Data Science and Applied Technology (DSAT) Core (RC4), a recent addition to the University of Florida (UF) Older Americans Independence Center (OAIC), provides an interactive data and technology ecosystem aimed at preserving mobility and preventing disability. Big data initiatives, applied technologies, and new methodological approaches for data science have grown rapidly in many various environments, and the world is moving toward a connected system of computing and sensing components. The broadly used term ?Internet of Things? refers to an environment in which detailed data are collected on health, activity, location, and other aspects of the participating entities. Flexible control of the different interconnected and frequently communicating components can provide a rich set of applications that can adapt dynamically to their environment. Additionally, mobile health (mHealth, smartphones and smartwatches) technologies are changing the landscape for how patients and research participants communicate about their health in real time. These possibilities have led the NIH to put forth large initiatives (Big Data to Knowledge (BD2K) and The Precision Medicine Initiative Cohort Program) for meeting this new demand for knowledge. DSAT investigators provide OAIC leadership to assure that researchers in Geriatrics in general, mobility and disability are prepared for the rapid advances in these expanding technologies. The RC4 provides many unique attributes, such as: developing software for interactive mobile technology (e.g., wearable sensors that are programmable in real time); validating new sensing technology; warehousing data; repurposing data; and applying machine learning techniques to domain problems. DSAT provides a central hub of expertise in computer science, biomedical engineering, biomedical informatics, data science, applied technology, epidemiology, and content expertise in the assessment of mobility to: ? Support OAIC cores, train Junior Scholars, and provide outreach to researchers and practitioners; ? Advance interactive monitoring for assessing mobility phenotypes; ? Warehouse and integrate multimodal data; ? Conduct machine-learning and pattern-discovery analyses; ? Harvest electronic health record (EHR) data to identify and recruit participants; ? Repurpose high-resolution biomedical data and physiological signals to derive mobility phenotypes; and ? Enhance externally supported projects. There is a growing demand for data science and applied technology for meeting the challenge of preserving mobility and preventing disability. The DSAT Core adds a highly innovative aspect to this challenge that will lead it into the future of connected systems of computing, sensing and biomedical informatics.
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