lifestyle analytics for personalized diabetes and weight management principal investigators: d....
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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