Young investigator award session II - Cardiac Rehabilitation
Bibliographic record
Abstract
BACKGROUND: In rehabilitation of Coronary Artery Disease (CAD) patients, exercise training is an important component. Telerehabilitation techniques have been proven successful to enable patients to continue rehabilitation at home after their hospital-based rehabilitation. However, achieving a sustained elevation in physical activity levels over a long term in the absence of supervised exercise intervention is difficult. We designed HeartHab, an appbased telerehabilitation program, by incorporating novel persuasive techniques to motivate CAD patients to reach personalized physical activity targets. In our study, we evaluated the effects of HeartHab on physical activity levels, modifiable risk factors and general health behaviour of patients who completed a hospital based rehabilitation program. METHODS: 32 CAD patients were recruited. Four patients had to be excluded and three others did not use the app at all. We compared the values of the remaining 25 patients before and after using HeartHab for a period of 8-10 weeks. We measured baseline values of weight, blood pressure, VO2max using ergo spirometry and physical activity levels using the International Physical Activity Questionnaire (IPAQ). We prescribed personalized exercise targets using recommendations from ESC's EXPERT tool. We translated the prescribed targets and physical activities logged by patients in the app into MET (Metabolic Equivalent of Task) values using ACSM's guidelines for exercise testing and prescription. We compared the mean MET values achieved after using the app against the prescribed targets and baseline values. RESULTS: On average, 52% of patient exceeded the prescribed weekly targets and 44% reached the prescribed goals. One patient did not register any physical activity in the app. For 68% of patients, the mean METs per week increased as compared to the baseline. For the remaining 32%, the lack of increase could be attributed to low app usage and low usage of the physical activity module of the app. Further evaluation showed no significant differences in body weight, systolic blood pressure, diastolic blood pressure or VO2 max. CONCLUSION: The use of HeartHab had a positive effect on increasing the mean physical activity levels of patients and motivated them to reach or exceed prescribed exercise targets in a non-supervised setting. No significant effects were seen on other outcomes, probably because of the short duration and relatively low intensity of the intervention.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".