Providing context to sedentary behaviour in cardiac rehab patients: Linking accelerometry and GPS data
Bibliographic record
Abstract
Purpose: The current work looked to provide context or location-based information to objective measures of sedentary time in cardiac rehab (CR) patients. Methods: Accelerometry and GPS data was collected simultaneously in 50 patients attending a CR program in Halifax, Nova Scotia. Patients wore the devices for 9 days at the beginning (i.e., within the 1st 3 weeks), end (i.e., within the last 2 weeks) of CR and 3-months after program completion. Minute-by-minute sedentary time calculated from the accelerometry data was linked to GPS data using ArcGIS software and used to identify where sedentary time occurred. Descriptive statistics (frequencies) were calculated to look at the different locations patients were found to be sedentary. Results: We linked > 1268 hours of sedentary and GPS data at each time point. Forty two different location codes were created to best categorize where participants' were when they were sedentary. At the beginning, end and 3-months following CR patients were found to be most sedentary within their home (74%, 67% and 63% of the data linked at Time 1, Time 2, and Time 3, respectively). Other common places included vehicles (different means of transportation), professional centres and residential dwellings different from their home. Conclusion: It appears that patients are most sedentary in their own homes. This work provides valuable information for informing future sedentary behaviour interventions in CR patients and highlights the home environment as an important intervention target in this patient population.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| 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) | 0.001 | 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".