/day of MVPA compared to those who were unemployed; (5.8, 95% CI: 1.5, 21.7) vs (1.5, 95% CI: 5.3, 20.0) respectively. CONCLUSION: High sedentary time, and low MVPA indicates the need to focus health promotion efforts on shifting sedentary time into LPA while trying to increase MVPA. Future studies need to be based on larger, representative samples of South Asians.
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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 | 0.002 |
| Science and technology studies | 0.002 | 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".