Co-Designing Technology to Reduce Health Disparities and Address New Norms Post–COVID-19: Proposal for a Mixed Methods Community-Based Participatory Research Approach
Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.344 | 0.204 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.012 | 0.024 |
| Scholarly communication | 0.013 | 0.017 |
| Open science | 0.009 | 0.027 |
| Research integrity | 0.015 | 0.014 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".