Classification of accelerometer wear and non-wear events in seconds for monitoring free-living physical activity

摘要

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 wearon-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 wearon-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 wearon-wear time when compared with methods that used acceleration data only (p0.01). 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.