Circadian computing: Sensing, modeling, and maintaining biological rhythms

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

Research output: Chapter in Book/Report/Conference proceedingChapter

3 Citations (Scopus)

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.

Original languageEnglish (US)
Title of host publicationMobile Health
Subtitle of host publicationSensors, Analytic Methods, and Applications
PublisherSpringer International Publishing
Pages35-58
Number of pages24
ISBN (Electronic)9783319513942
ISBN (Print)9783319513935
DOIs
StatePublished - Jul 12 2017

Fingerprint

Periodicity
Mental Health
Technology
Biological Phenomena
Physiology
Medical problems
Circadian Rhythm
Bipolar Disorder
Clocks
Sleep
Cardiovascular Diseases
Obesity
Health
Recurrence
Neoplasms
Therapeutics
Ecological Momentary Assessment
Drive

All Science Journal Classification (ASJC) codes

  • Medicine(all)
  • Computer Science(all)

Cite this

Abdullah, S., Murnane, E. L., Matthews, M., & Choudhury, T. (2017). Circadian computing: Sensing, modeling, and maintaining biological rhythms. In Mobile Health: Sensors, Analytic Methods, and Applications (pp. 35-58). Springer International Publishing. https://doi.org/10.1007/978-3-319-51394-2_3
Abdullah, Saeed ; Murnane, Elizabeth L. ; Matthews, Mark ; Choudhury, Tanzeem. / Circadian computing : Sensing, modeling, and maintaining biological rhythms. Mobile Health: Sensors, Analytic Methods, and Applications. Springer International Publishing, 2017. pp. 35-58
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Abdullah, S, Murnane, EL, Matthews, M & Choudhury, T 2017, Circadian computing: Sensing, modeling, and maintaining biological rhythms. in Mobile Health: Sensors, Analytic Methods, and Applications. Springer International Publishing, pp. 35-58. https://doi.org/10.1007/978-3-319-51394-2_3

Circadian computing : Sensing, modeling, and maintaining biological rhythms. / Abdullah, Saeed; Murnane, Elizabeth L.; Matthews, Mark; Choudhury, Tanzeem.

Mobile Health: Sensors, Analytic Methods, and Applications. Springer International Publishing, 2017. p. 35-58.

Research output: Chapter in Book/Report/Conference proceedingChapter

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Abdullah S, Murnane EL, Matthews M, Choudhury T. Circadian computing: Sensing, modeling, and maintaining biological rhythms. In Mobile Health: Sensors, Analytic Methods, and Applications. Springer International Publishing. 2017. p. 35-58 https://doi.org/10.1007/978-3-319-51394-2_3