title: Sleep estimates using microelectromechanical systems (MEMS).
abstract: OBJECTIVE
  Although currently more affordable than polysomnography, actigraphic sleep estimates have disadvantages. Brand-specific differences in data reduction impede pooling of data in large-scale cohorts and may not fully exploit movement information. Sleep estimate reliability might improve by advanced analyses of three-axial, linear accelerometry data sampled at a high rate, which is now feasible using microelectromechanical systems (MEMS). However, it might take some time before these analyses become available. To provide ongoing studies with backward compatibility while already switching from actigraphy to MEMS accelerometry, we designed and validated a method to transform accelerometry data into the traditional actigraphic movement counts, thus allowing for the use of validated algorithms to estimate sleep parameters.
  
  
  METHODS
  Simultaneous actigraphy and MEMS-accelerometry recording.
  
  
  METHODS
  Home, unrestrained.
  
  
  METHODS
  Fifteen healthy adults (23-36 y, 10 males, 5 females).
  
  
  METHODS
  None.
  
  
  METHODS
  Actigraphic movement counts/15-sec and 50-Hz digitized MEMS-accelerometry.
  
  
  METHODS
  Passing-Bablok regression optimized transformation of MEMS-accelerometry signals to movement counts. Kappa statistics calculated agreement between individual epochs scored as wake or sleep. Bland-Altman plots evaluated reliability of common sleep variables both between and within actigraphs and MEMS-accelerometers.
  
  
  RESULTS
  Agreement between epochs was almost perfect at the low, medium, and high threshold (kappa = 0.87 ± 0.05, 0.85 ± 0.06, and 0.83 ± 0.07). Sleep parameter agreement was better between two MEMS-accelerometers or a MEMS-accelerometer and an actigraph than between two actigraphs.
  
  
  CONCLUSIONS
  The algorithm allows for continuity of outcome parameters in ongoing actigraphy studies that consider switching to MEMS-accelerometers. Its implementation makes backward compatibility feasible, while collecting raw data that, in time, could provide better sleep estimates and promote cross-study data pooling.
authors: Department of Sleep and Cognition, Netherlands Institute for Neuroscience, an institute of the Royal Netherlands Academy of Arts and Sciences, Amsterdam, The Netherlands. b.te.lindert@nin.knaw.nl
authors: Bart H W
authors: BH
authors: te Lindert
authors: Eus J W
authors: EJ
authors: Van Someren
mesh_terms: D056044:Actigraphy; D000328:Adult; D000465:Algorithms; D015331:Cohort Studies; D004867:Equipment Design; D005260:Female; D006801:Humans; D008297:Male; D055617:Micro-Electrical-Mechanical Systems; D017286:Polysomnography; D015203:Reproducibility of Results; D012890:Sleep / Q000502:physiology*; D055815:Young Adult
keywords: accelerometry; actigraphy; cohort studies; sleep
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