TY - JOUR
T1 - Registration of 24-hour accelerometric rest-activity profiles and its application to human chronotypes
AU - McDonnell, Erin I.
AU - Zipunnikov, Vadim
AU - Schrack, Jennifer A.
AU - Goldsmith, Jeff
AU - Wrobel, Julia
N1 - Funding Information:
This work was supported in part by the National Institutes of Health under Grant [R01NS097423].J.A.S. is supported by the National Institutes of Health under Grant [R01AG061786 and Grant U01AG057545].V.Z. is supported by the National Institutes of Health under Grant [R01AG054771 and Grant U01AG057545].Data from this study were obtained from the Baltimore Longitudinal Study of Aging, a study of the Intramural Research Program of the National Institute on Aging
Publisher Copyright:
© 2021 Informa UK Limited, trading as Taylor & Francis Group.
PY - 2022
Y1 - 2022
N2 - By collecting data continuously over 24 hours, accelerometers and other wearable devices can provide novel insights into circadian rhythms and their relationship to human health. Existing approaches for analyzing diurnal patterns using these data, including the cosinor model and functional principal component analysis, have revealed and quantified population-level diurnal patterns, but considerable subject-level variability remained uncaptured in features such as wake/sleep times and activity intensity. This remaining informative variability could provide a better understanding of chronotypes, or behavioral manifestations of one’s underlying 24-hour rhythm. Curve registration, or alignment, is a technique in functional data analysis that separates “vertical” variability in activity intensity from “horizontal” variability in time-dependent markers like wake and sleep times; this data-driven approach is well-suited to studying chronotypes using accelerometer data. We develop a parametric registration framework for 24-hour accelerometric rest-activity profiles represented as dichotomized into epoch-level states of activity or rest. Specifically, we estimate subject-specific piecewise linear time-warping functions parametrized with a small set of parameters. We apply this method to data from the Baltimore Longitudinal Study of Aging and illustrate how estimated parameters give a more flexible quantification of chronotypes compared to traditional approaches.
AB - By collecting data continuously over 24 hours, accelerometers and other wearable devices can provide novel insights into circadian rhythms and their relationship to human health. Existing approaches for analyzing diurnal patterns using these data, including the cosinor model and functional principal component analysis, have revealed and quantified population-level diurnal patterns, but considerable subject-level variability remained uncaptured in features such as wake/sleep times and activity intensity. This remaining informative variability could provide a better understanding of chronotypes, or behavioral manifestations of one’s underlying 24-hour rhythm. Curve registration, or alignment, is a technique in functional data analysis that separates “vertical” variability in activity intensity from “horizontal” variability in time-dependent markers like wake and sleep times; this data-driven approach is well-suited to studying chronotypes using accelerometer data. We develop a parametric registration framework for 24-hour accelerometric rest-activity profiles represented as dichotomized into epoch-level states of activity or rest. Specifically, we estimate subject-specific piecewise linear time-warping functions parametrized with a small set of parameters. We apply this method to data from the Baltimore Longitudinal Study of Aging and illustrate how estimated parameters give a more flexible quantification of chronotypes compared to traditional approaches.
KW - Accelerometer
KW - cosinor model
KW - data-driven chronotypes
KW - diurnal registration
KW - functional principal components analysis
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U2 - 10.1080/09291016.2021.1929673
DO - 10.1080/09291016.2021.1929673
M3 - Article
C2 - 35784395
AN - SCOPUS:85107381126
VL - 53
SP - 1299
EP - 1319
JO - Biological Rhythm Research
JF - Biological Rhythm Research
SN - 0929-1016
IS - 8
ER -