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 model of network diffusion of obesity-related behavior. We then build social networks among agents using a more realistic approach. We calibrate our model first against national-level data. Our results show that network-based targeting may lead to greater population impact. We also present a new targeting method that outperforms other methods in terms of intervention effectiveness at the population level.

Keywords: Agent-based modeling; Effectiveness; Influence maximization; Intervention targeting; Obesity; Simulation; Social networks.

Fig. 1

Depiction of how the model specifies the influence of social networks and environment on agent behavior change. The process of updating energy intake (EI) is shown. A similar process can be imagined for physical activity (PA) by replacing all EIs with PA.

Fig. 2

Comparison between the average biennial change over weight in NLSY79 dataset (blue bars) and our model that was used for the simulation of weight changes (orange bars). (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

Fig. 3

Simulation results for 5 targeting scenarios after implementation of intervention to reduce dietary intake in 10% of the population. Average weight across the simulated population after applying intervention as obtained by five different targeting, and baseline scenario (no intervention) approaches are shown. Confidence intervals for the influence maximization method are shown using light blue color. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

Fig. 4

Simulation results for 5 targeting scenarios after implementation of intervention to increase physical activity in 10% of the population. Average weight across the simulated population after applying intervention as obtained by five different targeting, and baseline scenario (no intervention) approaches are shown. Confidence intervals for the influence maximization method are shown using light blue color. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)