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Circadian Computing: Sensing, Modeling, and Maintaining Biological Rhythms

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Abstract

Human physiology and behavior are deeply rooted in the daily 24 h temporal structure. Our biological processes vary significantly, predictably, and idiosyncratically throughout the day in accordance with these circadian rhythms, which in turn influence our physical and mental performance. Prolonged disruption of biological rhythms has serious consequences for physical and mental well-being, contributing to cardiovascular disease, cancer, obesity, and mental health problems. Here we present Circadian Computing, technologies that are aware of and can have a positive impact on our internal rhythms. We use a combination of automated sensing of behavioral traits along with manual ecological momentary assessments (EMA) to model body clock patterns, detect disruptions, and drive in-situ interventions. Identifying disruptions and providing circadian interventions is particularly valuable in the context of mental health—for example, to help prevent relapse in patients with bipolar disorder. More generally, such personalized, data-driven tools are capable of adapting to individual rhythms and providing more biologically attuned support in a number of areas including physical and cognitive performance, sleep, clinical therapy, and overall wellbeing. This chapter describes the design, development, and deployment of these “circadian-aware” systems: a novel class of technology aimed at modeling and maintaining our innate biological rhythms.

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Authors and Affiliations

  1. Cornell University, Ithaca, NY, USA

    Saeed Abdullah, Elizabeth L. Murnane, Mark Matthews & Tanzeem Choudhury

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  1. Saeed Abdullah
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Correspondence to Saeed Abdullah .

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  1. College of Computing, Georgia Institute of Technology, Atlanta, Georgia, USA

    James M. Rehg

  2. Department of Statistics, University of Michigan, Ann Arbor, Michigan, USA

    Susan A. Murphy

  3. Department of Computer Science, University of Memphis, Memphis, Tennessee, USA

    Santosh Kumar

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Abdullah, S., Murnane, E.L., Matthews, M., Choudhury, T. (2017). Circadian Computing: Sensing, Modeling, and Maintaining Biological Rhythms. In: Rehg, J., Murphy, S., Kumar, S. (eds) Mobile Health. Springer, Cham. https://doi.org/10.1007/978-3-319-51394-2_3

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  • DOI: https://doi.org/10.1007/978-3-319-51394-2_3

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  • Online ISBN: 978-3-319-51394-2

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