# Piloting a Sequential Multiple-Assignment Randomization Trial to evaluate AllyQuest: an mHealth intervention for HIV-positive young MSM to optimize HIV medication adherence and care outcomes

> **NIH NIH R34** · UNIV OF NORTH CAROLINA CHAPEL HILL · 2020 · $193,955

## Abstract

ABSTRACT
In the United States (U.S.), only about a third of HIV-positive young men who have sex with men (YMSM)
achieve sustained viral suppression which is crucial for improving individual health outcomes and reducing HIV
transmission. Daily adherence to antiretroviral therapy (ART) poses significant challenges for YMSM and is
complicated by multiple factors including high levels of depression, substance use, unstable housing, and
stigma. Tailored, adaptive interventions are needed that will equip YMSM with the skills, resources, and
support networks to face individual, structural, and societal challenges to daily ART adherence. Smartphone-
based interventions offer an accessible, customizable, and adaptable platform to improve and sustain
adherence. Ally Quest (AQ) is a theory-informed HIV ART adherence and social support application (app)
created by a diverse team of behavioral scientists, clinicians, and app developers with input from HIV-positive
YMSM. In a small pilot study, higher levels of AQ app usage were associated with significant increases in HIV
knowledge and confidence in ability to reliably take ART. The current project will expand the features of AQ to
improve intervention engagement and test its feasibility, acceptability, and preliminary efficacy through an
innovative Sequential Multiple-Assignment Randomization Trial (SMART) design that evaluates an escalation
versus de-escalation approach to intervention intensity. The study will advance mobile Health (mHealth) and
medication adherence science via three aims. First, with the guidance of a Youth Advisory Board and HIV care
providers, an intensified adherence support feature (AQ+) will be added offering in-app text-based adherence
counseling. Second, the feasibility and acceptability of the SMART will be tested through a 6-month pilot study
among 60 HIV-positive YMSM age 15 to 24, recruited from U.S. sites participating in the new HIV Adolescent
Trials Network. Viral suppression and self-reported ART adherence will be measured at 3-months to determine
whether a participant is escalated to a more intensive intervention condition, maintained in their current
condition, or de-escalated to a lower intensity condition. Lastly, AQ paradata metrics of participant app usage
and participant characteristics will be used to compare the treatment strategies embedded in the SMART to
determine optimal intervention engagement approaches for maximizing app use and daily in-app reported ART
adherence. This study has high public health significance because YMSM constitute the majority of all new
U.S. HIV diagnoses and experience the lowest rates of VS. This application is highly responsive to RFA-MH-
18-605 for mHealth interventions for the HIV continuum of care and the Division of AIDS Research priority
focus on high-risk vulnerable populations and behavioral interventions to improve HIV treatment outcomes.
Our innovative trial design will inform allocation of care resources to achieve optimal ...

## Key facts

- **NIH application ID:** 9935976
- **Project number:** 5R34MH118058-03
- **Recipient organization:** UNIV OF NORTH CAROLINA CHAPEL HILL
- **Principal Investigator:** Kathryn E Muessig
- **Activity code:** R34 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $193,955
- **Award type:** 5
- **Project period:** 2018-07-20 → 2022-11-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9935976

## Citation

> US National Institutes of Health, RePORTER application 9935976, Piloting a Sequential Multiple-Assignment Randomization Trial to evaluate AllyQuest: an mHealth intervention for HIV-positive young MSM to optimize HIV medication adherence and care outcomes (5R34MH118058-03). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/9935976. Licensed CC0.

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