Diffusion of an evidence-based smoking cessation intervention through Facebook: a randomised controlled trial study protocol

Introduction: Online social networks represent a potential mechanism for the dissemination of health interventions including smoking cessation; however, which elements of an intervention determine diffusion between participants is unclear. Diffusion is frequently measured using R, the reproductive rate, which is determined by the duration of use (t), the 'contagiousness' of an intervention (beta) and a participant's total contacts (z). We have developed a Facebook 'app' that allows us to enable or disable various components designed to impact the duration of use (expanded content, proactive contact), contagiousness (active and passive sharing) and number of contacts (use by non-smoker supporters). We hypothesised that these elements would be synergistic in their impact on R, while including non-smokers would induce a 'carrier' state allowing the app to bridge clusters of smokers. Methods and analysis: This study is a fractional factorial, randomised control trial of the diffusion of a Facebook application for smoking cessation. Participants recruited through online advertising are randomised to 1 of 12 cells and serve as 'seed' users. All user interactions are tracked, including social interactions with friends. Individuals installing the application that can be traced back to a seed participant are deemed 'descendants' and form the outcome of interest. Analysis will be conducted using Poisson regression, with event count as the outcome and the number of seeds in the cell as the exposure. Results: The results will be reported as a baseline R0 for the reference group, and incidence rate ratio for the remainder of predictors. Ethics and Dissemination: This study uses an abbreviated consent process designed to minimise barriers to adoption and was deemed to be minimal risk by the Institutional Review Board (IRB). Results will be disseminated through traditional academic literature as well as social media. If feasible, anonymised data and underlying source code are intended to be made available under an open source license.