%0期刊文章%@ 1438-8871 %I JMIR出版物%V 21%卡塔尔世界杯8强波胆分析 N 11% P e14849 %T研究分析服务，优化数字健康证据生成:多层案例研究Pham,Quynh %A Shaw,James %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，q.pham@mail.utoronto.ca %K研究分析%K有效参与%K数字健康%K移动健康%K实施%K日志数据%K服务设计%K慢性疾病%D 2019 %7 11.11.2019 %9原始论文%J J医学互联网Res %G英语%X背景:对慢性疾病自我管理的数字健康干预措施的广泛采用，已经催化了用于证明它们的方法选择的范例转变。最近，数字健康研究分析的应用已成为评估这些数据丰富的干预措施的有效方法。然而，从分析介导的试验中产生的有希望的证据基础与将这些新的研究方法引入评估实践的复杂性之间的不匹配越来越大。目的:本研究旨在对实施研究分析的过程产生可转移的见解，以评估数字卫生干预措施。我们试图回答以下两个研究问题:(1)如何设计研究分析服务来优化数字健康证据生成?(2)在评估实践中扩大、传播和维持这项服务的挑战和机遇是什么?方法:我们进行了在评估实践中实施研究分析的定性多层次嵌入式单案例研究，包括对安大略省政策和监管环境的回顾(宏观层面)，将数字健康分析平台引入评估实践的实地研究(中观层面)，以及对数字健康创新者关于分析和评估的看法的访谈(微观层面)。结果:研究分析的实践是支持数字健康证据生成的高效和有效的手段。 The introduction of a research analytics platform to evaluate effective engagement with digital health interventions into a busy research lab was ultimately accepted by research staff, became routinized in their evaluative practice, and optimized their existing mechanisms of log data analysis and interpretation. The capacity for research analytics to optimize digital health evaluations is highest when there is (1) a collaborative working relationship between research client and analytics service provider, (2) a data-driven research agenda, (3) a robust data infrastructure with clear documentation of analytic tags, (4) in-house software development expertise, and (5) a collective tolerance for methodological change. Conclusions: Scientific methods and practices that can facilitate the agile trials needed to iterate and improve digital health interventions warrant continued implementation. The service of research analytics may help to accelerate the pace of digital health evidence generation and build a data-rich research infrastructure that enables continuous learning and evaluation. %M 31710296 %R 10.2196/14849 %U //www.mybigtv.com/2019/11/e14849/ %U https://doi.org/10.2196/14849 %U http://www.ncbi.nlm.nih.gov/pubmed/31710296