Classification of accelerometer wear and non-wear events in seconds for monitoring free-living physical activity. Issue 5 (11th May 2015)

• Record Type: Journal Article
• Authors: Zhou, Shang-Ming; Hill, Rebecca A; Morgan, Kelly; Stratton, Gareth; Gravenor, Mike B; Bijlsma, Gunnar; Brophy, Sinead
• Is Part Of: BMJ open. Volume 5:Issue 5(2015)
• Publication Date: 2015-05-11
• Subjects: physical activity -- sedentary behaviour -- non-wear -- accelerometer -- PUBLIC HEALTH -- classification; Medicine -- Research -- Periodicals; 610.72
• DOI: 10.1136/bmjopen-2014-007447
• Languages: English
• ISSNs: 2044-6055

Abstract

Abstract : 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.