摘要：Objectives. We studied simulated interventions that leveraged social networks to increase physical activity in children. Methods. We studied a real-world social network of 81 children (average age = 7.96 years) who lived in low socioeconomic status neighborhoods, and attended public schools and 1 of 2 structured afterschool programs. The sample was ethnically diverse, and 44% were overweight or obese. We used social network analysis and agent-based modeling simulations to test whether implementing a network intervention would increase children’s physical activity. We tested 3 intervention strategies. Results. The intervention that targeted opinion leaders was effective in increasing the average level of physical activity across the entire network. However, the intervention that targeted the most sedentary children was the best at increasing their physical activity levels. Conclusions. Which network intervention to implement depends on whether the goal is to shift the entire distribution of physical activity or to influence those most adversely affected by low physical activity. Agent-based modeling could be an important complement to traditional project planning tools, analogous to sample size and power analyses, to help researchers design more effective interventions for increasing children’s physical activity. Although tremendous resources have been used to address the obesity epidemic, most interventions have been disappointing. Leading scientists and the National Institutes of Health have called for the application of system science methods for obesity prevention and treatment. 1,2 The multiple, interdependent, nonlinear, and often time-delayed forces that sustain the obesity epidemic are too complex to reverse by focusing attention on an individual’s behavior change, without concurrently addressing change in the larger systems within which that individual is embedded. 2–5 Social network analysis and agent-based modeling are 2 methods used to analyze and understand complex systems. Analysis of social networks can uncover how relationships constrain or enable our health behaviors, and how health behaviors shape which relationships we make or break. Agent-based models are social simulation models that can extend network models to predict future behavior from explicitly programmed, microlevel rules. In this article, we discussed the application of these models to obesity research, highlighted their utility in understanding social contagion (i.e., behavior spread), and identified scenarios that leveraged social contagion to accelerate behavior change. A “network intervention” uses social network data to achieve behavior change. 6 In a 2009 article, Bahr et al. used a cellular automata model to demonstrate that anti-obesity network interventions might require highly complex interventions, including locating the edges of network clusters and including “friends of friends” in the intervention. 7 This novel study was limited by a focus on a highly regular grid lattice network that might not correspond to real-world networks. Recently, El-Sayed et al. used agent-based simulation models in which artificial individuals were nested in a preferential attachment 8 social network to examine the potential for network interventions to reduce obesity. 9 The results suggested no added benefit on reducing population obesity rates using such interventions. Shortly thereafter, de la Haye critiqued several of the assumptions underlying the models of El-Sayed et al. for failing to reflect robust networks effects (e.g., obesity is not distributed at random in real-world networks; obese individuals tend to be connected to each other and marginalized in larger social networks). 10 Those studies evaluated network interventions in which an actor in the model was “treated” (lost weight) and examined how that influenced people in their social network to lose weight (or not become overweight). The mechanisms underlying behavior spread were generally unexamined. Social norms are thought to at least partially account for the network effects on obesity. Leahey et al. presented the first research to explicitly test this assumption. Their findings suggested that social norms specific to one’s social network (subjective norms) influenced weight more than broad societal-level social norms (injunctive norms). 11 In addition to conformity to group norms, there were other mechanisms that that might explain the effect of networks on behaviors associated with obesity (e.g., physical activity), including social facilitation, social learning, social comparison, social support, coercion, or competition. There is strong experimental, network, and theoretical literature that has shown that social networks (peers, family) influence physical activity in adolescents. 12–14 Multiple studies now suggest that network interventions to increase physical activity are warranted. 7,15–22 This becomes important in light of a recent meta-analysis that provided strong evidence that physical activity interventions to date have had small effects (amounting to an increase of 4 minutes of activity per day). 23 We recently demonstrated that children in afterschool programs adjusted their physical activity depending on the activity level of their friendship network. 22 These activity adjustments were 10% or greater and greater than 6 times more likely to happen than not, which suggested that children’s activity levels could be improved by intervening on their friendship network. We used computer simulations to test the expected outcome of implementing a network intervention to increase children’s physical activity by using a real-world social network that objectively measured physical activity, and we used agent-based modeling to test the comparative effectiveness of 3 intervention strategies. Accounting for network effects, we compared 3 intervention scenarios in an afterschool setting by (1) intervening on the most sedentary children within the network, (2) intervening on a random selection of children within the network, and (3) intervening on the most central, connected, or popular children within the network. We chose these intervention scenarios for the following reasons. First, they are theoretically amenable to a network intervention. The most sedentary children were selected because there is evidence that they can bring down the physical activity levels 22 of their immediate circle of friends in the afterschool setting. The most popular children were selected because some individuals (“opinion leaders”) play an especially important role in diffusion of disease and other contagion processes, including norms. 24 Opinion leaders have more incoming ties relative to others in the network; thus, they have more social power and control over behavior spread. 25 Network interventions that leverage opinion leaders as change agents have been implemented in contexts as diverse as preventing substance use, 26 reducing HIV risk behavior, 27 and implementing clinical practice guidelines, 28 but these intervention have not yet been used for obesity-related behavior. Because of the success of this approach in other settings (albeit in adolescents and adults), we hypothesized that it would produce a greater effect than intervening on children at random or focusing efforts on those in greatest need of intervention. Second, we also chose these 3 intervention strategies for their simplicity of execution; program staff in an afterschool setting might easily identify the most well-connected and the most sedentary children without requiring the specialized knowledge necessary for collecting and analyzing complete social networks. Comparing simulated intervention scenarios allowed us to estimate the effectiveness of these network interventions before real-world implementation.
