SLEEP - Quantification of the Fragmentation of Rest-Activity Patterns in Elderly Individuals Using a State Transition Analysis

VOLUME 34, ISSUE 11

STATE TRANSITION ANALYSIS TO QUANTIFY REST-ACTIVITY IN THE ELDERLY
Quantification of the Fragmentation of Rest-Activity Patterns in Elderly Individuals Using a State Transition Analysis

http://dx.doi.org/10.5665/sleep.1400

Andrew S.P. Lim, MD¹; Lei Yu, PhD²; Madalena D. Costa, PhD³^,⁴; Aron S. Buchman, MD²; David A. Bennett, MD²; Sue E. Leurgans, PhD²; Clifford B. Saper, MD, PhD¹

¹Department of Neurology, Program in Neuroscience and Division of Sleep Medicine, Harvard Medical School, Beth Israel Deaconess Medical Center, Boston, MA; ²Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL; ³Margaret and H.A. Rey Institute of Nonlinear Dynamics in Physiology, Beth Israel Deaconess Medical Center, Boston, MA; ⁴Wyss Institute for Biologically Inspired Engineering, Boston, MA

Table of Contents

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Objectives:

Recent interest in the temporal dynamics of behavioral states has spurred the development of analytical approaches for their quantification. Several analytical approaches for polysomnographic data have been described. However, polysomnography is cumbersome, perturbs behavior, and is limited to short recordings. Although less physiologically comprehensive than polysomnography, actigraphy is nonintrusive, amenable to long recordings, and suited to use in subjects' natural environments, and provides an indirect measure of behavioral state. We developed a probabilistic state transition model to quantify the fragmentation of human rest-activity patterns from actigraphic data. We then applied this to the study of the temporal dynamics of rest-activity patterns in older individuals.

Design:

Cross-sectional.

Setting:

Community-based.

Participants:

621 community-dwelling individuals without dementia participating in the Rush Memory and Aging Project.

Measurements and Results:

We analyzed actigraphic data collected for up to 11 days. We processed each record to give a series of transitions between the states of rest and activity, calculated the probabilities of such transitions, and described their evolution as a function of time. From these analyses, we derived metrics of the fragmentation of rest or activity at scales of seconds to minutes. Regression modeling of the relationship of these metrics with clinical variables revealed significant associations with age, even after adjusting for sex, body mass index, and a broad range of medical comorbidities.

Conclusions:

Probabilistic analyses of the transition dynamics of rest-activity data provide a high-throughput, automated, quantitative, and noninvasive method of assessing the fragmentation of behavioral states suitable for large scale human and animal studies; these methods reveal age-associated changes in the fragmentation of rest-activity patterns akin to those described using polysomnographic methods.

Citation:

Lim ASP; Yu L; Costa MD; Buchman AS; Bennett DA; Leurgans SE; Saper CB. Quantification of the fragmentation of rest-activity patterns in elderly individuals using a state transition analysis.