Perspectives from Underserved African Americans and their Healthcare Providers on the Development of a Diabetes Self-Management Smartphone Application: Exploratory Study (Preprint)
BACKGROUND Type 2 diabetes mellitus (T2DM) affects ~10% of the US population, disproportionately affecting African Americans. Smartphone applications (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 mockups were presented to receive comments and suggestions of improvements from smartphone users with prediabetes/T2DM, their friends and family members, and healthcare providers (in 6 interactive forums, 1 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 difficulties with access to healthcare 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 a person with T2DM’s engagement in self-management. 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 user's logs. CLINICALTRIAL