# Hybrid Type-1 Effectiveness-Implementation Trial of Motivation Matters! A Theory-Based mHealth Intervention to Support Early Antiretroviral Adherence in HIV+ African Women Who Engage in Sex Work

> **NIH NIH R01** · UNIVERSITY OF WASHINGTON · 2024 · $665,326

## Abstract

The United Nations Joint Programme on HIV/AIDS describes current global HIV data as frightening. In the
landmark year 2020, all global targets for HIV treatment and prevention were missed. During the past two years,
multiple overlapping crises have made the situation even worse, with a reversal of previous progress in many
areas of HIV treatment and prevention. Key populations have been disproportionately affected. Compared to
other groups, women who engage in sex work tend to have lower ART uptake, poorer adherence, and worse
treatment outcomes. They are frequently stigmatized, difficult to reach, and often do not benefit from treatment
and prevention efforts targeted to the general population. Despite several clinical trials of interventions to improve
the low rate of reaching an undetectable viral load (VL) in women with HIV who engage in sex work, none has
demonstrated efficacy, underscoring the need for evidence-based interventions (EBIs) to support antiretroviral
therapy (ART) adherence in this key population. To address this important HIV treatment and prevention gap,
we used an iterative development process in collaboration with women who engage in sex work to create
Motivation Matters! (MM!), an interactive mHealth intervention grounded in the Theory of Information, Motivation,
and Behavior. We generated preliminary data on efficacy and participant-level feasibility and acceptability of MM!
in a small randomized controlled trial (RCT). In the population of women engaged in sex work who were viremic
at baseline, undetectable VL at month six was achieved in 74.3% (26/35) of intervention and 46.2% (12/26) of
control participants (relative risk [RR] 1.61, 95%CI 1.02-2.55). These promising preliminary results informed this
proposal for a hybrid type 1 effectiveness-implementation trial to test the effectiveness of MM! for achieving
undetectable VL in women with HIV who engage in sex work. In parallel, we will evaluate key implementation,
service, and client outcomes including intervention fidelity, feasibility, acceptability, appropriateness, cost, safety,
patient-centeredness, engagement, and satisfaction to guide scale-up of MM!. Our aims are: 1) To conduct a
RCT to compare the safety and effectiveness of Motivation Matters! vs. standard of care (SOC, control) for
achieving undetectable viral load in women with HIV who engage in sex work and are initiating ART, 2) To
conduct a mixed-methods study, organized within the framework of the Implementation Research Logic Model
(IRLM), to examine patient-centeredness (service outcome) and key implementation outcomes including fidelity,
feasibility, acceptability, appropriateness, and cost of MM!, and 3) To conduct a mixed-methods study, organized
within the IRLM framework, to evaluate participants' satisfaction, engagement, and treatment related knowledge,
motivation, and behavior. A definitive trial demonstrating the effectiveness of MM! would shift the clinical practice
paradigm for supporting AR...

## Key facts

- **NIH application ID:** 10849520
- **Project number:** 1R01MH132536-01A1
- **Recipient organization:** UNIVERSITY OF WASHINGTON
- **Principal Investigator:** Joshua Kimani
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $665,326
- **Award type:** 1
- **Project period:** 2024-09-09 → 2028-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10849520

## Citation

> US National Institutes of Health, RePORTER application 10849520, Hybrid Type-1 Effectiveness-Implementation Trial of Motivation Matters! A Theory-Based mHealth Intervention to Support Early Antiretroviral Adherence in HIV+ African Women Who Engage in Sex Work (1R01MH132536-01A1). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/10849520. Licensed CC0.

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