Exploring Social Networks to Mitigate the Obesity Epidemic

1. Exploring Social Networks toMitigate the Obesity Epidemic James O. Hill, PhD Center for Human Nutrition University of Colorado Denver www.relenet.com

2. What role do social networks play in promoting Obesity? www.making-money-blogging.com

3. “Obesity appears to spread through social ties” Chistakis and Fowler (2007) • Framingham Heart Study • 5124 subjects • 12,067 in social network • Longitudinal Social network mapped/modeled • Clustering based on BMI • Increased chance of becoming obese: • 57% if friend became obese • 40% if sibling became obese • 37% if spouse became obese Subcomponent of the Social Network in the Framingham Heart Study in the Year 2000.

4. How can we use social network models in obesity research? • Better understand how and why social connections affect obesity • Develop better ways to reduce obesity

5. Modeling Social Networks • Predictive Social Network models • Individuals placed in social network • Rules of interaction established • Example: majority rule • Used to model: animal behavior, spread of infectious disease, voting behavior and other collective actions • Can be used to simulate spread of obesity and propose large-scale interventions

6. Social Network Model Examples Spread of Avian Flu in Indonesia Smoking Behavior Situngkir, 2004 Christakis and Fowler, 2008

7. Social Network Model • Large social network • 10,000 – 1,000,000 individuals • Network configurations/topologies • Square lattice, random, small-world • Individual BMI assigned • Four classes: underweight – obese • Interaction rules • Ten tested: simple (majority) to complex • Social Volatility (temperature) • Low volatility: rational behavior, stable weight • High volatility: irrational, change weight • Social Forces • Carrot or Stick • Food pricing, advertising, legislation A hypothetical social network with each individual’s initial state colored as normal weight (green) or overweight (blue). At each time step, the individuals update their current state to reflect the majority of their network neighbors (including themselves). At time 2, for example, the central figure changes from normal weight to overweight because the majority of its neighbors were overweight.

8. Model Simulation Parameters • 2005 BMI distribution (random) • 1% <18 (white) • 33%: 18-25 (green) • 33%: 25-29 (light blue) • 33%: >30 (dark blue) • Topology: square lattice w/8 nearest neighbors • Interaction rule: majority rules • Social Volatility: 0.6 (some irrational behavior) • Social Force: slight social force for overweight and obesity

9. Simulation Results • BMI groups cluster together • Increasing obesity • Matches population data • Suggests strong social forces at work • Weight loss interventions that ignore social network of participants less likely to succeed • Successful interventions can be designed

10. Clustering • Individuals with similar BMI cluster together • Results support Christakis and Fowler study • Clusters influence population-scale behaviors • Biased initial BMI distributions (e.g. more obese) lead to that BMI class dominating • Individual weight loss difficult • Middle of overweight/obese cluster Clustering of BMI shown for simulations with 10,000 individuals. Top panel, generation 5, bottom panel generation 100.

11. Social network contributing to increasing obesity • Strong Social Forces: Obesigenic environment • Driving increased BMI and larger obese clusters • Larger obese clusters perpetuate more obesity • Tipping point • Social acceptance of overweight/obesity • Social norms changing • Norms may be more important than behavioral imitation (Christakis and Fowler, 2007) • Weight gain by friend “permits” weight gain by individual • Behaviors may be different – not imitating friend • Suggests changing “acceptability” of obesity as important as modifying behavior

12. Using Social Network Models to Reduce Obesity

13. Target Specific Individuals • Target well-connected individuals • Social network celebrities • Can stabilize/convert an entire cluster • Target individuals on edge of cluster • Achieve/Maintain healthy weight (normal BMI) • Maintain cluster with small number of targeted individuals

14. Increase Social Volatility and Change Social Forces • Target small number in ALL BMI groups • Most effective when social volatility is increased (less rational behavior) • Change Social Forces • Advertising, Tax incentives • Best intervention will target well-connected while increasing social volatility and changing social forces

15. Weight Loss with Friends? • Individuals in middle of large cluster • Surrounded by similar BMI • Small group of friends not likely to succeed • Friends of Friends • Larger group • Better short term success • Best for individuals on edge of cluster Dieting with friends of friends may be a more effective strategy than dieting with friends. (a) A single overweight individual is selected near the border of the cluster, and eight surrounding friends of friends are also selected (all shown in red). Each is pinned at the normal weight value (green). (b) After 40 time steps, the normal-weight cluster has expanded to enclose the dieting cohort.

16. Conclusions • Social network models replicate the observed spread of obesity • Obesity spreads via social ties • Strong social forces are driving obesity • Individual weight loss difficult due to clustering • Social network should be considered in obesity interventions • Social network models can be used to design effective interventions • Successful interventions: map social network, target specific individuals • Change social forces to reduce acceptance of obesity • Increase irrational behavior (e.g. make controversial) to facilitate weight change

17. Acknowledgements • David Bahr, PhD, Regis University, dbahr@regis.edu • Ray Browning, PhD, Colorado State University, browning@cahs.colostate.edu • Holly Wyatt, MD, University of Colorado Denver • Model: http://academic.regis.edu/dbahr/GeneralPages/CellularAutomata/CA_Explorer/CA_Explorer_Home.html