A Health App Platform Providing a Budget to Purchase Preselected Apps as an Innovative Way to Support Public Health: Qualitative Study With End Users and Other Stakeholders
Notice bibliographique
Résumé
BACKGROUND: eHealth has the potential to improve health outcomes. However, this potential is largely untapped. Individuals face an overload of apps and have difficulties choosing suitable apps for themselves. In the FitKnip experiment, individuals were given access to a health app platform, where they could purchase reliable preselected health apps with a personal budget of €100 (US $107.35). By conducting a prospective study, we aimed to scientifically evaluate the FitKnip experiment as an innovative way to improve population health. OBJECTIVE: The aim of the experiment was to scientifically evaluate the FitKnip experiment as an innovative way to improve population health. More specifically, we conducted an in-depth qualitative evaluation of the concept and acceptability of FitKnip, its perceived impact on health empowerment, as well as the roles of stakeholders for the future implementation of a health app platform through focus group interviews. METHODS: This study followed a phenomenological research design and included 7 focus group interviews with end users and 1 with stakeholders, held between July and December 2020. End users were recruited through various institutions in the Netherlands, for example, insurance companies and local governments. All focus groups were semistructured using interview guides and were held via videoconferencing due to the COVID-19 pandemic measures. Each participant received access to a health app platform where they were enabled to purchase reliable, preselected health apps with a budget of €100 (US $107.35). The budget was valid for the entire research period. The health app platform offered 38 apps. A third party, a health care coalition, selected the apps to be included in FitKnip. The analyses were conducted according to the principles of the Framework Method. RESULTS: A priori formulated themes were concept, acceptability, health empowerment, and outcomes, and the roles of stakeholders for the future implementation of a health app platform. Both end users (n=31) and stakeholders (n=5) were enthusiastic about the concept of a health app platform. End users indicated missing apps regarding physical health and lifestyle and needing more guidance toward suitable apps. End users saw health empowerment as a precondition to using a health app platform and achieving health outcomes depending on the purchased mobile apps. End users and stakeholders identified potential providers and financing parties of FitKnip. Stakeholders recommended the establishment of a reputable national or international quality guidelines or certification for health and wellbeing apps, that can demonstrate the quality and reliability of mobile health applications. CONCLUSIONS: This study showed the need for a personalized and flexible platform. Next to this, a deeper understanding of the roles of stakeholders in such initiatives is needed especially on financing and reimbursement of health promotion and digital health services. A personalized, flexible health app platform is a promising initiative to support individuals in their health.
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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 | 0,019 | 0,022 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,008 | 0,006 |
| Communication savante | 0,003 | 0,005 |
| Science ouverte | 0,002 | 0,006 |
| Intégrité de la recherche | 0,002 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,001 |
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 ».