Experiences of Using a Self-management Mobile App Among Individuals With Heart Failure: Qualitative Study
Bibliographic record
Abstract
BACKGROUND: Interventions that focus on the self-management of heart failure are vital to promoting health in patients with heart failure. Mobile health (mHealth) apps are becoming more integrated into practice to promote self-management strategies for chronic diseases, optimize care delivery, and reduce health disparities. OBJECTIVE: The purpose of this study was to explore the experience of using a self-management mHealth intervention in individuals with heart failure to inform a future mHealth intervention study. METHODS: This study used a qualitative descriptive design. Participants were enrolled in the intervention groups of a larger parent study using a mobile app related to self-management of heart failure. The purposive, convenient, criterion-based sample for this qualitative analysis comprised 10 patients who responded to phone calls and were willing to be interviewed. Inclusion criteria for the parent study were adults who were hospitalized at Nebraska Medical Center with a primary diagnosis and an episode of acute decompensated heart failure; discharged to home without services such as home health care; had access to a mobile phone; and were able to speak, hear, and understand English. RESULTS: Study participants were middle-aged (mean age 55.8, SD 12 years; range 36-73 years). They had completed a mean of 13.5 (SD 2.2) years (range 11-17 years) of education. Of the 10 participants, 6 (60%) were male. Half of them (5/10, 50%) were New York Heart Association Classification Class III patients and the other half were Class IV patients. The intervention revealed four self-management themes, including (1) I didn't realize, and now I know; (2) It feels good to focus on my health; (3) I am the leader of my health care team; and (4) My health is improving. CONCLUSIONS: Participants who used a self-management mHealth app intervention for heart failure reported an overall positive experience. Their statements were organized into four major themes. The education provided during the study increased self-awareness and promoted self-management of their heart failure. The mHealth app supported patient empowerment, resulting in better heart failure management and improved quality of life. Participants advocated for themselves by becoming the leader of their health, especially when communicating with their health care team. Finally, the mHealth app was used by the participants as a self-management tool to assist in symptom management and improve their overall health. Future research should study symptom evaluation, medication tracking, and possibly serve as a health provider communication platform to empower individuals to be leaders in their chronic disease management.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| 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".