Aligned Accelerometer Data Can Improve Understanding of Chronotypes
Video
Team Information
Team Members
Erin McDonnell, PhD Candidate, Department of Biostatistics, Columbia University Mailman School of Public Health
Vadim Zipunnikov, Associate Professor, Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University
Jennifer Schrack, Associate Professor, Department of Epidemiology, Bloomberg School of Public Health, Johns Hopkins University
Julia Wrobel, Assistant Professor, Department of Biostatistics and Informatics, Colorado School of Public Health, University of Colorado Anschutz Medical Campus
Faculty Advisor: Jeff Goldsmith, Associate Professor of Biostatistics, Columbia University Mailman School of Public Health
Abstract
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.
Contact this Team
Contact: Erin McDonnell (use form to send email)
