Presentation from the 01-13-2017 LHS Collaboratory Meeting

1. Mobile Health Technologies
in Cardiovascular Disease
Mike Dorsch, PharmD, MS, FCCP, FAHA
Clinical Associate Professor
University of Michigan College of Pharmacy

2. Disclosures
• Grant/Research Support:
– AHRQ, NIA, Frankel CVC, LHS

3. Objectives
• Overview the advances in mobile
technologies
• Show a care model that lends itself to
mobile technologies
• Discuss two cases for using mobile
technologies in cardiovascular disease:
– Geofencing technology to help patients
reduce sodium intake
– Remote monitoring in heart failure

4. Happy 10th Anniversary!

5. • Cameras
• Displays
• GPS and GLONASS
• Wireless Communications
– LTE, WiFi, bluetooth, NFC
• Fingerprint sensors
• Gyro
• Accelerometer
• Proximity sensor
• Ambient light sensor
• Barometer
• Memory
• Microprocessors
What do mobile devices have to
offer?

6. Global Mobile Growth 2015-2020
Source: Cisco VNI Mobile, 2016

7. Global Connected Wearable
Devices 2015-2020
Source: Cisco VNI Mobile, 2016

8. US Mobile Connectivity Index
GSMA Connectivity
Index 2016

9. Smartphone Use in the US
Pew Research Center, 2015

10. Smartphone Use in the US
Pew Research Center, 2015

11. Model of Care
Credit: Larry An, MD

12. Cardiovascular Disease
Prevalence
Dariush Mozaffarian et al. Circulation. 2016;133:e38-e360

13. Deaths Due to Cardiovascular
Disease
Dariush Mozaffarian et al. Circulation. 2016;133:e38-e360

14. • Cardiovascular risk
assessment
– Aspirin
– Statin
• Blood pressure control
– Sodium intake
Million Hearts Initiative – Prevent
Cardiovascular Disease

15. 1. Start here
2. Check the
total calories
per serving
3. Limit these
nutrients
4. Get enough
of these
nutrients
5. Quick
Guide for %
Daily Value:
5% or less is
low and 20%
or more is
high
Green < 120 mg of sodium per serving
Yellow 120-480 mg of sodium per serving
Red > 480 mg of sodium per serving

16. Changes in Salt Intake in England
BMJ Open 2014;4: e004549.

17. Greater than 2300mg Sodium Daily
Incident Cardiovascular Disease
BMJ. 2009 Nov 24;339:b4567.

18. Sodium Intake and CVD Mortality
CVD Mortality
Public Health Nutr. 2015 Mar;18(4):695-704.

19. Usual Daily Intake of Sodium
Among US Adults
85-90% of US adults are above 2300mg/day
NHANES 2009-2012.

20. Dietary Sodium in the US

21. Total US food expenditures away
from home and at home
Dariush Mozaffarian et al. Circulation. 2016;133:e38-e360

22. Restaurant Average Sodium (mg)
Chili’s 2522
Burger King 894
Panera Bread 1113
Subway 929
Low Sodium Options at
Restaurants

23. Which foods contain high sodium?
Dariush Mozaffarian et al. Circulation. 2016;133:e38-e360

24. Patient Estimation of Sodium
Intake per Day
J Am Coll Nutr. 1991;10:383-393

25. Focus Groups About Sodium
Intake
• Patients say they struggle picking low
sodium options in restaurants
• Patients think restaurants don’t serve low-
sodium foods
• Patients have low confidence in picking low
sodium foods at restaurants
• Patients cannot tell if a grocery store item is
low in sodium
• Patients have low confidence estimating
how much sodium they eat in a day

26. One-on-one Interviews About
Following a Low Sodium Diet

27. Health IT and sodium
• Intervention – a mobile application
– Geofencing-based alerts at restaurants with low sodium
options
– Scanning foods at grocery stores and providing lower sodium
options
– Top 5 sodium containing foods
• Aim 1 – Develop the messages in focus groups
• Aim 2 – Study the efficacy of the mobile application in HTN
patients

28. Health IT and Sodium
What do I eat
That is low sodium
At McDs?

29. Health IT and Sodium

30. Health IT and Sodium
Week 0 Week 2 Week 4 Week 8
Dietary assessment -
ASA24, FFQ with
sodium screen
Self-care – SCFLDS
Clinical measures –
24-hr urinary sodium
excretion, spot urinary
excretion of sodium,
dipstick chloride,
dipstick creatinine,
blood pressure
Dietary assessment -
ASA24, FFQ with
sodium screen
Self-care – SCFLDS
Clinical measures - 24-
hr urinary sodium
excretion, spot urinary
excretion of sodium,
dipstick chloride,
dipstick creatinine,
blood pressure
Clinical measures -
dipstick chloride,
dipstick creatinine,
blood pressure
Week 6
Abbreviations – ASA24 = Automated Self-administered 24-Hour Dietary Recall,
FFQ = Food Frequency Questionnaire, SCFLDS = Self-care Confidence in Following a Low-sodium Diet Scale

31. Heart Failure
Prevalence:
5.7 million
>8 million by 2030
Mortality:
≈50% at 5 years
Economic costs:
≈$30.7 billion (direct and indirect)
$69.7 billion by 2030
Morbidity:
≈1 million hospitalizations/yr
Circulation 2015;131:e29-e322

32. Heart Failure
• Self-management is defined as an active
cognitive process undertaken by the patient
to manage their heart failure
• HF patients self-monitor weight, sodium,
fluid intake and symptoms
• Patients interpret self-monitoring and
perform self-management
• We developed a website application to
determine if self-monitoring improved HF
status

33. Health IT and Heart Failure
• Prospective single-center pre/post study
• Patients enrolled from the Advanced HF at the
FCVC
• Self-monitoring was performed for 12 weeks
• HF status was measured by:
– NYHA class, MLWHF, weight, exercise, walk
distance, physical exam
Telemed J E Health. 2015;21(4):267-70.

34. Health IT and Heart Failure
Telemed J E Health. 2015;21(4):267-70.

35. Health IT and Heart Failure
Telemed J E Health. 2015;21(4):267-70.
Variable Value (N=24)
Age (yrs) 59 ± 9
Female Gender (%) 63 (15)
Ejection Fraction (%) 28 ± 10
Hospitalizations in the last year (%)
0
1 or more
54 (13)
46 (11)
ICD (%) 83 (20)
Coronary Artery Disease (%) 58 (14)
HTN (%) 54 (13)
Atrial fibrillation (%) 38 (9)
Diabetes (%) 33 (8)
Median duration of monitoring was 67 days.

36. Health IT and Heart Failure
Telemed J E Health. 2015;21(4):267-70.

37. Health IT and Heart Failure
Telemed J E Health. 2015;21(4):267-70.

38. Health IT and Heart Failure
Telemed J E Health. 2015;21(4):267-70.
Parameter Pre Post P-value
Weight (lbs) 209 ± 9.6 207 ± 9.4 0.389
Exercise/week (no.) 1.29 ± 0.5 2.5 ± 0.6 0.3
Walk distance (yds) 572 ± 147 845 ± 187 0.119
JVD (cm) 8.1 ± 0.6 6.7 ± 0.3 0.083
Peripheral edema
(%)
29.2 16.7 0.375
JVD = jugular venous distention

39. Conceptual Model for Pre-clinical
Measures of Clinical Worsening
Self-Monitoring
Active
Clinical symptoms of
worsening HF
CURRENT STRATEGY
Self-management
Health care provider
support
Self-Monitoring
ActivePassive
Weight, steps (movement)
And sleep patterns
5 questions about how
patients are feeling
Self-regulation from visual
graphs of progress and push
notifications
Self-management
Health care provider
support
FUTURE STRATEGY
Pre-clinical measurements
of worsening HF
Clinical symptoms of
worsening HF

40. Heart Failure Progression
HealthStatus
Time
= Normal Progression = Progression with adaptive mobile technologies

41. Health IT and Heart Failure
• Developing a mobile application that
incorporates many aspects of the website
• Adding into the application passive
remote monitoring and motivational
messages
• Creating a predictive model to identify
pre-clinical markers of clinical worsening
using wearable devices

42. Conclusions
• Mobile technology offers a chance to collect
data about patients in their environment and
gives researchers access to data that has
not been collected previously
• GPS and geofencing are promising
technologies for contextual just-in-time
interventions
• Wearable devices may offer a key into pre-
clinical states in chronic disease
management

43. Special Thanks!
• Todd Koelling, MD
• Scott Hummel, MD, MS
• Larry An, MD
• CHCR
– Rex Timbs
– Emerson Delacroix
– Kristen Miller
– Juan Arzac
– Diane Egleston