@文章{info:doi/10.2196/15977，作者="Xin, Meiqi and Viswanath, Kasisomayajula and Li, Angela yuan - chun and CAO, Wangnan and HU, Yuhong and Lau, Joseph Tak-Fai and Mo, Phoenix kithan "，标题="电子健康干预对促进男男性行为中艾滋病预防行为的有效性:，期刊=《J Med Internet Res》，年=“2020”，月=“5”，日=“25”，卷=“22”，数=“5”，页=“e15977”，关键词=“HIV;艾滋病;性少数群体和性别少数群体;远程医疗;荟萃分析;背景:在男男性行为者(MSM)中，艾滋病毒的高流行率是一个全球关注的问题。尽管越来越多地使用电子健康(eHealth)技术提供艾滋病毒预防干预措施，但很少有研究系统地探索其有效性和与各种干预特征的关联。目的:本研究旨在对基于电子健康技术的干预措施的有效性进行荟萃分析，以促进MSM人群的艾滋病毒预防行为，并在整合设计和实施特征的框架内确定有效性预测因素。方法:使用5个数据库(即MEDLINE、PsycINFO、EMBASE、Web of Science和ProQuest dissertation)和其他来源(如相关评论和JMIR Publications的参考书目)进行系统的文献检索，使用与eHealth技术、HIV、MSM人群相关的术语，并进行实验研究设计。卡塔尔世界杯8强波胆分析 First, primary meta-analyses were conducted to estimate the effectiveness of eHealth interventions (d+) in changing 3 HIV-preventive behaviors among MSM: unprotected anal intercourse (UAI), HIV testing, and multiple sex partnership (MSP). Moderation analyses were then conducted to examine a priori effectiveness predictors including behavioral treatment components (eg, theory use, tailoring strategy use, navigation style, and treatment duration), eHealth technology components (eg, operation mode and modality type), and intervention adherence. Results: A total of 46 studies were included. The overall effect sizes at end point were small but significant for all outcomes (UAI: d+=−.21, P<.001; HIV testing: d+=.38, P<.001; MSP: d+=−.26, P=.02). The intervention effects on UAI were significantly larger when compared with preintervention groups than with concurrent groups. Greater UAI reductions were associated with the increased use of tailoring strategies, provision of feedback, and tunneling navigation in interventions with a concurrent group, whereas reductions were associated with the use of self-paced navigation in interventions with a preintervention group. Greater uptake of HIV testing was associated with longer treatment duration; computer-mediated communication; and the use of messaging, social media, or a combined technology modality. Higher intervention adherence consistently predicted larger effects on UAI and HIV testing. Conclusions: This study provided empirical evidence for the effectiveness of eHealth interventions in promoting HIV-preventive behaviors among MSM. Features of treatment content and eHealth technology might best predict the intervention effects on UAI and HIV testing, respectively. Most importantly, intervention adherence tended to play an important role in achieving better effectiveness. The findings could help inform the development of efficacious interventions for HIV prevention in the future. ", issn="1438-8871", doi="10.2196/15977", url="//www.mybigtv.com/2020/5/e15977", url="https://doi.org/10.2196/15977", url="http://www.ncbi.nlm.nih.gov/pubmed/32449685" }