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Nonparametric time series summary statistics for high-frequency accelerometry data from individuals with advanced dementia

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Listed:
  • Keerati Suibkitwanchai
  • Adam M Sykulski
  • Guillermo Perez Algorta
  • Daniel Waller
  • Catherine Walshe

Abstract

Accelerometry data has been widely used to measure activity and the circadian rhythm of individuals across the health sciences, in particular with people with advanced dementia. Modern accelerometers can record continuous observations on a single individual for several days at a sampling frequency of the order of one hertz. Such rich and lengthy data sets provide new opportunities for statistical insight, but also pose challenges in selecting from a wide range of possible summary statistics, and how the calculation of such statistics should be optimally tuned and implemented. In this paper, we build on existing approaches, as well as propose new summary statistics, and detail how these should be implemented with high frequency accelerometry data. We test and validate our methods on an observed data set from 26 recordings from individuals with advanced dementia and 14 recordings from individuals without dementia. We study four metrics: Interdaily stability (IS), intradaily variability (IV), the scaling exponent from detrended fluctuation analysis (DFA), and a novel nonparametric estimator which we call the proportion of variance (PoV), which calculates the strength of the circadian rhythm using spectral density estimation. We perform a detailed analysis indicating how the time series should be optimally subsampled to calculate IV, and recommend a subsampling rate of approximately 5 minutes for the dataset that has been studied. In addition, we propose the use of the DFA scaling exponent separately for daytime and nighttime, to further separate effects between individuals. We compare the relationships between all these methods and show that they effectively capture different features of the time series.

Suggested Citation

  • 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.
  • Handle: RePEc:plo:pone00:0239368
    DOI: 10.1371/journal.pone.0239368
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    References listed on IDEAS

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    1. 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.
    2. Ruben Fossion & Ana Leonor Rivera & Juan C Toledo-Roy & Jason Ellis & Maia Angelova, 2017. "Multiscale adaptive analysis of circadian rhythms and intradaily variability: Application to actigraphy time series in acute insomnia subjects," PLOS ONE, Public Library of Science, vol. 12(7), pages 1-21, July.
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