Specific Aims: Developing effective interventions to reduce disparities in treatment outcomes among populations disproportionately affected by HIV is a priority for NIH-funded HIV research in 2019. This study will explore the effects and uptake of an artificially intelligent, socially supportive chatbot ? a computer that texts with users via mobile phone ? among young women and men who have sex with men (YMSM) living with HIV in South Africa. Specifically, the study will (1) develop an online scale to measure social support from a chatbot among young adults; (2) evaluate associations of social support from a chatbot with changes in depressive symptoms and adherence to antiretroviral therapy (ART) among young women and YMSM in South Africa; and (3) describe barriers and facilitators to uptake of a chatbot among young women and YMSM in South Africa. Significance: Young South Africans between the ages of 15 to 24 are at high risk of contracting and dying from HIV. Though ART extends life and prevents transmission, many young adults have poor adherence. Research in high-income countries suggest that automated two-way messaging with chatbots improves adherence to health behaviors. Chatbots may also address depressive symptoms and lack of social support, which are consistently identified barriers to adherence. Chatbots are rarely used to improve HIV care in low- and middle-income countries. If this study?s aims are achieved, then (1) future researchers will be able to more accurately measure social support from chatbots; (2) chatbots could potentially be used to promote adherence to medications and increase access to mental health support; and (3) future design and implementation of chatbots to improve HIV care will be optimized. This study?s long-term objective is to generate evidence for an effective, scalable intervention that engages hard-to-reach populations living with HIV. Approach:
Aim 1 will develop a scale to measure social support from a chatbot using principal components and exploratory factor analyses applied to data gathered from 1,200 young adults worldwide who use Replika, a freely available mobile application (app).
Aims 2 and 3 will invite 160 young women and YMSM living with HIV in Cape Town, South Africa to use the Replika app for four weeks in a pre-post study design.
Aim 2 will employ an analysis of covariance using generalized multivariable linear regression models to assess the relationship between feelings of social support from the Replika app and changes in depressive symptoms and ART adherence.
Aim 3 will leverage surveys (quantitative) and interviews (qualitative) in a mixed methods study to characterize differences between users with high- and low-frequency engagement with the app. Fellowship Information: This study is the dissertation for Ms. Brooke Jarrett, a PhD student in the Department of Epidemiology at Johns Hopkins University. Her training will consist of research methods and science communication toward her career goal of becoming a researcher of state-of-the-art mobile technologies to improve the physical and mental health of young people affected by health disparities.

Public Health Relevance

Though medications for human immunodeficiency virus (HIV) can greatly increase life expectancy and prevent new HIV infections, many young adults living with HIV do not adhere to their prescribed daily regimen of medications. Artificially intelligent chatbots ? or a computer that texts back-and-forth with users ? may be able to promote adherence to health behaviors. This study will explore whether and how chatbots can provide social support, alleviate depressive symptoms, and improve medication adherence among young women and men who have sex with men living with HIV in South Africa.

National Institute of Health (NIH)
National Institute of Mental Health (NIMH)
Predoctoral Individual National Research Service Award (F31)
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Special Emphasis Panel (ZRG1)
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Allison, Susannah
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Johns Hopkins University
Public Health & Prev Medicine
Schools of Public Health
United States
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