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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

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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.

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Acknowledgements

This manuscript was supported by P01AA019072 (to PM) and L30AA023336 (to TW) from the National Institute on Alcohol Abuse and Alcoholism.

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Authors and Affiliations

  1. Department of Behavioral and Social Sciences, Center for Alcohol and Addictions Studies, Brown University School of Public Health, 121 South Main Street, Providence, RI, 02912, USA

    Tyler B. Wray, Daniel J. Carr & Peter M. Monti

  2. Department of Biostatistics, Brown University School of Public Health, Providence, RI, 02906, USA

    Xi Luo & Jun Ke

  3. Department of Biostatistics and Data Science, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, 77030, USA

    Xi Luo

  4. Department of Social and Behavioral Sciences, University of California, San Francisco, San Francisco, CA, 94118, USA

    Ashley E. Pérez

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  1. Tyler B. Wray
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Correspondence to Tyler B. Wray.

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Wray, T.B., Luo, X., Ke, J. et al. 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. Prev Sci 20, 904–913 (2019). https://doi.org/10.1007/s11121-019-01019-z

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