Using Mobile Technologies in Health Research at NIH


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  • Wireless penetration is 96% in the US and 78% of the world Wireless has leap-frogged the wired infrastructure in low and middle income countries with rates between 41-90% (average 78%) mHealth leverages existing mobile technology infrastructure (mobile phones, wireless networks, etc) for data collection, health monitoring and intervention
  • In 2007 the NIH funded a major effort to establish a foundation for large scale Gene-Environment Interaction studies, recognizing that in order to conduct meaningful studies we needed to have a much stronger technological base in both genome-wide association studies and personal exposure assessment. This project is one of 32 projects funded by the GEI’s Exposure Biology Program to establish that foundation in exposure assessment. The group lead by NJ Tao at Arizona State has developed a sensor system that measures volatile aromatic hydrocarbons and acidic vapors with a sensitivity right at the level that we need for environmental epidemiology studies – in the ppb to ppm range. The goal here is to be able to move beyond assigning everyone in a given geographical region an ‘average’ exposure level to assessing individual exposures as people go about their daily routine.
  • The proposal was submitted under the Frameworks Program for Global Health Signature Innovations Initiative and is co-funded with ARRA money from the Office of the NIH Director and FIC The intent of this one year ARRA pilot project is to train post-doctoral students from Schools of Medicine, Public Health and Engineering to work together to create, test, validate, and evaluate a device that contains a lens-free microscope attached to a portable hand-held device, such as wireless cell phone, for point-of-care surveillance and diagnosis of infectious diseases in resource-limited settings. The top image shows the Lenseless Ultra-wide-field Cell monitoring Array platform based on Shadow imaging (LUCAS) technology, developed by Aydogan Ozcan (co-PI), which relies on digital holography to generate signature images from the shadows of cells. The resolution of LUCAS is comparable to a conventional microscope (bottom left image), but at a fraction of the cost and in a portable format that can be used in the field or in low resource settings. Bottom right: The microscope can be inserted into a cell phone or other mobile device. The captured images are then sent to a computer equipped with software that uses the specific diffraction properties of cells for interpretation and results, much like in a microarray reader. In this proposal, the focus is on TB, malaria, HIV/AIDS, and water-borne diarrheal diseases. The LUCAS platform also has the capacity to measure CD4 cells, perform complete blood cell counts (CBC), and perform surveillance of water supplies for bacterial pathogens. Field tests and evaluation will be conducted in Brazil, Malawi, and Mozambique.
  • Dates? May How and where to apply? Information will be available shortly on the OBSSR website URL of OBSSR site come in on a click
  • Using Mobile Technologies in Health Research at NIH

    1. 1. NIH and mHealth Wendy Nilsen, PhD Office of Behavioral and Social Sciences Research National Institutes of Health
    2. 2. The NIH Mission: … science in pursuit of fundamental knowledge about the nature and behavior of living systems and the application of that knowledge to extend healthy life and reduce the burdens of illness and disability.
    3. 3. NIH Overview <ul><li>27 Institutes and Centers (ICs) </li></ul><ul><li>$31.2 billion in FY2010 </li></ul><ul><li>80% of funds for grants and contracts supporting extramural research </li></ul>
    4. 4. Office of Behavioral and Social Sciences Research (OBSSR) Mission <ul><li>… 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. </li></ul>
    5. 5. What is mHealth? <ul><li>Diverse application of wireless and mobile technologies designed to improve health research, health care services and health outcomes </li></ul>
    6. 6. Includes any wireless device carried by or on the person that is accepting or transmitting health data/information <ul><li>Sensors (e.g., implantable miniature sensors and “nanosensors”) </li></ul><ul><li>Monitors (e.g., wireless accelerometers, blood pressure & glucose monitors) </li></ul><ul><li>Mobile phones </li></ul>
    7. 7. Beyond Telemedicine <ul><li>Portable: Beyond POC Diagnostics </li></ul><ul><li>Scalable: Economical to scale </li></ul><ul><li>Richer data input: Continuous data sampling </li></ul><ul><li>Personal: Patient can receive & input information </li></ul><ul><li>Real-time: Data collection and feedback is in real-time using automated analyses and responses </li></ul>
    8. 8. <ul><li>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.” </li></ul><ul><li>Jay Parkinson of Future Well, 2011 </li></ul>Do it right or lose them
    9. 9. Leveraging the Ubiquity of Wireless
    10. 10. Moving “Hype” to Productivity
    11. 11. Continuum of mHealth tools
    12. 13. High Throughput Exposomics
    13. 14. “ At it’s most complete, the exposome encompasses life-course environmental exposures (including lifestyle factors), from the prenatal period onwards…” -- Christopher Paul Wild Exposomics
    14. 15. Genome Exposome Together these lead to whether disease occurs or health is promoted… Kevin Patrick, UCSD, NCI U01 CA130771
    15. 16. <ul><li>“ Unlike the genome, the exposome is a highly variable and dynamic entity that evolves throughout the lifetime of the individual…” </li></ul>-- Christopher Paul Wild
    16. 17. Implantable Biosensors <ul><li>Problem: Measurement of analytes ( glucose, lactate O2 and CO2) that indicate metabolic abnormalities </li></ul><ul><li>Solution: Miniaturized wireless implantable biosensor that continuously monitors metabolism </li></ul><ul><ul><li>Inserted by needle subcutaneously </li></ul></ul><ul><ul><li>Operated remotely using a PDA </li></ul></ul><ul><ul><li>Multi-analyte sensor </li></ul></ul><ul><ul><li>One month continuous monitoring </li></ul></ul>Diane J. Burgess, University of Connecticut NHLBI, R21HL090458
    17. 18. Wearable Chemical Sensor System <ul><li>Problem: Chemical exposure varies by context, need personal exposure </li></ul><ul><li>Solution: Selective detection of VOCs (hydrocarbon and acid vapors) </li></ul><ul><ul><li>Sensitive: ppb – ppm </li></ul></ul><ul><ul><li>Real-time: sec. – min. </li></ul></ul><ul><ul><li>Spatially resolved </li></ul></ul><ul><ul><li>Wearable: cell phone size </li></ul></ul><ul><ul><li>Cell phone based interface </li></ul></ul>Nongjian Tao, Arizona State University, NIEHS, U01 ES016064
    18. 19. <ul><li>Problem: Detection of salivary stress hormones in real-time is expensive and not practical in clinical settings </li></ul><ul><li>Solution: Develop wireless salivary biosensors </li></ul><ul><ul><li>Salivary α -amylase biosensor </li></ul></ul><ul><ul><li>Salivary cortisol biosensor </li></ul></ul>Stress Hormone Detection Vivek Shetty, DDS, UCLA, NIDA U01DA023815
    19. 20. Population Scale Activity Measures <ul><li>Problem: Population-scale measurement of physical activity </li></ul><ul><li>Solution: Miniature, low-cost devices that measure human motion using redesigned accelerometers in a user-friendly format </li></ul>Stephen Intille, PhD, Northeastern University NHLBI, U01HL091737
    20. 22. LUCAS- Mobile Microscope LUCAS images of CD4+ and CD8+ T cells compared to a regular microscope image .. LUCAS microscope Photos from Karin Nielsen and Aydogan Ozcan Computer software automatically interprets images at remote site Cell phone transmits image Karin Nielsen, UCLA, FIC, R24TW008811 A. OZCAN, 1R21EB009222-01 Problem: Create a low-cost quality microscope to use in low resources settings. Solution: A specially-developed lens fits to a cell phone to create a microscope Field testing : Malawi, Mozambique and Brazil
    21. 23. High-resolution fiber-optic microendoscope <ul><li>Problem: Methods to detect cancer from traditional biopsies are invasive for patients and require lab facilities. </li></ul><ul><li>Solution: A scientific charge-coupled device camera and a laptop computer for under $4,000 (clinical trials in China, Botswana, Guatemala) </li></ul><ul><li>Rebecca Richards-Kortum, Rice Univ. </li></ul><ul><li>NIBIB RO1 EB007594 </li></ul>
    22. 25. Body Sensor Networks <ul><li>Problem : Overweight and Obesity among urban, minority youth </li></ul><ul><li>Solution: KNOWME networks personalized tracking & feedback in Real-Time </li></ul><ul><ul><li>Immediate access to data allows nimble reactions to events, environments, & behavior </li></ul></ul><ul><ul><li>User interface for health professionals, children & families </li></ul></ul><ul><ul><li>User initiated data (SMS, speech notes, images/videos) </li></ul></ul><ul><ul><li>Real-time, personalized, adaptive interventions to correct energy balance </li></ul></ul>Donna Spruijt-Metz, PHD, USC, NSF ECG/ACC ACC Structure of Data Collecting Software End-to-end Encryption of Sensitive Data Device Manager Local Storage [User Configuration] [Analyzed Data] [Raw Data] Transmitter [Encrypt/Decrypt] Analyzer [Plug-in modules] GPS ACC ECG Data Collector Service Manager Client Application with GUI Local Socket or IPC
    23. 26. Chronic Disease Management <ul><li>Problem: Chronic diseases are difficult and expensive to manage within traditional healthcare settings </li></ul><ul><li>Solution: CHESS: Disease self-management programs for asthma, alcohol dependence and lung cancer </li></ul><ul><li>Information provided the user needs it </li></ul><ul><li>Intervene remotely with greater frequency than traditional care </li></ul><ul><ul><li>Real-time management </li></ul></ul><ul><ul><li>More efficient triage </li></ul></ul><ul><ul><li>Reduces acute care </li></ul></ul>David Gustafson, University of Wisconsin, NIAAA R01 AA 017192-04
    24. 27. Problem: Patients with CVD have symptoms that frequently bring them to emergency care where there is limited baseline data Solution: Remote monitoring to create physiological cardiac activity “fingerprints” that alert professionals and patient when there are irregularities based on their own cardiac patterns Vladimir Shusterman, PinMed, NHLBI, R43-44 HL0771160, R41HL093953 Cardiac Disease Management Longitudinal pattern recognition Subject Center Cell Phone or Computer Connection Adapting parameters Subject Healthcare professional Adapting parameters
    25. 28. Wireless Pain Prevention Program <ul><li>Problem: Treatment of pain and quality of life improvement for youths with Sickle Cell Disease </li></ul><ul><li>Solution: Wireless Pain Prevention Program </li></ul><ul><ul><li>Cell phone with e-Ouch software (support and information for pain in real-time) </li></ul></ul><ul><ul><li>Web link connecting to educational materials, a psychologist, and a nurse practitioner </li></ul></ul><ul><ul><li>Peer social support network through cell phone </li></ul></ul>Eufemia Jacob, UCLA, NHLBI, RC1HL100301
    26. 29. Remote Clinical Trials Participation from home
    27. 30. Aging in Place: Smart Environment/Mobile Technologies <ul><li>Problem: Assessment of and intervention for everyday functional limitations of persons with early-stage dementia without need of assisted living (aging in place) </li></ul><ul><li>Solution: Automated wireless and fixed monitoring and assistance to help people cope with age-related limitations </li></ul>Diane J. Cook, Washington State NIBIB, R01EB009675
    28. 32. Necessity for Global Health <ul><li>Lack of providers in developing world </li></ul><ul><li>No wired infrastructure </li></ul><ul><ul><li>Well-developed and rapidly growing wireless </li></ul></ul><ul><li>Healthcare needs to be provided through low-cost and immediate, scalable services </li></ul><ul><li>Potential for reverse technology </li></ul><ul><li>transfer </li></ul><ul><ul><li>Knowledge from developing </li></ul></ul><ul><ul><li>world informs domestic research </li></ul></ul><ul><ul><li>and practice </li></ul></ul>
    29. 33. Adherence Monitoring (Uganda) Jessica Haberer, Partners Healthcare NIMH K23MH087228 Problem: Adherence to chronic disease medications is poor. In resource-poor settings, getting people medication is only part of the solution Solution: Wireless medication canisters that signal medication timing, transmit adherence data and allow resources to target the non-compliant
    30. 34. Walter Curiso, MD, University of Peruana FIC R01TW007896 Adverse Event Monitoring (Peru) Problem: Following at-risk patients for adverse events in low- to medium resource countries is expensive/impractical Solution: Wireless adverse events reporting and database improves patient and community care Real time data via IVR on cell phones Secure database Queries on demand via Internet Real time alerts via E-mail Real time alerts via SMS Communication back to the field via cell phones Urban and rural areas Of Peru
    31. 35. Rapid Explosion of Inputs & Data <ul><li>Continuous data integration with: </li></ul><ul><ul><li>Genetic data </li></ul></ul><ul><ul><li>Electronic medical record data </li></ul></ul><ul><ul><li>Behavioral Sensors (e.g. actigraphy, sleep sensors) </li></ul></ul><ul><ul><li>Environmental Sensors (e.g., personal & fixed sensors) </li></ul></ul><ul><ul><li>Location Sensors (e.g. GIS, GPS) </li></ul></ul><ul><ul><li>Contextual Sensors (e.g. passive sensing of sound, noise, interpersonal nearness) </li></ul></ul><ul><ul><li>Ecological momentary sampling of symptoms, QOL, etc. </li></ul></ul><ul><li>High throughput analyses needed for complex, streaming data </li></ul>
    32. 36. Research/Funding Challenges <ul><li>Technology development (rapid) versus NIH funding process (slow) timelines. </li></ul><ul><li>Interdisciplinary research teams needed versus traditional academic model. </li></ul><ul><li>Research methodology for data collection/analysis. </li></ul><ul><li>NIH study sections – grant reviewers. </li></ul><ul><li>IRBs/HIPAA. </li></ul><ul><li>Getting to know who to talk to at NIH </li></ul>
    33. 37. What NIH has done thus far: Meeting and Groups <ul><li>NIH mHealth Interest Groups </li></ul><ul><li>mHealth Training Institutes (OBSSR) in 6/11 & 12/11 </li></ul><ul><li>mHealth Evidence Workshop: RWJ, McKesson & NSF to better match research design to the pace of mHealth technology development (8/11) </li></ul><ul><li>NIA/NIBIB Personal Motion Sensing meeting (6/10) </li></ul><ul><li>Organizing sponsor of the mHealth Summit (2009-2011) </li></ul>
    34. 38. NIH Awards-Cell Phones 2010
    35. 39. NIH Announcements <ul><li>Request for Applications (RFA): A formal announcement that invites grants or cooperative agreement applications in a well-defined scientific area to support scientific program initiatives, indicating the amount of funds set aside for the competition and the estimated number of awards to be made. </li></ul><ul><li>Program Announcement (PA): A formal statement that describes and gives notice to the grantee community of the existence of an NIH-wide or individual Institute/Center extramural research activity/interest or announces the initiation of a new or modified activity/interest or mechanism of support and invites applications for grant or cooperative agreement support. Funds may or may not be set-aside for PAs . </li></ul><ul><li> </li></ul>
    36. 40. Current NIH Research Support <ul><ul><li>RFA-DA-12-007 Remote Monitoring System for Detecting Cocaine Ingestion/Intoxication (R01) </li></ul></ul><ul><ul><li>RFA-EB-11-001 Development and Translation of Medical Technologies to Reduce Health Disparities (R43/R44) </li></ul></ul><ul><ul><li>PAR-11-020 Technologies for Healthy Independent Living (R01) </li></ul></ul><ul><ul><li>RFA-MH-12-060 Promoting Engagement in Care and Timely Antiretroviral Initiation Following HIV Diagnosis (R01) </li></ul></ul><ul><ul><li>PA-11-063 Translating Basic Behavioral and Social Science Discoveries into Interventions to Improve Health-Related Behaviors </li></ul></ul>
    37. 41. Important NIH Websites <ul><li>NIH Office of Extramural Research: </li></ul><ul><ul><li> </li></ul></ul><ul><ul><li> </li></ul></ul><ul><li>NIH Center for Scientific Review: </li></ul><ul><ul><li> </li></ul></ul><ul><li>NIH RePORTer Database: </li></ul><ul><ul><li> </li></ul></ul>
    38. 42. Workshop on mHealth Evidence <ul><li>Collaboration between Robert Wood Johnson, McKesson foundation, NSF and NIH </li></ul><ul><li>Randomized control trials are challenging in the fast-paced world of technology. Need alternate methods </li></ul><ul><li>Workshop to assess the design and analytic possibilities for developing evidence in mHealth </li></ul><ul><li>August 16, 2011 at NIH </li></ul><ul><li>    </li></ul>
    39. 43. 2011 NIH mHealth Training Institutes <ul><li>Need </li></ul><ul><ul><li>Improved use of mHealth products in clinical and behavioral research </li></ul></ul><ul><ul><li>Increased collaboration and cross-fertilization across disciplines </li></ul></ul><ul><li>Plan </li></ul><ul><ul><li>5-day training for 28 participants </li></ul></ul><ul><ul><li>Develop skills to improve the design and research of mobile technologies </li></ul></ul>
    40. 44. Thank you! <ul><li>Thank you! </li></ul><ul><ul><li>Wendy Nilsen, NIH Office of Behavioral and Social Sciences Research </li></ul></ul><ul><ul><li>301-496-0979 </li></ul></ul><ul><ul><li>[email_address] </li></ul></ul>