Andrew D Brown1, Jillian Whelan2, Kristy A Bolton3, Phoebe Nagorcka-Smith4, Joshua Hayward4, Penny Fraser4, Claudia Strugnell4, Tiana Felmingham4, Melanie Nichols4, Colin Bell2, Ha N D Le5, Steven Allender4. 1. Global Obesity Centre (GLOBE), Institute for Health Transformation, School of Health and Social Development, Deakin University, Geelong, Australia. Electronic address: andrew.brown@deakin.edu.au. 2. Global Obesity Centre (GLOBE), Institute for Health Transformation, School of Medicine, Deakin University, Geelong, Australia. 3. Global Obesity Centre (GLOBE), Institute for Health Transformation, School of Health and Social Development, Deakin University, Geelong, Australia; Institute for Physical Activity and Nutrition, School of Exercise and Nutrition Sciences, Deakin University, Geelong, Australia. 4. Global Obesity Centre (GLOBE), Institute for Health Transformation, School of Health and Social Development, Deakin University, Geelong, Australia. 5. Global Obesity Centre (GLOBE), Institute for Health Transformation, School of Health and Social Development, Deakin University, Geelong, Australia; Deakin Health Economics, Institute for Health Transformation, School of Health and Social Development, Deakin University, Geelong, Australia.
Abstract
INTRODUCTION: Applying systems science in public health trials is a recent innovation in childhood obesity prevention. This paper aims to use systems science conventions to propose a theory of change for community-based interventions aiming to build capacity and use exemplars from systems science for obesity prevention to describe how this approach works. METHODS: Participants were community-based researchers. A dynamic hypothesis was created in workshops conducted in 2020 and 2021 by identifying variables critical to building community capacity for systems thinking. These were used to develop stock and flow diagrams representing individual causal relationships, feedback loops, and the overall theory of change. RESULTS: The resultant model identified 9 stocks and 4 pairs of central balancing and reinforcing feedback loops. These represented building commitment through relationships, mutual learning, strengthening collaboration, and embedding capacity. The model is described using examples from 3 trials involving 25 communities across Victoria, Australia. CONCLUSIONS: This nonlinear and practice-based model illustrates the process of community-based obesity prevention. The model integrates >20 years of community-based intervention implementation experience, providing an overarching theory of how such interventions work to create change and prevent obesity.
INTRODUCTION: Applying systems science in public health trials is a recent innovation in childhood obesity prevention. This paper aims to use systems science conventions to propose a theory of change for community-based interventions aiming to build capacity and use exemplars from systems science for obesity prevention to describe how this approach works. METHODS: Participants were community-based researchers. A dynamic hypothesis was created in workshops conducted in 2020 and 2021 by identifying variables critical to building community capacity for systems thinking. These were used to develop stock and flow diagrams representing individual causal relationships, feedback loops, and the overall theory of change. RESULTS: The resultant model identified 9 stocks and 4 pairs of central balancing and reinforcing feedback loops. These represented building commitment through relationships, mutual learning, strengthening collaboration, and embedding capacity. The model is described using examples from 3 trials involving 25 communities across Victoria, Australia. CONCLUSIONS: This nonlinear and practice-based model illustrates the process of community-based obesity prevention. The model integrates >20 years of community-based intervention implementation experience, providing an overarching theory of how such interventions work to create change and prevent obesity.
Authors: Pippa McKelvie-Sebileau; David Rees; David Tipene-Leach; Erica D'Souza; Boyd Swinburn; Sarah Gerritsen Journal: Int J Environ Res Public Health Date: 2022-04-19 Impact factor: 4.614