Examining Use Behavior of a Goal-Supporting mHealth App in Primary Care Among Patients With Multiple Chronic Conditions: Qualitative Descriptive Study
Notice bibliographique
Résumé
BACKGROUND: Although mobile health (mHealth) apps are increasingly being used to support patients with multiple chronic conditions (multimorbidity), most mHealth apps experience low interaction and eventual abandonment. To tackle this engagement issue, when developing an mHealth program, it is important to understand the social-behavioral factors that affect patients' use behavior. OBJECTIVE: The aim of this study was to explore the social and behavioral factors contributing to patients' use behavior of an mHealth app called the electronic Patient-Reported Outcome (ePRO). The ePRO app supports goal-oriented care delivery in interdisciplinary primary care models. METHODS: A descriptive qualitative study was used to analyze interview data collected for a larger mixed methods pragmatic trial. The original 15-month trial was conducted in 6 primary care teams across Ontario, Canada, between 2018 and 2019. The eligibility criteria for patients were being aged ≥60 years with ≥10 visits within the previous 12 months of study enrollment. For this analysis, patients were classified as long-term or short-term users based on their length of use of the ePRO app during the trial. The Social Cognitive Theory by Bandura was used to categorize social-behavioral factors that contributed to patients' decision to continue or discontinue using the app. RESULTS: The patient-provider relationship emerged as a key factor that shaped patients' experiences with the app and subsequent decision to continue using the app. Other factors that contributed to patients' decision to continue using the app were personal and social circumstances, perceived usefulness, patients' previous experience with goal-related behaviors, and confidence in one's capability. There was an overlap of experience between long- and short-term app users but, in general, long-term users perceived the app to be more useful and their goals to be more meaningful than short-term app users. This observation was complicated by the fact that patient health-related goals were dynamic and changed over time. CONCLUSIONS: Complex patients' use behavior of a goal-supporting mHealth app is shaped by an array of sociobehavioral factors that can evolve. To tackle this dynamism, there should be an emphasis on creating adaptable health technologies that are easily customizable by patients and able to respond to their changing contexts and needs. TRIAL REGISTRATION: ClinicalTrials.gov NCT02917954; https://clinicaltrials.gov/ct2/show/NCT02917954.
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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 tête enseignante, 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 ».