UCLA

Young sexual minority men (SMM) experience higher rates of intimate partner violence (IPV) compared to their heterosexual counterparts, and these experiences were associated with adverse health and mental health outcomes including HIV, substance use, and psychological distress. However, little is known about the association between IPV victimization and HIV prevention services such as pre-exposure prophylaxis (PrEP). This dissertation aimed to describe the prevalence of IPV victimization, perpetration, and bi-directional violence among a sample of cisgender young SMM who use substances. It examined the associations between different types of IPV victimization and PrEP utilization, explores the mediating effects of PrEP stigma and alcohol, tobacco, and other drug use on the relationship between IPV and PrEP utilization, and built and verified a machine learning model to predict self-reported IPV victimization using social media and mobile phone use data. The study found alarmingly high rates of IPV victimization, perpetration, and bi-directional violence among a sample of cisgender young SMM recruited from 2021 to 2023, particularly emotional IPV. The results indicated that experiencing emotional, monitoring, and controlling IPV victimization were associated with suboptimal PrEP utilization, such as lower likelihood of using PrEP for HIV prevention and suboptimal PrEP adherence. Additionally, the study found that PrEP stigma and nicotine use mediate the relationship between IPV victimization and PrEP use and adherence. Finally, social media data and mobile phone use activities can be used to predict self-reported IPV victimization through machine learning techniques when adequate data is available. These findings highlight the elevated vulnerabilities of cisgender young SMM to IPV and underscore the need to promote IPV screening and services across different settings. Future research is necessary to understand the longitudinal impact of IPV experiences on PrEP utilization and to explore the underlying mechanisms. There is potential to utilize machine learning, social media data, and mobile phone activities to identify young SMM who experience or are at risk of IPV, indicating opportunities for integrated eHealth and technology-based IPV services and interventions.