%0 Journal Article
%T Measuring the impact of apnea and obesity on circadian activity patterns using functional linear modeling of actigraphy data
%A Jia Wang
%A Hong Xian
%A Amy Licis
%A Elena Deych
%A Jimin Ding
%A Jennifer McLeland
%A Cristina Toedebusch
%A Tao Li
%A Stephen Duntley
%A William Shannon
%J Journal of Circadian Rhythms
%D 2011
%I BioMed Central
%R 10.1186/1740-3391-9-11
%X A statistical method for testing differences in activity patterns measured by actigraphy across subgroups using functional data analysis is described. For illustration this method is used to statistically assess the impact of apnea-hypopnea index (apnea) and body mass index (BMI) on circadian activity patterns measured using actigraphy in 395 participants from 18 to 80 years old, referred to the Washington University Sleep Medicine Center for general sleep medicine care. Mathematical descriptions of the methods and results from their application to real data are presented.Activity patterns were recorded by an Actical device (Philips Respironics Inc.) every minute for at least seven days. Functional linear modeling was used to detect the association between circadian activity patterns and apnea and BMI. Results indicate that participants in high apnea group have statistically lower activity during the day, and that BMI in our study population does not significantly impact circadian patterns.Compared with analysis using summary measures (e.g., average activity over 24 hours, total sleep time), Functional Data Analysis (FDA) is a novel statistical framework that more efficiently analyzes information from actigraphy data. FDA has the potential to reposition the focus of actigraphy data from general sleep assessment to rigorous analyses of circadian activity rhythms.Activity measured by wrist actigraphy has been shown to be a valid marker of entrained Polysomnography (PSG) sleep phase and is strongly correlated with entrained endogenous circadian phase [1]. Actigraphy data is recorded densely, such as every minute or every 15 seconds, for each patient over multiple days. This data is generally analyzed by reducing the time series activity values to summary statistics such as sleep/wake ratios,[2,3] total sleep time,[2,4] sleep efficiency,[5,6] wake after sleep onset, [2,3,6] ratio of nighttime activity to daytime activity or total activity,[7,8] standard deviation of sle
%K Apnea
%K BMI
%K circadian activity patterns
%K Functional Data Analysis
%U http://www.jcircadianrhythms.com/content/9/1/11