VA FitHeart, a Mobile App for Cardiac Rehabilitation: Usability Study
Bibliographic record
Abstract
BACKGROUND: Cardiac rehabilitation (CR) improves outcomes for patients with ischemic heart disease or heart failure but is underused. New strategies to improve access to and engagement in CR are needed. There is considerable interest in technology-facilitated home CR. However, little is known about patient acceptance and use of mobile technology for CR. OBJECTIVE: The aim of this study was to develop a mobile app for technology-facilitated home CR and seek to determine its usability. METHODS: We recruited patients eligible for CR who had access to a mobile phone, tablet, or computer with Internet access. The mobile app includes physical activity goal setting, logs for tracking physical activity and health metrics (eg, weight, blood pressure, and mood), health education, reminders, and feedback. Study staff demonstrated the mobile app to participants in person and then observed participants completing prespecified tasks with the mobile app. Participants completed the System Usability Scale (SUS, 0-100), rated likelihood to use the mobile app (0-100), questionnaires on mobile app use, and participated in a semistructured interview. The Unified Theory of Acceptance and Use of Technology and the Theory of Planned Behavior informed the analysis. On the basis of participant feedback, we made iterative revisions to the mobile app between users. RESULTS: We conducted usability testing in 13 participants. The first version of the mobile app was used by the first 5 participants, and revised versions were used by the final 8 participants. From the first version to revised versions, task completion success rate improved from 44% (11/25 tasks) to 78% (31/40 tasks; P=.05), SUS improved from 54 to 76 (P=.04; scale 0-100, with 100 being the best usability), and self-reported likelihood of use remained high at 76 and 87 (P=.30; scale 0-100, with 100 being the highest likelihood). In interviews, patients expressed interest in tracking health measures ("I think it'll be good to track my exercise and to see what I'm doing"), a desire for introductory training ("Initially, training with a technical person, instead of me relying on myself"), and an expectation for sharing data with providers ("It would also be helpful to share with my doctor, it just being a matter of clicking a button and sharing it with my doctor"). CONCLUSIONS: With participant feedback and iterative revisions, we significantly improved the usability of a mobile app for CR. Patient expectations for using a mobile app for CR include tracking health metrics, introductory training, and sharing data with providers. Iterative mixed-method evaluation may be useful for improving the usability of health technology.
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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 | 0.008 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".