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An Architecture to Support Real-World Studies that Investigate the Autonomic Nervous System

  • Danielle GroatEmail author
  • Ramkiran Gouripeddi
  • Randy Madsen
  • Yu Kuei Lin
  • Julio C. Facelli
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11721)

Abstract

Diabetes is a chronic disease with complications related to the autonomic nervous system (ANS) that can affect quality of life and lead to mortality. Clinicians and researchers currently rely on subjective and/or invasive means that don’t necessarily translate to real-world setting when assessing severity of certain diabetes complications. We elicited use-cases of studies aimed at understanding ANS in the context of diabetes to gather system requirements for designing an architecture to support sensor-based studies. Real-world studies would need to be capable of gathering contextual data as well as proxies for ANS symptoms from digital markers from an evolving sensor landscape, while also supporting the data needs of researchers before, during, and after data acquisition. The proposed architecture makes use of open source and commercially available mobile health technologies, and informatics platforms to meet the design criteria. Building and testing a prototype of the proposed architecture is planned to confirm the system performs as expected.

Keywords

Real-world studies Diabetes complications Autonomic nervous system Software architecture Sensors Digital biomarkers 

Notes

Acknowledgements

This research is supported by NIH/NIDDK Ruth L. Kirschstein National Research Service Award, Diabetes & Metabolism Research Center at the University of Utah, the England Family Foundation, the Ardene Bullard “Of Love” Tennis Tournament, Jacobsen Construction, NIH/NCATS UL1TR002538, and NIH/NIBIB U54EB021973. Computational resources were provided by the Utah Center for High Performance Computing, which is partially funded by the NIH Shared Instrumentation.

Computational resources were provided by the Utah Center for High Performance Computing, which is partially funded by the NIH Shared Instrumentation Grant 1S10OD021644-01A1.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Danielle Groat
    • 1
    Email author
  • Ramkiran Gouripeddi
    • 1
    • 2
  • Randy Madsen
    • 2
  • Yu Kuei Lin
    • 3
  • Julio C. Facelli
    • 1
    • 2
  1. 1.Department of Biomedical InformaticsUniversity of UtahSalt Lake CityUSA
  2. 2.Center for Clinical and Translational ScienceUniversity of UtahSalt Lake CityUSA
  3. 3.Department of EndocrinologyUniversity of UtahSalt Lake CityUSA

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