Recommendations for Designing a Digital Health Tool for Blindness Prevention Among High-Risk Diabetic Retinopathy Patients: Qualitative Focus Group Study of Adults With Diabetes
Bibliographic record
Abstract
Background: Diabetic retinopathy (DR) is a leading cause of preventable blindness among working-aged adults. Black, Latine, and low-income individuals are screened less for DR, diagnosed later, treated less often, and go blind more than White individuals. Objective: This study aimed to engage members to co-design a digital health tool that is accessible, user-friendly, and culturally relevant, through a community-led research approach,. Methods: Using a qualitative approach, we conducted 4 semistructured focus group interviews with 19 individuals from the Greater New Haven area, aged 18 years or older, and diagnosed with diabetes. We transcribed and coded the focus group interviews and categorized them into themes using affinity mapping. The specific aims were to complete a comprehensive needs assessmen for the development of a community-responsive digital health tool and to increase access to information about DR screening in high-risk populations. We transcribed the focus group interviews, used rapid qualitative analysis to generate themes, and completed affinity mapping to identify content and features for a digital health tool for preventing blindness from DR. Results: We interviewed 19 individuals (68% [13/19] female, 47% [9/19] Black, 26% [5/19]) Hispanic) in 4 focus groups. Over 80% (15/19) had access to smart devices, including smartphones (17/19, 89%), smartwatches (4/19, 21%), computers (14/19, 74%), and tablets (11/19, 58%). Many participants had access to multiple devices (17/19, 89%). Participants self-reported hemoglobin A1c (mean hemoglobin A1c 6.77, SD 1.93) and age (mean age 58.79, SD 19.54). Education levels among participants varied. Almost half of all the participants (9/19, 47%) completed some college, a little less than a quarter (4/19, 21%) achieved a high school diploma or general education development certificate, and a little less than a quarter (4/19, 21%) completed less than a high school equivalent of education. Household income levels across nearly all participants (14/19, 74%) were below US $50,000, but household size data were not collected. Participants reported extensive experience with diabetes or prediabetes (mean years with diabetes or prediabetes 17.06, SD 17.53). The themes obtained from coding focus group interviews included the mental toll of diabetes, peer support like accountability and local community events, education about diabetes management, barriers to DR screening like long wait times for appointments or cost of medications, and diet-related topics like how to find cost-effective healthy food. Conclusions: DR is one of the leading causes of blindness, and many treatments exist. Despite the existence of treatments, historically marginalized populations experience poor health outcomes, including blindness. Our community-based approach aids in the creation of a culturally responsive digital health tool.
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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 | 0.085 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.005 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| 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".