Correlates of sitting time in adults with type 2 diabetes
Bibliographic record
Abstract
BACKGROUND: Studies suggest a relationship between sitting time and cardiovascular disease mortality. Our aim was to identify socio-demographic, contextual, and clinical (e.g., body composition, diabetes duration) correlates of self-reported sitting time among adults with type 2 diabetes, a clinical population at high risk for cardiovascular disease. We sought to determine if there was an inverse relationship between sitting and step counts in a diabetes cohort in whom we had previously identified low step counts with further lowering in fall/winter. METHODS: The cohort included 198 adults (54 % men; age 60.0 SD 11.5 years; Body mass index 30.4 SD 5.6 kg/m(2)) (Montréal, Canada). Socio-demographic, contextual and clinical factors were assessed using standardized questionnaires and step counts with a pedometer over 14 days (concealed viewing windows). Total sitting time was estimated once per season (up to 4 times per year at -month intervals) using the International Physical Activity Questionnaire-Short version. Potential sitting time correlates were evaluated using Bayesian longitudinal hierarchical linear regression models in participants with sitting time data (n = 191). RESULTS: The average sitting time was 308 (SD 161) minutes/day without variation across seasons. Sitting time correlates were being an immigrant (56 fewer minutes/day spent sitting compared to non- immigrants, 95 % credible interval, CrI: -100, -11) and having a university degree (55 more minutes/day spent sitting compared to those without a university degree, 95 % CrI: 10, 100) after adjustment for potential correlates observed in univariate analyses (sex, age, job status, waist circumference, depressed mood, steps). Correlation between sitting and steps, adjusted for age and sex, was -0.144 (95 % CI: -0.280, 0.002). CONCLUSION: There was low correlation between sitting time and step counts. Therefore, high sitting time and low step counts are behaviours that may need to be independently targeted. Interventions to reduce sitting time in adults with type 2 diabetes may need to target non-immigrants and those with a university degree.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.001 | 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".