Article Text

Classification of accelerometer wear and non-wear events in seconds for monitoring free-living physical activity
  1. Shang-Ming Zhou1,
  2. Rebecca A Hill1,
  3. Kelly Morgan1,
  4. Gareth Stratton2,
  5. Mike B Gravenor1,
  6. Gunnar Bijlsma1,
  7. Sinead Brophy1
  1. 1College of Medicine, Swansea University, Wales, UK
  2. 2College of Engineering, Swansea University, Wales, UK
  1. Correspondence to Dr Shang-Ming Zhou; s.zhou{at}


Objective To classify wear and non-wear time of accelerometer data for accurately quantifying physical activity in public health or population level research.

Design 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.

Setting Local residents in Swansea, Wales, UK.

Participants 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.

  • physical activity
  • sedentary behaviour
  • non-wear
  • accelerometer
  • classification

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