ArticlePrevention science : the official journal of the Society for Prevention Research2019
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.
Article in Prevention science : the official journal of the Society for Prevention Research, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 2 of them syntheses that pooled it.
What it found
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
20 citing papers in PubMed, 2 syntheses or guidelines pooled it, 29 citations in OpenAlex.
- Identifying Best Practices for Increasing HIV Pre-exposure Prophylaxis (PrEP) Use and Persistence in the United States: A Systematic Review.AIDS and behavior · 2024Pooled it
- A Systematic Review of Recent Methodological Approaches for Using Ecological Momentary Assessment to Examine Outcomes in U.S. Based HIV Research.Current HIV/AIDS reports · 2020Pooled it
- External validity of analogue sexual risk laboratory data.Experimental and clinical psychopharmacology · 2023Trial
- A bibliometric analysis of research on the application of just-in-time adaptive interventions in mental health.Medicine · 2026Review
- Enhancing PrEP adherence through person-centred mobile app interventions: a real-world data and machine learning approach using UPrEPU among gay, bisexual and other men who have sex with men in Taiwan.Journal of the International AIDS Society · 2025Article
- Identifying Substance Use and High-Risk Sexual Behavior Among Sexual and Gender Minority Youth by Using Mobile Phone Data: Development and Validation Study.Online journal of public health informatics · 2025Article
- The use of artificial intelligence in sexual and reproductive health: a comprehensive scoping review.npj women's health · 2025Article
- Explainable artificial intelligence and domain adaptation for predicting HIV infection with graph neural networks.Annals of medicine · 2024Article
- Behind Prep Decisions: Understanding User Patterns and Discontinuation Factors in Real-World.AIDS and behavior · 2024Article
- Predicting the HIV/AIDS Knowledge among the Adolescent and Young Adult Population in Peru: Application of Quasi-Binomial Logistic Regression and Machine Learning Algorithms.International journal of environmental research and public health · 2023Article
- Effects of Alcohol Intoxication on Sexual Decision-Making among Men Who Have Sex with Men (MSM): Alcohol's Influences on Self-Control Processes.Clinical psychological science : a journal of the Association for Psychological Science · 2023Article
- Addressing Health Inequities in Digital Clinical Trials: A Review of Challenges and Solutions From the Field of HIV Research.Epidemiologic reviews · 2022Review
- Using machine learning approaches to predict timely clinic attendance and the uptake of HIV/STI testing post clinic reminder messages.Scientific reports · 2022Article
- Current Artificial Intelligence (AI) Techniques, Challenges, and Approaches in Controlling and Fighting COVID-19: A Review.International journal of environmental research and public health · 2022Review
- Determinants and prediction ofFrontiers in public health · 2022Article
- Prediction of HIV status based on socio-behavioural characteristics in East and Southern Africa.PloS one · 2022Article
- Detection and Prevention of Virus Infection.Advances in experimental medicine and biology · 2022Article
- Daily Associations Among Alcohol Intoxication, Partner Familiarity, Participant Effortful Control, Urgency, and PrEP Uptake on Sexual Behavior in Men Who Have Sex with Men.Archives of sexual behavior · 2021Article
- Ecological Momentary Assessment and mHealth Interventions Among Men Who Have Sex With Men: Scoping Review.Journal of medical Internet research · 2021Article
- Artificial Intelligence and Machine Learning for HIV Prevention: Emerging Approaches to Ending the Epidemic.Current HIV/AIDS reports · 2020Review
Corrections and comments
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Authors and funding
6 authors at 2 institutions in 1 country.
Funding
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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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.