RT Journal Article
SR Electronic
T1 Scale-invariant time registration of 24-hour accelerometric rest-activity profiles and its application to human chronotypes
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2020.10.13.337550
DO 10.1101/2020.10.13.337550
A1 Erin I. McDonnell
A1 Vadim Zipunnikov
A1 Jennifer A. Schrack
A1 Jeff Goldsmith
A1 Julia Wrobel
YR 2020
UL http://biorxiv.org/content/early/2020/10/14/2020.10.13.337550.abstract
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 components 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.Competing Interest StatementThe authors have declared no competing interest.