mHealth Support in Cardiac Care Pathways for Patient Self-Management During Transitions From Hospital to Rehabilitation: Exploratory Field Study
Bibliographic record
Abstract
Background: Cardiac rehabilitation (CR) is essential for recovery from cardiovascular disease. However, patients often encounter challenges in navigating the transition from acute hospital care to CR. Mobile health (mHealth) technologies may support this critical phase; however, evidence regarding their clinical practice remains limited. The HERO app (developed by REDOX GmbH) was developed to address the needs of patients with cardiovascular disease for orientation, emotional support, and motivation during this transition. Objective: This study aims (1) to explore how mHealth technologies tailored for patients with cardiovascular disease can support their needs regarding orientation, emotional balance, and motivation during the transition from the acute hospital to CR and (2) to evaluate the user experience and acceptance of the HERO app as targeted pathway support. Methods: A mixed methods study was conducted with patients with cardiovascular disease using study diaries, questionnaires, and semistructured interviews. Participants were purposively recruited in acute hospitals and rehabilitation settings. Quantitative data were analyzed descriptively, and qualitative data were analyzed using content analysis after Mayring. Results: Eight participants used the app for an average of 14 (range 4-23) days. The app was perceived as a helpful short-term resource. It supported patients in understanding their condition, planning for CR, and regaining motivation. Participants highlighted the value of combining objective information with peer experiences. Suggestions for improvement included more personalized self-management guidance and a precise onboarding process to increase accessibility and usability. Conclusions: Based on the findings, we propose 4 pillars of mHealth support for cardiac care transitions, including timely access, actionable guidance, peer support, and short-term usability. These pillars could inform the design of patient-centered mHealth tools for care transitions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 teacher head, 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".