摘要：Objectives. We evaluated the social network structure of QuitNet, one of the largest online communities for behavior change, and compared its characteristics to other known social networks. Methods. Using modern network analysis methods, we identified QuitNet members who were active during a 60-day period, along with their ties. We then derived multiple subgroups, such as key players and integrators, from connections and communication patterns. Results. Among 7569 participants, we identified 103 592 connections to other members. Metrics of social network integration were associated with increased likelihood of being female, being older, having been in the system longer, and not smoking. Conclusions. The QuitNet community is a large-scale social network with the characteristics required for sustainability of social support and social influence to promote smoking cessation and abstinence. These characteristics include persistence of members over time, heterogeneity of smoking status, and evidence of rich, bidirectional communications. Some of the influential subgroups we identified may provide targets for future network-level interventions. Despite decades of research, tobacco use remains the most deadly of behaviors, causing 5 million deaths worldwide annually 1 and projected to cause 10 million per year by 2030. 2 The United States has an estimated 44.5 million smokers, leading to 430 000 premature deaths annually. 3 Evidence-based cessation interventions exist but are vastly underutilized by smokers. 4 There is a pressing need to maximize the population impact of cessation with innovations that are attractive and accessible to consumers. 3 One method is to leverage social network effects, which play a prominent role in the induction of smoking cessation and the perpetuation of abstinence. 5 Observational studies support a robust relationship between social support and positive outcomes for smoking, other health behaviors, and health status. 6 , 7 Higher levels of connectedness and positive social support are associated with smoking cessation and relapse prevention. 8 – 11 Negative social support (e.g., a spouse who smokes or is critical of attempts at cessation) are barriers to cessation. 11 After these associations were established, intervention studies manipulated supportive interactions outside the context of cessation treatment as a means to improve outcomes, with disappointing results. 8 , 10 – 15 Consequently, enthusiasm for social support interventions waned, 16 and the focus shifted to delivering the briefer treatments preferred by smokers. 17 Online social networks, which have proliferated in the past decade, offer a novel way to address the gap between observational data and lackluster intervention effects. Social network interventions may work through multiple mechanisms, including social support, information transfer, social influence, modeling, and the transmission of social norms. Despite the growth of online communities and networks, few published reports describe their characteristics. 18 – 21 Moreover, health behavior studies containing social network features have not documented the characteristics of the social network itself. 22 – 25 Before network effects are studied, it is critical to determine whether a true social network has developed. Otherwise, efforts to evaluate the efficacy of a social network intervention may fail if researchers unwittingly study a system that has not yet developed into a functional, sufficiently heterogeneous, large, and stable network or in which the ties between participants are weak or insufficient. Finally, no social network studies to our knowledge have examined the mechanisms that might underlie their effectiveness in changing behavior. We used formal network methods and analytic techniques to explore key structural and functional characteristics of a large, known online community for smoking cessation. Specifically, we sought to (1) characterize the social network and participants of this community, (2) describe its structure and establish that it shared characteristics with other known online networks, and (3) identify subgroups whose existence and characteristics might inform the design of cessation interventions. Our intent was to establish the necessary foundation for subsequent investigations into the effectiveness of online social networks in influencing cessation outcomes as well as to advance understanding of social network effects in tobacco treatment.
