Demographic and clinical correlates of sedentary behaviour in heart disease patients
Bibliographic record
Abstract
Background: Emerging literature suggests that sedentary behaviour has negative health consequences. To date, however, very little is known about the sedentary behaviours of individuals living with heart disease. The purpose of this study was to objectively measure sedentary behaviours of this population and to examine its relationship with demographic and clinical variables. Methods: 287 patients diagnosed with coronary heart disease at 8 sites across Canada wore an accelerometer and GPS unit for 7 days and completed a questionnaire that included demographic and clinical characteristics. Accelerometers had to be worn for a minimum of 10 hours/day for at least 3 days to be used in the analyses. ActiLife 6 software was used to calculate the number of sedentary bouts and the total time in sedentary bouts / day. Results: Data for 278 patients (mean age = 65.72; 72.7% male; 90.9% white) were analyzed. On average, patients engaged in 12.3 (SD=3.96) bouts/day and 257.61 (SD=100) minutes/day of sedentary activity. Zero-order correlations showed that being older (r=.221, p<.001), male (r=-.212; p=.001) and married/ common-law (r=.161; p=.01) were significantly associated with increased number of sedentary bouts/day, and being older (r=.269, p<.001), male (r=-.188; p<.005), and not employed (r=.159; p=.01) were significantly associated with increased sedentary minutes / day. No clinical variables were associated with sedentary bouts or minutes. Conclusions: Sedentary behaviour appears to be problematic for heart disease patients. Based on the preliminary findings, age, gender, marital status, and employment status should be considered in the design of sedentary behaviour interventions. Acknowledgments: Canadian Institutes of Health Research (CIHR)
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".