technologyenabled& remote&monitoring& andsupport · • sensor!sampling!in! real!5me!...
TRANSCRIPT
Wendy Nilsen, PhD
Office of Behavioral and Social Sciences Research Na5onal Ins5tutes of Health
May 17, 2013
Technology-‐Enabled Remote Monitoring
and Support
Office of Behavioral and Social Sciences Research (OBSSR) Mission … to stimulate behavioral and social science
research throughout NIH and to integrate these areas of research more fully into others of the NIH health research enterprise, thereby improving our understanding, treatment, and prevention of disease.
Leveraging the Ubiquity of Wireless
Includes any wireless device carried by or on the person that is accepBng or transmiCng health data/
informaBon • Sensors (e.g., implantable miniature
sensors and “nanosensors”) • Monitors (e.g., wireless accelerometers, blood pressure & glucose monitors)
• Mobile phones
Beyond Telemedicine • Portable: Beyond Point-‐of-‐Care diagnos5cs
• Scalable: Economical to scale
• Richer data input: Con5nuous data sampling
• Personal: Pa5ent can receive & input informa5on
• Real-‐6me: Data collec5on and feedback is in real-‐5me using automated analyses and responses
I think we can safely assume the promise of apps radically revolutionizing our health is heavily inflated. So, then, what good are health apps? Health apps are the equivalent of old school public health advertising. Just as I see an ad when I get on the subway telling me this soft drink has 40 packets of sugar, I whip out my iPhone and see the Livestrong app on my homescreen reminding me that I need to eat well. I don’t really want to use it because it’s such a drag.” Jay Parkinson of Future Well, 2011
6 Do it right or lose them
The Poten5al • Technologies can expand health research and health care beyond the lab/hospital into the person's environment.
• Technologies also can change the quesBons we ask and the way we do research.
• Remote clinical trials offer new possibili5es.
Moving “Hype” to ProducBvity
Con5nuum of Technology tools
Measurement • Sensor sampling in real 5me
• Integra5on with health data
• Ecological momentary assessment (EMA)
DiagnosBc • POC Diagnos5cs • Portable imaging • Biomarker sensing • Clinical decision making
Treatment • Chronic disease management
• Remote Clinical trials
• Disaster support/care
Global • Service Access • Remote treatment
• Dissemina5on of health informa5on
• Disease surveillance
• Medica5on tracking and safety
• Preven5on and wellness interven5ons
Measurement and Assessment
Implantable Biosensors • Problem: Measurement of analytes (glucose, lactate O2 and
CO2) that indicate metabolic abnormali5es • Solu6on: Miniaturized wireless implantable biosensor that
con5nuously monitors metabolism – Inserted by needle subcutaneously – Operated remotely using a PDA – Mul5-‐analyte sensor – One month con5nuous monitoring
Diane J. Burgess, University of Connec5cut NHLBI, R21HL090458
Aging in Place: Smart Environment/Mobile Technologies
• Problem: Assessment of and interven5on for everyday func5onal limita5ons of persons with early-‐stage demen5a without need of assisted living (aging in place)
• Solu6on: Automated wireless and fixed monitoring and assistance to help people cope with age-‐related limita5ons
Diane J. Cook, Washington State NIBIB, R01EB009675
• Problem: Understanding activity, which impacts health through multiple channels, is a challenge using self-report and accelerometers alone do not provide enough information
• Solution: Develop an end to end system of sensors that detect movement, a host of sensors and self-report to explore activity multimodally
Physical AcBvity and LocaBon
Kevin Patrick, UCSD, NCI U01CA130771
• Problem: Detection of salivary stress hormones in real-time is expensive and not practical in clinical settings
• Solution: Develop wireless salivary biosensors – Salivary α-amylase biosensor – Salivary cortisol biosensor
Stress Hormone DetecBon
Vivek Shefy, DDS, UCLA, NIDA U01DA023815
DiagnosBcs
LUCAS images of CD4+ and CD8+ T cells compared to a regular microscope image..
LUCAS microscope
Cell phone transmits image
Karin Nielsen, UCLA, FIC, R24TW008811
A. OZCAN, 1R21EB009222-01
LUCAS-‐ Mobile Microscope Problem: Create a low-‐cost quality microscope to use in low resources seings. Solu6on: A specially-‐developed lens fits to a cell phone to create a microscope Field tes6ng: Malawi, Mozambique and Brazil
LUCAS microscope Heart Afack Predic5on Problem: Create a low-‐cost, non-‐burdensome measure of heart func5on. Solu6on: A specially-‐developed 14mm implant that reacts with blood to generate a signal
Ecole Polytechnique Fédérale de Lausanne
Molecular Analysis of Cells Problem: Detec5on a variety of biologics rapidly and without a laboratory. Solution: A chip based micro NMR unit Smartphone powered analysis: Ca Protein bio-‐markers, DNA, bacteria and virus drugs
Ralph Weissleder, MIT, NIBIB RO1 EB004626
Nanosensor Tafoos • Problem: Detection of biomarkers in a non-invasive platform • Solution: Nanosensor skins
– EMG, glucose and sodium measured in real time
John Rogers, University of Illinois
Treatment
Chronic Disease Management Problem: Chronic diseases are difficult and expensive to manage
within tradi5onal healthcare seings Solu6on: CHESS: Disease self-‐management programs for
asthma, alcohol dependence and lung cancer – Informa5on provided the user needs it – Intervene remotely with greater frequency than tradi5onal care
– Real-‐5me management – More efficient triage – Reduces acute care
David Gustafson, University of Wisconsin, NIAAA R01 AA 017192-‐04
Longitudinal pattern recognition
Subject Center
Cell Phone or
Computer Connection Subject
Healthcare professional
Adap5ng parameters
Problem: Pa5ents with CVD have symptoms that frequently bring them to emergency care where there is limited baseline data Solu6on: Remote monitoring to create physiological cardiac ac5vity “fingerprints” that alert professionals and pa5ent when there are irregulari5es based on their own cardiac paferns
Vladimir Shusterman, PinMed, NHLBI, R43-‐44 HL0771160, R41HL093953
Cardiac Disease Management
ECG/ACC
ACC
Structure of Data Collecting Software
End-to-end Encryption
Device Manager
Local Storage
[User Configuration] [Analyzed Data]
[Raw Data]
Transmitter [Encrypt/Decrypt]
Analyzer [Plug-in
modules]
GPS ACC ECG
Data Collector
Service Manager
Application with Graphic User Interface
Local Socket or IPC
• Problem: Overweight and Obesity among urban, minority youth • Solu6on: KNOWME networks personalized monitoring & feedback in real-‐5me
q Immediate access to data allows nimble reac5ons to events, environments, & behavior q User interface for health professionals, children & families
Donna Spruijt-‐Metz, PHD, USC, NSF
Body Sensor Networks
Global
Necessity for Global Health • Lack of providers in developing world • No wired infrastructure
– Well-‐developed and rapidly growing wireless
• Healthcare needs to be provided through low-‐cost and immediate, scalable services
• Poten5al for reverse technology transfer
– Knowledge from developing world informs domes5c research and prac5ce
Adherence Monitoring (Uganda)
Jessica Haberer, Partners Healthcare NIMH K23MH087228
Problem: Adherence to chronic disease medica5ons is poor. In resource-‐poor seings, geing people medica5on is only part of the solu5on Solu6on: Wireless medica5on canisters that signal medica5on 5ming, transmit adherence data and allow resources to target the non-‐compliant
Walter Curiso, MD, University of Peruana FIC R01TW007896
Real time data via IVR on cell phones
Secure database
Queries on demand via
Internet
Real time alerts via
Real time alerts via SMS
Communication back to the field via cell phones
Urban and rural areas
Of Peru
Adverse Event Monitoring (Peru) Problem: Following at-‐risk pa5ents for adverse events in low-‐ to medium resource countries is expensive/imprac5cal Solu6on: Wireless adverse events repor5ng and database improves pa5ent and community care
Remote Clinical Trials
ParBcipaBon from home
The Challenges • Tech development & research
in silos • Heterogeneity of devices • No interoperability/ harmoniza5on standards
• Logis5cal barriers to scaling • No guidance on mobile
cybersecurity or privacy • IRB/HIPAA • Lifle known about engaging
research par5cipants remotely
Workshop on mHealth Evidence • Collabora5on between Robert Wood Johnson, McKesson founda5on, NSF and NIH
• Workshop in August 2011 at NIH to assess mHealth study design and analy5c possibili5es
• hfp://obssr.od.nih.gov/scien5fic_areas/methodology/mhealth/mhealth-‐workshop.aspx
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2013 NIH mHealth Training Ins5tutes § Need
§ Improved use of mHealth products in clinical, behavioral and technology research
§ Increased collabora5on and cross-‐fer5liza5on across disciplines
§ Plan § 5-‐day training for 28 par5cipants § Develop skills to improve the design and research of mobile technologies
§ August 26-‐30, 2013, UCLA
Join our Listserv • mHealth-‐[email protected]
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Thank you!
• Thank you! – Wendy Nilsen, NIH Office of Behavioral and
Social Sciences Research – 301-496-0979 – [email protected]
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Rapid Explosion of Inputs & Data • Con5nuous data integra5on with:
– Gene5c data – Electronic medical record data – Behavioral Sensors (e.g. ac5graphy, sleep sensors) – Environmental Sensors (e.g., personal & fixed sensors) – Loca5on Sensors (e.g. GIS, GPS) – Contextual Sensors (e.g. passive sensing of sound, noise, interpersonal nearness)
– Ecological momentary sampling of symptoms, QOL, etc. • High throughput analyses needed for complex, streaming data