Using Smartphone Survey Data and Machine Learning to Identify Situational and Contextual Risk Factors for HIV Risk Behavior Among Men Who Have Sex with Men Who Are Not on PrEP.

ABSTRACT

"Just-in-time" interventions (JITs) delivered via smartphones have considerable potential for reducing HIV risk behavior by providing pivotal support at key times prior to sex. However, these programs depend on a thorough understanding of when risk behavior is likely to occur to inform the timing of JITs. It is also critical to understand the most important momentary risk factors that may precede HIV risk behavior, so that interventions can be designed to address them. Applying machine learning (ML) methods to ecological momentary assessment data on HIV risk behaviors can help answer both questions. Eighty HIV-negative men who have sex with men (MSM) who were not on PrEP completed a daily diary survey each morning and an experience sampling survey up to six times per day via a smartphone application for 30 days. Random forest models achieved the highest area under the curve (AUC) values for classifying high-risk condomless anal sex (CAS). These models achieved 80% specificity at a sensitivity value of 74%. Unsurprisingly, the most important contextual risk factors that aided in classification were participants' plans and intentions for sex, sexual arousal, and positive affective states. Findings suggest that survey data collected throughout the day can be used to correctly classify about three of every four high-risk CAS events, while incorrectly classifying one of every five non-CAS days as involving high-risk CAS. A unique set of risk factors also often emerge prior to high-risk CAS events that may be useful targets for JITs.