Quality of Mobile Apps for Care Partners of People With Alzheimer Disease and Related Dementias: Mobile App Rating Scale Evaluation
Notice bibliographique
Résumé
BACKGROUND: Over 11 million care partners in the United States who provide care to people living with Alzheimer disease and related dementias (ADRD) cite persistent and pervasive unmet needs related to their caregiving role. The proliferation of mobile apps for care partners has the potential to meet care partners' needs, but the quality of apps is unknown. OBJECTIVE: This study aims to evaluate the quality of publicly available apps for care partners of people living with ADRD and identify design features of low- and high-quality apps to guide future research and user-centered app development. METHODS: We searched the US Apple App and Google Play stores with the criteria that included apps needed to be available in the US Google Play or Apple App stores, accessible to users out of the box, and primarily intended for use by an informal (family or friend) care partner of a person living with ADRD. We classified and tabulated app functionalities. The included apps were then evaluated using the Mobile App Rating Scale (MARS) using 23 items across 5 dimensions: engagement, functionality, aesthetics, information, and subjective quality. We computed descriptive statistics for each rating. To identify recommendations for future research and app development, we categorized rater comments on score-driving factors for each MARS rating item and what the app could have done to improve the item score. RESULTS: We evaluated 17 apps. We found that, on average, apps are of minimally acceptable quality. Functionalities supported by apps included education (12/17, 71%), interactive training (3/17, 18%), documentation (3/17, 18%), tracking symptoms (2/17, 12%), care partner community (3/17, 18%), interaction with clinical experts (1/17, 6%), care coordination (2/17, 12%), and activities for the person living with ADRD (2/17, 12%). Of the 17 apps, 8 (47%) had only 1 feature, 6 (35%) had 2 features, and 3 (18%) had 3 features. The MARS quality mean score across apps was 3.08 (SD 0.83) on the 5-point rating scale (1=inadequate to 5=excellent), with apps scoring highest on average on functionality (mean 3.37, SD 0.99) and aesthetics (mean 3.24, SD 0.92) and lowest on average on information (mean 2.95, SD 0.95) and engagement (mean 2.76, SD 0.89). The MARS subjective quality mean score across apps was 2.26 (SD 1.02). CONCLUSIONS: We identified apps whose mean scores were more than 1 point below minimally acceptable quality, whereas some were more than 1 point above. Many apps had broken features and were rated as below acceptable for engagement and information. Minimally acceptable quality is likely to be insufficient to meet care partner needs. Future research should establish minimum quality standards across dimensions for care partner mobile apps. Design features of high-quality apps identified in this study can provide the foundation for benchmarking these standards.
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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 | 0,004 | 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 | 0,002 | 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 ».