New Methodology for Characterizing Circadian Rhythms in Actigraphy Data Collected from a Wearable Device

• Activity Number: 443 - Making an Impact on Physical Activity and Sleep Research by Developing New Statistical Methods
• Type: Invited
• Date/Time: Wednesday, July 31, 2019 : 8:30 AM to 10:20 AM
• Sponsor: Korean International Statistical Society
• Author(s): Paul Albert * and Sungduk Kim
• Companies: National Cancer Insititute and NIH
• Keywords: circadian ; longitudinal ; actigraphy; Bayesian methods

Abstract

Circadian rhythms are defined as a biological endogenous process that repeats at an approximate 24-hour period. Increasingly these processes are recognized in their importance in understanding disease processes. This talk will focus on our recent work on the statistical modeling of longitudinally collected circadian rhythm data from intensively collected wearable data. I will begin with a discussion of a shape invariant model for Gaussian data that can be easily be fit with standard software (Albert and Hunsberger, Biometrics, 2005). This model was subsequently extended for modeling longitudinal count data (Ogbagaber et al., Journal of Circadian Rhythms, 2012). More recently we developed a statistical model for assessing the degree of disturbance or irregularity in a circadian pattern for count sequences that are observed over time in a population of individuals (Kim and Albert, Journal of the American Statistical Association, 2018). We develop a latent variable Poisson modeling approach that allows for studying the degree of circadian versus stochastic short-term trend (irregular process) in longitudinal count data collected from a wearable device.