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Activity Number: 62 - Large Population Physical Activity Studies Using Wearable Devices: Challenges and Future Directions
Type: Invited
Date/Time: Monday, August 9, 2021 : 10:00 AM to 11:50 AM
Sponsor: Section on Statistics in Epidemiology
Abstract #316941
Title: New Methodology for Characterizing Circadian Rhythm in Actigraphy Data Collected from a Wearable Device
Author(s): Sungduk Kim* and Paul S. Albert
Companies: National Cancer Institute and National Cancer Institute
Keywords: Circadian rhythm; intensive longitudinal data; Latent process ; Poisson data ; Longitudinal data ; Time series

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. 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. We develop a latent variable Poisson modeling approach with both circadian and stochastic short-term trend (autoregressive latent process) components that allow for individual variation in the degree of each component. A parameterization is proposed for modeling covariate dependence on the proportion of these two model components across individuals. In addition, we incorporate covariate dependence in the overall mean, the magnitude of the trend, and the phase-shift of the circadian pattern. Innovative Bayesian methodology is used for parameter estimation.

Authors who are presenting talks have a * after their name.

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