Alberta Rating Index for Apps (ARIA): An Index to Rate the Quality of Mobile Health Applications
Notice bibliographique
Résumé
Introduction: The number of mobile health applications (m-health apps) available to the public through online application (app) stores is rapidly increasing. In addition, the general public’s interest to use m-health apps as an adjunct to conventional health care services is increasing. However, because of the inadequate quality control mechanisms on app stores and lack of stringent regulations for m-health apps, there is a risk of apps to have inferior quality or even be harmful. In the absence of formal guidelines, users may choose apps based on unreliable information such as reviews and ratings on the app download page, or the number of downloads for an app. Rating scales to evaluate apps exist for m-health app users, health care providers, and researchers. Most are long and complicated scales that are not appropriate for the general public. None have been developed using a theoretical framework. The purpose of this thesis was to develop the Alberta Rating Index for Apps (ARIA) based on the theories of technology acceptance, frameworks of app evaluation, and lived experience of users of mobile health applications. Methods: A multi strategy study was conducted in three phases. In phase one, the investigator conducted six focus groups with users of mobile health applications including older adults, adults with a mental health condition, health care providers, and app developers to identify quality criteria that were important to users and developers of m-health apps. Next, an item pool was generated based on a review of app rating scales. In phase two, the content of the item pool was validated using an online survey and a calculation of the content validation index for each item. Also in phase two, a sample of participants from the online survey participated in a focus group to shortlist the item pool and develop the first draft of ARIA. In phase three, ARIA was piloted by nine potential users of m-health apps, including older adults and adults with a mental health condition. Also, in phase three, the inter-rater reliability and criterion-related validity of ARIA were examined using 16 participants consisting of 4 older adults, 4 adults with a mental health condition, and 4 health care providers. The scores of ARIA were correlated with the scores of users’ version of Mobile Apps Rating Scale (U-MARS) to examine the criterion-related validity. Results: Nine quality criteria measure the quality of m-health apps: the purpose of the app, trustworthiness, privacy, security, affordability, ease of use, functionality, appropriateness to target users, and usefulness and satisfaction. Generalizability coefficients (G-coefficients) were calculated using ARIA total scores as the measure of reliability. High G-coefficients for health care providers (G = 0.98), older adults (G = 0.83) and adults with mental health conditions (G = 0.88) indicated that users could reliably rate the quality of m-health apps based on total scores of ARIA. The positive but low correlation of ARIA’s total scores with U-MARS indicated that both assessment tools measure the quality of m-health apps. However, the quality criteria were different between the ARIA and U-MARS. Participants in phase three reported that ARIA was easier and more convenient compared to U-MARS. Conclusion: ARIA is the first mobile application rating index developed based on theories of technology acceptance and frameworks of app evaluation. ARIA was designed to be used by health care providers and the general public, including older adults, adults with mental health conditions, and family caregivers. The content of ARIA was validated through a rigorous process. Moreover, three types of app users tested the inter-rater reliability of ARIA. Users perceived ARIA to be easier and more convenient compared to U-MARS. Clinical implications of ARIA are to help patients, family caregivers, and healthcare providers rate the quality of mobile health applications and identify the ones that are acceptable. Health informatics researchers may use ARIA to develop health apps that are useful and acceptable to end users, especially older adults.
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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 | 0,001 | 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 | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 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 ».