Methodological approaches in developing and implementing digital health interventions amongst underserved women.

Abstract

Background: Minority populations are utilizing mobile health applications more frequently to access health information. One group that may benefit from using mHealth technology is underserved women, specifically those on community supervision.

Objective: Discuss methodological approaches for navigating digital health strategies to address underserved women's health disparities.

Description of the innovative method: Using an intersectional lens, we identified strategies for conducting research using digital health technology and artificial intelligence amongst the underserved, particularly those with community supervision.

Description of its effectiveness: We explore (1) methodological approaches that combine traditional research methods with precision medicine, digital phenotyping, and ecological momentary assessment; (2) implications for artificial intelligence; and (3) ethical considerations with data collection, storage, and engagement.

Discussion: Researchers must address gendered differences related to health, social, and economic disparities concurrently with an unwavering focus on the protection of human subjects when addressing the unique needs of underserved women while utilizing digital health methodologies.

Public contribution: Women on community supervision in South Central Texas helped inform the design of JUN, the mHealth app we reported in the case exemplar. JUN is named after the Junonia shell, a native shell to South Texas, which means strength, power, and self-sufficiency, like the participants in our preliminary studies.