Lin Bing, Li Jiayan, Liu Jiaxiu, He Wei, Pan Haiying, Zhong Xiaoni
School of Public Health, Chongqing Medical University, Chongqing, China.
Research Center for Medicine and Social Development, Chongqing, China.
JMIR Mhealth Uhealth. 2024 Dec 12;12:e58920. doi: 10.2196/58920.
Preexposure prophylaxis (PrEP) is an effective strategy to reduce the risk of HIV infection. However, the efficacy of PrEP is highly dependent on adherence. Meanwhile, adherence changes over time, making it difficult to manage effectively.
Our study aimed to explore and predict the patterns of change in PrEP adherence among men who have sex with men (MSM) and evaluate the impact of the WeChat-based reminder intervention on adherence, thus providing more information for PrEP implementation strategies.
From November 2019 to June 2023, in a randomized controlled longitudinal study of the PrEP demonstration project in Western China (Chongqing, Sichuan, and Xinjiang) based on a mobile health (mHealth) reminder intervention, participants were randomly divided into reminder and no-reminder groups, with those in the reminder group receiving daily reminders based on the WeChat app. Participants were followed up and self-reported their medication adherence every 12 weeks for a total of 5 follow-up visits. We used the growth mixture model (GMM) to explore potential categories and longitudinal trajectories of adherence among MSM, and patterns of change in PrEP adherence were predicted and evaluated based on the decision tree.
A total of 446 MSM were included in the analysis. The GMM identified 3 trajectories of adherence: intermediate adherence group (n=34, 7.62%), low adherence ascending group (n=126, 28.25%), and high adherence decline group (n=286, 64.13%). We included 8 variables that were significant in the univariate analysis in the decision tree prediction model. We found 4 factors and 8 prediction rules, and the results showed that HIV knowledge score, education attainment, mHealth intervention, and HIV testing were key nodes in the patterns of change in adherence. After 10-fold cross-validation, the final prediction model had an accuracy of 75%, and the classification accuracy of low and intermediate adherence was 78.12%.
The WeChat-based reminder intervention was beneficial for adherence. A short set of questions and prediction rules, which can be applied in future large-scale validation studies, aimed at developing and validating a short adherence assessment tool and implementing it in PrEP practices among MSM.
暴露前预防(PrEP)是降低艾滋病毒感染风险的有效策略。然而,PrEP的疗效高度依赖于依从性。同时,依从性会随时间变化,难以有效管理。
我们的研究旨在探索和预测男男性行为者(MSM)中PrEP依从性的变化模式,并评估基于微信的提醒干预对依从性的影响,从而为PrEP实施策略提供更多信息。
2019年11月至2023年6月,在中国西部(重庆、四川和新疆)基于移动健康(mHealth)提醒干预的PrEP示范项目的随机对照纵向研究中,参与者被随机分为提醒组和非提醒组,提醒组参与者通过微信应用程序接收每日提醒。对参与者进行随访,他们每12周自我报告一次药物依从性,共进行5次随访。我们使用生长混合模型(GMM)探索MSM中依从性的潜在类别和纵向轨迹,并基于决策树预测和评估PrEP依从性的变化模式。
共有446名MSM纳入分析。GMM确定了3种依从性轨迹:中等依从性组(n = 34,7.62%)、低依从性上升组(n = 126,28.25%)和高依从性下降组(n = 286,64.13%)。我们在决策树预测模型中纳入了单因素分析中有显著意义的8个变量。我们发现了4个因素和8条预测规则,结果表明艾滋病毒知识得分、教育程度、mHealth干预和艾滋病毒检测是依从性变化模式中的关键节点。经过10折交叉验证,最终预测模型的准确率为75%,低依从性和中等依从性的分类准确率为78.12%。
基于微信的提醒干预对依从性有益。一组简短的问题和预测规则可应用于未来的大规模验证研究,旨在开发和验证一种简短的依从性评估工具,并在MSM的PrEP实践中实施。