Use of Mobile Apps in Heart Failure Self-management: Qualitative Study Exploring the Patient and Primary Care Clinician Perspective
Notice bibliographique
Résumé
BACKGROUND: Mobile apps have the potential to support patients with heart failure and facilitate disease self-management, but this area of research is recent and rapidly evolving, with inconsistent results for efficacy. So far, most of the published studies evaluated the feasibility of a specific app or assessed the quality of apps available in app stores. Research is needed to explore patients' and clinicians' perspectives to guide app development, evaluation, and implementation into models of care. OBJECTIVE: This study aims to explore the patient and primary care clinician perspective on the facilitators and barriers to using mobile apps, as well as desired features, to support heart failure self-management. METHODS: This is a qualitative phenomenological study involving face-to-face semistructured interviews. Interviews were conducted in a general practice clinic in Sydney, Australia. Eligible participants were adult patients with heart failure and health care professionals who provided care to these patients at the clinic. Patients did not need to have previous experience using heart failure mobile apps to be eligible for this study. The interviews were audio-recorded, transcribed, and analyzed using inductive thematic data analysis in NVivo 12. RESULTS: A total of 12 participants were interviewed: 6 patients (mean age 69 [SD 7.9] years) and 6 clinicians. The interviews lasted from 25 to 45 minutes. The main facilitators to the use of apps to support heart failure self-management were communication ability, personalized feedback and education, and automated self-monitoring. Patients mentioned that chat-like features and ability to share audio-visual information can be helpful for getting support outside of clinical appointments. Clinicians considered helpful to send motivational messages to patients and ask them about signs and symptoms of heart failure decompensation. Overall, participants highlighted the importance of personalization, particularly in terms of feedback and educational content. Automated self-monitoring with wireless devices was seen to alleviate the burden of tracking measures such as weight and blood pressure. Other desired features included tools to monitor patient-reported outcomes and support patients' mental health and well-being. The main barriers identified were the patients' unwillingness to engage in a new strategy to manage their condition using an app, particularly in the case of low digital literacy. However, clinicians mentioned this barrier could potentially be overcome by introducing the app soon after an exacerbation, when patients might be more willing to improve their self-management and avoid rehospitalization. CONCLUSIONS: The use of mobile apps to support heart failure self-management may be facilitated by features that increase the usefulness and utility of the app, such as communication ability in-between consultations and personalized feedback. Also important is facilitating ease of use by supporting automated self-monitoring through integration with wireless devices. Future research should consider these features in the co-design and testing of heart failure mobile apps with patients and clinicians.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
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 | 0,011 | 0,019 |
| 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 | 0,006 |
| Communication savante | 0,004 | 0,003 |
| Science ouverte | 0,002 | 0,005 |
| Intégrité de la recherche | 0,002 | 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 ».