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Wearable Computing and Sensor Technology:
What’s the Fuss?
Amy R. Sheon, PhD, MPH
Executive Director, Urban Health Initiative
Case Western Reserve University School of Medicine
June 11, 2014
6/11/2014
SHEON
1
• Increase familiarity with ubiquitous wearable activity tracking devices
• Stimulate interest in using commercially available devices for:
• Personal health improvement
• Clinical care
• Employee health
• Population health management
• Clinical research
• Consider other applications
Talk Objectives
6/11/2014 SHEON 2
Roadmap
•Disclosure of commercial and personal interests
•Review device development and features
•Highlight important features, limitations
•Consider current and future uses in obesity
•Other uses, including clinical epidemiology
6/11/2014 SHEON 3
Research/ Clinical Care
• Evidence-based
• Gold standard
• Patient safety
• Reimbursement/cost
Population Health/ Entrepreneurial
• Does it improve health
• Can people afford/access it
• Will people buy it
• Will they use it
Do they work well enough? Do they work?
6/11/2014 SHEON 4
Intake
Energy Expenditure
+ Balance -
6/11/2014 SHEON 5
Energy Balance
Intake
Energy Expenditure
+ Balance -
6/11/2014 SHEON 6
Energy Balance
Energy Expenditure Estimation
• Expert Panel advises “clinical judgment regarding when to accept estimated RMR using predictive equations in any given individual”
• “….indirect Calorimetry may be an important tool when, in the judgment of the clinician, the predictive methods fail an individual in a clinically relevant way.”
• For groups “underrepresented by existing validation studies of predictive equations, a high level of suspicion regarding the accuracy of the equations is warranted.”
Comparison of Predictive Equations for Resting Metabolic Rate in Healthy Nonobese and Obese Adults: A Systematic Review. Frankenfield, et al; J Am Diet Assoc 2005;105:775-589
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1 2
3
4 5
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Gold Standard Metabolism Measurement Methods
1. Metabolic chamber
2. Doubly labeled water
3. Metabolic cart
4. Hood
5. Actigraph
SHEON
Underutilized RMR Measurement Method?
• FDA 510K-cleared Class II Medical Device for measuring RMR
• Reimbursable in primary care
• Available at 121 Fitness for modest fee
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Gold Standard---Or “Good Enough”
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Metabolic Variation: Intra-person
• Meta-analysis: 15% variation at 52 weeks; range 3% -36% (Black & Cole Eur J Clin Nutr; 2000;54)
• Haugan: Over 2 weeks, little change at same time of day; difference equivalent to 99 kcal/day or 6% from morning to afternoon (Am J Clin Nutr 2003;78).
• After prolonged calorie restriction: 10% drop; not restored after free living. Weyer, Am J Clin Nutr 2000;72)
6/11/2014 SHEON 11
Inter-Person Metabolic Variation
• Meta analysis: Homogenous groups: 1.6 – 18.8% (Black & Cole Eur J Clin Nutr 2000;54)
• 150 adults in Scotland: BMR range 1027 -2499 kcal/day; 27% of variation not explained by body composition etc (Johnstone et al, Am J Clin Nutr 2005;82)
• Among subjects with similar body mass, top 5% burn 28-32% more at rest than those with slowest burn rate. Most extreme difference: 2 people with 43kg lean body mass: 715 kcal/day difference in BMR. (Speakman et al, Phys Biochem Zoology 2004;77)
6/11/2014 12 SHEON
6/11/2014 SHEON 13
Disclosure #4: Ambition for Case Western Reserve
• Increase of 2183-2491 steps per day
• 27% increase in physical activity
• .38 decrease in BMI
• 3.8 mm decrease in systolic BP
• Success associated with having a step goal, being older, non-workplace setting, and having greater baseline BP
6/11/2014 SHEON 14
Effectiveness of Pedometers
Bravata, et al, Jama 2007;298(19)2296-2304
• Provides total daily steps & energy expenditure estimate
• Nearly all overestimate caloric expenditure; often fail to account for resting EE
• Don’t measure intensity
• Accuracy varies by speed
6/11/2014 SHEON 15
Pedometer Accuracy in Energy Estimation
Crouter, SE et al, Med Sci Sports Exerc 2003;35(8);1455-60
6/11/2014 SHEON 16
Accelerometer-Based Devices
• Steps • Distance • Movement • Calories
• Sleep • Personalization • Interactivity • Calorie balance • Activity-specific metrics
Smartphone Displays
6/11/2014 SHEON 17
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Goal Setting, Gamification
Ben
chm
arki
ng
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“Interpretation”
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Download Daily Totals
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Limitation: Pseudo-Personalized
Pedometer Accelerometer
Age, Gender No Yes
Height/Weight (body composition)
No Yes
Nutrition intake No User input
Energy Expenditure Daily total; translate steps via
formula
Real-time; translate movement
via formula
Smart Bands: The Next Frontier
0.3 0.5 8
23
45
2012 2013 2014* 2015* 2017*
Smart Band Sales In Millions*
Though currently a relatively small market serving fitness enthusiasts, wearable bands
represent a massive opportunity in the medical and wellness segment. 2014 will be the year
that wearables become a key consumer technology….12 Feb 2014
http://www.canalys.com/newsroom/16-million-smart-bands-shipped-h2-2013
*Predicted
6/11/2014 SHEON 23
Tracking styles
• Documentary
• Directive: goal driven
• Diagnostic
• Collect Rewards
• Social
Rooksby , SIGCHI Conf on Human Factors in Computing 2014
6/11/2014 SHEON 24
http://quantifiedself.com/guide/
Circa 2014
6/11/2014 SHEON 25
Circa 2014
Moodies Speak into smartphone; receive emotional analysis for self diagnosis
Fingermill Let your fingers do the walking on a smartphone treadmill
Spreadsheets Ranks states according to duration of sexual activity
6/11/2014 SHEON 26
Spreadsheets
6/11/2014 SHEON 27
Moodies Fingermill
Sensing What
• Dehydration
• Glucose
• Nutrition
• Intake
• Composition
• ECG
• Alcohol concentration
What/How
• Clothing
• Glasses
• Smartphone—with attachments
• Smart Fork
• Jaw movement 6/11/2014 SHEON 28
Wearable Sensors—Now and Soon
FDA Guidance re Mobile Medical Apps Issued September 25th, 2013
“FDA intends to apply its regulatory oversight to only those mobile apps and devices whose functionality could pose a risk to a patient’s safety if the mobile app were to not function as intended.” (p. 13)
http://www.fda.gov/downloads/MedicalDevices/DeviceRegulationandGuidance/GuidanceDocuments/UCM263366.pdf 6/11/2014 SHEON 29
Subject to Regulatory Oversight “Enforcement Discretion”
• Connected to device to control, display, store, analyze or transmit patient-specific medical device data
• Apps that transform mobile platform into a regulated device by attachments, display screens or sensor or by including functionalities similar to those of currently regulated devices
• Mobile app software that does patient specific diagnosis or treatment recommendations (e.g calculates radiation dose
• Help users manage disease or conditions without specific treatment or treatment suggestions including coaching and prompting; eg for “cardiovascular disease, hypertension, diabetes or obesity, and promote strategies for maintaining a healthy weight, getting optimal nutrition, exercising and staying fit.”
• Simple tools to organize and track health info
• Document conditions to share with providers
• Automate tasks for providers
• Enable pt or provider engagement with EHR
Samsung Simband
Coming Soon….
http://www.techhive.com/article/2198147/samsung-announces-simband-a-wearable-dev-kit-to-cement-leadership-in-digital-health.html/
6/11/2014 SHEON 30
Coming Soon….
6/11/2014 SHEON 31
Apple Healthbook
Apple Healthkit-Mayo Clinic
• Epic Systems
• Designed Apple’s Health app and HealthKit API as central repository for personal health information
• Integrates information from devices & allows user input via entry or device re glucose, heart rate, etc
• Integrates with EMR
UCSF-Samsung Digital Health Innovation Lab
• Goal: accelerate validation and commercialization of promising new sensors, algorithms, and digital health technologies for preventive health solutions
• Lab space for clinical testing and trials
• First of a kind test bed where entrepreneurs and innovators will be able to validate their technologies and accelerate adoption of new preventive health solutions
• On new bioscience campus with 50 startups, 9 pharma/biotechs and 10 venture capital firms
Robust Academic Partnerships
http://www.fastcompany.com/3031385/most-innovative-companies/inside-apple-the-mayo-clinics-new-partnership
N. Ungerleider, Fast Company
http://www.ucsf.edu/news/2014/02/111976/samsung-ucsf-partner-accelerate-new-innovations-preventive-health-technology
UCSF News
6/11/2014 SHEON 32
Current Wearable Sensor-Based Devices
• Must be worn on body
• Provide individualized measurements
• Transformational potential in weight management, research, and more?
• Few consumers and tech reviewers differentiate sensor-based versus other devices
6/11/2014
SHEON
33
6/11/2014 SHEON 34
Key Value of Sensor –Based Devices
Pedometer Accelerometer Sensor
Age, Gender No Yes Yes
Height/Weight (body composition)
No Yes Yes
Nutrition intake No User input User input or biosensing
Energy Expenditure
Translate steps via formula
Translate movement via
Formula
Proprietary method of translating individual
biometric measures into calories
6/11/2014 SHEON 35
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Highly Sensitive for Episodic Activity
6/11/2014 SHEON 37
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6/11/2014 SHEON 39
6/11/2014 SHEON 40
Basis BodyMedia
Steps 7363 9895
Calories 2045 2269
Walking/Running :34/0
Moderate/Vigorous 1:30/19
Sleep hours 11:47p – 6:29 a 11:48 p – 6:22 a
Sleep +lying down 6:42 6:07/27
Toss/turn 25
Interruptions 0 6
Efficiency 91% 93%
Deep 1:00
Light 3:57
REM 1:45 6/11/2014 SHEON 41
http://www.bodymedia.com/bibliography.html
Validation Studies
6/11/2014 SHEON 42
EE Validation Study
Lee, J-M, Validity of consumer based physical activity monitors and calibration of smartphone for prediction of physical activity energy expenditure, 2013 (Iowa State U)
6/11/2014 SHEON 43
http://www.bodymedia.com/bibliography.html
Outcome Studies
6/11/2014 SHEON 44
• Goal setting and monitoring are proven effective strategies
• Greater weight loss seen when used in clinical setting:
• Usual care: -.89 kg
• Group weight loss -1.86 kg*
• Armband alone: -3.55 kg***
• Armband +group weight loss (-6.59 kg)***
• Better retention
Shugar et al Int J Behav Nutr Phys Act 2011 (8) Pellegrini, Obesity 2012 (2) Barry Diabetes, Metab Syndr Obesity 2011(4)
6/11/2014 SHEON 45
Effectiveness in Clinical Setting
Easy to Connect Apps
6/11/2014 SHEON 46
6/11/2014 47
Genetic Test Results: Obesity & Metabolism
Marker Topic Result Interpretation
Rs9939609 Effect of physical activity on BMI
TT Tendency toward lower BMI. Exercise is associated with a slight reduction in BMI of about .85 units on average
Rs1801282 Improvement in glucose tolerance with regular exercise
CC Little or no change in glucose tolerance with regular exercise
Rs1800588 Insulin sensitivity response to exercise
CC Exercise is associated with a 5% improvement in insulin sensitivity, on average
Rs4994 Effect of behavioral intervention on weight loss
AA Decreasing calorie intake and increasing physical activity through walking is associated with weight loss SHEON
The Microbiome
6/11/2014 SHEON 48
Gut Microbiota from Twins Discordant for Obesity
Modulate Metabolism in Mice
Increased total body and fat mass, as well as obesity-
associated metabolic phenotypes, were transmissible with
uncultured fecal communities and with their corresponding
fecal bacterial culture collections. Cohousing mice harboring
an obese twin’s microbiota (Ob) with mice containing the
lean co-twin’s microbiota (Ln) prevented the development of
increased body mass and obesity-associated metabolic
phenotypes in Ob cage mates.
Ridaura, et al Science 2013;341
6/11/2014 SHEON 49
Transfer of the gut microbiota from RYGB-treated mice to
nonoperated, germ-free mice resulted in weight loss and decreased
fat mass in the recipient animals relative to recipients of microbiota
induced by sham surgery, potentially due to altered microbial
production of short-chain fatty acids. These findings provide the first
empirical support for the claim that changes in the gut microbiota
contribute to reduced host weight and adiposity after RYGB surgery.
Liou et al. Sci Transl Med 2013;5:178
Conserved Shifts in the Gut Microbiota Due to Gastric
Bypass Reduce Host Weight and Adiposity
6/11/2014 SHEON 50
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• Children
• Worksite Wellness Programs
• Athletes
• Military
• Clinical programs re metabolic-sensitive conditions (HIV, thyroid, CA)
• Population Health
6/11/2014 SHEON 53
Other Use Cases
Subject with Healthy
Parkinson’s Disease Subject
6/11/2014 SHEON 54
Activity Level During Sleep
Community Health Applications
http://labs.strava.com/heatmap/#8/-84.18145/40.85058/gray/both 6/11/2014 SHEON 55
Community Health Applications
https://www.newschallenge.org/challenge/healthdata/evaluation/our-health-using-sensor-journalism-data-and-storytelling-to-explore-public-health-issues-in-kentucky
6/11/2014 SHEON 56
Community Health Innovations
6/11/2014 SHEON 57
Community Health
6/11/2014 SHEON 58
http://therealdeal.com/blog/2014/04/14/nyu-to-track-activity-at-relateds-hudson-yards/
Core Team Amy Sheon, PhD, MPH, Inventor Lynn Kam, PhD, MBA, MA, Dept of Nutrition
Other Contributors
Eileen Seeholzer, MD, MA, Internal Medicine; MetroHealth Mehran Mehregany, PhD, EE, Case Wireless Health Program, San Diego Meral Ozsoyoglu, PhD,EE/CS Colin Drummond, PhD, MBA, MS, FPBSN Nora Nock, PhD, MS, Epidemiology/Biostatistics Michael Hadley, MBA, MD (2017) Jeno Mozes Andrea Marks, Intern
Management Advisors
Blair Geho, MD, PhD, School of Medicine Chief Tech Officer Michael Haag, MBA, MS, Executive Director, CWRU TTO Dalia Abou-Zeki, Coulter Case Translational Research Partnership Nitin Charaparambil, CCTRP Commercialization Associate
6/11/2014 59
Development Support
SHEON
6/11/2014 SHEON 61
Illustrating our Differences
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