Development of a Digital Health Intervention for the Secondary Prevention of Cardiovascular Disease (INTERCEPT): Co-Design and Usability Testing Study
Bibliographic record
Abstract
BACKGROUND: Secondary prevention is an important strategy to reduce the burden of cardiovascular disease (CVD), a leading cause of death worldwide. Despite the growing evidence for the effectiveness of digital health interventions (DHIs) for the secondary prevention of CVD, the majority are designed with minimal input from target end users, resulting in poor uptake and usage. OBJECTIVE: This study aimed to optimize the acceptance and effectiveness of a DHI for the secondary prevention of CVD through co-design, integrating end users' perspectives throughout. METHODS: A theory-driven, person-based approach using co-design was adopted for the development of the DHI, known as INTERCEPT. This involved a 4-phase iterative process using online workshops. In phase 1, a stakeholder team of health care professionals, software developers, and public and patient involvement members was established. Phase 2 involved identification of the guiding principles, content, and design features of the DHI. In phase 3, DHI prototypes were reviewed for clarity of language, ease of navigation, and functionality. To anticipate and interpret DHI usage, phase 4 involved usability testing with participants who had a recent cardiac event (<2 years). To assess the potential impact of usability testing, the System Usability Scale was administered before and after testing. The GUIDED (Guidance for Reporting Intervention Development Studies in Health Research) checklist was used to report the development process. RESULTS: Five key design principles were identified: simplicity and ease of use, behavioral change through goal setting and self-monitoring, personalization, system credibility, and social support. Usability testing resulted in 64 recommendations for the app, of which 51 were implemented. Improvements in System Usability Scale scores were observed when comparing the results before and after implementing the recommendations (61 vs 83; P=.02). CONCLUSIONS: Combining behavior change theory with a person-based, co-design approach facilitated the development of a DHI for the secondary prevention of CVD that optimized responsiveness to end users' needs and preferences, thereby potentially improving future engagement.
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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 | 0.034 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".