Comparing methods of targeting obesity interventions in populations: An agent-based simulation.

ABSTRACT: Social networks as well as neighborhood environments have been shown to effect obesity-related behaviors including energy intake and physical activity. Accordingly, harnessing social networks to improve targeting of obesity interventions may be promising to the extent this leads to social multiplier effects and wider diffusion of intervention impact on populations. However, the literature evaluating network-based interventions has been inconsistent. Computational methods like agent-based models (ABM) provide researchers with tools to experiment in a simulated environment. We develop an ABM to compare conventional targeting methods (random selection, based on individual obesity risk, and vulnerable areas) with network-based targeting methods. We adapt a previously published and validated mo

SUBMITTER: Beheshti R 

PROVIDER: S-EPMC5769018 | biostudies-literature | 2017 Dec

REPOSITORIES: biostudies-literature