mobile health technologies in cardiovascular disease

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

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Page 1: Mobile Health Technologies in Cardiovascular Disease

Mobile Health Technologies in Cardiovascular Disease

Mike Dorsch, PharmD, MS, FCCP, FAHAClinical Associate Professor

University of Michigan College of Pharmacy

Page 2: Mobile Health Technologies in Cardiovascular Disease

Disclosures

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

Page 3: Mobile Health Technologies in Cardiovascular Disease

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

Page 4: Mobile Health Technologies in Cardiovascular Disease

Happy 10th Anniversary!

Page 5: Mobile Health Technologies in Cardiovascular Disease

• 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?

Page 6: Mobile Health Technologies in Cardiovascular Disease

Global Mobile Growth 2015-2020

Source: Cisco VNI Mobile, 2016

Page 7: Mobile Health Technologies in Cardiovascular Disease

Global Connected Wearable Devices 2015-2020

Source: Cisco VNI Mobile, 2016

Page 8: Mobile Health Technologies in Cardiovascular Disease

US Mobile Connectivity Index

GSMA Connectivity Index 2016

Page 9: Mobile Health Technologies in Cardiovascular Disease

Smartphone Use in the US

Pew Research Center, 2015

Page 10: Mobile Health Technologies in Cardiovascular Disease

Smartphone Use in the US

Pew Research Center, 2015

Page 11: Mobile Health Technologies in Cardiovascular Disease

Model of Care

Credit: Larry An, MD

Page 12: Mobile Health Technologies in Cardiovascular Disease

Cardiovascular Disease Prevalence

Dariush Mozaffarian et al. Circulation. 2016;133:e38-e360

Page 13: Mobile Health Technologies in Cardiovascular Disease

Deaths Due to Cardiovascular Disease

Dariush Mozaffarian et al. Circulation. 2016;133:e38-e360

Page 14: Mobile Health Technologies in Cardiovascular Disease

• Cardiovascular risk assessment– Aspirin– Statin

• Blood pressure control– Sodium intake

Million Hearts Initiative – Prevent Cardiovascular Disease

Page 15: Mobile Health Technologies in Cardiovascular Disease

1.Starthere

2.Checkthetotalcaloriesperserving

3.Limitthesenutrients

4.Getenoughofthesenutrients

5.QuickGuidefor%DailyValue:5%orlessislowand20%ormoreishigh

Green < 120 mg of sodium per serving Yellow 120-480 mg of sodium per serving Red > 480 mg of sodium per serving

Page 16: Mobile Health Technologies in Cardiovascular Disease

Changes in Salt Intake in England

BMJ Open 2014;4: e004549.

Page 17: Mobile Health Technologies in Cardiovascular Disease

Greater than 2300mg Sodium Daily

Incident Cardiovascular Disease

BMJ. 2009 Nov 24;339:b4567.

Page 18: Mobile Health Technologies in Cardiovascular Disease

Sodium Intake and CVD Mortality

CVD Mortality

Public Health Nutr. 2015 Mar;18(4):695-704.

Page 19: Mobile Health Technologies in Cardiovascular Disease

Usual Daily Intake of Sodium Among US Adults

85-90% of US adults are above 2300mg/dayNHANES 2009-2012.

Page 20: Mobile Health Technologies in Cardiovascular Disease

Dietary Sodium in the US

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Total US food expenditures away from home and at home

Dariush Mozaffarian et al. Circulation. 2016;133:e38-e360

Page 22: Mobile Health Technologies in Cardiovascular Disease

Restaurant Average Sodium (mg)Chili’s 2522

Burger King 894Panera Bread 1113

Subway 929

Low Sodium Options at Restaurants

Page 23: Mobile Health Technologies in Cardiovascular Disease

Which foods contain high sodium?

Dariush Mozaffarian et al. Circulation. 2016;133:e38-e360

Page 24: Mobile Health Technologies in Cardiovascular Disease

Patient Estimation of Sodium Intake per Day

J Am Coll Nutr. 1991;10:383-393

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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

Page 26: Mobile Health Technologies in Cardiovascular Disease

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

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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

Page 28: Mobile Health Technologies in Cardiovascular Disease

Health IT and Sodium

What do I eatThat is low sodium

At McDs?

Page 29: Mobile Health Technologies in Cardiovascular Disease

Health IT and Sodium

Page 30: Mobile Health Technologies in Cardiovascular Disease

Health IT and Sodium

Week 0 Week 2 Week 4 Week 8

Dietary assessment -ASA24, FFQ with sodium screenSelf-care – SCFLDSClinical measures –24-hr urinary sodium excretion, spot urinary excretion of sodium, dipstick chloride, dipstick creatinine, blood pressure

Dietary assessment -ASA24, FFQ with sodium screenSelf-care – SCFLDSClinical 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

Page 31: Mobile Health Technologies in Cardiovascular Disease

Heart Failure

Prevalence:5.7 million

>8 million by 2030

Mortality:≈50% at 5 yearsEconomic costs:

≈$30.7 billion (direct and indirect)$69.7 billion by 2030

Morbidity:≈1 million hospitalizations/yr

Circulation 2015;131:e29-e322

Page 32: Mobile Health Technologies in Cardiovascular Disease

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

Page 33: Mobile Health Technologies in Cardiovascular Disease

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.

Page 34: Mobile Health Technologies in Cardiovascular Disease

Health IT and Heart Failure

Telemed J E Health. 2015;21(4):267-70.

Page 35: Mobile Health Technologies in Cardiovascular Disease

Health IT and Heart Failure

Telemed J E Health. 2015;21(4):267-70.

Variable Value (N=24)Age (yrs) 59 ± 9Female Gender (%) 63 (15)Ejection Fraction (%) 28 ± 10Hospitalizations 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.

Page 36: Mobile Health Technologies in Cardiovascular Disease

Health IT and Heart Failure

Telemed J E Health. 2015;21(4):267-70.

Page 37: Mobile Health Technologies in Cardiovascular Disease

Health IT and Heart Failure

Telemed J E Health. 2015;21(4):267-70.

Page 38: Mobile Health Technologies in Cardiovascular Disease

Health IT and Heart Failure

Telemed J E Health. 2015;21(4):267-70.

Parameter Pre Post P-valueWeight (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

Page 39: Mobile Health Technologies in Cardiovascular Disease

Conceptual Model for Pre-clinical Measures of Clinical Worsening

Self-Monitoring

Active

Clinical symptoms ofworsening HF

CURRENT STRATEGY

Self-managementHealth care provider

support

Self-Monitoring

ActivePassive

Weight, steps (movement)And sleep patterns

5 questions about howpatients are feeling

Self-regulation from visualgraphs of progress and push

notifications

Self-managementHealth care provider

support

FUTURE STRATEGY

Pre-clinical measurementsof worsening HF

Clinical symptoms of worsening HF

Page 40: Mobile Health Technologies in Cardiovascular Disease

Heart Failure Progression

Hea

lth S

tatu

s

Time

= Normal Progression = Progression with adaptive mobile technologies

Page 41: Mobile Health Technologies in Cardiovascular Disease

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

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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

Page 43: Mobile Health Technologies in Cardiovascular Disease

Special Thanks!

• Todd Koelling, MD• Scott Hummel, MD, MS• Larry An, MD• CHCR

– Rex Timbs– Emerson Delacroix– Kristen Miller– Juan Arzac– Diane Egleston