ABSTRACT Modifiable risk factors, including diet quality, have a significant impact on disability and life expectancy. Social network analyses demonstrate that social connections play a role in initiating and reinforcing these risk factors through mechanisms such as mutual support and information exchange, as well as social influence pathways like modeling, the establishment of social norms, and persuasion. Though the literature on social networks and health behaviors, including network interventions, has grown substantially, key populations and issues remain under-investigated, such as networks of midlife adults, the impact of social selection (e.g., homophily) alongside peer influence, and the equitable distribution of intervention effects. Further, given that network interventions target small numbers of recipients and benefit larger numbers in a population, cost-efficiency is an important, yet often neglected, metric of intervention success. The proposed study will address these research gaps by conducting simulations to identify how workplace health behavior interventions can best capitalize on social connections to have the most positive, equitable, and cost-effective impact on health behavior outcomes for midlife adults. Our simulations will be built from a stochastic actor-oriented model (SAOM), a statistical network model that simultaneously estimates the formation and dissolution of social ties alongside the transmission of health behaviors. The model will be estimated using empirical data from prior studies testing the impact of a healthy eating intervention employing social norms feedback and other behavioral nudges on the healthfulness of employee cafeteria purchases within a workplace social network. We will map out the scenarios in which interventions leveraging social networks have the most positive and equitable impact on employees’ healthy purchasing. We will also assess how changing model parameters will shape intervention costs and cost- effectiveness. Our specific aims are: 1) Develop, parameterize, and calibrate an SAOM simulating a network including midlife employees (ages 50-64) within a socioeconomically diverse workforce based on empirical data. 2) Use the SAOM to manipulate the targeting strategy, effect size, durability, and social transmissibility of a healthy eating intervention and run simulations to understand which characteristics of the intervention maximize the healthfulness of employee food purchases. At the same time, we will assess the incremental cost- effectiveness of alternate targeting strategies. 3) Across intervention targeting scenarios, use the SAOM to manipulate aspects of social selection and heterogeneous peer influence by sociodemographic characteristics to identify circumstances where interventions mitigate or exacerbate sociodemographic disparities in healthy purchasing. Throughout, we will focus on employees in midlife (ages 50-64) embedded in this broader social network. This work responds dir...