Beikzadeh Mehrab, Holloway Ian W, Kärkkäinen Kimmo, Hong Chenglin, Cascalheira Cory, Wu Elizabeth S C, Boka Callisto, Avendaño Alexandra C, Yonko Elizabeth A, Sarrafzadeh Majid
Department of Computer Science, UCLA Samueli School Of Engineering, University of California, Los Angeles, 7400 Boelter Hall, Los Angeles, CA, 90034, United States, 1 4245664464.
Department of Social Welfare, University of California, Los Angeles, Los Angeles, CA, United States.
Online J Public Health Inform. 2025 Aug 12;17:e68013. doi: 10.2196/68013.
Sexual and gender minority (SGM) individuals are at heightened risk for substance use and sexually transmitted infections than their non-SGM peers. Collecting mobile phone usage data passively may open new opportunities for personalizing interventions, as behavioral risks could be identified without user input.
This study aimed to determine (1) whether passively sensed mobile phone data can be used to identify substance use and sexual risk behaviors for sexually transmitted infection (STI) and HIV transmission among young SGM who have sex with men, (2) which outcomes can be predicted with a high level of accuracy, and (3) which passive data sources are most predictive of these outcomes.
We developed a mobile phone app to collect participants' messaging, location, and app use data and trained a machine learning model to predict risk behaviors for STI and HIV transmission. We used Scikit-learn to train logistic regression and gradient boosting classification models with simple linear model specification to predict participants' substance use and sexual behaviors (ie, condomless anal sex, number of sexual partners, and methamphetamine use), which were validated using self-report questionnaires. F1-scores were used to quantify prediction accuracy of the model using different data sources (and combinations of these sources) for prediction. Differences between text, location, app use, and Linguistic Inquiry and Word Count (LIWC) domains by outcome were investigated using independent t tests where associations were considered significant at P<.05.
Among participants (n=82) who identified as SGM, were sexually active, and reported recent substance use, our model was highly predictive of methamphetamine use and having ≥6 sexual partners (F1-scores as high as 0.83 and 0.69, respectively). The model was less predictive of condomless anal sex (highest F1-score 0.38). Overall, text-based features were found to be most predictive, but app use and location data improved predictive accuracy, particularly for detecting ≥6 sexual partners. Methamphetamine use was significantly associated with dating app use (P=.01) and use of sex-related words (P=.002). Having ≥6 sex partners was associated with dating app use (0.02), use of sex-related words (P=.001), and traveling a further distance from home (P=.03), on average, compared to participants with fewer sex partners. Methamphetamine users were more likely to use social (P=.002) and affect words (P=.003) and less likely to use drive-related words (P=.02). People having 6 or more partners were more likely to use social, affect words, and cognitive process-related words (P=.003 and .004 respectively).
Our results show that passively collected mobile phone data may be useful in detecting sexual risk behaviors. Expanding data collection may improve the results further, as certain behaviors, such as injection drug use, were quite rare in the study sample. These models may be used to personalize STI and HIV prevention as well as substance use harm reduction interventions.
性取向和性别少数群体（SGM）比非SGM同龄人面临更高的物质使用和性传播感染风险。被动收集手机使用数据可能为个性化干预带来新机会，因为无需用户输入即可识别行为风险。
本研究旨在确定（1）被动感知的手机数据是否可用于识别与男性发生性行为的年轻SGM中性传播感染（STI）和艾滋病毒传播的物质使用和性风险行为，（2）哪些结果可以高精度预测，以及（3）哪些被动数据源最能预测这些结果。
我们开发了一款手机应用程序来收集参与者的信息、位置和应用使用数据，并训练了一个机器学习模型来预测STI和艾滋病毒传播的风险行为。我们使用Scikit-learn训练逻辑回归和梯度提升分类模型，采用简单线性模型规范来预测参与者的物质使用和性行为（即无保护肛交、性伴侣数量和甲基苯丙胺使用情况），并使用自我报告问卷进行验证。F1分数用于量化模型使用不同数据源（以及这些数据源的组合）进行预测的准确性。使用独立t检验研究文本、位置、应用使用和语言查询与字数统计（LIWC）领域之间按结果的差异，当P<0.05时，关联被认为具有统计学意义。
在82名自我认同为SGM、性活跃且报告近期有物质使用的参与者中，我们的模型对甲基苯丙胺使用和有≥6个性伴侣具有高度预测性（F1分数分别高达0.83和0.69）。该模型对无保护肛交的预测性较低（最高F1分数为0.38）。总体而言，基于文本的特征被发现最具预测性，但应用使用和位置数据提高了预测准确性，特别是对于检测≥6个性伴侣。甲基苯丙胺使用与约会应用使用（P=0.01）和使用与性相关的词汇（P=0.002）显著相关。与性伴侣较少的参与者相比，有≥6个性伴侣与约会应用使用（P=0.02）、使用与性相关的词汇（P=0.001）以及平均离家更远（P=0.03）相关。甲基苯丙胺使用者更可能使用社交（P=0.002）和情感词汇（P=0.003），而较少使用与驾驶相关的词汇（P=0.02）。有六个或更多性伴侣的人更可能使用社交、情感和认知过程相关的词汇（分别为P=0.003和0.004）。
我们的结果表明，被动收集的手机数据可能有助于检测性风险行为。扩大数据收集可能会进一步改善结果，因为某些行为，如注射吸毒，在研究样本中相当罕见。这些模型可用于个性化STI和艾滋病毒预防以及物质使用危害减少干预措施。
