1. Lifestyle Analytics for Personalized Diabetes and Weight Management Principal Investigators: D. Bertsimas at MIT, collaborative with I. Paschalidis and W. Adams at Boston Univ.  Other contributor/collaborator: Allison O’ Hair  This work was supported in part by the National Science Foundation under grant IIS-1237022. 2000 2010 1990 Source: Behavioral Risk Factor Surveillance System, Centers for Disease Control and Prevention Obesity Trends Among U.S. Adults Our Approach The incidence of type II diabetes is increasing with obesity  25.8 million people in the US have diabetes (8.5% of the population)  According to the CDC, 20-30% of the US population will have diabetes by 2050  35% of adults have pre-diabetes, 50% of adults 65+ In the US, diabetes costs ~$174 billion per year. Diabetes is the leading cause of heart disease, stroke, blindness, amputation and kidney failure. Many of these complications and costs can be avoided with lifestyle changes We have developed LiA (Lifestyle Analytics): a personalized, comprehensive, and dynamic system for diabetes management Learn food preferences to improve diet adherence Model blood glucose dynamics Propose a daily food and exercise plan Blood Glucose Dynamics Blood glucose level is measured in milligrams per decilitre (mg/dL) Blood glucose levels follow a trajectory (BG curve) We model the blood glucose levels as a function of  Fasting level  Food consumed  Classify foods into categories using the glycemic index  Measures the effects of carbohydrates on blood glucose levels  Exercise performed BG measurements can be used to learn the BG curve  Regression to update  Robust optimization to account for error Plan Generation with MIO Maximize preferences  Penalties for high blood glucose levels, nutritional violations Bounds on max/min blood glucose levels Nutritional requirements  Calories, carbs, protein, fat, etc. Food group requirements  Fruits, vegetables, dairy, meat, starch “Appeal” constraints  Variety, timing, balanced meals The Future of LiA LiA aspires to affect the lives of millions of people LiA currently proposes a diet and exercise plan LiA is now in beta testing with a hospital in the US and a hospital in Singapore. In the future, LiA will have more features like:  incorporate drugs for diabetes  provide a weekly supermarket list  give advice on restaurants and what to order  minimize cost  allow more flexibility with recipes  provide a family meal plan Finding a Diet and Exercise Plan Use mixed integer optimization Personalized results for each person Data used:  Blood glucose data  Food database from USDA and recipes  Preferences  Personal attributes to calculate nutritional needs Adaptive Questionnaires Introduced in marketing [Toubia et al. 2003, 2004] Results often suffer from response errors  Incorrect responses influence later questions Previous work using complexity parameters or a Bayesian framework [Abernethy et al. 2008, Toubia et al. 2007] We use integer and robust optimization Acknowledgements