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Joint and Individual Representation of Domains of Physical Activity, Sleep, and Circadian Rhythmicity

Author

Listed:
  • Junrui Di

    (Johns Hopkins Bloomberg School of Public Health)

  • Adam Spira

    (Johns Hopkins Center on Aging and Health
    Johns Hopkins Bloomberg School of Public Health
    Johns Hopkins School of Medicine)

  • Jiawei Bai

    (Johns Hopkins Bloomberg School of Public Health)

  • Jacek Urbanek

    (Johns Hopkins University School of Medicine)

  • Andrew Leroux

    (Johns Hopkins Bloomberg School of Public Health)

  • Mark Wu

    (Johns Hopkins University School of Medicine)

  • Susan Resnick

    (National Institute on Aging, National Institutes of Health)

  • Eleanor Simonsick

    (National Institute on Aging, National Institutes of Health)

  • Luigi Ferrucci

    (National Institute on Aging, National Institutes of Health)

  • Jennifer Schrack

    (Johns Hopkins Center on Aging and Health
    Johns Hopkins Bloomberg School of Public Health)

  • Vadim Zipunnikov

    (Johns Hopkins Center on Aging and Health
    Johns Hopkins Bloomberg School of Public Health)

Abstract

Developments in wearable technology have enabled researchers to continuously and objectively monitor various aspects and physiological domains of real life including levels of physical activity, quality of sleep, and strength of circadian rhythm in many epidemiological and clinical studies. Current analytical practice is to summarize each of these three domains individually via a standard inventory of interpretable features, and explore individual associations between the features and clinical variables. However, the features often exhibit significant interaction and correlation both within and between domains. Integration of features across multiple domains remains methodologically challenging. To address this problem, we propose to use joint and individual variation explained, a dimension reduction technique that efficiently deals with multivariate data representing multiple domains. In this paper, we review the most frequently used features to characterize the domains of physical activity, sleep, and circadian rhythmicity and illustrate the approach using wrist-worn actigraphy data from 198 participants of the Baltimore Longitudinal Study of Aging.

Suggested Citation

  • Junrui Di & Adam Spira & Jiawei Bai & Jacek Urbanek & Andrew Leroux & Mark Wu & Susan Resnick & Eleanor Simonsick & Luigi Ferrucci & Jennifer Schrack & Vadim Zipunnikov, 2019. "Joint and Individual Representation of Domains of Physical Activity, Sleep, and Circadian Rhythmicity," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 11(2), pages 371-402, July.
    • Handle: RePEc:spr:stabio:v:11:y:2019:i:2:d:10.1007_s12561-019-09236-4
    DOI: 10.1007/s12561-019-09236-4
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    References listed on IDEAS

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    1. Eric Jondeau & Emmanuel Jurczenko & Michael Rockinger, 2018. "Moment Component Analysis: An Illustration With International Stock Markets," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 36(4), pages 576-598, October.
    2. Jeff Goldsmith & Vadim Zipunnikov & Jennifer Schrack, 2015. "Generalized multilevel function-on-scalar regression and principal component analysis," Biometrics, The International Biometric Society, vol. 71(2), pages 344-353, June.
    3. Andrew Leroux & Junrui Di & Ekaterina Smirnova & Elizabeth J Mcguffey & Quy Cao & Elham Bayatmokhtari & Lucia Tabacu & Vadim Zipunnikov & Jacek K Urbanek & Ciprian Crainiceanu, 2019. "Organizing and Analyzing the Activity Data in NHANES," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 11(2), pages 262-287, July.
    4. Haochang Shou & Vadim Zipunnikov & Ciprian M. Crainiceanu & Sonja Greven, 2015. "Structured functional principal component analysis," Biometrics, The International Biometric Society, vol. 71(1), pages 247-257, March.
    5. Josephine Y Chau & Anne C Grunseit & Tien Chey & Emmanuel Stamatakis & Wendy J Brown & Charles E Matthews & Adrian E Bauman & Hidde P van der Ploeg, 2013. "Daily Sitting Time and All-Cause Mortality: A Meta-Analysis," PLOS ONE, Public Library of Science, vol. 8(11), pages 1-1, November.
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