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