摘要：Objectives. To examine the diffusion of an evidence-based smoking cessation application (“app”) through Facebook social networks and identify specific intervention components that accelerate diffusion. Methods. Between December 2012 and October 2013, we recruited adult US smokers (“seeds”) via Facebook advertising and randomized them to 1 of 12 app variants using a factorial design. App variants targeted components of diffusion: duration of use ( t ), “contagiousness” (β), and number of contacts ( Z ). The primary outcome was the reproductive ratio ( R ), defined as the number of individuals installing the app (“descendants”) divided by the number of a seed participant’s Facebook friends. Results. We randomized 9042 smokers. App utilization metrics demonstrated between-variant differences in expected directions. The highest level of diffusion ( R = 0.087) occurred when we combined active contagion strategies with strategies to increase duration of use (incidence rate ratio = 9.99; 95% confidence interval = 5.58, 17.91; P < .001). Involving nonsmokers did not affect diffusion. Conclusions. The maximal R value (0.087) is sufficient to increase the numbers of individuals receiving treatment if applied on a large scale. Online interventions can be designed a priori to spread through social networks. Given tobacco’s deadly and addictive properties, there continues to be a pressing need for effective and broadly disseminated tobacco cessation interventions. Most smokers attempt to quit without any form of assistance, 1,2 and tobacco use rates have remained relatively static over the past 5 years at around 20%. 3 There have been numerous efforts to build effective online interventions for tobacco cessation. 4,5 Internet smoking cessation programs have the potential to reach a large number of people in a cost-efficient manner, but their uptake is often tied to expensive marketing and promotion efforts. 6 The literature on diffusion of innovations suggests an alternative model for treatment dissemination in which an intervention proliferates through an existing social network, “virally” spreading from smoker to smoker. 7 Many studies have documented the role of interpersonal influence in behavior change, 8–12 including the viral spread of cessation in large networks. 13 The explosion of online social networks, such as Facebook, has potentially changed the concept of how network-based interventions might function by shifting interpersonal influence to mediated communications platforms. 14 The broad diffusion of Facebook applications (“apps”) and games has become part of the national discourse, making the phrase “to go viral” part of the vernacular. Viral applications are generally disseminated either by active mechanisms (e.g., via invitations, e-mail) or passive mechanisms (e.g., via observation). As with the spread of an infectious disease, the diffusion of online social network applications can be modeled with the reproductive ratio ( R ), which is determined by the duration that an individual remains “infectious” ( t ), the relative “contagiousness” of the application itself (β), and the number of contacts ( Z ). In cases in which the mean value of R exceeds 1.0 (i.e., when a typical user spreads the application to more than 1 person), an application will diffuse exponentially through a network. When R is less than but close to 1.0, diffusion depends on recurring new “infections” but can spread across multiple generations. For example, an application with an R value of 0.9 will reach an average of 6 additional participants when traced out to 10 generations. For online applications with rapid diffusion cycles (days or even hours), R values well below 1.0 still have the potential to promulgate an intervention. The concept of the reproductive rate is common across network science, public health, epidemiology, and social application design, making it a unifying metric that can be used both for intervention design and evaluation while also encouraging cross-program comparisons. We hypothesized that the Facebook social network could be used to virally disseminate an evidence-based smoking cessation application. We constructed a Facebook app that varied specific features of the 3 components of R : (1) the length of time ( t ) participants used the application, (2) how aggressive the application was in promoting itself to a participant’s friends (β, or contagiousness), and (3) the number of Facebook friends ( Z , or contacts). The app was designed so that these components of R (time, contagiousness, contacts) could be enabled or disabled for individual participants and evaluated in an experimental design. The rate of intervention diffusion ( R ) was the outcome of interest.
