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Adaptive Engagement and Support Model for People With Chronic Health Conditions: Using Combined Content Analysis to Assess Online Health Communities

Author

J Med Internet Res. 2020 Jun 3. doi: 10.2196/17338. Online ahead of print.

Brian M Green 1, Katelyn Tente Van Horn 1, Ketki Gupte 1, Megan Evans 2, Sara Hayes 1, Amrita Bhowmick 1 2

Author Information

1 Health Union, LLC, 1217 Sansom Street2nd Floor, Philadelphia, US.

2 Department of Health Behavior, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, US.

Free article

Abstract

Background: With the pervasiveness of social media, online health communities (OHC) are an important tool for facilitating information sharing and support among people with chronic health conditions. Importantly, OHCs offer insight into conversations about the lived experiences of people with particular health conditions. What is less known is what aspects of OHCs are important to maintain safe and productive conversations that support health.

Objective: This research aims to assess the provision of social support and the role of active moderation in OHCs developed in accordance with and managed by an adaptive engagement model. This study aims to identify key elements of the model that are central to the development, maintenance, and adaptation of OHCs for people with chronic health conditions.

Methods: This research uses Combined Content Analysis, a mixed-methods approach, to analyze sampled Facebook post comments from 6 OHCs to understand how key aspects of the adaptive engagement model facilitate different types of social support. OHCs included in this study are for people living with multiple sclerosis, migraine, IBS, rheumatoid arthritis, lung cancer, and prostate cancer. An exploratory approach was taken in the analysis and initial codes were grouped into thematic categories and then confirmed through thematic network analysis using the Dedoose™ qualitative analysis software tool. Thematic categories were compared for similarities and differences for each of the 6 OHCs, and by content descriptive category.

Results: The Facebook posts reach and engagement data and analysis of the sample of 5,881 comments demonstrate that people with chronic health conditions want to engage online and find value in supporting and sharing their experiences with others. By far, most comments made in these Facebook posts were expressions of social support for others living with the same health condition (57.9%). Among the comments where there was an element of support, those where community members validated knowledge or experiences of others were most frequent (46%), followed by the expression of empathy and understanding (32%). Even among posts with more factual content, like insurance coverage issues, user comments still had frequent expressions of support for others (37%).

Conclusions: The analysis of this OHC Adaptive Engagement model-in-action shows that the foundational elements--social support, engagement, and moderation-can effectively be used to provide a rich and dynamic community experience for individuals with chronic health conditions. Social support is demonstrated in a variety of ways including through sharing information or validating information shared by others, expressions of empathy, and sharing encouraging statements with others.