Location-Based Sedentary Time and Physical Activity in People Living With Coronary Artery Disease
Bibliographic record
Abstract
PURPOSE: Sedentary time (ST) and lack of physical activity increase the risk of adverse outcomes for those living with coronary artery disease (CAD). Little is known about how much ST, light physical activity (LPA), and moderate to vigorous physical activity (MVPA) that CAD participants not attending cardiac rehabilitation engage in, the locations where they engage in these behaviors, and how far from home the locations are. METHODS: Participants completed a survey and wore an accelerometer and global positioning system receiver for 7 d at baseline and 6 mo later. RESULTS: Accelerometer analyses (n = 318) showed that participants averaged 468.4 ± 102.7 of ST, 316.1 ± 86.5 of LPA, and 32.9 ± 28.9 of MVPA min/d at baseline. ST and LPA remained stable at 6 mo, whereas MVPA significantly declined. The global positioning system (GPS) analyses (n = 315) showed that most of participant ST, LPA, and MVPA time was spent at home followed by other residential, retail/hospitality, and work locations at baseline and 6 mo. When not at home, the average distance to a given location ranged from approximately 9 to 18 km. CONCLUSIONS: Participants with CAD spent the majority of their time being sedentary. Home was the location used the most to engage in ST, LPA, and MVPA. When not home, ST, LPA, and MVPA were distributed across a variety of locations. The average distance from home to a given location suggests that proximity to home may not be a barrier from an intervention perspective.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".