Sahiti Myneni*, University of Texas Health Science Center at Houston, Houston, United States Nathan K Cobb, Georgetown University, Washington, United States Trevor Cohen, University of Texas Health Science Center at Houston, Houston, United States Track: Research Presentation Topic: The nature and dynamics of social networks in health Presentation Type: Oral presentation Submission Type: Single Presentation Building: Sheraton Maui Resort Room: B - Kapalua Date: 2014-11-13 11:50 AM – 12:35 PM Last modified: 2014-09-04

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Background: In most prior research involving online communities, the focus has been on examining network structure to understand mechanisms underlying behavior change. Content analysis of the messages exchanged in these communities has been limited to the "social support" perspective. However, existing behavior change theories suggest that message content plays a prominent role reflecting several socio-cognitive factors (including and beyond social support) affecting an individual’s efforts to make a lifestyle change. An understanding of these factors is required if we are to identify the mechanisms of behavior change in the Health 2.0 era, where consumers are often the drivers of technological interventions and digitized information sources.
Objective: The objective of this work is two-fold: 1) to harness digital communication data to capture essential meaning of communication and factors affecting a desired behavior change, and 2) to understand the applicability of existing behavior change theories to characterize peer-to-peer communication in online platforms.
Methods: In this paper, we describe grounded theory based qualitative analysis of digital communication in QuitNet, an online community promoting smoking cessation. A database of 16,492 de-identified public messages from 1456 users between March 1, 2007 and April 30, 2007 was used in our study. A total of 795 messages were analyzed using grounded theory techniques to reach and ensure thematic saturation. This analysis enabled identification of key concepts contained in the messages exchanged by QuitNet members, allowing us to understand the socio-behavioral intricacies underlying an individual’s efforts to cease smoking in a group setting. The findings derived from this data-driven analysis were then interpreted in the light of existing behavior change theories (Social Cognitive Theory, Stages of Change Model, Health Belief Model, Theory of Reasoned Action) in an attempt to understand the interplay between the behavior change constructs facilitated by Health 2.0 based interventions and existing health behavior models. This analysis enhances our understanding of the applicability of behavior change theories, which were formulated based on face-to-face communication, in the context of online social relationships.
Results: A total of 43 different concepts were identified, which were then grouped under 12 themes based on analysis of 795 messages. Examples of concepts include “sleepiness”, “pledge”, “patch”, “spouse”, and “slip”. Examples of themes include “Traditions”, “Social support”, “Obstacles”, “Relapse” and “Cravings”. Results indicate that themes comprised of member-generated strategies such as "virtual bonfires" and "pledges" were related to the highest number of theoretical constructs from the existing behavior change theories. In addition, results indicate that the member generated communication content supports socio-cognitive constructs from more than one behavior change model, unlike majority of the existing theory-driven interventions.
Conclusions: With the onset of mobile smart phones and ubiquitous internet connectivity, online social network data reflect the intricacies of human health behavior as experienced by real people in real time. This study offers insights into the various kinds of behavioral constructs prevalent in the messages exchanged among QuitNet users. Theoretically, this study establishes the manifestation of existing behavior change theories in QuitNet-like Health 2.0 platforms. Pragmatically, it sets the stage for real-time data-driven socio-behavioral interventions promoting healthy lifestyle modifications by allowing us to understand the emergent user needs to sustain a desired behavior change.