Insights Into the Use of a Digital Healthy Aging Coach (AGATHA) for Older Adults From Malaysia: App Engagement, Usability, and Impact Study
Notice bibliographique
Résumé
BACKGROUND: Digital inclusion is considered a pivotal social determinant of health, particularly for older adults who may face significant barriers to digital access due to physical, sensory, and social limitations. Avatar for Global Access to Technology for Healthy Aging (AGATHA) is a virtual healthy aging coach developed by the World Health Organization to address these challenges. Designed as a comprehensive virtual coach, AGATHA comprises a gamified platform that covers multiple health-related topics and modules aimed at fostering user engagement and promoting healthy aging. OBJECTIVE: The aim of this study was to explore the perception and user experience of Malaysian older adults in their interactions with the AGATHA app and its avatar. The focus of this study was to examine the engagement, usability, and educational impact of the app on health literacy and digital skills. METHODS: We performed a qualitative study among adults 60 years and older from suburban and rural communities across six states in Malaysia. Participants were purposefully recruited to ensure representation across various socioeconomic and cultural backgrounds. Each participant attended a 1-hour training session to familiarize themselves with the interface and functionalities of AGATHA. Subsequently, all participants were required to engage with the AGATHA app two to three times per week for up to 2 weeks. Upon completion of this trial phase, an in-depth interview session was conducted to gather detailed feedback on their experiences. RESULTS: Overall, the participants found AGATHA to be highly accessible and engaging. The content was reported to have a comprehensive structure and was delivered in an easily understandable and informative manner. Moreover, the participants found the app to be beneficial in enhancing their understanding pertaining to health-related issues in aging. Some key feedback gathered highlighted the need for increased interactive features that would allow for interaction with peers, better personalization of content tailored to the individual's health condition, and improvement in the user-experience design to accommodate older users' specific needs. Furthermore, enhancements in decision-support features within the app were suggested to better assist users in making health decisions. CONCLUSIONS: The prototype digital health coaching program AGATHA was well received as a user-friendly tool suitable for beginners, and was also perceived to be useful to enhance older adults' digital literacy and confidence. The findings of this study offer important insights for designing other digital health tools and interventions targeting older adults, highlighting the importance of a user-centered design and personalization to improve the adoption of digital health solutions among older adults. This study also serves as a useful starting point for further development and refinement of digital health programs aimed at fostering an inclusive, supportive digital environment for older adults.
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Prédiction machine sur la base complète
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Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,004 | 0,008 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,001 | 0,003 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 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 ».