Re‐examination of accelerometer data processing and calibration for the assessment of physical activity intensity

Abstract

This review re‐examines the use of accelerometer and oxygen uptake data for the assessment of activity intensity. Accelerometers capture mechanical work, while oxygen uptake captures the energy cost of this work. Frequency filtering needs to be considered when processing acceleration data. A too restrictive filter attenuates the acceleration signal for walking and, to a higher degree, for running. This measurement error affects shorter (children) more than taller (adults) individuals due to their higher movement frequency. Less restrictive filtering includes more movement‐related signals and provides measures that better capture mechanical work, but may include more noise. An optimal filter cut‐point is determined where most relevant acceleration signals are included. Further, accelerometer placement affects what part of mechanical work being captured. While the waist placement captures total mechanical work and therefore contributes to measures of activity intensity equivalent by age and stature, the thigh and wrist placements capture more internal work and do not provide equivalent measures. Value calibration of accelerometer measures is usually performed using measured oxygen uptake with the metabolic equivalent of task (MET) as reference measure of activity intensity. However, the use of MET is not stringent and is not a measure of activity intensity equivalent by age and stature. A candidate measure is the mass‐specific net oxygen uptake, VO2net (VO2tot − VO2stand). To improve measurement of physical activity intensity using accelerometers, research developments are suggested concerning the processing of accelerometer data, use of energy expenditure as reference for activity intensity, and calibration procedure with absolute versus relative intensity.

Wearable Inertial Sensors to Assess Gait during the 6-Minute Walk Test: A Systematic Review

“…The high variability of cut-off frequencies is justified by the variety of parameters extracted by the authors of the papers, and the locations of the sensors on the human body. It has been shown that low cut-off frequencies attenuate inertial sensor signals, while less restrictive filtering may provide more movement-related signals, but with the risk of higher noise [75]. Future studies should include more technical details regarding pre-processing of raw data used to calculate gait metrics, justifying their choices in light of existing literature.…”

Resemblance in accelerometer-assessed physical activity in families with children: the Lolland-Falster Health Study

“…The subjects were divided into a preschool group (5-6 years, N = 29), a child group (9-11 years, N = 35), an adolescent group (14-16 years, N = 31), and an adult group (> 18 years, N = 38). Identifying age-independent MPA and VPA intensity-specific counts thresholds is challenging, and adjusting for basal metabolic rate to account for body weight, height, and maturation is not an accurate solution [30]. The metabolic and mechanical cost of walking at self-selected speed is similar across a large age range [31] and performed at an intensity corresponding to 30-35% of individuals' VO 2max .…”

Association of Basal Metabolic Rate and Nutrients Oxidation with Cardiometabolic Risk Factors and Insulin Sensitivity in Sedentary Middle-Aged Adults

“…Advertisements in electronic media, social networks, and leaflets were used to recruit the participants of the study. Exclusion criteria were: being physically active (more than 20 min of moderate-vigorous physical activity on more than 3 d/week) [25], being participants of a structured exercise intervention during the last 3 months, presenting a non-stable body weight (i.e., >3 kg) during the previous 3 months, being smokers, suffering from cardiometabolic disease, and/or being pregnant. The baseline assessment of the participants of the FIT-AGEING study was conducted during September (2016 and 2017) in the Sport and Health Joint University Institute (iMUDS, Granada, Spain).…”