Perspectives From Underserved African Americans and Their Health Care Providers on the Development of a Diabetes Self-Management Smartphone App: Qualitative Exploratory Study

Abstract

Background Type 2 diabetes mellitus (T2DM) affects approximately 10% of the US population, disproportionately afflicting African Americans. Smartphone apps have emerged as promising tools to improve diabetes self-management, yet little is known about the use of this approach in low-income minority communities. Objective The goal of the study was to explore which features of an app were prioritized for people with T2DM in a low-income African American community. Methods Between February 2016 and May 2018, we conducted formative qualitative research with 78 participants to explore how a smartphone app could be used to improve diabetes self-management. Information was gathered on desired features, and app mock-ups were presented to receive comments and suggestions of improvements from smartphone users with prediabetes and T2DM, their friends and family members, and health care providers; data were collected from six interactive forums, one focus group, and 15 in-depth interviews. We carried out thematic data analysis using an inductive approach. Results All three types of participants reported that difficulty with accessing health care was a main problem and suggested that an app could help address this. Participants also indicated that an app could provide information for diabetes education and self-management. Other suggestions included that the app should allow people with T2DM to log and track diabetes care–related behaviors and receive feedback on their progress in a way that would increase engagement in self-management among persons with T2DM. Conclusions We identified educational and tracking smartphone features that can guide development of diabetes self-management apps for a low-income African American population. Considering those features in combination gives rise to opportunities for more advanced support, such as determining self-management recommendations based on data in users’ logs.