IDEAS home Printed from https://ideas.repec.org/a/spr/stabio/v11y2019i2d10.1007_s12561-018-09230-2.html
   My bibliography  Save this article

Measuring Variability in Rest-Activity Rhythms from Actigraphy with Application to Characterizing Symptoms of Depression

Author

Listed:
  • Robert T. Krafty

    (University of Pittsburgh)

  • Haoyi Fu

    (University of Pittsburgh)

  • Jessica L. Graves

    (University of Pittsburgh)

  • Scott A. Bruce

    (George Mason University)

  • Martica H. Hall

    (University of Pittsburgh)

  • Stephen F. Smagula

    (University of Pittsburgh)

Abstract

The twenty-four hour sleep-wake pattern known as the rest-activity rhythm (RAR) is associated with many aspects of health and well-being. Researchers have utilized a number of interpretable, person-specific RAR measures that can be estimated from actigraphy. Actigraphs are wearable devices that dynamically record acceleration and provide indirect measures of physical activity over time. One class of useful RAR measures are those that quantify variability around a mean circadian pattern. However, current parametric and non-parametric RAR measures used by applied researchers can only quantify variability from a limited or undefined number of rhythmic sources. The primary goal of this article is to consider a new measure of RAR variability: the log-power spectrum of stochastic error around a circadian mean. This functional measure quantifies the relative contributions of variability about a circadian mean from all possibly frequencies, including weekly, daily, and high-frequency sources of variation. It can be estimated through a two-stage procedure that smooths the log-periodogram of residuals after estimating a circadian mean. The development of this measure was motivated by a study of depression in older adults and revealed that slow, rhythmic variations in activity from a circadian pattern are correlated with depression symptoms.

Suggested Citation

  • Robert T. Krafty & Haoyi Fu & Jessica L. Graves & Scott A. Bruce & Martica H. Hall & Stephen F. Smagula, 2019. "Measuring Variability in Rest-Activity Rhythms from Actigraphy with Application to Characterizing Symptoms of Depression," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 11(2), pages 314-333, July.
    • Handle: RePEc:spr:stabio:v:11:y:2019:i:2:d:10.1007_s12561-018-09230-2
    DOI: 10.1007/s12561-018-09230-2
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s12561-018-09230-2
    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/s12561-018-09230-2?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. 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.
    2. Morris, Jeffrey S. & Arroyo, Cassandra & Coull, Brent A. & Ryan, Louise M. & Herrick, Richard & Gortmaker, Steven L., 2006. "Using Wavelet-Based Functional Mixed Models to Characterize Population Heterogeneity in Accelerometer Profiles: A Case Study," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 1352-1364, December.
    3. Philippe Besse & J. Ramsay, 1986. "Principal components analysis of sampled functions," Psychometrika, Springer;The Psychometric Society, vol. 51(2), pages 285-311, June.
    4. Simon N. Wood, 2011. "Fast stable restricted maximum likelihood and marginal likelihood estimation of semiparametric generalized linear models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 73(1), pages 3-36, January.
    5. Robert T. Krafty & William O. Collinge, 2013. "Penalized multivariate Whittle likelihood for power spectrum estimation," Biometrika, Biometrika Trust, vol. 100(2), pages 447-458.
    6. Kehui Chen & Jing Lei, 2015. "Localized Functional Principal Component Analysis," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(511), pages 1266-1275, September.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Keerati Suibkitwanchai & Adam M Sykulski & Guillermo Perez Algorta & Daniel Waller & Catherine Walshe, 2020. "Nonparametric time series summary statistics for high-frequency accelerometry data from individuals with advanced dementia," PLOS ONE, Public Library of Science, vol. 15(9), pages 1-23, September.

    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. 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.
    2. 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.
    3. Sun, Xuxue & Cai, Wenjun & Li, Mingyang, 2021. "A hierarchical modeling approach for degradation data with mixed-type covariates and latent heterogeneity," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    4. 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.
    5. Cederbaum, Jona & Scheipl, Fabian & Greven, Sonja, 2018. "Fast symmetric additive covariance smoothing," Computational Statistics & Data Analysis, Elsevier, vol. 120(C), pages 25-41.
    6. Peijun Sang & Liangliang Wang & Jiguo Cao, 2017. "Parametric functional principal component analysis," Biometrics, The International Biometric Society, vol. 73(3), pages 802-810, September.
    7. Zhang, Xiaoke & Zhong, Qixian & Wang, Jane-Ling, 2020. "A new approach to varying-coefficient additive models with longitudinal covariates," Computational Statistics & Data Analysis, Elsevier, vol. 145(C).
    8. Gerhard Tutz & Moritz Berger, 2018. "Tree-structured modelling of categorical predictors in generalized additive regression," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 12(3), pages 737-758, September.
    9. Yixuan Qiu & Jing Lei & Kathryn Roeder, 2023. "Gradient-based sparse principal component analysis with extensions to online learning," Biometrika, Biometrika Trust, vol. 110(2), pages 339-360.
    10. Tommaso Luzzati & Angela Parenti & Tommaso Rughi, 2017. "Spatial error regressions for testing the Cancer-EKC," Discussion Papers 2017/218, Dipartimento di Economia e Management (DEM), University of Pisa, Pisa, Italy.
    11. Davide Fiaschi & Andrea Mario Lavezzi & Angela Parenti, 2020. "Deep and Proximate Determinants of the World Income Distribution," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 66(3), pages 677-710, September.
    12. Conor Waldock & Bernhard Wegscheider & Dario Josi & Bárbara Borges Calegari & Jakob Brodersen & Luiz Jardim de Queiroz & Ole Seehausen, 2024. "Deconstructing the geography of human impacts on species’ natural distribution," Nature Communications, Nature, vol. 15(1), pages 1-15, December.
    13. Xiongtao Dai & Zhenhua Lin & Hans‐Georg Müller, 2021. "Modeling sparse longitudinal data on Riemannian manifolds," Biometrics, The International Biometric Society, vol. 77(4), pages 1328-1341, December.
    14. Longhi, Christian & Musolesi, Antonio & Baumont, Catherine, 2014. "Modeling structural change in the European metropolitan areas during the process of economic integration," Economic Modelling, Elsevier, vol. 37(C), pages 395-407.
    15. Park, So Young & Xiao, Luo & Willbur, Jayson D. & Staicu, Ana-Maria & Jumbe, N. L’ntshotsholé, 2018. "A joint design for functional data with application to scheduling ultrasound scans," Computational Statistics & Data Analysis, Elsevier, vol. 122(C), pages 101-114.
    16. Sihvonen, Markus, 2021. "Yield curve momentum," Research Discussion Papers 15/2021, Bank of Finland.
    17. Roberto Basile & Luigi Benfratello & Davide Castellani, 2012. "Geoadditive models for regional count data: an application to industrial location," ERSA conference papers ersa12p83, European Regional Science Association.
    18. Dillon T. Fogarty & Caleb P. Roberts & Daniel R. Uden & Victoria M. Donovan & Craig R. Allen & David E. Naugle & Matthew O. Jones & Brady W. Allred & Dirac Twidwell, 2020. "Woody Plant Encroachment and the Sustainability of Priority Conservation Areas," Sustainability, MDPI, vol. 12(20), pages 1-15, October.
    19. Fang Yao & Hans-Georg Müller & Andrew J. Clifford & Steven R. Dueker & Jennifer Follett & Yumei Lin & Bruce A. Buchholz & John S. Vogel, 2003. "Shrinkage Estimation for Functional Principal Component Scores with Application to the Population Kinetics of Plasma Folate," Biometrics, The International Biometric Society, vol. 59(3), pages 676-685, September.
    20. E. Zanini & E. Eastoe & M. J. Jones & D. Randell & P. Jonathan, 2020. "Flexible covariate representations for extremes," Environmetrics, John Wiley & Sons, Ltd., vol. 31(5), August.

    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_s12561-018-09230-2. 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.