# Developing an artificial intelligence-based mHealth intervention to increase HIV testing in Malaysia

> **NIH NIH R33** · YALE UNIVERSITY · 2023 · $311,997

## Abstract

Project Summary
 HIV testing jumpstarts entry into the HIV prevention and treatment cascade. HIV testing levels, however, are
especially low in men who have sex with men (MSM), who increasingly contribute to heightened HIV transmission
in the presence of high levels of stigma and discrimination. For high risk MSM, new guidelines recommend
frequent HIV testing, ranging from every 3 to 6 months. Yet, HIV testing in MSM often occurs less frequently due
to individual (e.g., heightened concerns about risk disclosure), clinic (e.g., confidentiality breaches, and
discrimination from healthcare providers) and policy (criminalization of same-sex sexual behaviors) barriers. HIV
prevalence in MSM in Malaysia has soared to 21.6% nationally, exceeding 40.9% in Kuala Lumpur. While
surveillance surveys of MSM in Malaysia who meet criteria for PrEP suggest that ever tested is 70.3%, past-
year tested is 40.9%, and only 9.5% were tested more than 1 time per year, despite extraordinary levels of self-
reported risk. Once tested, however, MSM with HIV in Malaysia are likely to be treated with ART and achieve
viral suppression, making HIV testing a central focus for HIV prevention and treatment.
 Innovative strategies that motivate and provide guidance for testing among MSM in Malaysia are therefore
urgently needed. Intervening using Information-Motivation-Behavioral Skills (IBM) model is ideally suited to
overcome barriers to recommended HIV testing in MSM. Moreover, in settings like Malaysia where the HIV
epidemic has transitioned from primarily concentrated in PWID to a volatile epidemic in MSM, theory-guided
behavioral change strategies that inform, motivate and provide pragmatic skills to more fully engage in
recommended HIV testing are poised to accelerate the HIV prevention and care continuum. Given that there are
many individual, clinic and policy barriers to HIV testing, mobile health (mHealth) interventions that reduce “in
person” contact and offer a menu of behavioral skills is ideally suited to increase access to MSM in highly
stigmatized settings and promote recommended HIV testing. Recent studies in the U.S., China, South Africa,
and Peru show that mHealth interventions using smartphones and apps have the potential to increase HIV testing
while maintaining MSM’s confidentiality. Such mHealth interventions are feasible and acceptable among MSM,
including in Malaysia where most MSM find sexual partners using social-networking apps with similar interfaces
and functionalities to the proposed intervention. Current mHealth strategies, however, are limited by their lack of
automation and need for high-intensity and sustained human inputs, which restricts their scale-up. Artificial
intelligence (AI) using machine learning (ML) may overcome such limitations, but has yet to be applied to
mHealth-based HIV testing algorithms. We therefore aim to develop and pilot test an AI-chatbot (R21 phase).
Findings from the R21 phase will inform a Type 1 Hybrid Implementatio...

## Key facts

- **NIH application ID:** 10669814
- **Project number:** 5R33TW011663-04
- **Recipient organization:** YALE UNIVERSITY
- **Principal Investigator:** FREDERICK LEWIS ALTICE
- **Activity code:** R33 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2023
- **Award amount:** $311,997
- **Award type:** 5
- **Project period:** 2020-09-11 → 2025-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10669814

## Citation

> US National Institutes of Health, RePORTER application 10669814, Developing an artificial intelligence-based mHealth intervention to increase HIV testing in Malaysia (5R33TW011663-04). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10669814. Licensed CC0.

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