The Future Digital Health Consumer Here Today – Toward Personalized Preventive Medicine Invited Talk Johnson and Johnson Pharmaceutical Research & Development.

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The Future Digital Health Consumer Here Today – Toward Personalized Preventive Medicine Invited Talk Johnson and Johnson Pharmaceutical Research & Development Center La Jolla, CA February 4, 2011 Dr. Larry Smarr Director, California Institute for Telecommunications and Information Technology Harry E. Gruber Professor, Dept. of Computer Science and Engineering Jacobs School of Engineering, UCSD Follow me on Twitter: lsmarr 1

Calit2 Has Been Had a Vision of the Digital Transformation of Health for a Decade Next StepPutting You On-Line! –Wireless Internet Transmission –Key Metabolic and Physical Variables –Model -- Dozens of Processors and 60 Sensors / Actuators Inside of our Cars Post-Genomic Individualized Medicine –Combine –Genetic Code –Body Data Flow –Use Powerful AI Data Mining Techniques The Content of This Slide from 2001 Larry Smarr Calit2 Talk on Digitally Enabled Genomic Medicine

Nine Years Later I Am Recording My Metabolic Self 25 Week Average: 2473 Calories Burned/Day 1:19 hr Physical Activity/Day (>3 METs) 6887 Steps/Day (~3.4 Miles) 25 Week Ave: 6:51 hrs with 81% Efficiency Elliptical Gardening Up and Down House Steps Measure Quantity and Quality of Sleep

Quantifying My Sleep Pattern Using Zeo - Surprisingly About Half My Sleep is REM! REM is Normally 20% of Sleep Mine is Between 45-65% of Sleep An Infant Typically Has 50% REM

Psychological & Social sensors Biological sensors Diet & Physical Activity sensors Air quality (particulate, ozone, etc) Temperature, GPS, Sound, Video, Other devices & embedded sensors BP, Resp, HR, Blood (e.g. glucose, electrolytes, pharmacological, hormone), Transdermal, Implants Mood, Social network (peers/family) Attention, voice analysis Physical activity (PAEE, type), sedentary Posture/orientation, diet intake (photo/bar code) Wearable Environmental sensors Sensor data + Clinical & Personal Health Record Data + Ecological data on determinants of health + Analysis & comparison of parameters in near-real time (normative and ipsative) + Sufficient population-level data to comprehend trends, model them and predict health outcomes + Feedback in near real-time via SMS, audio, haptic or other cues for behavior or change in Rx device = True Preventive Medicine! Sensors embedded in the environment Geocoded data on safety, location of recreation, food, hazards, etc Over the Next Decade an Explosion of Health Sensing: Center for Wireless &Population Health Systems

Center for Wireless and Population Health Systems Center for Wireless and Population Health Systems: Driven by Major Health Challenges Research on Systems of Wireless, Clinical, & Home Technologies to Measure & Improve Lifestyle & Other Health-Related Behaviors In: –Adolescents –Healthy & to Prevent Weight Gain –Overweight & Obese –At Risk for Type 2 Diabetes –With Chronic Disease –Recovering from Leukemia –Adults –Depression –Schizophrenia –Promoting Successful Aging –Exposure Biology Research –Cancer Comparative Effectiveness Research Kevin Patrick, UCSD SOM, Director

Center for Wireless & Population Health Systems: Cross-Disciplinary Collaborating Investigators UCSD School of Medicine –Kevin Patrick, MD, MS, Greg Norman, PhD, Fred Raab, Jacqueline Kerr, PhD –Jeannie Huang, MD, MPH UCSD Jacobs School of Engineering –Bill Griswold, PhD, Ingolf Krueger, PhD, Tajana Simunic Rosing, PhD San Diego Supercomputer Center –Chaitan Baru, PhD UCSD Department of Political Science –James Fowler, PhD SDSU Departments of Psychology & Exercise/Nutrition Science –James Sallis, PhD, Simon Marshall, PhD Santech, Inc. –Jennifer Shapiro, PhD, Ram Seshan, MS, MBA PhD students and Post-doctoral Fellows (current) –Jordan Carlson, Barry Demchak, Laura Pina, Ernesto Ramirez, Celal Zifti 7

Genetic & Biological Factors Interpersonal & Psychosocial Factors Environmental/Ecological Factors Medical & Exercise Sciences Behavioral & Social Sciences Environment, Population & Policy Sciences Center for Wireless & Population Health Systems: Integrative View to Support Interventions Source: Kevin Patrick, UCSD SOM & Calit2

Interpersonal & Psychosocial Factors NanoTech, Drug Delivery, Sensors, Body Area Networks (BANs) BAN-to-Mobile-to- Database, SMS/MMS Social networks Ubicomp, Location-Aware Services, Data Mining, Systems Sciences Genetic & Biological Factors Environmental/Ecological Factors Center for Wireless &Population Health Systems: Developing and Testing Engineering-Based Solutions Source: Kevin Patrick, UCSD SOM & Calit2

Two Projects in the Calit2 Center for Wireless & Population Health Systems CitiSense –Sensing of Individuals Environment SMART –Use of Social Networks to Change Behavior

CitiSense –New NSF Grant for Fine-Grained Environmental Sensing Using Cell Phones CitiSense contribute distribute sense display discover retrieve Seacoast Sci. 4oz 30 compounds 4oz 30 compounds EPA CitiSense Team PI: Bill Griswold Ingolf Krueger Tajana Simunic Rosing Sanjoy Dasgupta Hovav Shacham Kevin Patrick C/A L S W F Intel MSP

SMART Social Mobile Approach to Reduce Weight mobilesocial PURPOSE To leverage social networks, social media, mobile phones, and the web for weight loss among year old young adults. Funded with a 5-year grant from NHLBI/NIH Source: Kevin Patrick, UCSD SOM & Calit2

SMART Social Mobile Approaches to Reduce Weight mobilesocial 1 - Participant 2 - Friend of Participant 3 - Friend of Friend SMART Study INTERVENTION Facebook an Intact Social Network + Mobile Phone txt Messaging + Smartphone Mobile Apps + Website Source: Kevin Patrick, UCSD SOM & Calit2

Study Design: 400 Subjects, years old male & female, Weight: 25 < BMI < 34.9 University students at four colleges in the San Diego area Owns a personal computer Owns a mobile phone and uses text messaging Facebook user / willing to start using Facebook RECRUITMENT BEGINS: March 2011 ENROLLMENT BEGINS: April/May, 2011 n = 200 Comparison n = 200 Treatment Collaborators and platforms:

LifeChips: the merging of two major industries, the microelectronic chip industry with the life science industry LifeChips medical devices Lifechips--Merging Two Major Industries: Microelectronic Chips & Life Sciences 65 UCI Faculty

I am the Future Digital Health Consumer: Measuring the State of Your Body and Tuning It 20-pounds-by-becoming-a-quantified-self/ I Arrived from 20 Years in the Midwest in 2000 and Decided to Move Against the Obesity Trend Age 52 Age 62

Goal: Lose Weight by Changing What &How Much I Eat, While Increasing Aerobic Exercise Gradually Moving to Zone Diet and Regular Exercise Losing Diet Discipline Back on Track, Fewer Calories More Exercise Exercise is Elliptical and Walking Reached Desired Weight Blood Pressure 134/73 Pulse 55 Resting Pulse Lowered to 45

Goal: Reduce My Body Fat Through Nutrition and Exercise I Lost More Than 1/3 of My Original Body Fat Fat Loss: Chest First, Then Thighs, & Finally Abs Most of Loss in First 3-4 Years, Then Very Slow Measurements by Trainer Terry Martin % Body Fat Drops From 21% TO 15% Higher Body Fat Can Increase the Risk of Heart Disease, Diabetes, and Cancer

Goal: Quantify Your Food Intake So You Can Tune Your Glucose/Insulin System and Lower Inflammation Quality of Food –All Organic and Mostly Locally Grown –Carbs are Low Glycemic Index –No Added Sugar or Refined Flour – Mostly Fruits and Vegetables –Proteins are Lean –Meat is Grass Fed – No Corn or Antibiotics –Fish is Wild, Often Locally Caught –Fats are Omega-3 Rich –Supplemented by 7g Daily Pharmaceutically Purified Fish Oil Pills Computed Average Over 12 Days When at Home for Maximum Accuracy Measure All Food and Drink Components, Then Use USDA Lookup to Compute Each Item Still Need to Lower Sugar & Increase Protein and Decrease Fat by 15%

Goal: Improve My Omega-3 ScoresTo Reduce Inflammation & Protect Against Future Heart Disease If your Omega-3 Score is at least 7.2 and your DHA Score is at least 4.5, you are 32% less likely to develop heart disease If your EPA+DHA Score is at least 4.6, you are 70% less likely of dying from a heart attack. Ref: Based on Lemaitre et al., n-3 Polyunsaturated fatty acids, fatal ischemic heart disease, and nonfatal myocardial infarction in older adults: the Cardiovascular Health Study. Am. J. Clin. Nutr. 77: (2003). Graphics from = My Values Tested by yourfuturehealth.com

In Spite of My High Levels of Omega-3s, Blood Measurements Show Chronic Inflammation 21 hsCRP from Blood Tests Come Back When You Have a Symptom Symptom: Acute Diverticulitis Antibiotics High Sensitivity Complex Reactive Protein is the Standard Blood Test for Inflammation hsCRP Should Be <100

Blood Tests Did Not Reveal Colon Immune Response-- Stool Tests Provide Additional Insight Come Back When You Have a Symptom Invisible Episodic Colon Immune Response Symptom: Acute Diverticulitis Antibiotics hsCRP from Blood Tests Others from Stool Tests by yourfuturehealth.com Lactorferrin Should Be <73 25x Normal 15x Normal Future Challenge: Danger of Persistent hsCRP Inflammation and Destabilized Microbiome 30x Normal

Goal: Monitor the Colon Microbiome- an Ecological Battle Between Beneficial and Harmful Bacteria All 3+ or 4+ Three Weeks Before Taking Antibiotics Two 0+ Two Years After 10 Days of Antibiotics Levaquin & Metronidaloze Next Step Get DNA Microbe, Parasite, Yeast Test These Tests Culture Bacteria

Goal: Determine the Structural State of Each Internal Organ and Major Body Subsystems 3D Full Body Scan mm Accuracy Small Tumors Organ Damage Plaque Sites Virtual Colonoscopy 64 Slice Heart CT Scan Plaque Sites Valve Anomalies Carotid Ultrasound Imaging Plaque Thickness Physical Colonoscopy Detect Polyps Colon Disease Stress Test with Echocariogram Heart Fitness Structural Weakness

Amazing Improvements Are Possible Through Nutrition, Exercise, and Stress Management The Quantified Body of Professor Ramesh Rao, UCSD Division Director, Calit2 Broad Spectrum Amino Acid Vegetarian Diet, 1000 Miles of Running, 1000 Hours of Yoga

Should You Keep Your Health Data Private or Share to Gain the Most Knowledge?

Where I Believe We are Headed: Predictive, Personalized, Preventive, & Participatory Medicine Quantify ~2500 Blood Proteins, 50 Each from 50 Organs or Cell Types from a Single Drop of Blood To Create a Time Series

You Can Download This Presentation at lsmarr.calit2.net