Rapid advances in technology, are leading to field-deployable mobile sensing devices that can now be used to quantify the complex dynamics across time of key physical, biological, behavioral, social, and environmental factors that contribute to health and disease risk. These data will enable us to break through to the next level of biomedical understanding of causation in complex disorders. While ongoing efforts have made significant strides in the analysis of "big data" in the areas of genomics, imaging, and EHR, significant new investment is needed to develop and disseminate data analytics tools specific to the unique features of mobile sensor data (e.g., high volume, velocity, variety, variability, and versatility) to convert this wealth of data into information, knowledge, and action. Investment in a strong, open, scientific, and computational infrastructure for mobile "big data" at this early stage promises outsize returns to advance science and improve health. The Data Science Research (DSR) Core of the Center of Excellence for Mobile Sensor Data to Knowledge (MD2K) will generate generalizable theory, methods, tools, and software, to address major barriers to processing complex mobile sensor data (e.g., transition from early lab feasibility to robust field utility) so as to enable the use of mobile sensor data by the broader community for biomedical knowledge discovery and just-in-time care delivery, laying the foundation for P5 Medicine. The DSR Core will demonstrate the feasibility, utility, and generalizability of the MD2K approach by implementing the entire MD2K data analytics system in the context of two biomedical applications - reducing relapse among abstinent daily smokers and reducing readmission among congestive heart failure (CHF) patients. All the tools and software developed by MD2K will be freely available as open-source projects to engage data science researchers in advancing the science of MD2K. Biomedical researchers will be able to install the MD2K software on mobile devices to collect mobile sensor data and the MD2K analytics software on their servers to analyze these data for biomedical discovery.

Public Health Relevance

By enabling the use of mobile sensor data to detect and predict person-specific disease risk factors ahead of the onset of adverse clinical events, this project will make it possible to intervene anytime, anywhere, to anticipate and prevent disease complications at the earliest timepoint, ultimately supporting wellness. These tools, made freely available, will reduce the burden of complex chronic disorders on health and healthcare.

Agency
National Institute of Health (NIH)
Institute
National Institute of Biomedical Imaging and Bioengineering (NIBIB)
Type
Specialized Center--Cooperative Agreements (U54)
Project #
1U54EB020404-01
Application #
8906240
Study Section
Special Emphasis Panel (ZRG1-BST-N (52))
Project Start
Project End
Budget Start
2014-09-29
Budget End
2015-05-31
Support Year
1
Fiscal Year
2014
Total Cost
$1,077,065
Indirect Cost
$168,234
Name
University of Memphis
Department
Type
DUNS #
055688857
City
Memphis
State
TN
Country
United States
Zip Code
38152