A Smartphone App (AnSim) With Various Types and Forms of Messages Using the Transtheoretical Model for Cardiac Rehabilitation in Patients With Coronary Artery Disease: Development and Usability Study
Bibliographic record
Abstract
BACKGROUND: Despite strong evidence of clinical benefit, cardiac rehabilitation (CR) programs are currently underutilized and smartphone-based CR strategies are thought to address this unmet need. However, data regarding the detailed process of development are scarce. OBJECTIVE: This study focused on the development of a smartphone-based, patient-specific, messaging app for patients who have undergone percutaneous coronary intervention (PCI). METHODS: The AnSim app was developed in collaboration with a multidisciplinary team that included cardiologists, psychiatrists, nurses, pharmacists, nutritionists, and rehabilitation doctors and therapists. First, a focus group interview was conducted, and the narratives of the patients were analyzed to identify their needs and preferences. Based on the results, health care experts and clinicians drafted messages into 5 categories: (1) general information regarding cardiovascular health and medications, (2) nutrition, (3) physical activity, (4) destressing, and (5) smoking cessation. In each category, 90 messages were developed according to 3 simplified steps of the transtheoretical model of behavioral change: (1) precontemplation, (2) contemplation and preparation, and (3) action and maintenance. After an internal review and feedback from potential users, a bank of 450 messages was developed. RESULTS: The focus interview was conducted with 8 patients with PCI within 1 year, and 450 messages, including various forms of multimedia, were developed based on the transtheoretical model of behavioral change in each category. Positive feedback was obtained from the potential users (n=458). The mean Likert scale score was 3.95 (SD 0.39) and 3.91 (SD 0.39) for readability and usefulness, respectively, and several messages were refined based on the feedback. Finally, the patient-specific message delivery system was developed according to the baseline characteristics and stages of behavioral change in each participant. CONCLUSIONS: We developed an app (AnSim), which includes a bank of 450 patient-specific messages, that provides various medical information and CR programs regarding coronary heart disease. The detailed process of multidisciplinary collaboration over the course of the study provides a scientific basis for various medical professionals planning smartphone-based clinical research.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".