So in this last section, we're going to look at the relationship between obesity and social networks. So in the last decade there's been an explosion of research looking at how obesity clusters in the social networks of children and adults. And this research has consistently found that people who are socially connected to each other tend to be similar in their risk for overweight or their weight status. And it's also found that having a social connection who's obese predicts obesity overtime. So when we observe these social networks like the one you see on the graph here, it's a social network of, again, school children. And here, the nodes are colored by gender, so the blue nodes are the boys, and the yellow nodes are the girls. And the relationships represent friendships. When we track this social network over time, and this one's over the course of a year, and we look at how BMI is distributed in this social network. So in this case the node size represents the BMI of the youth. So larger nodes represent those kids who were on the higher end of the BMI scale and potentially overweight or obese. And what we observe is that there's a tendency for similarity among kids who are connected and who are friends. But that over time, this similarity increases. And so we start to see clusters and smaller friendship groups that become more similar in terms of their weight status. So, on the graph to the right here you see some pockets of friends who are some of these lower BMI scores in the school. And other pockets of friends who are on a higher under the BMI scale. So when some of this research first came out, some of the conclusions that were drawn were that obesity must be socially contagious. This must be happening because we somehow are influenced by the people that we're socially connected to to become overweight or obese. But a number of studies over the past decade have started to look at some of the dynamics and the processes that play out in these social networks overtime. And have identified some more nuance processes that are important to understand and that give rise to the social clustering of obesity that we see in networks. And the first of these processes is social selection. And in these studies where we look at the dynamics of networks over time, and what drives people to become connected to people in the first place. We see really convincing evidence that individuals select peers whose weight status or who's risk for overweight is similar to their own. And we also have some understanding of why this happens, and one of the key reasons is weight based stigma, or the social exclusion of people who are overweight. So when we studied this in youth, we really see evidence where youth who are overweight, aren't really biased at all in who they want to be friends with. But that their friendships are typically not reciprocated from peers who are not overweight. So, through this process of exclusion and stigma, over weight youth tend to be friends with other overweight youth. And they tend to be pushed to the periphery of the margins of these social networks. And we also see a lot of friendship selection around other types of characteristics and behaviors that are related to weight status and BMI. And so we see, hemophily on things like gender, and age, there's economic status. And a range of health behaviors, where youth tend to become socially connected to others who are similar to them. But similar to them on characteristics that are correlated with obesity that contribute to this similarity in obesity in the long run. And the last is what's called propinquity, and this is the tendency for people to become socially connected to people that they're just close to. So this happens when you're exposed to people at school, in your neighborhood and in different geographic spaces. And because a lot of these geographic spaces help explain our risk for obesity we also see similarities in socially connected youth being explained by some of these propinquity processes. But this social network research gives us a much better handle on understanding to what extent their social influence on obesity. And so it allows us to account for these social selection processes and then to examine whether. Over and above these social selection effects, these tendencies for kids who are similar to each other in weight status in the first place. Do we see evidence of social influence? And what we're finding with this research is that we do. That weight status is fairly often predicted by the weight status of our social connections. Once we've accounted for these social selection processes. So this suggests some sort of social contagion of obesity process. Our research, again, over the last decade is giving us more insights into why that's occurring. And this process seems to be playing out through social influences on our weight norms, what we think of as being an acceptable or a healthy weight. And also, direct influences from these social connections on our weight related behaviors. Things like diet and physical activity and how much time we spend in a day being sedentary. And a lot of the research looking at dyads and small groups that's happened over the last 50 years in social psychology and public health. Gives us a lot of information about why that might happen. We know a lot about these interpersonal processes and social influence processes. Things I mentioned previously like imitation and mimicry of other people's eating behaviors. Or the role that social norms and social influences of the people around us play on our behaviors. That shape our behaviors over time. And probably explain some of these social influence processes. So some of this research has drilled down to see if we examine the dynamics of social networks and obesity related behaviors over time, what do we see? And do we see evidence that some social network affects on these behaviors, it might be what's explaining social influence on obesity. So a lot of this research has focused on youth and their friendship networks, and has looked at a range of obesity related behaviors. And what this body of research has found is that friendship selection among youth in these social networks is predicted by things like BMI and weight status. And this strong role of social exclusion and marginalization of overweight youth. But that it's also predicted by kids having similar levels of physical activity here and being in the same types of sports. And we also see some evidence that kids who consume the most junk food tend to become more popular in their social networks. We also see the role of gender and race ethnicity and network structure predicting friendship selection processes. And things that might give rise, again, to the clustering of similar behaviors in these smaller social groups and cliques. And then we find over and above these selection effects. There's strong evidence for social and network effects on behavior change. So we see that the behavior and the characteristics of people that youth are socially connected to. Predict changes in their weight norms, and changes in their physical activity, and the sports that they do, and changes in their screen time, and how much junk food they're eating. And so we see strong evidence for network effects on behavior change and behaviors that are related to obesity risk. And so when we bring this evidence together and start to look at what are the dynamics and the system properties of social networks and obesity and related behaviors. First we see strong evidence that obesity and related behavior is cluster in our social networks. That people who are socially connected tend to be similar in these attributes. And that this is explained by behaviors and BMI shaping our peer selection. And these processes of homophily, and the role of these behaviors, and being popular or being socially marginalized. And then over and above this process where peers and friends in turn shape our behaviors and our BMI. And so we see these types of feedback loops that you see on the screen here where youth or adults who are particularly at risk for obesity are more likely to select into social networks. And that we might think of as obesogenic, or having higher obesity risk. And that there's this feedback loop where these networks then in turn reinforce these risky health behaviors or potentially risky weight norms and reinforce the obesity risk of these individuals. Likewise, we see individuals who have lower risk and for obesity being more likely to select in to what we might think of as a healthy social network. So they tend to be surrounded by people who engage in healthier behaviors or who have lower BMI and that in turn re enforce and influence, and lower their obesity risk. And sort of create this feedback loop that we see in social networks. So, one of the next steps with this research is to bring together all of this information. And think about how our understanding of these social network dynamics and these phenomena over time, can help us think beyond just dietetic level or small group level affects of social influence on obesity. And think about other avenues and processes that we might want to intervene on when we're thinking about social network interventions for obesity. So for a while we've been thinking about social influence and the role that other people's behaviors and ideas play in shaping our obesity risk. But this research also invites us to think about processes of social selection. And how we might think about helping people create or select into healthier social networks, and networks that might then influence healthier behaviors and outcomes. And we can also think about how people who are socially connected are exposed to similar risk factors and how we might work within these social groups. And their shared exposure to risk factors for obesity, and to help them reduce their risk for obesity over time. So, in this last slide I'll talk about a couple of intervention strategies that are being tested and worked through at the moment. And network strategies that, we think are going to be effective at lowering obesity risk. One of these approaches is to thinking about targeting at risk social groups. So this is where we use information and knowledge that we have about the social network structure and the behavior, their BMI of people within that social network. And we identify clusters of potentially peers or family members who are socially connected to each other, and we think more strategically about how we develop group based interventions. Interventions where that whole social group who shares that social risk and typically similar behaviors, and might be able to change their risk for obesity together over a time. And, reinforce those healthy norms, and reduce their risk together. A second approach is thinking about how we might foster healthy social selection and influence. And by this, I don't mean that we want to tell people to stop being friends with the people that they're connected to, or their existing family members. But we're really thinking of this as helping people build healthy adjacent social networks to the people that they might allready be connected to in their lives. And adjacent social networks that are going to help them support healthy change, or their health behavior goals. And so these types of interventions wouldn't just have the goal of creating healthy behavior change and reducing obesity risk. But they'd equally have the goal of helping create and foster social connections among people in the intervention. So that they develop established and sustainable relationships that are going to be these new healthy social networks that will help support them overtime. And so, in conclusion, the goal of this lecture was really to look at how social network analysis and the set of tools within system science. Help us to understand social network dynamics and how this relates to obesity risk, and how we think about utilizing this knowledge for interventions. And then how the social network dynamics fit into larger social systems and our understanding of these as important constructs within these larger system dynamics. And lastly, to think about systems level solution for obesity prevention that incorporates some of these network processes and network dynamics.
