title: Quantification of the fragmentation of rest-activity patterns in elderly individuals using a state transition analysis.
abstract: OBJECTIVE
  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.
  
  
  METHODS
  Cross-sectional.
  
  
  METHODS
  Community-based.
  
  
  METHODS
  621 community-dwelling individuals without dementia participating in the Rush Memory and Aging Project.
  
  
  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.
authors: Department of Neurology, Program in Neuroscience and Division of Sleep Medicine, Harvard Medical School, Beth Israel Deaconess Medical Center, Boston, MA 02215, USA.
authors: Andrew S P
authors: AS
authors: Lim
authors: Lei
authors: L
authors: Yu
authors: Madalena D
authors: MD
authors: Costa
authors: Aron S
authors: AS
authors: Buchman
authors: David A
authors: DA
authors: Bennett
authors: Sue E
authors: SE
authors: Leurgans
authors: Clifford B
authors: CB
authors: Saper
mesh_terms: D056044:Actigraphy / Q000379:methods; D000367:Age Factors; D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D015992:Body Mass Index; D003430:Cross-Sectional Studies; D003627:Data Interpretation, Statistical; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D012737:Sex Factors; D012890:Sleep; D014851:Wakefulness