Using Social Network Analysis to Delineate Mechanisms of Behaviour Change in Peer-led Interventions.

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Social network-based interventions capitalize on influential individuals to alter health behaviour, and are recognised as promising alternatives to individual-based strategies. However, the design of these interventions rests on the ability to identify influential peers (e.g., 'peer leaders'). Large, full-scale trials of varying network intervention strategies are often prohibitive in cost and time. Thus, this project will employ simulation-based computational models to overcome this barrier. Using a combination of social network analysis and agent-based modelling, the project will compare the effectiveness of a range of strategies for selecting influential individuals in network-based interventions (e.g., selection based on friendship popularity, connectedness, peer nomination of effective leaders), and assess how the effectiveness of each strategy differs according to simulated environmental context. Stochastic actor-oriented models (SAOMs) will be fit to data from two large social network interventions with young people, ages 13-16, the A Stop Smoking in Schools Trial (n=7,730) (ASSIST), and the Sexually Transmitted Infections and Sexual Health trial (n = 1,376) (STASH), to provide empirically-grounded parameters of network and behaviour change. Agent-based models will simulate varying intervention scenarios. Peer leader selection will be manipulated first, to assess which strategy results in the largest behaviour change. Manipulations to the social (e.g., network structure, relationship strength) and environmental context (e.g., demographics of students) will then be compared to assess the interplay between intervention strategy and wider context. Social network models, and agent-based extensions to these models, are well-suited to evaluate network interventions given their ability to be grounded in empirical data, and simulate counterfactuals to the observed data. The findings will inform how social network interventions can be tailored according to the specific contextual features of individual schools, and support the development of future schools health interventions.