Digital Phenotyping & Deep Learning: Substance Use Impact on PrEP Adherence among Black Sexual and Gender Minorities

National Institute On Drug Abuse

Investigators

Linked publications, trials & patents

Abstract

The United States has grappled with multiple epidemics, notably the persistent HIV epidemic, predominantly affecting Black sexual minority men and gender minorities. These groups register elevated HIV incidence and prevalence. Parallelly, as several states are gravitating towards cannabis legalization, we witness a surge in its usage. Black sexual and gender minorities consuming cannabis have shown heightened levels of high-risk behaviors, including binge drinking, unprotected sex under the influence, and intercourse with partners potentially HIV-positive. Intriguingly, modern geosocial networking and dating applications are incorporating features allowing users to match specifically with cannabis users. This blend of sexualized cannabis use may be amplifying HIV transmission due to heightened engagement in condomless sex and an increased number of sexual partners. With HIV pre-exposure prophylaxis (PrEP) showcasing near 95% efficacy in adherent individuals, it emerges as a potential combatant against this epidemic. Despite having two PrEP formulations availabledaily oral and long-acting injectablethe awareness, usage, and adherence amongst the target demographic remains alarmingly low. Preliminary data suggests that adherence among Black sexual minority men and gender minorities is contingent on their substance use. To address these complex intersections, we use a holistic examination of the relationships among HIV transmission risk, cannabis consumption, and PrEP outcomes. This would harness innovative digital phenotyping, social media language and online behaviors, event-level measures, and critical biomarker data. Given the ubiquity of smartphones (used by over 95% of the target group), these devices were chosen for continuous monitoring and data collection. An application, developed by our lab and previously validated, was deployed to accumulate passive mobile data and administer timely ecological momentary assessments. Employing cutting-edge deep learning techniques, we will synthesize this data into dynamic digital phenotypes, capable of allotting daily risk scores to participants. The overarching objective is to ascertain correlations between cannabis consumption and two pivotal outcomes: risky sexual practices and diminished PrEP adherence. To accomplish the goals listed above, we will follow Black sexual minorities and gender minorities (who have sex with men) who use cannabis longitudinally to generate a dataset that will be used to train the algorithm behind the digital phenotypes. This includes examining online communication regarding sexualized cannabis use and/or PrEP intentions and attitudes. This project will address the following concepts: Advance Computational Algorithms to Identify Cannabis Use and Cravings We will use advanced computational algorithms to predict cannabis use, including sexualized cannabis use, and cannabis cravings. Our first goal will be to carry out a comparative analysis of machine learning algorithms to determine the optimal classifier we can use to provide accurate classifications. Our second goal is to implement a feature selection algorithm that will extract the most relevant features (from clinical, EMA, and digital phenotype data) that provide the best classification of cannabis cravings and use, especially in the context of sexual behavior. Artificial Intelligence to Identify PrEP Non-Adherence and Risky Sexual Behaviors High-risk sexual behaviors and non-adherence to PrEP occur for many reasonsincluding cannabis use. The best approach to tackling these problems will involve big data that is context-aware and individually tailored. Digital phenotype data contains a rich set of information that includes demographics, mood, sexual behavior, social support, dating behavior, substance use, and location. We will also have access to clinical health records. In combination, we will be able to develop and validate a PrEP adherence and HIV risk artificial intelligence (AI) tool to identify participants who are at risk for PrEP non-adherence and risky sexual encounters. This AI tool will be programmed to deliver tailored messaging when elevations in risk are detected. In summary, this project delves into the intricate dynamics of cannabis use, its socio-technological implications, and its intersection with HIV risk and PrEP adherence. The methodologies and results would be invaluable for key stakeholders striving for comprehensive solutions to this multifaceted challenge.