通过使用手机数据来识别性和性别少数群体青年的物质使用和高风险性行为:开发和验证研究
Mehrab Beikzadeh1, Ian W Holloway2, Kimmo Kärkkäinen3
1Department of Computer Science, UCLA Samueli School Of Engineering, University of California, Los Angeles, 7400 Boelter Hall, Los Angeles, CA, 90034, United States, 1 4245664464.
Online journal of public health informatics
|August 12, 2025
概括
被动收集的手机数据可以在年轻的性和性别少数群体 (SGM) 个人中识别物质使用和性风险. 这项技术可以个性化艾滋病毒和性传播疾病预防干预措施.
科学领域:
- 数字流行病学数字流行病学
- 移动卫生干预措施 移动卫生干预
- 机器学习在公共卫生中的应用
背景情况:
- 性和性别少数群体 (SGM) 个人面临更高的物质使用和性传播感染 (STIs) 风险.
- 通过在没有用户直接输入的情况下识别行为风险,被动手机数据收集为个性化干预提供了一种新的方法.
研究的目的:
- 确定被动感应的手机数据是否可以识别SGM人口中与男性发生性关系的年轻男性 (MSM) 的物质使用和性风险行为.
- 评估确定结果的预测准确性,并确定这些行为最具预测性的被动数据源.
主要方法:
- 一个移动应用程序收集了参与者的消息,位置和应用程序使用数据.
- 机器学习模型 (逻辑回归,梯度提升) 被训练来预测物质使用和性行为,与自我报告问卷进行验证.
- F1分数量化了预测准确性;独立的t测试分析了数据领域的结果差异.
主要成果:
- 该模型在预测甲基胺使用和有六个或更多的性伴侣方面取得了高准确性 (F1得分高达0.83).
- 不使用避孕套进行交的预测准确度较低 (最高F1得分为0.38).
- 基于文本的功能是最具预测性的,应用程序的使用和位置数据增强了预测,特别是在多个性伴侣的情况下. 甲胺使用和更多的性伴侣与特定的应用程序使用模式和语言使用有关.
结论:
- 被动收集的手机数据显示,在SGM个体中检测性风险行为是有前途的.
- 扩大数据收集可以提高对注射药物使用等不太常见行为的预测准确度.
- 这些预测模型可以为减少物质使用危害和预防性感染/艾滋病毒提供个性化干预信息.
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