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Connecting Senior Care: Wearables & Analytics Drive Results
Session 141, February 22, 2017
Ken Smith, Senior Research Scholar, Stanford Center on Longevity
Ginna Baik, Senior Care Strategist, CDW
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Speaker Introduction
Ginna Baik Senior Care StrategistCDW Healthcare
Ken Smith Senior Research Scholar Stanford Center on Longevity
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Conflict of Interest
Ken Smith and Ginna Baik have no real or apparent conflicts of interest to report.
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Agenda
• 24-hour activity cycle (HAC) – what is it and why it is important
• Wearable technologies and data capture – key considerations
• At work today – senior care wearables pilot
• Wearables data in action
• Questions
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Learning Objectives
• Evaluate state of the science regarding the health effects of physical activity and sedentary behavior as established by self-report and device measurements for each of the 24-HAC domains
• Identify innovation and issues regarding the accurate and reliable measurement of the 24-HAC domains using wearable devices
• Assess the status of the collection, storage, management and analysis of data in each 24-HAC domain
• Evaluate research priorities to advance objective assessment in each of the 24-HAC domains
• Discuss outcomes and lessons learned from the research and implications these findings have for population health/senior care
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An Introduction of How Benefits Were Realized for the Value of Health IT• Driving results through wearables and analytics can:
Patient Engagement:
Improve lives by enabling patients to take a more active
role in their care
Treatment:
Transform care by delivering a more complete view of patient
health, supporting more informed decisions
Savings:
Lower costs by reducing readmissions
T P S
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Rethinking Physical Activity:Time to Consider the Complete Picture
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A Typical (Healthy) 24-Hour Day Light
Activity
Sleep
Light Activity
Sedentary behavior (sitting)
Light activity
Exercise 5% of our day80% of our focus
Sleep
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• Heart disease• High blood pressure• Stroke• Diabetes• Weight gain• Slowed cognitive
processing• Depression• Accidents
• Heart disease• Obesity• Diabetes• Cancer• Cognitive
impairment• Mood
enhancement
• Heart disease• Obesity• Diabetes• Metabolic shifts
• Least understood domain
• Currently viewed as “exercise light”
• Often linked to social benefits
Health Implications of the Four Domains (Independently)
10Sleep
Source: Duck-chul Lee, Russell R. Pate, Carl J. Lavie, Xuemei Sui, Timothy S. Church, Steven N. BlairLeisure-Time Running Reduces All-Cause and Cardiovascular Mortality RiskJournal of the American College of Cardiology, Volume 64, Issue 5, 5 August 2014, Pages 472-481
How Much Activity is Enough?
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Domains Also Interact
Physical Activity
Sedentary Sleep
12SleepData Quality
Wearab
ilit
y
Wearables: Balancing Usability With Data Quality
13Sleep
Four Questions about the Data
1. How good does the data need to be?
2. Can we establish standard datasets and
formats?
3. What about privacy?
4. How do we deal with iso-temporal data?
14Sleep
Source: Buman MP, Winkler EA, Kurka
JM, et al. Reallocating time to sleep,
sedentary behaviors, or active behaviors:
associations with cardiovascular disease
risk biomarkers, NHANES 2005-2006.
American Journal of Epidemiology.
2014;179:323–34.
Example of Iso-Temporal Effects
0.7
1
1.2Relative Risk
Association
HDL cholesterolSleep to MVPASB to MVPALIPA to MVPASleep to LIPASB to LIPASB to sleep
TriglyceridesSleep to MVPASB to MVPALIPA to MVPASleep to LIPASB to LIPASB to sleep
15Sleep
Call to Action
• Explore ways to integrate wearables into your environment
• Recognize the value of non-exercise activities (especially light activity and sedentary behavior)
• Encourage development of a wearable 24-hour activity monitor
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• Glucose• Lactate• Hydration
• Saliva monitoring• Cortisol (stress)• Optical approaches
• Mood• Depression• Internal noises
• Real-time glucose
A Flavor of the (Near) Future
The Next Frontier
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Wearables at Work Today
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Mayo Clinic Research reports a synergistic interaction between computer activities and moderate exercise in “protecting” the brain function of people older than age 70
Those who exercised and used a computer decreased their risk of mild cognitive impairment by 50%
Computer Use/Exercise Combination Reduces Memory Loss
Source: Geda YE, Silber TC, Roberts RO, et al. Computer Activities, Physical Exercise, Aging, and Mild Cognitive Impairment: A Population-Based
Study. Mayo Clinic Proceedings. 2012;87(5):437-442. doi:10.1016/j.mayocp.2011.12.020.
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• 77% of seniors say activity and sleep trackers are, or have the potential to be, useful
• 45% of seniors said using a health tracker increased their motivation for healthier living
• 46% reported being more active, sleeping better or eating more healthfully
“Wearable health monitoring devices can enable seniors to catch problems early and avoid hospitalizations or long-term care stays. By giving caregivers access to this data, seniors can improve their outcomes and have better quality of life.”
(Senior Housing News, February 2015)
Benefits of Wearable Technology
Source: AARP.org, “Building a Better Tracker: Older Consumers Weigh in on Activity and Sleep
Monitoring Devices 2015”
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What are 600 seniors doing across America, and why is it ground-breaking?
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Data from the Wearable Pilot
• What do you do
with the data?
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Data Analysis Trends Around Heart Rate
• Looking at wearables
versus analytics wellness
trends—
Why is it important to
differentiate?
• Heart rate on wearables—
Is it a reliable measure?
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Gender & Mobility Aid Differences in Sleep/Activity Data
• What are we learning about residents’ data with mobility aids?
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Identifying Potential for Adverse Events
2
3
11
2
3BCA Sleep Index
Activity Level
Sleep Range
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A Summary of How Benefits Were Realized for the Value of Health IT
Reduce costs
Transformcare
Improve lives
Expanding the use of wearable devices to:
T P S
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Questions?
Ginna Baik Senior Care Strategist
CDW Healthcare [email protected]
Ken SmithSenior Research Scholar
Stanford Center on [email protected]