Developing a Process for the Analysis of User Journeys and the Prediction of Dropout in Digital Health Interventions: Machine Learning Approach.
Developing a Process for the Analysis of User Journeys and the Prediction of Dropout in Digital Health Interventions: Machine Learning Approach.
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DOI：
10.2196/17738
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发表时间：
2020-10-28
影响因子：
7.4
通讯作者：
Ritterband LM
中科院分区：
文献类型：
--
作者：
Bremer V;Chow PI;Funk B;Thorndike FP;Ritterband LM
在提供和评估数字(即网络和移动应用程序)健康干预措施时，用户辍学是一个普遍关注的问题。研究人员尚未充分认识到这些基于技术的程序产生的大量数据的潜力。尤其令人感兴趣的是预测谁将退出干预的能力。这可能是通过分析用户旅程数据--自我报告的以及系统生成的数据--由个人通过数字健康干预导航所采用的路径(或旅程)产生的。这项研究的目的是为用户旅程数据的分析提供一个循序渐进的过程，并最终在数字健康干预的背景下预测辍学。这一过程被应用于基于互联网的失眠干预的数据，以此来说明其用途。该计划的完成取决于完成7个连续的核心，其中包括一个初始的教程核心。辍学的定义是没有完成第七个核心。给出了用户行程分析的步骤，包括数据转换、特征工程和统计模型分析与评价。辍学预测基于151名参与者的数据，这些参与者来自一个全自动的基于网络的项目(使用互联网健康睡眠)，该项目为失眠提供认知行为疗法。使用L1和L2正则化的Logistic回归、支持向量机和Boost决策树，并根据它们的预测性能进行评估。报告了数据中的相关特征，这些特征预测了用户辍学。预测辍学(曲线下面积[AUC]值)的精度因程序核心和机器学习技术的不同而不同。经过模型评估，Boost决策树的AUC值在0.6到0.9之间。其他手工制作的特征，包括完成干预的特定步骤的时间，起床的时间，以及上次与系统交互的天数，都有助于预测性能。这些结果支持了分析用户行程数据以预测辍学的可行性和潜力。理论驱动的手工制作特征提高了预测性能。在个体水平上预测辍学的能力可以用于提高研究人员和临床医生的决策能力，以及为动态干预方案提供信息。
User dropout is a widespread concern in the delivery and evaluation of digital (ie, web and mobile apps) health interventions. Researchers have yet to fully realize the potential of the large amount of data generated by these technology-based programs. Of particular interest is the ability to predict who will drop out of an intervention. This may be possible through the analysis of user journey data—self-reported as well as system-generated data—produced by the path (or journey) an individual takes to navigate through a digital health intervention. The purpose of this study is to provide a step-by-step process for the analysis of user journey data and eventually to predict dropout in the context of digital health interventions. The process is applied to data from an internet-based intervention for insomnia as a way to illustrate its use. The completion of the program is contingent upon completing 7 sequential cores, which include an initial tutorial core. Dropout is defined as not completing the seventh core. Steps of user journey analysis, including data transformation, feature engineering, and statistical model analysis and evaluation, are presented. Dropouts were predicted based on data from 151 participants from a fully automated web-based program (Sleep Healthy Using the Internet) that delivers cognitive behavioral therapy for insomnia. Logistic regression with L1 and L2 regularization, support vector machines, and boosted decision trees were used and evaluated based on their predictive performance. Relevant features from the data are reported that predict user dropout. Accuracy of predicting dropout (area under the curve [AUC] values) varied depending on the program core and the machine learning technique. After model evaluation, boosted decision trees achieved AUC values ranging between 0.6 and 0.9. Additional handcrafted features, including time to complete certain steps of the intervention, time to get out of bed, and days since the last interaction with the system, contributed to the prediction performance. The results support the feasibility and potential of analyzing user journey data to predict dropout. Theory-driven handcrafted features increased the prediction performance. The ability to predict dropout at an individual level could be used to enhance decision making for researchers and clinicians as well as inform dynamic intervention regimens.
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