A groundbreaking research initiative led by Yale School of Medicine's Jeffrey Wickersham, PhD, aims to combat HIV transmission through an innovative mobile application targeting high-risk behaviors associated with chemsex practices. The National Institute of Drug Abuse has awarded an R01 grant to support this cutting-edge intervention study.
The Evolution of HIV Transmission in Southeast Asia
The HIV landscape in Malaysia has undergone a significant transformation since the first case was detected in 1986. What began primarily as an injection drug use-driven epidemic has shifted dramatically, with sexual transmission now accounting for 90% of new cases. More than half of these cases occur among gay and bisexual men or other men who have sex with men.
"When methadone became more accessible in Malaysia, those cases started to account for a smaller share of new diagnoses. And sexually-driven transmission of HIV exploded," explains Wickersham, associate professor of medicine at Yale School of Medicine.
Innovative Mobile Intervention: The JomCare App
The research team has developed JomCare, a mobile application designed to intervene in high-risk behaviors associated with chemsex - the use of drugs in sexual contexts. The app implements a sophisticated monitoring system that engages with users twice daily to assess:
- Substance cravings
- Intentions to engage in drug use
- Actual engagement in sexualized drug use activities
Participants will undergo a 90-day trial period, including objective measures through urine testing for stimulant drugs. The study employs a micro-randomized trial design to evaluate three different types of interventions.
Addressing a Critical Treatment Gap
The research addresses a significant void in current treatment options for stimulant drug use. "Sexualized drug use is hard to intervene on. Unfortunately, there are no medication-based therapies like those that are available for opioid dependence, such as buprenorphine or naltrexone," Wickersham notes.
Machine Learning Integration
The study incorporates advanced machine learning techniques, led by Dr. Premananda Indic from UT Tyler, to optimize intervention effectiveness. The technology will analyze patterns to determine:
- Optimal timing for interventions
- Most effective types of prompts
- Conditions that yield the greatest reduction in risk behaviors
Collaborative Research Team
The project brings together experts from multiple institutions:
- Roman Shrestha, PhD, MPH (University of Connecticut) - Co-principal investigator
- James Dziura, MPH, PhD (Yale School of Medicine) - Biostatistics expert
- Edward Boyer, MD, PhD (The Ohio State University) - Emergency medicine specialist
- Iskandar Azwa, MBChB, MRCP (Universiti Malaya) - Regional medical expert
The research represents a significant step forward in HIV prevention strategies, particularly in addressing the complex intersection of substance use and sexual behavior in high-risk populations.
