Using an interactive web application to identify pre-exposure prophylaxis adherence among men who have sex with men

IF 5.3 1区心理学 Q1 PSYCHOLOGY, CLINICAL

International Journal of Clinical and Health Psychology Pub Date : 2024-07-01 DOI:10.1016/j.ijchp.2024.100490

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引用次数: 0

Abstract

Background

Men who have sex with men (MSM) are at a high risk for HIV infection. While pre-exposure prophylaxis (PrEP) is an effective oral preventive strategy, its success is largely dependent on consistent medication adherence.

Objective

The aim of this study was to develop the machine learning web application and evaluate the performance in predicting PrEP adherence.

Methods

The PrEP prospective cohort study of the MSM population conducted in Western China from 2019 to 2023, and we collected adherence data and personal characteristics data from 747 MSM. Predictor variables were screened and the performance of several machine learning methods in predicting nonadherent behaviors were compared.

Results

A total of 11 candidate variables were screened that predicted nonadherent behaviors. We developed and evaluated five machine learning models that performed well in predicting adherence. Attitudes of male sexual partners, self-efficacy, HIV testing, number of male sexual partners, and risk perception were the most important predictors of adherence. The optimal prediction model was displayed in a shiny web application for online calculation of the probability of occurrence of nonadherent behaviors among MSM.

Conclusions

Machine learning performed well in predicting nonadherent behaviors among MSM. An interactive and intuitive web application can help identify individuals who may have nonadherent behaviors, resulting in improved medication adherence and increased prevention efficacy.

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利用交互式网络应用程序确定男男性行为者是否坚持暴露前预防措施

男男性行为者（MSM）是艾滋病毒感染的高危人群。虽然暴露前预防（PrEP）是一种有效的口服预防策略，但其成功与否在很大程度上取决于是否坚持服药。本研究旨在开发机器学习网络应用程序，并评估其在预测PrEP依从性方面的性能。从2019年到2023年，我们在中国西部开展了针对MSM人群的PrEP前瞻性队列研究，收集了747名MSM的依从性数据和个人特征数据。我们筛选了预测变量，并比较了几种机器学习方法在预测非依从行为方面的性能。共筛选出 11 个可预测不依从行为的候选变量。我们开发并评估了五个机器学习模型，这些模型在预测依从性方面表现出色。男性性伴侣的态度、自我效能感、HIV 检测、男性性伴侣的数量和风险认知是预测依从性的最重要因素。最佳预测模型显示在一个闪亮的网络应用程序中，用于在线计算男男性行为者不依从行为发生的概率。机器学习在预测 MSM 不依从行为方面表现良好。交互式直观网络应用程序可帮助识别可能有不依从行为的人，从而改善服药依从性并提高预防效果。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

International Journal of Clinical and Health Psychology PSYCHOLOGY, CLINICAL-

CiteScore

10.70

自引率

5.70%

发文量

审稿时长

33 days

期刊介绍： The International Journal of Clinical and Health Psychology is dedicated to publishing manuscripts with a strong emphasis on both basic and applied research, encompassing experimental, clinical, and theoretical contributions that advance the fields of Clinical and Health Psychology. With a focus on four core domains—clinical psychology and psychotherapy, psychopathology, health psychology, and clinical neurosciences—the IJCHP seeks to provide a comprehensive platform for scholarly discourse and innovation. The journal accepts Original Articles (empirical studies) and Review Articles. Manuscripts submitted to IJCHP should be original and not previously published or under consideration elsewhere. All signing authors must unanimously agree on the submitted version of the manuscript. By submitting their work, authors agree to transfer their copyrights to the Journal for the duration of the editorial process.