Abstract
Complex systems modeling can provide useful insights when designing and anticipating the impact of public health interventions. We developed an agent-based, or individual-based, computation model (ABM) to aid in evaluating and refining implementation of behavior change interventions designed to increase physical activity and healthy eating and reduce unnecessary weight gain among school-aged children. The potential benefits of applying an ABM approach include estimating outcomes despite data gaps, anticipating impact among different populations or scenarios, and exploring how to expand or modify an intervention. The practical challenges inherent in implementing such an approach include data resources, data availability, and the skills and knowledge of ABM among the public health obesity intervention community. The aim of this article was to provide a step-by-step guide on how to develop an ABM to evaluate multifaceted interventions on childhood obesity prevention in multiple settings. We used data from 2 obesity prevention initiatives and public-use resources. The details and goals of the interventions, overview of the model design process, and generalizability of this approach for future interventions is discussed.
Highlights
Researchers have called for the development of systems-science models to study topics in public health [1,2,3,4,5,6] and evaluate interventions [7,8]
Using Agent-based modeling (ABM) to assess how heterogeneous agents respond to changes in their environment provides a laboratory to examine outcomes while accounting for pathways through which an intervention may influence behavioral change
The body mass index (BMI) distribution for town A represented the average US community according to current childhood obesity rates
Summary
Researchers have called for the development of systems-science models to study topics in public health [1,2,3,4,5,6] and evaluate interventions [7,8]. Such models have made advances in infectious disease epidemiology [9,10] but have not been broadly used for studying chronic disease prevention. Using ABMs to assess how heterogeneous agents respond to changes in their environment provides a laboratory to examine outcomes while accounting for pathways through which an intervention may influence behavioral change
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
