Measuring Variability in Rest-Activity Rhythms with Application to Characterizing Symptoms of Depression

• Activity Number: 45 - Recent Development in Mobile/Wearable Device Data Analysis
• Type: Invited
• Date/Time: Sunday, August 7, 2022 : 4:00 PM to 5:50 PM
• Sponsor: Health Policy Statistics Section
• Author(s): Robert T Krafty* and Haoyi Fu and Jessica Graves and Scott Bruce and Martica Hall and Stephen Smagula
• Companies: Emory University and University of Pittsburgh and University of Pittsburgh and Texas A&M University and University of Pittsburgh and University of Pittsburgh
• Keywords: Mobile Health; Depression; Activity; Time Series; Sleep; Circadian

Abstract

The twenty-four hour sleep-wake pattern known as the rest-activity rhythm (RAR) is associated with many aspects of health and well-being. Researchers have utilized a number of interpretable, person-specific RAR measures that can be estimated from actigraphy, which are wearable devices that dynamically record acceleration and provide indirect measures of physical activity over time. One class of useful RAR measures are those that quantify variability around a mean circadian pattern. In this talk, we consider a new measure of RAR variability: the log-power spectrum of stochastic error around a circadian mean. This functional measure quantifies the relative contributions of variability about a circadian mean from all possibly frequencies, including weekly, daily, and high-frequency sources of variation. It can be estimated through a two-stage procedure that smooths the log-periodogram of residuals after estimating a circadian mean. The development of this measure was motivated by a study of depression in older adults and revealed that slow, rhythmic variations in activity from a circadian pattern are correlated with depression symptoms.