Objective Fibromyalgia patients face particular challenges in building relationships with health care providers. In this study, we examine, from patients’ perspectives, factors that influence the formation of effective patient–provider relationships. Design This research employed a qualitative approach to analyze data collected from a study that employed semistructured interviews. Methods Multiple methods were used to recruit 23 fibromyalgia patients for interviews. Semistructured interviews were conducted to explore how participants’ information behaviors, including their communication with and relationships to providers, changed over time. The interview data were analyzed using a qualitative analytic method based on interpretative phenomenological analysis and constructivist grounded theory. Results We identified three important factors that influenced the building of effective relationships: patients and providers’ interactions involving information, identifying health care providers that fit patients’ needs, and realizing shared responsibilities. With regard to information, we described three important themes: information gaps, providers as educators/facilitators, and collaborative information behavior. Conclusions Understanding of the key elements of relationship development between patients and providers can be utilized in various ways to improve clinical care. First, the knowledge gained in this study can inform the design of patient education materials that assist patients to identify providers that fit their needs, prepare for consultations, and develop realistic expectations for providers. The findings of this study can also inform the design of resources and tools to enable clinicians to communicate and relate better with their patients.

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 (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. Objective 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. Methods 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. Results 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. Conclusions 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.

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