Perception and Evaluation of a Knowledge Transfer Concept in a Digital Health Application for Patients With Heart Failure: Mixed Methods Study
Bibliographic record
Abstract
Background: Digital health education can enhance the quality of life of patients with heart failure by providing accessible and tailored information, which is essential for effective self-care and self-management. Objective: This work aims to develop a mobile health knowledge transfer concept for heart failure in a user-centered design process grounded in theoretical frameworks. This approach centers on enhancing the usability, patient engagement, and meaningfulness of mobile health education in the context of heart failure. Methods: A user-centered design process was employed. First, semistructured stakeholder interviews were conducted with patients (n=9) and medical experts (n=5). The results were used to develop a health knowledge transfer concept for a mobile health app for heart failure. This concept was implemented as a digital prototype based on an existing German mobile health app for patients with heart failure. We used this prototype to evaluate our concept with patients with heart failure in a study composed of user testing and semistructured patient interviews (n=7). Results: Stakeholder interviews identified five themes relevant to mobile health education: individualization, content relevance, media diversity, motivation strategies, and trust-building mechanisms. The evaluation of our prototype showed that patients value the adaptation of content to individual interests and prior knowledge. Digital rewards such as badges and push notifications can increase motivation and engagement but should be used with care to avoid overload, irrelevance, and repetition. Conclusions: Our findings emphasize the importance of tailoring mobile health education to the specific needs and preferences of patients with heart failure. At the same time, they also highlight the careful implementation of motivation strategies to promote user engagement effectively. These implications offer guidance for developing more impactful interventions to improve health outcomes for this population.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".