Daily Path Areas and Location Use During and After Cardiac Rehabilitation
Bibliographic record
Abstract
PURPOSE: Little research has focused on the potential impact that the environment plays in shaping cardiac rehabilitation (CR) patient sedentary time (ST) and physical activity (PA). To address this, the current study generated daily path areas (DPAs) based on the locations they visited during and after they completed CR. METHODS: Patients in CR (n = 66) completed a survey and wore an accelerometer and Global Positioning System receiver for 7 days early (first month), late (last 2 weeks of program), and 3 months after completing CR. RESULTS: Individual DPAs were approximately 24 km 2 at baseline and remained stable over time. Location-based analyses showed that most patients' ST and PA time was spent at home, followed by other residential, commercial, work, and CR locations. However, the time spent in certain locations (eg, parks and recreation locations) fluctuated during and after CR by intensity. CONCLUSIONS: CR patient DPA was stable over time. Within this space, they primarily engaged in ST and PA at home. However, when not home, the distribution of location use varied across a number of locations that extended well beyond their neighborhoods. Therefore, proximity to home may not be a barrier for CR patients in relation to their ST and PA.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".