Benefits of mHealth Co-design for African American and Hispanic Adults: Multi-Method Participatory Research for a Health Information App
Notice bibliographique
Résumé
BACKGROUND: Participatory research methodologies can provide insight into the use of mobile health (mHealth) apps, cultural preferences and needs, and health literacy issues for racial and ethnic groups, such as African Americans and Hispanics who experience health disparities. OBJECTIVE: This methodological paper aims to describe a 1-year multi-method participatory research process that directly engaged English-speaking African American and bilingual or Spanish-speaking Hispanic adults in designing a prevention-focused, personalized mHealth, information-seeking smartphone app. We report design team participants' experiences with the methods to show why our approach is valuable in producing apps that are more aligned with their needs. METHODS: Three design sessions were conducted to inform the iteration of a prevention-focused, personalized mHealth, information-seeking app. The research team led sessions with 2 community member design teams. Design team participants described their goals, motives, and interests regarding prevention information using different approaches, such as collage and card sorting (design session 1), interaction with the app prototype (design session 2), and rating of cultural appropriateness strategies (design session 3). RESULTS: Each design team had 5 to 6 participants: 2 to 3 male participants and 3 female participants aged between 30 and 76 years. Design team participants shared their likes and dislikes about the sessions and the overall experience of the design sessions. Both African American and Hispanic teams reported positive participation experience. The primary reasons included the opportunity for their views to be heard, collectively working together in the design process, having their apprehension about mHealth reduced, and an opportunity to increase their knowledge of how they could manage their health through mHealth. The feedback from each session informed the following design sessions and a community-engaged process. In addition, the specific findings for each design session informed the design of the app for both communities. CONCLUSIONS: This multi-method participatory research process revealed 4 key lessons learned and recommendations for future research in mHealth app design for African Americans and Hispanics. Lesson 1-community partnerships are key because they provide the chain of trust that helps African American and Hispanic participants feel comfortable participating in app research. Lesson 2-community-based participatory research principles continue to yield promising results to engage these populations in mHealth research. Lesson 3-interactive design sessions uncover participants' needs and development opportunities for mHealth tools. Lesson 4-multiple design sessions with different methods provide an in-depth understanding of participants' mHealth preferences and needs. Future developers should consider these methods and lessons to ensure health apps in the marketplace contribute to eliminating health disparities and achieving health equity.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
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 | 0,025 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,004 | 0,001 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,002 |
| 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 ».