Influences on the Uptake of Health and Well-being Apps and Curated App Portals: Think-Aloud and Interview Study
Notice bibliographique
Résumé
BACKGROUND: Health and well-being smartphone apps can provide a cost-effective solution to addressing unhealthy behaviors. The selection of these apps tends to occur in commercial app stores, where thousands of health apps are available. Their uptake is often influenced by popularity indicators. However, these indicators are not necessarily associated with app effectiveness or evidence-based content. Alternative routes to app selection are increasingly available, such as via curated app portals, but little is known about people's experiences of them. OBJECTIVE: The aim of this study is to explore how people select health apps on the internet and their views on curated app portals. METHODS: A total of 18 UK-based adults were recruited through social media and asked during an in-person meeting to verbalize their thoughts while searching for a health or well-being app on the internet on a platform of their choice. The search was then repeated on 2 curated health app portals: the National Health Service Apps Library and the Public Health England One You App portal. This was followed by semistructured interviews. Data were analyzed using framework analysis, informed by the Capability, Opportunity, Motivation-Behavior model and the Theoretical Domains Framework. RESULTS: Searching for health and well-being apps on the internet was described as a minefield. App uptake appeared to be influenced by participants' capabilities such as app literacy skills and health and app awareness, and opportunities including the availability of apps, app esthetics, the price of an app, and social influences. Motivation factors that seemed to affect the uptake were perceived competence, time efficiency, perceived utility and accuracy of an app, transparency about data protection, commitment and social identity, and a wide range of emotions. Social influences and the perceived utility of an app were highlighted as particularly important. Participants were not previously aware of curated portals but found the concept appealing. Curated health app portals appeared to engender trust and alleviate data protection concerns. Although apps listed on these were perceived as more trustworthy, their presentation was considered disappointing. This disappointment seemed to stem from the functionality of the portals, lack of user guidance, and lack of tailored content to an individual's needs. CONCLUSIONS: The uptake of health and well-being apps appears to be primarily affected by social influences and the perceived utility of an app. App uptake via curated health app portals perceived as credible may mitigate concerns related to data protection and accuracy, but their implementation must better meet user needs and expectations.
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Comment cette classification a été obtenuedéplier
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 | 0,005 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,003 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| 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 ».