Experiences of Using a Self-management Mobile App Among Individuals With Heart Failure: Qualitative Study
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,008 | 0,012 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,008 | 0,005 |
| Communication savante | 0,003 | 0,004 |
| Science ouverte | 0,002 | 0,005 |
| Intégrité de la recherche | 0,001 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».