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

Allison D Crawford1, Rocky Slavin2, Maryam Tabar2

  • 1School of Nursing, The University of Texas Health at San Antonio, San Antonio, Texas, USA.

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Summary
This summary is machine-generated.

Mobile health (mHealth) strategies can address health disparities for underserved women, particularly those on community supervision. Research must integrate digital health, AI, and ethical considerations to protect participants.

Keywords:
artificial intelligencecriminal justice systemdigital healthgendered differenceshealth disparitiesmHealthmethodologysocial justicewomen's health

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Area of Science:

  • Digital Health
  • Health Disparities Research
  • Artificial Intelligence in Healthcare

Background:

  • Minority populations increasingly use mobile health (mHealth) applications for health information access.
  • Underserved women, especially those on community supervision, represent a group that could significantly benefit from mHealth interventions.
  • Addressing health disparities in this population requires innovative digital health strategies.

Purpose of the Study:

  • To discuss methodological approaches for digital health strategies aimed at reducing health disparities among underserved women.
  • To explore the integration of precision medicine, digital phenotyping, and ecological momentary assessment in mHealth research.
  • To examine the implications of artificial intelligence (AI) and ethical considerations in data management for this population.

Main Methods:

  • Utilized an intersectional lens to identify research strategies for digital health and AI among underserved populations.
  • Combined traditional research methods with advanced techniques like precision medicine and digital phenotyping.
  • Explored ecological momentary assessment for real-time data collection in mHealth studies.

Main Results:

  • Identified key strategies for conducting research with underserved women on community supervision using digital health technologies.
  • Highlighted the importance of integrating AI and ethical frameworks for data collection, storage, and participant engagement.
  • Developed and informed the design of JUN, an mHealth app, based on input from women in South Central Texas.

Conclusions:

  • Researchers must concurrently address gendered health, social, and economic disparities.
  • Protecting human subjects is paramount when implementing digital health methodologies for underserved women.
  • An unwavering focus on ethical considerations and participant well-being is crucial for successful mHealth interventions.