期刊文章%@ 1438-8871 %I JMIR出版物%V 20% N卡塔尔世界杯8强波胆分析 12 %P 11491 %T用自我引导的电子健康干预检查现实世界用户参与的预测因素:使用新数据集的移动应用程序和网站分析% a Baumel,Amit % a Kane,John M %+海法大学社区精神卫生系，Abba Khoushy Ave 199，海法，3498838，以色列，972 48288496,abaumel@univ.haifa.ac.il %K eHealth %K mHealth %K用户参与%K用户体验%K治疗联盟%K说服性设计%K行为改变%D 2018 %7 14.12.2018 %9原创论文%J J Med Internet Res %G英文%X背景:文献表明，自我引导电子健康(eHealth)干预的产品设计影响用户参与。然而，传统的试验设置不能在实际应用中检查这些关系。目的:本研究旨在检验产品设计的质量、研究证据和公开可得的数据是否能预测现实世界用户对移动和基于web的自我引导电子健康干预的参与度。方法:该分析包括可向公众提供的自我引导的移动和基于网络的电子健康干预措施，并使用光线量表套件对其质量进行评估。量表包括可用性、视觉设计、用户参与、内容、治疗说服力、治疗联盟、可信度和研究证据。基于2016年11月1日至2018年4月30日这18个月的时间窗口，我们从一个提供用户对网站和移动应用参与的汇总非个人信息的面板中获得了现实世界使用行为数据。真实的用户粘性变量包括平均使用时间(手机应用和网站)和下载后30天的手机应用用户留存率。结果:分析包括52款手机应用(平均下载量38600次; interquartile range [IQR] 116,000) and 32 websites (monthly unique visitors median 5689; IQR 30,038). Results point to moderate correlations between Therapeutic Persuasiveness, Therapeutic Alliance, and the 3 user engagement variables (.31≤rs≤.51; Ps≤.03). Visual Design, User Engagement, and Content demonstrated similar degrees of correlation with mobile app engagement variables (.25≤rs≤.49; Ps≤.04) but not with average usage time of Web-based interventions. Positive correlations were also found between the number of reviews on Google Play and average app usage time (r=.58; P<.001) and user retention after 30 days (r=.23; P=.049). Although several product quality ratings were positively correlated with research evidence, the latter was not significantly correlated with real-world user engagement. Hierarchical stepwise regression analysis revealed that either Therapeutic Persuasiveness or Therapeutic Alliance explained 15% to 26% of user engagement variance. Data on Google Play (number of reviews) explained 15% of the variance of mobile app usage time above Enlight ratings; however, publicly available data did not significantly contribute to explaining the variance of the other 2 user-engagement variables. Conclusions: Results indicate that the qualities of product design predict real-world user engagement with eHealth interventions. The use of real-world behavioral datasets is a novel way to learn about user behaviors, creating new avenues for eHealth intervention research. %M 30552077 %R 10.2196/11491 %U //www.mybigtv.com/2018/12/e11491/ %U https://doi.org/10.2196/11491 %U http://www.ncbi.nlm.nih.gov/pubmed/30552077