JMIR Publications

ABSTRACT

Background:

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 (online coaching) is a common component of Internet interventions, 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.

Objective:

In this study, we employ 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.

Methods:

We explored the main themes in coaches’ and participants’ messages using an automated textual analysis method, topic modeling. Cluster analysis was employed to identify subgroups of participants with similar engagement patterns.

Results:

First, we performed topic modeling on coaches’ messages. The themes in coaches’ messages fell into three categories: Treatment Content, Administrative/Technical, and Rapport Building. We next employed topic modeling to identify topics from participants’ message histories. Similar to the coaches’ topics, these were subsumed under three high-level categories: Health Management and Treatment Content, Questions and Concerns, and Activities and Interests. Last, the cluster analysis identified four clusters, each with a distinguishing characteristic: Assignment-Focused, Short Message Histories, Pain-Focused, and Activity-Focused. The names of each cluster exemplify the main engagement patterns of that cluster.

Conclusions:

In this secondary data analysis, we demonstrated how automated text analysis techniques can be used to identify messages of interest, such as questions and concerns from users. In addition, we demonstrated how cluster analysis can be used to identify subgroups of individuals who share communication and engagement patterns, which could inform personalization of interventions for different sub-groups of patients. To our knowledge, this is the first example of the use of cluster analysis to identify similar patterns of behavior in textual data collected from an Internet intervention.