Rapid advances in technology are leading to field-deployable mobile sensing devices that can be used to quantify complex dynamics of key physical, biological, behavioral, social, and environmental factors that contribute to health and disease risk across time and help us thus break through to the next level of biomedical understanding of causation in complex disorders. While ongoing efforts have focused on and made significant strides in the analysis of """"""""big data"""""""" in the areas of genomics, imaging, and EHR, significant new investment is needed to provide training in data analytics tools specific to the unique features of mobile sensor data to enable scientists 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, but only if accompanied by easily-accessible training and education. 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 and using it for biomedical discovery. The Training Core of the MD2K Center will enable the broader biomedical research community beyond the Center to use the MD2K data analysis tools for biomedical discovery in a range of biomedical applications by providing online tutorials, training videos, virtual seminars and a comprehensive web-based resource library. It will engage the data science research community in advancing the science of MD2K itself by equipping them with data sets, open-source software, documentation, and online forums so they can contribute new theories, software, and data sets, and integrate new sensors and other data types into the MD2K platform. It will develop curricular materials for use in classrooms, in both data science and biomedical disciplines, to provide MD2K training to students, and begin the process of building the next generation of scientific workforce that is capable of using MD2K in increasingly sophisticated biomedical applications.

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.

National Institute of Health (NIH)
National Institute of Biomedical Imaging and Bioengineering (NIBIB)
Specialized Center--Cooperative Agreements (U54)
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Special Emphasis Panel (ZRG1-BST-N (52))
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University of Memphis
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