Supporting Health Behavior Among Persons Living With HIV Using Tech, MOST, and Behavioral Economics

Adherence, Medication, HIV Seropositivity

HIV, behavioral economics, adherence, HIV antiretroviral therapy, ART non-persistence, HIV non-suppression

The present study focuses on adult persons living with HIV in the New York City and Newark, New Jersey metropolitan areas who are not highly adherent to HIV medication and who do not evidence undetectable HIV viral load (the ultimate goal of HIV treatment). Those who have not decided whether they wish to take HIV medications are invited to enroll. The study uses technology, is grounded in principles of behavioral economics, and uses the multiphase optimization strategy (MOST) to examine the acceptability, feasibility, and evidence of efficacy of three intervention components. The components are intended to increase rates of HIV viral suppression in the sample. The three components are: prizes for viral suppression (fixed compensation \[$275\] or lottery prizes \[up to $500\]), text messages and quiz questions that generate points to earn prizes (to foster engagement), and counseling sessions grounded in the motivational interviewing approach to help participants articulate goals with respect to health and viral suppression, identify and resolve barriers to HIV medication use, and build motivation for viral suppression. Participants are assessed at baseline and then 5- and 8-months later.

The present study focuses on adult persons living with HIV who are not highly adherent to HIV medication and who do not evidence undetectable HIV viral load (the ultimate goal of HIV treatment). The study uses technology, is grounded in principles of behavioral economics, and uses the multiphase optimization strategy (MOST) to examine the acceptability, feasibility, and evidence of efficacy of three intervention components. The components are intended to increase rates of HIV viral suppression in the sample. All participants receive a core intervention comprised of referrals to care and case management. The three components are: compensation for viral suppression (fixed compensation or lottery-type prizes), weekly text messages and quiz questions (TMQQ) for 21 weeks that generate points to earn compensation (to foster engagement), and three counseling sessions grounded in the motivational interviewing (MI) approach to help participants articulate goals with respect to health and viral suppression, identify and resolve barriers to HIV medication use, and build motivation for viral suppression. We have previously tested variations of these components together as a packaged intervention and found high acceptability, feasibility, and evidence of efficacy. We now seek to understand this approach with more precision by testing the effects of individual components using MOST. The components will be tested in a factorial design with 8 intervention conditions. Consistent with the factorial design, each condition will comprise a unique combination of components. Each condition includes at least one component. Participants will be recruited by a proven hybrid strategy that includes peer recruitment, ads placed in a free newspaper, and direct recruitment from an NYU Recruitment Registry managed by Dr. Gwadz. Participants will be screened for eligibility and a total of 80 adult persons living with HIV and with detectable HIV viral load will be enrolled in the study and randomly assigned to an intervention condition. Participants will be assessed at baseline and 5- and 8-months post-baseline with a structured assessment battery. A subset will receive in-depth interviews. The primary outcome is viral suppression. This is a pilot study. It is not powered for efficacy but we will examine evidence of efficacy. All study activities can be carried out in a virtual format, and we can switch to in-person contact for screening, assessment, and some components when appropriate. In-person activities would be conducted at the New York University Silver field site in Manhattan. The study is funded through the Constance and Martin Silver Artificial Intelligence and Social Impact Fund at the New York University Silver School of Social Work.

United States