Co-Designing Technology to Reduce Health Disparities and Address New Norms Post–COVID-19: Proposal for a Mixed Methods Community-Based Participatory Research Approach
Notice bibliographique
Résumé
BACKGROUND: Everyday life has changed since the COVID-19 pandemic. Existing health disparities among underserved communities have been exacerbated. Latino and Native Hawaiian and Pacific Islander (NHPI) populations disproportionately experience health disparities, even when compared to other minority populations. Both populations have heart disease, cancer, and diabetes as the leading causes of death, and both have high rates of obesity. As we recover from the pandemic, we must consider the intersection of continued health disparities, new social norms and attitudes, and new patterns of health behavior. OBJECTIVE: The overarching goal of this project is to reduce health disparities among Latino and NHPI populations, considering new health behavior patterns, social norms, and increased technology use. The research project-specific aims are to (1) conduct key informant interviews and focus groups among Latino and NHPI populations; (2) develop and implement a community health and health behavior survey; and (3) co-design, develop, and test new technology that is meaningful and responsive to community needs and preferences. METHODS: Using community-based participatory research (CBPR) and mixed methods approaches, the interdisciplinary research team will develop new technology based on community insights (key informant interviews, focus groups, and a community health survey). With our community liaisons, we will recruit adult (18+ years old) Latino and NHPI community members from the northern region in San Diego County (ie, Oceanside, San Marcos, and Escondido), largely from culturally related groups and organizations, such as dance schools (hālaus) and churches. Qualitative data will be analyzed using directed content analysis, and quantitative data will be analyzed using descriptive and multivariate statistics. The main outcomes include the identification of community health needs, culturally appropriate interventions, desired modality of intervention strategies, and acceptability of the technology. We expect the new technology to be focused on mobile health (mHealth) smartphone apps. Components will likely include strategies to improve obesity-related health behaviors and mental health. RESULTS: This study received funding from the National Institute of General Medical Sciences in April 2022 as part of the Support for Research Excellence (SuRE) Program (R16). Key informant interviews and focus groups were completed in July 2023. Community health surveys were completed in August 2024. The development of the beta mHealth app began in September 2024 in partnership with California State University San Marcos (CSUSM) computer science students. Beta testing and evaluation will be completed by December 2025. The qualitative findings, identifying themes for a new mHealth app, were published in June 2025. CONCLUSIONS: A major strength of this study is that it works with the communities of intended impact to directly inform new innovations to promote health behaviors. This study includes unique partnerships and an interdisciplinary team of researchers, students, community members, and consultants/collaborators to inform practices that can impact health disparities among Latino and NHPI populations with technology and strategies that are innovative and effective. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/73927.
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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 | 0,344 | 0,204 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,002 |
| Méta-épidémiologie (sens large) | 0,002 | 0,004 |
| Bibliométrie | 0,004 | 0,003 |
| Études des sciences et des technologies | 0,012 | 0,024 |
| Communication savante | 0,013 | 0,017 |
| Science ouverte | 0,009 | 0,027 |
| Intégrité de la recherche | 0,015 | 0,014 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 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 ».