order EVP-6124 We address this problem from

We address this problem from a computational modeling point of view, and build an ABM that simulates the outcomes of different targeting methods including selected realistic factors that may interact. There exists a limited but rapidly developing literature for modeling social influence on obesity patterns, and studying network-based obesity interventions. However, the literature seems to provide contradictory conclusions. On one side, Zhang, Tong, Lamberson, Durazo-Arvizu, and Luke (2015) finds no differences between selecting random vs. overweight opinion leaders. El-Sayed et al. (2013) claims that interventions that target the most well-connected individuals in a population will have little or no added value compared with at-random implementation. On the other hand, Bahr, Browning, Wyatt, and Hill (2009) find that random targeting approaches require more individuals to effect the same change as targeting well-connected individuals on cluster edges. Similarly, Trogdon and Allaire (2014) show that the effect of population-level interventions depend on the underlying social network, and selecting the most popular obese agents for weight loss interventions resulted in greater population impact. These models have been estimated using different datasets in both adult and adolescent populations. Moreover, different network structures have been used to build simulated networks. This includes random, lattice, scale-free, small-world and online social networks (Barabasi, 2009). In all of existing work, the concept of behavioral induction has been used to implement peer influence, which leads to order EVP-6124 of behavior change throughout the network. The structure of the network, for instance small-world vs. scale-free, does not affect intervention outcomes significantly (El-Sayed et al., 2013; Trogdon & Allaire, 2014). However, the social diffusion dynamics have differed dramatically, which may explain differences in results. Since the population effectiveness of any simulated intervention is directly determined by the model\'s assumptions about the diffusion process, it is critical to validate this part of the model before exploring intervention strategies with the model. In this paper, we limit ourselves by holding the diffusion dynamics under consideration constant, focusing exclusively on how different targeting strategies alter population impacts. The question of whether alternate diffusion dynamics may magnify or weaken the impact of interventions across targeting strategies will be the subject of a subsequent analysis.
Materials and methods
Results The results of model validation is shown in Fig. 2. The reported results relate to two years of running our model without any intervention. The mean and standard deviation for the average weight change is equal to 1.8 and 4.0 (pounds) in the NLSY dataset, and 1.5 and 6.6 for our model. Next, we compared the performance of five targeting methods described earlier, as shown in Figs. 3 and 4 below. Fig. 3 plots change in average agent weight over two years after a behavioral intervention is delivered to reduce EI. As specified by the model, EI is reduced by 15% in agents assigned to the intervention (on average). The figure shows the combined population impact on average body weight, taking account of both diffusion and environmental effects. A dashed line shows the average weight change of the population, had the simulation continued without any intervention. This shows a slight increase in average body mass consistent with population trends, and represents the control condition or the causal scenario of no intervention against which the 5 targeting methods can be compared. Across all five targeting scenarios, the range of change in population average weight was between -0.35 and -1.60kg (with an average loss of -0.69kg). Random targeting and vulnerable (agents residing in a more obesogenic area) showed the least overall impact (change in mean weight at 2 years of -0.49kg and -0.47kg compared to no intervention (CTNI for short). Targeting high-risk agents (obese) and those with more network connections resulted in more weight loss (-0.63 and -0.75 CTNI respectively). Results showed that selecting subjects on the basis of our IM model resulted in the most average weight loss (-1.7kg CTNI). Confidence intervals (CIs) of vulnerable and random targeting were overlapping, as well as CIs of high-risk and centrality methods. CIs of these two groups were separate, as well as CI of the IM method (shown in the chart) and others. For energy intake, the rate of aggregate weight loss was similar over time with evidence of convergence to a steady state by day 500. The rate of average weight loss was steeper early in the post-intervention period in the IM targeting scenario.