Cardiomeds, an mHealth App for Self-Management to Support Swiss Patients With Heart Failure: 2-Stage Mixed Methods Usability Study
Bibliographic record
Abstract
BACKGROUND: Mobile health apps have shown promising results in improving self-management of several chronic diseases in patients. We have developed a mobile health app (Cardiomeds) dedicated to patients with heart failure (HF). This app includes an interactive medication list; daily self-monitoring of symptoms, weight, blood pressure, and heart rate; and educational information on HF delivered through various formats. OBJECTIVE: This study aimed to perform a mixed methods usability study of Cardiomeds. METHODS: Smartphone users with HF were recruited from the HF outpatient clinic at the University Hospital of Geneva. The usability test was conducted in 2 stages, with modifications made to the app after the first stage to address major usability issues. Each stage required 10 participants to perform 14 tasks, such as entering vital signs, entering a new medication and time of intake, or finding information about HF. Each task was timed, sessions were recorded, and all data were anonymized. After completing the tasks, patients completed the System Usability Scale 10-item questionnaire and answered 5 open questions about their perceptions of Cardiomeds. RESULTS: Twenty patients with HF, 75% (15/20) of whom were men, with a mean age of 55 years, were included in this study. The average time to complete all 14 tasks was 18 (SD 5.7) minutes. Manual medication entry was the most time-consuming task, taking an average of 154.40 (SD 68.08) seconds in the first stage, 103.10 (SD 42.76) seconds in the second stage, and 128 (SD 63) seconds overall. The mean overall success rate was 77% (SD 0.23%) for the first stage and 94% (SD 0.07%) for the second stage. A total of 30% (3/10) of participants in the first stage completed all tasks without any help compared with 50% (5/10) of participants during the second stage. The average System Usability Scale score was 80% (SD 17%), showing a slight increase from 79% (SD 16%) in the first stage to 80% (SD 28%) in the second stage, which qualifies the app as "good" in terms of usability. Between the 2 stages, part of the app interface was redesigned to address the key issues identified in the first stage. Despite these improvements, problems related to guidance were frequent and comprised 36% (8/22) of the problems in the first stage and 40% (6/15) in the second stage. In response to open questions, 85% (17/20) of the participants responded that they would like to use the app when it became available. CONCLUSIONS: The usability test indicated that Cardiomeds is a suitable and user-friendly app for patients with HF. The app will be further tested in a randomized clinical trial (2022-00731) after acute HF hospitalization to assess its impact on patients' knowledge about HF, self-care, and quality of life.
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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 | 0.016 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".