title: Classification of accelerometer wear and non-wear events in seconds for monitoring free-living physical activity.
abstract: OBJECTIVE
  To classify wear and non-wear time of accelerometer data for accurately quantifying physical activity in public health or population level research.
  
  
  METHODS
  A bi-moving-window-based approach was used to combine acceleration and skin temperature data to identify wear and non-wear time events in triaxial accelerometer data that monitor physical activity.
  
  
  METHODS
  Local residents in Swansea, Wales, UK.
  
  
  METHODS
  50 participants aged under 16 years (n=23) and over 17 years (n=27) were recruited in two phases: phase 1: design of the wear/non-wear algorithm (n=20) and phase 2: validation of the algorithm (n=30).
  
  
  METHODS
  Participants wore a triaxial accelerometer (GeneActiv) against the skin surface on the wrist (adults) or ankle (children). Participants kept a diary to record the timings of wear and non-wear and were asked to ensure that events of wear/non-wear last for a minimum of 15 min.
  
  
  RESULTS
  The overall sensitivity of the proposed method was 0.94 (95% CI 0.90 to 0.98) and specificity 0.91 (95% CI 0.88 to 0.94). It performed equally well for children compared with adults, and females compared with males. Using surface skin temperature data in combination with acceleration data significantly improved the classification of wear/non-wear time when compared with methods that used acceleration data only (p<0.01).
  
  
  CONCLUSIONS
  Using either accelerometer seismic information or temperature information alone is prone to considerable error. Combining both sources of data can give accurate estimates of non-wear periods thus giving better classification of sedentary behaviour. This method can be used in population studies of physical activity in free-living environments.
authors.affiliation: College of Medicine, Swansea University, Wales, UK.
authors.forename: Shang-Ming
authors.initials: SM
authors.lastname: Zhou
authors.affiliation: College of Medicine, Swansea University, Wales, UK.
authors.forename: Rebecca A
authors.initials: RA
authors.lastname: Hill
authors.affiliation: College of Medicine, Swansea University, Wales, UK.
authors.forename: Kelly
authors.initials: K
authors.lastname: Morgan
authors.affiliation: College of Engineering, Swansea University, Wales, UK.
authors.forename: Gareth
authors.initials: G
authors.lastname: Stratton
authors.affiliation: College of Medicine, Swansea University, Wales, UK.
authors.forename: Mike B
authors.initials: MB
authors.lastname: Gravenor
authors.affiliation: College of Medicine, Swansea University, Wales, UK.
authors.forename: Gunnar
authors.initials: G
authors.lastname: Bijlsma
authors.affiliation: College of Medicine, Swansea University, Wales, UK.
authors.forename: Sinead
authors.initials: S
authors.lastname: Brophy
mesh_terms: D000054:Acceleration; D061725:Accelerometry / Q000379:methods*; D000293:Adolescent; D000328:Adult; D000465:Algorithms; D000842:Ankle; D001831:Body Temperature; D002648:Child; D015444:Exercise; D005260:Female; D006801:Humans; D008297:Male; D018670:Monitoring, Ambulatory / Q000379:methods*; D009043:Motor Activity; D057185:Sedentary Behavior; D012867:Skin; D014852:Wales; D014953:Wrist; D055815:Young Adult
keywords: PUBLIC HEALTH; accelerometer; classification; non-wear; physical activity; sedentary behaviour