{"title":"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","authors":"","doi":"10.1016/j.ijchp.2024.100490","DOIUrl":null,"url":null,"abstract":"<div><h3>Background</h3><p>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.</p></div><div><h3>Objective</h3><p>The aim of this study was to develop the machine learning web application and evaluate the performance in predicting PrEP adherence.</p></div><div><h3>Methods</h3><p>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.</p></div><div><h3>Results</h3><p>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.</p></div><div><h3>Conclusions</h3><p>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.</p></div>","PeriodicalId":47673,"journal":{"name":"International Journal of Clinical and Health Psychology","volume":null,"pages":null},"PeriodicalIF":5.3000,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1697260024000553/pdfft?md5=91f8f92cdf511192f75a5b7b2f7380e9&pid=1-s2.0-S1697260024000553-main.pdf","citationCount":"0","resultStr":"{\"title\":\"Using an interactive web application to identify pre-exposure prophylaxis adherence among men who have sex with men\",\"authors\":\"\",\"doi\":\"10.1016/j.ijchp.2024.100490\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><h3>Background</h3><p>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.</p></div><div><h3>Objective</h3><p>The aim of this study was to develop the machine learning web application and evaluate the performance in predicting PrEP adherence.</p></div><div><h3>Methods</h3><p>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.</p></div><div><h3>Results</h3><p>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.</p></div><div><h3>Conclusions</h3><p>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.</p></div>\",\"PeriodicalId\":47673,\"journal\":{\"name\":\"International Journal of Clinical and Health Psychology\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":5.3000,\"publicationDate\":\"2024-07-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.sciencedirect.com/science/article/pii/S1697260024000553/pdfft?md5=91f8f92cdf511192f75a5b7b2f7380e9&pid=1-s2.0-S1697260024000553-main.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Journal of Clinical and Health Psychology\",\"FirstCategoryId\":\"102\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S1697260024000553\",\"RegionNum\":1,\"RegionCategory\":\"心理学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"PSYCHOLOGY, CLINICAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Clinical and Health Psychology","FirstCategoryId":"102","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1697260024000553","RegionNum":1,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"PSYCHOLOGY, CLINICAL","Score":null,"Total":0}
Using an interactive web application to identify pre-exposure prophylaxis adherence among men who have sex with men
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.
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.
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