Nathan K Cobb1, Megan A Jacobs1, Paul Wileyto1, Thomas Valente1, Amanda L Graham1. 1. Nathan K. Cobb is with the Department of Pulmonary and Critical Care, Georgetown University Medical Center, Washington, DC, and the Department of Health, Behavior and Society, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD. Megan A. Jacobs is with the Schroeder Institute for Tobacco Research and Policy Studies, Truth Initiative, Washington, DC. Paul Wileyto is with the Department of Biostatistics & Epidemiology, University of Pennsylvania School of Medicine, Philadelphia. Thomas Valente is with the Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles. Amanda L. Graham is with the Schroeder Institute for Tobacco Research and Policy Studies, Truth Initiative, Washington, DC, and the Department of Oncology, Georgetown University Medical Center/Cancer Prevention and Control Program, Lombardi Comprehensive Cancer Center, Washington, DC.
Abstract
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.
RCT Entities:
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.
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