IDEAS home Printed from https://ideas.repec.org/a/spr/stabio/v11y2019i2d10.1007_s12561-019-09236-4.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s12561-019-09236-4
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s12561-019-09236-4?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    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. 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.
    3. 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.
    4. 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.
    5. 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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Yukun Zhang & Haocheng Li & Sarah Kozey Keadle & Charles E. Matthews & Raymond J. Carroll, 2019. "A Review of Statistical Analyses on Physical Activity Data Collected from Accelerometers," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 11(2), pages 465-476, July.
    2. Li, Yehua & Qiu, Yumou & Xu, Yuhang, 2022. "From multivariate to functional data analysis: Fundamentals, recent developments, and emerging areas," Journal of Multivariate Analysis, Elsevier, vol. 188(C).
    3. Lassance, Nathan & Vrins, Frédéric, 2021. "Portfolio selection with parsimonious higher comoments estimation," Journal of Banking & Finance, Elsevier, vol. 126(C).
    4. Esparcia, Carlos & Díaz, Antonio, 2024. "The football world upside down: Traditional equities as an alternative for the new fan tokens? A portfolio optimization study," Research in International Business and Finance, Elsevier, vol. 71(C).
    5. Selene Yue Xu & Sandahl Nelson & Jacqueline Kerr & Suneeta Godbole & Eileen Johnson & Ruth E. Patterson & Cheryl L. Rock & Dorothy D. Sears & Ian Abramson & Loki Natarajan, 2019. "Modeling Temporal Variation in Physical Activity Using Functional Principal Components Analysis," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 11(2), pages 403-421, July.
    6. Boudt, Kris & Cornilly, Dries & Verdonck, Tim, 2020. "Nearest comoment estimation with unobserved factors," Journal of Econometrics, Elsevier, vol. 217(2), pages 381-397.
    7. Wenyi Lin & Jingjing Zou & Chongzhi Di & Dorothy D. Sears & Cheryl L. Rock & Loki Natarajan, 2023. "Longitudinal Associations Between Timing of Physical Activity Accumulation and Health: Application of Functional Data Methods," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 15(2), pages 309-329, July.
    8. Li, Kan & Luo, Sheng, 2019. "Bayesian functional joint models for multivariate longitudinal and time-to-event data," Computational Statistics & Data Analysis, Elsevier, vol. 129(C), pages 14-29.
    9. Marta Karas & Jiawei Bai & Marcin Strączkiewicz & Jaroslaw Harezlak & Nancy W. Glynn & Tamara Harris & Vadim Zipunnikov & Ciprian Crainiceanu & Jacek K. Urbanek, 2019. "Accelerometry Data in Health Research: Challenges and Opportunities," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 11(2), pages 210-237, July.
    10. 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.
    11. Boyao Wu & Difang Huang & Muzi Chen, 2023. "Estimating contagion mechanism in global equity market with time‐zone effect," Financial Management, Financial Management Association International, vol. 52(3), pages 543-572, September.
    12. Boyao Wu & Difang Huang & Muzi Chen, 2024. "Estimating Contagion Mechanism in Global Equity Market with Time-Zone Effect," Papers 2404.04335, arXiv.org.
    13. Domino, Krzysztof, 2020. "Multivariate cumulants in outlier detection for financial data analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 558(C).
    14. Tirman, Laura & Biggs, Hannah & Morrison, Kathleen & Manner, Jillian & Sivaramakrishnan, Divya & Baker, Graham & Jepson, Ruth, 2021. "Stand Up for Health: Programme theory for an intervention to reduce sedentary behaviour in contact centres," Evaluation and Program Planning, Elsevier, vol. 89(C).
    15. Wanbo Lu & Guanglin Huang & Kris Boudt, 2024. "Estimation of Non-Gaussian Factors Using Higher-order Multi-cumulants in Weak Factor Models," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 24/1085, Ghent University, Faculty of Economics and Business Administration.
    16. Gertheiss, Jan & Goldsmith, Jeff & Staicu, Ana-Maria, 2017. "A note on modeling sparse exponential-family functional response curves," Computational Statistics & Data Analysis, Elsevier, vol. 105(C), pages 46-52.
    17. Lassance, Nathan & Vrins, Frédéric, 2019. "Robust portfolio selection using sparse estimation of comoment tensors," LIDAM Discussion Papers LFIN 2019007, Université catholique de Louvain, Louvain Finance (LFIN).
    18. Gergely Ráthonyi & Karolina Kósa & Zoltán Bács & Kinga Ráthonyi-Ódor & István Füzesi & Péter Lengyel & Éva Bácsné Bába, 2021. "Changes in Workers’ Physical Activity and Sedentary Behavior during the COVID-19 Pandemic," Sustainability, MDPI, vol. 13(17), pages 1-25, August.
    19. Feliu Serra-Burriel & Pedro Delicado & Fernando M. Cucchietti, 2021. "Wildfires Vegetation Recovery through Satellite Remote Sensing and Functional Data Analysis," Mathematics, MDPI, vol. 9(11), pages 1-22, June.
    20. Daniel Backenroth & Russell T. Shinohara & Jennifer A. Schrack & Jeff Goldsmith, 2020. "Nonnegative decomposition of functional count data," Biometrics, The International Biometric Society, vol. 76(4), pages 1273-1284, December.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:stabio:v:11:y:2019:i:2:d:10.1007_s12561-019-09236-4. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.