Format

Send to

Choose Destination

See 1 citation found by title matching your search:

Obesity (Silver Spring). 2019 Jul 25. doi: 10.1002/oby.22553. [Epub ahead of print]

Activating a Community: An Agent-Based Model of Romp & Chomp, a Whole-of-Community Childhood Obesity Intervention.

Author information

1
Center on Social Dynamics and Policy, Economics Studies Program, The Brookings Institution, Washington, DC.
2
Friedman School of Nutrition Science and Policy, Tufts University, Boston, Massachusetts.
3
Global Obesity Centre (GLOBE), Centre for Population Health Research, Deakin University, Geelong, Victoria, Australia.
4
School of Population Health, University of Auckland, Auckland, New Zealand.

Abstract

OBJECTIVE:

Successful whole-of-community childhood obesity prevention interventions tend to involve community stakeholders in spreading knowledge about and engagement with obesity prevention efforts through the community. This process is referred to by the authors as stakeholder-driven community diffusion (SDCD). This study uses an agent-based model in conjunction with intervention data to increase understanding of how SDCD operates.

METHODS:

This agent-based model retrospectively simulated SDCD during Romp & Chomp, a 4-year whole-of-community childhood obesity prevention intervention in Victoria, Australia. Stakeholder survey data, intervention records, and expert estimates were used to parameterize the model. Model output was evaluated against criteria derived from empirical data and experts' estimates of the magnitude and timing of community knowledge and engagement change.

RESULTS:

The model was able to produce outputs that met the evaluation criteria: increases in simulated community knowledge and engagement driven by SDCD closely matched expert estimates of magnitude and timing.

CONCLUSIONS:

Strong suggestive evidence was found in support of a hypothesis that SDCD was a key driver of the success of the Romp & Chomp intervention. Model exploration also provided additional insights about these processes (including where additional data collection might prove most beneficial), as well as implications for the design and implementation of future interventions.

PMID:
31343115
DOI:
10.1002/oby.22553

Supplemental Content

Full text links

Icon for Wiley
Loading ...
Support Center