Utility and Efficacy of a Smartphone Application to Enhance the Learning and Behavior Goals of Traditional Cardiac Rehabilitation
Bibliographic record
Abstract
PURPOSE: Most eligible patients do not participate in traditional clinic-based cardiac rehabilitation (CR) despite well-established benefits. Novel approaches to overcome logistic obstacles and increase efficiencies of learning, behavior modification, and exercise surveillance may increase CR participation. In an observational study, the feasibility and utility of a mobile smartphone application for CR, Heart Coach (HC), were assessed as part of standard care. Ultimately, innovative CR models incorporating HC may facilitate better CR usage and value. METHODS: Twenty-six patients enrolled in CR installed HC. Over the next 30 days, they were prompted by HC to complete a daily "task list" that included medications, walking, education (text and videos), and surveys. Cardiac rehabilitation providers monitored each patient's progress through a HC-based Web dashboard and also sent them personalized feedback and support. Completion of the tasks and feedback (qualitative and quantitative) from patients and clinicians were tracked. RESULTS: Patients engaged with HC 90% of days during the study period, with uniformly favorable impact on compliance and adherence. Eighty-three percent of patients reported a positive/very positive HC experience. Providers reported that HC enhanced their provision of therapy by improving communication, clinical insight, patient participation, and program efficiency. CONCLUSIONS: Integrating a mobile care delivery platform into CR was feasible, safe, and agreeable to patients and clinicians. It enhanced patient perceptions of CR care and physician perceptions of the CR caregiving process. Mobile-enabled technologies hold promise to extend the quality and reach of CR, and to better achieve contemporary accountable care goals.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.002 | 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".