OBJECTIVE: Adolescents' drinking is influenced by their friends' drinking. However, it is unclear whether individually-targeted alcohol interventions reduce drinking in the friends of individuals who receive the intervention. This study used simulations of drinking in simulated longitudinal social networks to test whether individually-targeted alcohol interventions may be expected to spread to non-targeted individuals. METHOD: Stochastic actor-based models simulated longitudinal social networks where changes in drinking and friendships were modeled using parameters from a meta-analysis of high school 10th grade social networks. Social influence (i.e., how much one's friends' drinking affects their own drinking) and social selection (i.e., how much one's drinking affects who they select as friends) were manipulated at several levels. At the midpoint of each simulation, a randomly-selected heavy-drinking individual was experimentally assigned to an intervention (changing their drinking status to non-drinking) or a control condition (no change in drinking status) and the drinking statuses of that individual's friends were recorded at the end of the simulation. RESULTS: Friends of individuals who received the intervention significantly reduced their drinking, with higher reductions occurring in networks with greater social influence. However, all effect sizes were small (e.g., average per-friend reduction of .07 on a 5-point drinking scale). CONCLUSIONS: Individually-targeted alcohol interventions may have small effects on reducing the drinking of non-targeted adolescents, with social influence being a mechanism that drives such effects. Due to small effect sizes, many adolescents may need to receive alcohol interventions to produce measurable effects on drinking outcomes for non-targeted individuals. (PsycInfo Database Record (c) 2021 APA, all rights reserved).
OBJECTIVE: Adolescents' drinking is influenced by their friends' drinking. However, it is unclear whether individually-targeted alcohol interventions reduce drinking in the friends of individuals who receive the intervention. This study used simulations of drinking in simulated longitudinal social networks to test whether individually-targeted alcohol interventions may be expected to spread to non-targeted individuals. METHOD: Stochastic actor-based models simulated longitudinal social networks where changes in drinking and friendships were modeled using parameters from a meta-analysis of high school 10th grade social networks. Social influence (i.e., how much one's friends' drinking affects their own drinking) and social selection (i.e., how much one's drinking affects who they select as friends) were manipulated at several levels. At the midpoint of each simulation, a randomly-selected heavy-drinking individual was experimentally assigned to an intervention (changing their drinking status to non-drinking) or a control condition (no change in drinking status) and the drinking statuses of that individual's friends were recorded at the end of the simulation. RESULTS: Friends of individuals who received the intervention significantly reduced their drinking, with higher reductions occurring in networks with greater social influence. However, all effect sizes were small (e.g., average per-friend reduction of .07 on a 5-point drinking scale). CONCLUSIONS: Individually-targeted alcohol interventions may have small effects on reducing the drinking of non-targeted adolescents, with social influence being a mechanism that drives such effects. Due to small effect sizes, many adolescents may need to receive alcohol interventions to produce measurable effects on drinking outcomes for non-targeted individuals. (PsycInfo Database Record (c) 2021 APA, all rights reserved).
Authors: Grace C Huang; Jennifer B Unger; Daniel Soto; Kayo Fujimoto; Mary Ann Pentz; Maryalice Jordan-Marsh; Thomas W Valente Journal: J Adolesc Health Date: 2013-09-03 Impact factor: 5.012
Authors: Melissa J Cox; Angelo M DiBello; Matthew K Meisel; Miles Q Ott; Shannon R Kenney; Melissa A Clark; Nancy P Barnett Journal: Psychol Addict Behav Date: 2019-03-14
Authors: Jade Benjamin-Chung; Benjamin F Arnold; David Berger; Stephen P Luby; Edward Miguel; John M Colford; Alan E Hubbard Journal: Int J Epidemiol Date: 2018-02-01 Impact factor: 7.196