Physical activity spectrum discriminant analysis—A method to compare detailed patterns between groups

Investigating physical activity (PA) patterns as a detailed intensity spectrum instead of crude intensity categories have improved the ability to analyze the relationship between measured PA and health variables. The aim of this methodological study was to introduce and investigate the utility of using detailed PA intensity spectrum compared to crude PA intensity categories for comparison of PA between groups and between repeated measures. The study sample consisted of two groups of children, where one group was scheduled for extended physical education (PE) by daily classes while the other group followed usual PE schedule. Accelerometer data was processed into traditional crude PA intensity categories and into detailed PA intensity spectrum. Multivariate partial least squares regression for discriminant analysis (PLS-DA) was applied for PA intensity spectrum group comparison and compared to traditional univariate statistical analysis. Repeated measures were investigated using independent PLS-DA as well as multilevel PLS-DA for paired analysis. While traditional analysis of crude PA intensity categories was unable to find any group differences, multivariate analysis of the PA intensity spectrum identified statistically significant differences. By the extension of multilevel PLS-DA for paired comparison, a clear difference in the PA intensity spectrum was demonstrated between repeated measures. In conclusion, analysis of detailed PA intensity spectrum demonstrates utility for comparing detailed PA data between groups and between repeated measures in interventional and observational research

Physical activity spectrum discriminant analysis—A method to compare detailed patterns between groups

Investigating physical activity (PA) patterns as a detailed intensity spectrum instead of crude intensity categories have improved the ability to analyze the relationship between measured PA and health variables. The aim of this methodological study was to introduce and investigate the utility of using detailed PA intensity spectrum compared to crude PA intensity categories for comparison of PA between groups and between repeated measures. The study sample consisted of two groups of children, where one group was scheduled for extended physical education (PE) by daily classes while the other group followed usual PE schedule. Accelerometer data was processed into traditional crude PA intensity categories and into detailed PA intensity spectrum. Multivariate partial least squares regression for discriminant analysis (PLS-DA) was applied for PA intensity spectrum group comparison and compared to traditional univariate statistical analysis. Repeated measures were investigated using independent PLS-DA as well as multilevel PLS-DA for paired analysis. While traditional analysis of crude PA intensity categories was unable to find any group differences, multivariate analysis of the PA intensity spectrum identified statistically significant differences. By the extension of multilevel PLS-DA for paired comparison, a clear difference in the PA intensity spectrum was demonstrated between repeated measures. In conclusion, analysis of detailed PA intensity spectrum demonstrates utility for comparing detailed PA data between groups and between repeated measures in interventional and observational research

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

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

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

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 measure-ment error affects shorter (children) more than taller (adults) individuals due to their higher movement frequency. Less restrictive filtering includes more movement‐re-lated 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 me-chanical 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 stat-ure. A candidate measure is the mass‐specific net oxygen uptake, VO2net (VO2tot − VO2stand). To improve measurement of physical activity intensity using accel-erometers, research developments are suggested concerning the processing of ac-celerometer data, use of energy expenditure as reference for activity intensity, and calibration procedure with absolute versus relative intensity