Advancing mechanistic understanding of social influence of obesity through personal networks.

In the current and previous issues of Obesity, three papers address mechanisms by which obesity might spread from person to person: norms around desirable body size and behaviors and social support from important others. These papers are notable for using egocentric designs, whereby individuals are drawn from a larger population and asked about their own personal networks, allowing inferences to be made on the larger network from which the sample is drawn. The disadvantages when compared with a whole-network (sociometric) approach are that one must rely on the egos’ reports of their alters’ characteristics (such as friend’s body size), that salient network characteristics cannot be calculated at all, or can only be calculated with bias (3), and that the state-of-the-art method for teasing apart network dynamics and social influence (the stochastic actor-based SIENA model) cannot be used (4). Nevertheless, egocentric approaches are much more feasible and generalizable because they do not entail the complete enumeration of single networks. The study by Shakya et al. (5) examined 1-year changes in dieting, alcohol consumption, and physical activity given comparisons between individual study participants (egos) and their named friends and family members (alters). This study is noteworthy because it used a large, nationally representative sample and a variation on the widely used General Social Survey network instrument. The investigators used a social comparison model, whereby egos rated whether they saw themselves as fitter (or less fit) or thinner (heavier) than alters. This study found that individuals who reported themselves to be less fit than their average alters at baseline were less likely to exercise 1 year later, whereas those who thought themselves thinner than their average alters were less likely to diet. These results suggest that peer comparisons can both promote and harm health. Winston et al. (6) used a cross-sectional analysis of a “small change” weight loss program. Participants were asked to place alters within one of three circles representing importance in the ego’s life and to rate whether each alter helped or harmed the ego in achieving his or her weight loss and physical activity goals. Having helpful alters was associated with greater weight loss in the program, whereas having only harmful alters was associated with weight gain. Intriguingly, having a mixture of helpful and harmful alters was associated with similar weight loss to having only helpful alters, highlighting the need for social support. A third study by Leahey et al. (7) examined the normative environment of participants in a weight loss program. Participants reported whether their social contacts were overweight. Participants also reported acceptability of unhealthy eating habits and frequency of encouragement for unhealthy eating and physical inactivity in their personal network. Although having overweight family members and best friends was associated with baseline BMI, social norms were not. Nevertheless, obesity-promoting social norms and number of overweight casual friends were associated with smaller weight loss, suggesting that an overweight norm in the larger network can undermine weight loss. All of these studies point to the double-edged nature of networks: they not only offer social support and healthy exemplars but also undermine efforts to change behavior. However, none of these studies can disentangled peer influence from network selection, which means that they are almost certainly confounded. People who choose thinner friends may be more predisposed to dieting in the first place. Whether confounding is so great as to completely invalidate these results is unknown. As noted above, there are tools available to control for confounding, but they require whole networks followed over time, and the trade-off is with facility and generalizability. A final concern is the lack of clarity on how to translate intriguing findings from observational studies into actionable network-based