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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?

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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

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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

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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

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3

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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 ginbaik@cdw.com

Ken SmithSenior Research Scholar

Stanford Center on Longevityksmith62@stanford.edu