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

Transcription

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

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

3 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

4 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

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

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

7 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

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

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

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

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

12 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

13 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

14 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

15 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?

16 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 studies

17 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

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

19 Introduction

20 Introduction

21 Consent

22 Consent

23 Consent

24 Consent

25 Consent

26 Share Data with HealthKit

27 Tasks and Dashboard

28 Tasks and Dashboard

29 40,000 Enrolled in MyHeart Counts

30 40,000 Enrolled in MyHeart Counts

31 Initial Data: Physical Activity

32 Initial Data: Happiness

33 Initial Data: Happiness

34 Initial Data: Diet (Daily Fruits/Vegetables)

35 Initial Data: Sleep 0.03 Bed Time B e d t i m e density 0.02 status late early age A g e

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

37 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

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

39 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

40 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

41 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 Security/privacy of connected health data How to safely link and share mhealth data science is why

42 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

43 Q & A