杂志文章%@ 2291-5222 %I JMIR出版物%V 7% N 卡塔尔世界杯8强波胆分析1% P e11941 %T一个分析指标库，用于评估消费者移动健康应用程序对慢性疾病的有效参与:范围审查%A Pham,Quynh %A Graham,Gary %A Carrion,Carme %A Morita,Plinio P %A Seto,Emily %A Stinson,Jennifer N %A Cafazzo，多伦多大学达拉拉娜公共卫生学院，健康科学大楼，学院街155号425套房，加拿大ON, M5T 3M6, 1 416 340 4800 ext 4765，约瑟夫卫生政策、管理和评估研究所，q.pham@mail.utoronto.ca %K分析%K有效参与%K参与%K坚持%K日志数据%K移动健康%K移动应用程序%K慢性疾病%K范围审查%D 2019 %7 18.01.2019 %9原始论文%J JMIR移动健康Uhealth %G英文%X背景:有混合证据支持当前移动健康(Mhealth)应用程序改善慢性健康和福祉的雄心。对于这种可变影响的一种解释是，用户并没有按照预期的方式使用应用。分析的应用，定义为使用数据产生新的见解，是一种新兴的方法，用于研究和解释与移动健康干预措施的接触。目的:本研究旨在巩固敬业度分析指标之前在临床和技术背景下的应用，以告知它们如何在未来的评估中得到最佳应用。方法:我们进行了范围审查，对用于评估消费者移动健康应用程序慢性疾病的分析指标的范围进行了分类。我们根据应用程序结构和用户粘性数据的应用对研究进行了分类，并为每个类别计算了描述性数据。应用卡方和Fisher精确独立检验来计算编码变量之间的差异。结果:共有41项研究符合我们的纳入标准。 The average mHealth evaluation included for review was a two-group pretest-posttest randomized controlled trial of a hybrid-structured app for mental health self-management, had 103 participants, lasted 5 months, did not provide access to health care provider services, measured 3 analytic indicators of engagement, segmented users based on engagement data, applied engagement data for descriptive analyses, and did not report on attrition. Across the reviewed studies, engagement was measured using the following 7 analytic indicators: the number of measures recorded (76%, 31/41), the frequency of interactions logged (73%, 30/41), the number of features accessed (49%, 20/41), the number of log-ins or sessions logged (46%, 19/41), the number of modules or lessons started or completed (29%, 12/41), time spent engaging with the app (27%, 11/41), and the number or content of pages accessed (17%, 7/41). Engagement with unstructured apps was mostly measured by the number of features accessed (8/10, P=.04), and engagement with hybrid apps was mostly measured by the number of measures recorded (21/24, P=.03). A total of 24 studies presented, described, or summarized the data generated from applying analytic indicators to measure engagement. The remaining 17 studies used or planned to use these data to infer a relationship between engagement patterns and intended outcomes. Conclusions: Although researchers measured on average 3 indicators in a single study, the majority reported findings descriptively and did not further investigate how engagement with an app contributed to its impact on health and well-being. Researchers are gaining nuanced insights into engagement but are not yet characterizing effective engagement for improved outcomes. Raising the standard of mHealth app efficacy through measuring analytic indicators of engagement may enable greater confidence in the causal impact of apps on improved chronic health and well-being. %M 30664463 %R 10.2196/11941 %U http://mhealth.www.mybigtv.com/2019/1/e11941/ %U https://doi.org/10.2196/11941 %U http://www.ncbi.nlm.nih.gov/pubmed/30664463