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.
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.
Entities:
Keywords:
Actigraphy; aging; mathematical modeling; state transition analysis
Authors: Chung-Chuan Lo; Thomas Chou; Thomas Penzel; Thomas E Scammell; Robert E Strecker; H Eugene Stanley; Plamen Ch Ivanov Journal: Proc Natl Acad Sci U S A Date: 2004-12-06 Impact factor: 11.205
Authors: Mark L Unruh; Susan Redline; Ming-Wen An; Daniel J Buysse; F Javier Nieto; Jeun-Liang Yeh; Anne B Newman Journal: J Am Geriatr Soc Date: 2008-05-14 Impact factor: 5.562
Authors: David G Harper; Edward G Stopa; Victoria Kuo-Leblanc; Ann C McKee; Kentaro Asayama; Ladislav Volicer; Neil Kowall; Andrew Satlin Journal: Brain Date: 2008-03-27 Impact factor: 13.501
Authors: Aron S Buchman; Robert S Wilson; Lei Yu; Bryan D James; Patricia A Boyle; David A Bennett Journal: Arch Gerontol Geriatr Date: 2013-08-15 Impact factor: 3.250
Authors: Shahmir Sohail; Lei Yu; Julie A Schneider; David A Bennett; Aron S Buchman; Andrew S P Lim Journal: Mov Disord Date: 2017-10-30 Impact factor: 10.338
Authors: David A Bennett; Julie A Schneider; Aron S Buchman; Lisa L Barnes; Patricia A Boyle; Robert S Wilson Journal: Curr Alzheimer Res Date: 2012-07 Impact factor: 3.498
Authors: Andrew S P Lim; Brian A Ellison; Joshua L Wang; Lei Yu; Julie A Schneider; Aron S Buchman; David A Bennett; Clifford B Saper Journal: Brain Date: 2014-08-20 Impact factor: 13.501
Authors: Elizabeth M Cespedes Feliciano; Mirja Quante; Jia Weng; Jonathan A Mitchell; Peter James; Catherine R Marinac; Sara Mariani; Susan Redline; Jacqueline Kerr; Suneeta Godbole; Alicia Manteiga; Daniel Wang; J Aaron Hipp Journal: Sleep Date: 2017-12-01 Impact factor: 5.849
Authors: Andrew S P Lim; Debra A Fleischman; Robert J Dawe; Lei Yu; Konstantinos Arfanakis; Aron S Buchman; David A Bennett Journal: Sleep Date: 2016-01-01 Impact factor: 5.849
Authors: Pasquale F Innominato; Andrew S Lim; Oxana Palesh; Mark Clemons; Maureen Trudeau; Andrea Eisen; Cathy Wang; Alex Kiss; Kathleen I Pritchard; Georg A Bjarnason Journal: Support Care Cancer Date: 2015-08-11 Impact factor: 3.603