Michael V. McConnell, MD, MSEE Professor of CV Medicine and Electrical Engineering (courtesy) Director, Cardiovascular Health

Michael V. McConnell, MD, MSEE Professor of CV Medicine and Electrical Engineering (courtesy) Director, Cardiovascular Health Innovation @StopHeartDz

Disclosures Apple, Inc. In-kind software development support GE Healthcare, Inc. MRI research grant

Stanford Team Euan Ashley MD PhD Associate Professor, Cardiovascular Medicine and Genetics Chair, Stanford Biomedical Data Science Initiative Alan Yeung MD Professor and Chief, Cardiovascular Medicine Director, Stanford Cardiovascular Health Michael Halaas Chief Information Officer, Stanford School of Medicine Aleksandra Pavlovic Research Coordinator Sharat Israni PhD; Janet Kalesnikoff PhD Stanford Biomedical Data Science Initiative

Stanford Advisors Mary Rosenberger PhD, William Haskell PhD, Abby King PhD Stanford Preventive Research Center David Maron MD, Mary Ann Champagne RN CNS Preventive Cardiology Mildred Cho PhD; Kelly Ormond PhD Stanford Center for Biomedical Ethics Emmanuel Mignot MD PhD Director, Stanford Center for Sleep Sciences and Medicine Robert Harrington MD Professor and Chair, Department of Medicine Program Chair, American Heart Association

Apple/Sage Bionetworks Team Michael O Reilly, MD Stephen Friend, PhD John Wilbanks Michael Kellen

Who gets heart disease? Half of all men and one-third of all women. #1 in world, and growing World U.S.

Prevention: What can we do? Fewer risk factors substantially lowers your risk of death from CVD, for both men and women Men Women Berry JD, et al., NEJM 2012

AHA s 7 Key CV Health Behaviors/Indicators Life s Simple 7

CV Health: How are we doing in the US? NHANES: <50% for most risk factors <10% overall at ideal risk

Prevention: Physical activity Regular physical activity adds years to your life Moore SC, et al., PLoS Med 2012

What is the role of mhealth? It s fundamentally our daily habits, which currently go unmeasured, that primarily determine our risk for cardiovascular disease

What is the role of mhealth? It s fundamentally our daily habits, which currently go unmeasured, that primarily determine our risk for cardiovascular disease We now have mobile devices and apps to image our activities and health the other 362 days a year

What is the role of mhealth? mhealth aligns with AHA 2020 goals of promoting cardiovascular health Reaches people not just patients Empowers people and providers for more personalized and continuous care Reaches the world and not just US

Background/Aims: Prevention and Daily Activity Physical activity is our cheapest and best drug Reduces heart attacks, strokes, diabetes, obesity, etc. Survey data overestimate measured activity, so we don t really know the optimal dose Few studies also incorporate sedentary activity and sleep measures

Background/Aims: Prevention and Daily Activity Key unanswered questions What dose of measured daily activities is associated with optimal heart health? Type, duration, frequency, intensity? Exercise, sedentary, sleep? Goal: Transform our activity guidelines based on measured, global data What interventions can we implement that help people improve their heart-healthy activities and risk? Does empowering individuals with their personalized risk information help? What are the best behavioral interventions that lead to sustained improvement?

ResearchKit: Phone-based Medical Research Open-source tools to build a research study (researchkit.org) Consent screens Surveys Tasks (sensors) Scheduling system Data (not to Apple) Leverages HealthKit mhealth data aggregator Initial launch March 2015 5 studies

MyHeart Counts Methods: 3 Core Tasks Task 1: Activity data collection [iphone motion chip or wearable] Task 2: Fitness assessment [6-min walk test, +/- HR] Task 3: Risk assessment [AHA s 10-year ASCVD risk score] v1: Follow for changes in activity/fitness, risk factors/outcomes v2: Randomize A/B testing of behavioral interventions

Study Protocol/Timeline Cohorts: 1) General population 2) Patient groups 3) Research cohorts/biobanks

Introduction

Consent

Share Data with HealthKit

Tasks and Dashboard

40,000 Enrolled in MyHeart Counts

Initial Data: Physical Activity

Initial Data: Happiness

Initial Data: Diet (Daily Fruits/Vegetables)

Initial Data: Sleep 0.03 Bed Time B e d t i m e density 0.02 status late early 0.01 0.00 20 40 60 80 20 40 age 60 80 A g e

Initial Data: Sleep vs. Happiness * Life Satisfaction Retire Wake early late late early early early late late

Data Feedback, Analysis Personalized feedback to participants: Heart health age (based on 10 yr risk score), Fitness level (relative) Weekly review of active/sedentary/sleep time Educational links/resources for all of the above Primary analyses will compare active/sedentary/sleep min to: 10-yr and lifetime risk (e.g., AHA/ACC ASCVD pooled analysis calculator) Fitness assessment (e.g., 6-min walk distance) Distribution by age, gender, race, location Other health markers (BP, HR, lipids, glucose) Self-report survey data Added analyses for data-rich cohorts: Real-time activity vs. HR response (via Watch/wearable) Genomics, Biomarkers EMR/PMR

Challenges Mobile health sensors are not always gold standards Majority are consumer/commercial devices

Challenges Mobile health sensors are not always gold standards Majority are consumer/commercial devices Data quality more dependent on the participant Opt in, Motivation/engagement, tech savvy/access

Challenges Mobile health sensors are not always gold standards Majority are consumer/commercial devices Data quality more dependent on the participant Opt in, Motivation/engagement, tech savvy/access Use of sensors and data feedback can modify behavior Can be difficult to have a placebo or keep user blinded

Next Steps Incorporate randomized behavioral interventions Collaborating with AHA on MyLifeCheck feedback/coaching Broaden availability (Android version, outside US) Collaborating with Oxford Expand to assess daily activity/fitness in other research studies and cohorts Stanford and outside collaborators

Q & A