Assessment of Adaptive Engagement and Support Model for People With Chronic Health Conditions in Online Health Communities: Combined Content Analysis

Abstract

<jats:sec> <jats:title>Background</jats:title> <jats:p>With the pervasiveness of social media, online health communities (OHCs) 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. Little is known about the aspects of OHCs that are important to maintain safe and productive conversations that support health.</jats:p> </jats:sec> <jats:sec> <jats:title>Objective</jats:title> <jats:p>This study aimed 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 also aimed to identify key elements of the model that are central to the development, maintenance, and adaptation of OHCs for people with chronic health conditions.</jats:p> </jats:sec> <jats:sec> <jats:title>Methods</jats:title> <jats:p>This study used 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, irritable bowel syndrome, rheumatoid arthritis, lung cancer, and prostate cancer. An exploratory approach was used 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 topic discussed.</jats:p> </jats:sec> <jats:sec> <jats:title>Results</jats:title> <jats:p>Data on the reach and engagement of the Facebook posts and the analysis of the sample of 5881 comments demonstrate that people with chronic health conditions want to engage on the web and find value in supporting and sharing their experiences with others. Most comments made in these Facebook posts were expressions of social support for others living with the same health condition (3405/5881, 57.89%). Among the comments with an element of support, those where community members validated the knowledge or experiences of others were most frequent (1587/3405, 46.61%), followed by the expression of empathy and understanding (1089/3405, 31.98%). Even among posts with more factual content, such as insurance coverage issues, user comments still had frequent expressions of support for others (80/213, 37.5%).</jats:p> </jats:sec> <jats:sec> <jats:title>Conclusions</jats:title> <jats:p>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 sharing information or validating information shared by others, expressions of empathy, and sharing encouraging statements with others.</jats:p> </jats:sec>

Identifying Self-Management Support Needs for Pregnant Women With Opioid Misuse in Online Health Communities: Mixed Methods Analysis of Web Posts

“…Thematic analyses are commonly applied to sociobehavioral studies using semistructured interviews with an average of 30 participants (SD 18.7) [ 79 ]. Qualitative studies of online content have varying sample sizes, usually ranging from 100 to 2000 [ 31 , 41 , 43 , 45 , 46 , 50 , 80 ]. We iteratively coded 200 posts and reached concept saturation within this number.…”

“…Third, comments in response to the initiating posts by women with opioid misuse were not analyzed, as this study focused on the beliefs and actions of those attempting self-management at the moment of posting. Previous research shows that most comments in OHCs for chronic health conditions have an element of social support, primarily including validation and empathy [ 80 ]. In light of our observation on the imbalance of requests for informational and emotional support, future research should examine how the OHC audience responds to the posts.…”

Telemedicine Use and Health-Related Concerns of Patients With Chronic Conditions During COVID-19: Survey of Members of Online Health Communities

“…Data for this study were collected in an online survey distributed through Qualtrics Survey Software (Qualtrics International Inc) to members of 26 online health communities hosted by Health Union. Each community provides patients and caregivers a digital platform to learn about their diagnosis and both provide and receive social support from peers and health care providers [ 12 ]. Between May 12-14, 2020, the survey was emailed to 98,983 community members who had previously opted into receiving emails from Health Union.…”

“…Thus, the purpose of this study is to describe the telehealth use, resource needs, and information sources of individuals with chronic conditions during the COVID-19 pandemic. Data for this study were collected from members of online health communities hosted by the digital publishing platform, Health Union, for patients with chronic conditions (eg, communities for those with migraines, lung cancer, chronic obstructive pulmonary disease, or rheumatoid arthritis) [ 12 ]. This paper also explores differences in telehealth use among sociodemographic characteristics to identify those who may be in most critical need of support.…”

Identifying Self-Management Support Needs for Pregnant Women With Opioid Misuse in Online Health Communities: Mixed Methods Analysis of Web Posts (Preprint)

“…Thematic analyses are commonly applied to sociobehavioral studies using semistructured interviews with an average of 30 participants (SD 18.7) [79]. Qualitative studies of online content have varying sample sizes, usually ranging from 100 to 2000 [31,41,43,45,46,50,80]. We iteratively coded 200 posts and reached concept saturation within this number.…”