# An Adaptive Strategy for Preventing and Treating Lapses of Retention in Adult HIV Care II (ADAPT-R II)

> **NIH NIH R34** · WASHINGTON UNIVERSITY · 2020 · $251,025

## Abstract

Abstract
This R34 application is best understood in the context of both a NIH-funded sequential multiple
assignment randomized trial (SMART) “Adaptive Strategies to Prevent and Treat Lapses of
Retention (ADAPT-1)” nearing completion and a future trial (ADAPT-3) motivated by observations
from the ADAPT-1. Retention in HIV treatment over long periods of time represents an archetypal
complex public health problem and requires innovative solutions. The diversity of intensities and
types of barriers to engagement mean that no single intervention is needed by all nor will work for
all in need. For example, counseling could help a patient experiencing stigma, but will not help
an individual who wants to come but cannot afford transportation. To respond to this conundrum,
we carried out a SMART (ADAPT-1) to test a family of adaptive retention strategies. By
maintaining lower intensity interventions in those doing well, adaptive strategies optimize
efficiency, while escalating in those not doing well enhances effectiveness. In ADAPT-1, we
initially randomized patients to one of three lower intensity interventions (standard of care (SOC),
SMS messages and a conditional cash transfer). Only those who fail to be consistently retained
are re-randomized to one of three more intensive interventions (SOC outreach, SMS message
with a conditional cash transfer, or a navigator). Emerging ADAPT-1 results (in forthcoming
publications) confirm our original hypothesis that pegging the retention intervention to patient
behavior improves outcomes, the study also revealed additional opportunities to extend a
“precision public health” paradigm. Specifically, we observed that different patients (based on
sociodemographic, clinical and laboratory characteristics) respond differentially to different
adaptive retention strategies. This observation begs a further hypothesis: use of predictive
analytics (optimized with cutting-edge machine learning techniques) to distribute each
intervention (e.g., SOC, cash transfer, SMS) to those patients most likely to respond to that
intervention can achieve further gains in effectiveness and efficiency over any single sequenced
retention strategy, even if strategy is itself already adaptive. We plan a future R01 application to
test a machine learning based distribution of retention interventions as compared to best single
sequential adaptive interventions (from ADAPT-1). To prepare for the novel trial, we propose this
R34, to (1) develop and test the information technology basis for delivering on-demand predictions
to health care workers in the field, (2) refine the statistical foundations of machine learning ability
to predict through simulations and (3) assess the fit of machine learning based recommendations
in the organizational, policy and ethical context of health systems in Kenya.

## Key facts

- **NIH application ID:** 10017320
- **Project number:** 5R34MH121224-02
- **Recipient organization:** WASHINGTON UNIVERSITY
- **Principal Investigator:** Elvin H. Geng
- **Activity code:** R34 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $251,025
- **Award type:** 5
- **Project period:** 2019-09-12 → 2022-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10017320

## Citation

> US National Institutes of Health, RePORTER application 10017320, An Adaptive Strategy for Preventing and Treating Lapses of Retention in Adult HIV Care II (ADAPT-R II) (5R34MH121224-02). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10017320. Licensed CC0.

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