Patient perspectives on the use of mobile apps to support heart failure management: A qualitative descriptive study
Bibliographic record
Abstract
BACKGROUND: Adherence to diet and medical therapies are key to improving heart failure (HF) outcomes; however, nonadherence is common. While mobile apps may be a promising way to support patients with adherence via education and monitoring, HF patient perspectives regarding the use of apps for HF management in unknown. This data is critical for these tools to be successfully developed, implemented, and adopted to optimize adherence and improve HF outcomes. OBJECTIVE: To determine patients' needs, motivations, and challenges on the use of mobile apps to support HF management. METHODS: A qualitative descriptive study using focus groups (n = 4,60 minutes) was conducted among HF patients from outpatient HF clinics in Toronto, Canada. The Diffusion of Innovation theory informed a ten-question interview guide. Interview transcripts were independently coded by two researchers and analyzed using content analysis. RESULTS: Nineteen HF patients (65 ± 10 yrs, 12 men) identified a total of four key themes related to the use of mobile apps. The theme 'Factors impacting technology use by patients' identified motivations and challenges to app use, including access to credible information, easy and accessible user-interface. Three themes described patients' needs on the use of mobile apps to support HF management: 1) 'Providing patient support through access to information and self-monitoring', apps could provide education on HF-related content (e.g., diet, medication, symptoms); 2) 'Facilitating connection and communication', through information sharing with healthcare providers and connecting with other patients; 3) 'Patient preferences', app features such as reminders for medication, and visuals to show changes in HF symptoms were favoured. CONCLUSIONS: HF patients perceive several benefits and challenges to app use for HF self-management. Capitalizing on the benefits and addressing the challenges during the app development process may maximize adoption of such tools in this patient population.
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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 | 0.013 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| 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".