Accepted for/Published in: JMIR Formative Research
Date Submitted: Feb 12, 2020
Date Accepted: Dec 17, 2020
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Perspectives from Underserved African Americans and their Healthcare Providers on the Development of a Diabetes Self-Management Smartphone Application: Exploratory Study
ABSTRACT
Background:
Type 2 diabetes mellitus (T2DM) affects ~10% of the US population, disproportionately affecting African Americans. Smartphone applications (apps) have emerged as a promising tool to improve diabetes self-management, yet little is known about the use of this approach in low-income communities of color.
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. Data were gathered directly from potential prediabetic/T2DM end-users, their friends and family members, and health care providers (7 discussion-group forums, and 15 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 the patient's engagement in diabetes self-management.
Conclusions:
We identified educational and tracking smartphone features that can guide development of diabetes self-management apps for this population. Considering those features in combination gives rise to opportunities for more advanced support, such as using artificial intelligence to make self-management recommendations based on data in user's logs.
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Copyright
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