Benefits of mHealth Co-design for African American and Hispanic Adults: Multi-Method Participatory Research for a Health Information App
Bibliographic record
Abstract
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.
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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 | 0.100 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".