7 31 2012 rapid presentation 1 designingmobile

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RAPID PRESENTATION 1:

Designing Mobi le

Shilo Anders, PhD, Vanderbilt University Prabir Dut t a, PhD, t he Ohio St at e Universit y Thienne Johnson, PhD, University of Arizona

Julie Kient z, PhD, Universit y of Washington Yali ni Senat hir aj ah, PhD, SUNY Down st at e Medical Cent er Jacob Sorber, PhD, Dart mout h College Gang Zhou, PhD, College of William and Mary


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Shilo H. Anders, Ph.D.

Center for Research and Innovation in Systems Safety

Vanderbilt University Medical Center

Nashville, TN


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Problem Statement

How to enhance care through interactive user-friendly mobile device technology

Currently paper-based system Studying sickle cell patients initially, expanding

into other patient populations

Design user interface for mobile devices to

monitor and coordinate their care


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Future Research Interests

How to deal with health care data overload

Long-term assessment of integration of increased

data streams into EHR in support of cliniciandecision making


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Development of sensors for quantitation

of disease biomarkers in exhaled breath

Prabir Dutta

Department of Chemistry

The Ohio State University

In collaboration with Cleveland Clinic, NASA Glenn, MakelEngineering


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Breath Markers in Diseased States

Disease

Oxidative Stress:

- Lipid Peroxidation

- Asthma, COPD, Bronchiectasis

Lung Disease:

- Asthma

- COPD

- Cystic Fibrosis

- Pulmonary Allograft Dysfunction

Breath Markers

Pentane, Ethane

H2O2

NO, CO, H2O2 NO, H2O2 NO, CO, H2O2

NO

Lung transplant rejection

Metabolic Disorder: Diabetes

Gastroenteric Diseases: Disorders ofDigestion

Gastritis, Gastric Ulcer

Carbonyl sulfide

Acetone

Hydrogen

13CO, 14 CO2

Clin Chem. 52, 800, 2006


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NO: Important Breath Marker for Lung

Disease

Activation of NOS2 by damage to airway epithelial cells and byinflammation

NO > 10-20 ppb; 90-95% predictive value for asthma (1993)

American Thoracic Society/ European Respiratory Society have

defined Exhaled Breath Collection Protocol (FENO) Therapy adjustment based on NO analysis

NO analysis a cost effective method for screening largepopulations : diagnosis, compliance, drug efficacy, dosage

2003: FDA approved NO chemiluminescence analyzer (25-45kg, $20-$45K)

Significant commercial activity for developing mobiletechnology: hand-held device (15 million asthma patients)


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0 6 12 18 24 30 36 42

630

700

770

0 6 12 18 24 30 36 42

770

840

910

0 6 12 18 24 30 36 42

700

770

560

630

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Breath Sample (8 ppb NO)OFFON

Breath Sample (17 ppb NO)

EMF(mV

)

Time mins

Breath Sample (46 ppb NO)

403530252015100

Breath Sample (82 ppb NO) 5

30 36 42

605

610

615

620

625

630

OFF

EMF(mV)

Time (mins)

ON

20 Sensor Array: Breath samples

0 15 30 45 60 75 900

20

40

60

80

20 Sensor Array

EMF(mV)

NO (ppb)

Slope ~1.0

Water as background: breath samples Impractical device, too large for mobile applications: high thermal

load

Sensors & Actuators, 2011, B158, 292


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Miniaturization : effort atNASA Glenn

ON

10ppb

20ppb

40ppb

ON

ON

OFF

OFF

OFF

J Breath Res. 5. 2011 (037111)


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Breath Analyzer Second Generation Prototype:Handheld Sampler Detail (Makel Engineering)

Mouth-piece withdisposable filterSensor manifold

Rechargeablebattery

Wirelesselectronics

Check valve withsample port(tubing not shown)

Sensor array

Spirosure, licensed OSU technology :https://gust.com/c/spirosure_inc


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What Next?? Mobile Breath

Detection Technology

Biologists : What are the biochemical links between breath markers anddisease If volatile molecules in blood, probably in breath Mass Spectrometric analysis is advanced, so identification inbreath is not an issue

What will be the relevance for disease or treatment?

Physical scientists: What new sensing principles are required? Development of materials/catalysts Demonstration of selectivity/sensitivity

Engineers : Device Fabrication, miniaturization, ideally cell phone platforms,data handling

Clinicians Are devices field applicable (point of care) ?

How is the information relevant?


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Thienne Johnson

Post-doctoral researcher / ECEThe University of Arizona

[email protected]

http://www.cs.arizona.edu/~thienne

2012 NIH MHEALTH SUMMER INSTITUTE


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Context-aware system provide the user with adaptive recommendations frominformation available on a social application.

The use of context will enrich recommendation and personalization decisions.

Use case: A context-aware

recommendation system platform


mHealth intelligent platforms help users to be responsible for their own health treatment.


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Building trust relationships. A key component is to have a set oftrusted users. Trusted users are knowledgeable about food and nutrition but are not

necessarily computer experts. What are good ways to build referral-based trust relationships between

users and a central server without having to engage in cumbersomeprotocols?

Privacy: great availability of personal data (name, email, photographyetc) may be used to reasonably identify a user. Its imprudent to sharesome info in public and it would violate most privacy legislation ifrelease by health care professionals.

Selected problems on security2012 NIH MHEALTH SUMMER INSTITUTE


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Related solutions

Use of a central authentication server/service

or ad-hoc negotiation


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Addressing the issues


Design and implementation of a trust framework (using exchange of securitycertificates between mobile devices and monitoring servers) to allowrecommendations from trusted users.

Use of known security techniques for storing sensitive information on mobile device,server and secure communications protocols for data transmission.


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How mHealth will help?2012 NIH MHEALTH SUMMER INSTITUTE

Collaboration with health researchers to:

Engage the platform and protocols with the HIPAAsecurityguidelines.

(but avoiding exhaustion of devices battery)

Test and define new context types

What else can we use to improve user experience and userwillingness to use a mHealth system?

[email protected]


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University of Washington

Julie A. Kientz, Ph.D.Assistant ProfessorUniversity of Washing ton

Contact:

[email protected]

http://juliekientz.com


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About Me

Background: Ph.D. in Computer Sc ienc e from theGeorg ia Institute of Tec hnology

Research Focus: Human-Computer Interac tion,User-Centered Design, Health Informatics

Currently: Completing 4th year as Assistant Professorin Human Cente red Design & Eng ineering


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Research Mission

To d esign, develop , and eva lua te app lic a tions to support

ind ividua ls and families in p ursing the ir hea lth goa ls. Weexp lore how novel tec hnolog ies, suc h as ub iquitous andc ollabora tive c omputing, c an help with rec ord -keep ing,da ta review, and b ehavior c hange.

Computing for HealthyLiving & Learning Lab


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Current mHealth Projec ts

How c an tec hnology support trac kingchildrens developmental progress?

How c an tec hnology supporthea lthy sleep behaviors?


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Supporting Healthy Sleep Behaviors

Mobile a pp lic a tions for trac king sleep , p romotingawareness, assessing sleep environment, andmeasuring sleep iness

ShutEyesleep hygiene

Lullaby

sleep environment

PVT-Touchsleepiness test

SleepTightinsomnia CBT


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Tracking Childrens Development

Baby Steps: Parents rec eive SMS notific a tionsasking developmenta l milestone questions

Can respond to messages, whic h are stored andsynced with online da tabase a nd c omp anion web site

Can Jose throw a ballusing both hands?(Reply Y for yes, S forsometimes, N for no)

Y


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Yalini Senathirajah, PhD

Assistant Professor

Department of Medical Informatics

SUNY Downstate Medical Center

Brooklyn NY

Creating Adaptive User-composable

Healthcare Information Systems


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Problem: Healthcare IT Information-intensive, complex1,2 rapidly changing

Information varies Integration and aggregation ofsnippets of multiple information

sources (e.g. a lab result, x-ray, paper, patient preference) Chronic conditions , complex multiplayer care Social, collaborative, high-stakes, security needs Different populations have different needs; unknown to

designers/researchersHistorically: design by programmers/vendors

Vendor lock-in: monolithic systems require vendor/programmer,agreement, time, cost to change

Bad fit to clinician or patient conceptions/needs Poor usability affecting MU adoption

1. Computational Technology for Effective Health Care: Immediate Steps and Strategic Directions. National Research Council, 2009.2. Wears, RL. Computer Technology and Clinical Work: Still Waiting for Godot. JAMA 2005.


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Web 2.0Is a core

philosophy and setofprinciples &practices

Framework orplatform, not

application

Public Web user control & participation


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MedWISE3 Metadesign = design to let others design

Widgets can be

plots, notes, RSS,

alerts, timelines,

any web-enabled

program


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Expected & Found Benefits Better task-technology congruence

HCI/cognitive/usability efficiencies: decreased back and forth navigation;decreased work and time due to aggregation, sharing; cognitive ease

Users do use new affordances to solve problems, develop new things thatfit their needs

User acceptance/satisfaction great enthusiasm Mashups - accommodate rapid change (e.g. H1N1) Problems unknown by designers can be solved by users; evolutionary

development

Christensen - Disruptive Innovation:

Bring a different value proposition to the market Initially under perform established products in mainstream market Superior in ways that are not valued by the established market more

reliable, easier to use, or cheaper


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Why mHealth?

Accessible, low-cost, BYOD policies give user control devices improving - disruptive innovation? Consensus (NIHI-Canada); in future you will mix and match your

apps

Wheres the framework for this?

Advantages for patients, clinicians, researchers: Design in a flash, change in a flash Access, control, rapid updates, fit to task Inferior now but rapidly increasing power, screen res, functions Ubiquitous, where you are

App is existing paradigm; composability intuitively understood App Frame with medical vocabularies, hooks, data specialization,

visualizations

Special populations can design their own; unpredicted


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Recent History- letting users do it

Wordprocessing

anyone can print GUI anyone can use a computer

Graphical browseranyone can use internet

Framework + MedAppInventor ? Anyone can program?


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2012 NIH Mobile HealthSummer Institute

Gang Zhou

Assistant Professor

Computer Science DepartmentThe College of William and Mary

Web: www.cs.wm.edu/~gzhou


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College of William and Mary

What is the problem?

How to provide:P1: practical body sensor networking?

P2: sensing performance assurance?

P3: communication performance assurance?

P4: energy efficiency for battery powered devices?

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ZigBee

Bluetoothetc.

WiFi/3G/4G + Internet

P1: Practical Body Sensor


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P1: Practical Body SensorNetworking?

E.g., for activity recognition,Requirements:

Portable and user friendly

Computationally lightweight

Accurate

Not Invasive

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Our Solution(ACM SenSys11)

TinyOS-based motes + Android phone

Activity recognition approachappropriate for phones (no servers)

Identify redundant sensors to reducetraining costs

Classify difficult activities with nearly90% accuracy

Retraining detection without groundtruth

P2/P3: Sensing/Communication


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P2/P3: Sensing/CommunicationPerformance Assurance?

P2: Need to ensure sensing performance We accuratelycharacterizing and exploitingsensing diversity

Sensing diversity is defined as: the sensing capability differencesamong individual sensors or sensor clusters, no matterheterogeneous or homogeneous ones, in a specific deployment.

Compared with existing model-driven approaches that depend onmodality specific sensing models for data fusion

They need to mitigate sensing diversity, but we utilize sensingdiversity for our benefit: IEEE INFOCOM11

P3: Need to ensure sensing performance over multiple

heterogeneous hops: ZigBee, Bluetooth, WiFi, 3G/4G, etc.

We use a radio-agnostic MAC/PHY abstraction to supportplatform portability

IEEE INFOCOM11, ACM TOSN11, INFOCOM08

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P4: Energy Efficiency for Battery


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P4: Energy Efficiency for BatteryPowered Devices?

Energy savings for both sensors and smartphones Opportunisticallysharing sensing and computing

resources among multiple body sensor networks

Under submission to ACM TOSN

More energy savings for smartphones

Classify applications into high/low priorities, put WiFi radioto power save model for delay-tolerant applications.

ACM Ubicomp12, up to 56% energy savings in Androidsmartphones compared with adaptive PSM

For real-time applications like VoIP, sense silence datapacket and put WiFi radio to power save mode.

ACM Ubicomp11, about 40% energy savings in Androidsmartphones compared with adaptive PSM

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How will mHealth help?

New applications that can use our smartphone-centered, performance-ensured, body sensor networks

New hardware that can be integrated into or collaborate

with our smartphone-centered body sensor networks

New research partners

New funding opportunities

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7 31 2012 rapid presentation 1 designingmobile

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