Chen Annie T, Swaminathan Aarti, Kearns William R, Alberts Nicole M, Law Emily F, Palermo Tonya M
Department of Biomedical Informatics and Medical Education, School of Medicine, University of Washington, Seattle, WA, United States.
Department of Psychology, St Jude Children's Research Hospital, Memphis, TN, United States.
J Med Internet Res. 2019 Apr 15;21(4):e11756. doi: 10.2196/11756.
Delivery of behavioral health interventions on the internet offers many benefits, including accessibility, cost-effectiveness, convenience, and anonymity. In recent years, an increased number of internet interventions have been developed, targeting a range of conditions and behaviors, including depression, pain, anxiety, sleep disturbance, and eating disorders. Human support (coaching) is a common component of internet interventions that is intended to boost engagement; however, little is known about how participants interact with coaches and how this may relate to their experience with the intervention. By examining the data that participants produce during an intervention, we can characterize their interaction patterns and refine treatments to address different needs.
In this study, we employed text mining and visual analytics techniques to analyze messages exchanged between coaches and participants in an internet-delivered pain management intervention for adolescents with chronic pain and their parents.
We explored the main themes in coaches' and participants' messages using an automated textual analysis method, topic modeling. We then clustered participants' messages to identify subgroups of participants with similar engagement patterns.
First, we performed topic modeling on coaches' messages. The themes in coaches' messages fell into 3 categories: Treatment Content, Administrative and Technical, and Rapport Building. Next, we employed topic modeling to identify topics from participants' message histories. Similar to the coaches' topics, these were subsumed under 3 high-level categories: Health Management and Treatment Content, Questions and Concerns, and Activities and Interests. Finally, the cluster analysis identified 4 clusters, each with a distinguishing characteristic: Assignment-Focused, Short Message Histories, Pain-Focused, and Activity-Focused. The name of each cluster exemplifies the main engagement patterns of that cluster.
In this secondary data analysis, we demonstrated how automated text analysis techniques could be used to identify messages of interest, such as questions and concerns from users. In addition, we demonstrated how cluster analysis could be used to identify subgroups of individuals who share communication and engagement patterns, and in turn facilitate personalization of interventions for different subgroups of patients. This work makes 2 key methodological contributions. First, this study is innovative in its use of topic modeling to provide a rich characterization of the textual content produced by coaches and participants in an internet-delivered behavioral health intervention. Second, to our knowledge, this is the first example of the use of a visual analysis method to cluster participants and identify similar patterns of behavior based on intervention message content.
在互联网上提供行为健康干预有诸多益处，包括可及性、成本效益、便利性和匿名性。近年来，已开发出越来越多的互联网干预措施，针对一系列病症和行为，包括抑郁症、疼痛、焦虑症、睡眠障碍和饮食失调。人力支持（辅导）是互联网干预措施的常见组成部分，旨在提高参与度；然而，对于参与者如何与辅导人员互动以及这可能如何与他们的干预体验相关，我们知之甚少。通过检查参与者在干预期间产生的数据，我们可以描述他们的互动模式并改进治疗方法以满足不同需求。
在本研究中，我们采用文本挖掘和可视化分析技术来分析在一项针对患有慢性疼痛的青少年及其父母的互联网疼痛管理干预中，辅导人员与参与者之间交换的信息。
我们使用一种自动文本分析方法——主题建模，来探索辅导人员和参与者信息中的主要主题。然后，我们对参与者的信息进行聚类，以识别具有相似参与模式的参与者子群体。
首先，我们对辅导人员的信息进行了主题建模。辅导人员信息中的主题分为三类：治疗内容、行政和技术以及建立融洽关系。接下来，我们使用主题建模从参与者的信息历史中识别主题。与辅导人员的主题类似，这些主题归为三个高级类别：健康管理和治疗内容、问题与担忧以及活动与兴趣。最后，聚类分析识别出4个聚类组，每个聚类组都有一个显著特征：任务导向型、短信息历史型、疼痛导向型和活动导向型。每个聚类组的名称体现了该聚类组的主要参与模式。
在这项二次数据分析中，我们展示了如何使用自动文本分析技术来识别感兴趣的信息，例如用户的问题与担忧。此外，我们展示了如何使用聚类分析来识别具有共同沟通和参与模式的个体子群体，进而促进针对不同患者子群体的干预个性化。这项工作做出了两个关键的方法学贡献。首先，本研究在使用主题建模以丰富描述互联网行为健康干预中辅导人员和参与者产生的文本内容方面具有创新性。其次，据我们所知，这是使用可视化分析方法对参与者进行聚类并基于干预信息内容识别相似行为模式的首个实例。
