Tracing coalition changes in knowledge in and engagement with childhood obesity prevention to improve intervention implementation

Abstract

Background While most coalition research focuses on studying the effects of peer relationship structure, this study examines the coevolution of coalition structure and behavior across three communities in the U.S. with the goal of identifying coalition dynamics that impact a childhood obesity prevention intervention. Methods Over two years (2018–2020), three communities within the U.S. participated in a childhood obesity prevention intervention at different times. This intervention was guided by the Stakeholder-Driven Community Diffusion theory, which describes an empirically testable mechanism for promoting community change. Measures are part of the Stakeholder-driven Community Diffusion (SDCD) survey with demonstrated reliability, which include knowledge of and engagement with childhood obesity prevention and social networks. Data from three coalition-committees and their respective networks were used to build three different stochastic actor-oriented models. These models were used to examine the coevolution of coalition structure with coalition behavior (defined a priori as knowledge of and engagement with obesity prevention) among coalition-committee members and their nominated alters (Network A) and coalition-committee members only (Network B). Results Overall, coalitions decrease in size and their structure becomes less dense over time. Both Network A and B show a consistent preference to form and sustain ties with those who have more ties. In Network B, there was a trend for those who have higher knowledge scores to increase their number of ties over time. The same trend appeared in Network A but varied based on their peers’ knowledge in and engagement with childhood obesity prevention. Across models, engagement with childhood obesity prevention research was not a significant driver of changes in either coalition network structure or knowledge. Conclusions The trends in coalition Network A and B’s coevolution models may point to context-specific features (e.g., ties among stakeholders) that can be leveraged for better intervention implementation. To that end, examining tie density, average path length, network diameter, and the dynamics of each behavior outcome (i.e., knowledge in and engagement with childhood obesity prevention) may help tailor whole-of-community interventions. Future research should attend to additional behavioral variables (e.g., group efficacy) that can capture other aspects of coalition development and that influence implementation, and to testing the efficacy of network interventions after trends have been identified.

Exploratory Analysis of Social Networks Linked to the Provision of Beverages in Costa Rican Schools

“…We adapted the network dimension portion of the stakeholder-driven community diffusion survey, which has been implemented in the United States with acceptable reliability to assess social and work network properties [ 22 , 23 ]. Given the objective of exploring beverage discussion networks across schools, the original survey went through several revisions and adaptations before administration.…”

The Integration of Complex Systems Science and Community-Based Research: A Scoping Review

“…In recent years, there has been a growing trend among researchers to integrate CSS and CBR to address seemingly intractable concerns, such as childhood obesity, tobacco use, and HIV [10][11][12]. For instance, in the Catalyzing Communities project at Tufts University, researchers collaboratively merged group model building, social network analysis, and computational modeling to evaluate the diffusion of knowledge and engagement in childhood obesity prevention from a small community coalition to the broader community, with the goal of creating and sustaining local changes in policies and practices that influence child nutrition and physical activity [13][14][15]. This innovative approach integrated CSS concepts and analytical techniques with CBR principles and methods, and has recently been cited as an exemplar systems-informed implementation of a science project [16].…”