Comparability of children's sedentary time estimates derived from wrist worn GENEActiv and hip worn ActiGraph accelerometer thresholds. Issue 10 (October 2018) - Boddy, Lynne M.; Noonan, Robert J.; Kim, Youngwon; Rowlands, Alex V.; Welk, Greg J.; Knowles, Zoe R.; Fairclough, Stuart J.
Abstract: Objectives: To examine the comparability of children's free-living sedentary time (ST) derived from raw acceleration thresholds for wrist mounted GENEActiv accelerometer data, with ST estimated using the waist mounted ActiGraph 100 count · min −1 threshold. Design: Secondary data analysis. Method: 108 10–11-year-old children (n = 43 boys) from Liverpool, UK wore one ActiGraph GT3X+ and one GENEActiv accelerometer on their right hip and left wrist, respectively for seven days. Signal vector magnitude (SVM; mg) was calculated using the ENMO approach for GENEActiv data. ST was estimated from hip-worn ActiGraph data, applying the widely used 100 count · min −1 threshold. ROC analysis using 10-fold hold-out cross-validation was conducted to establish a wrist-worn GENEActiv threshold comparable to the hip ActiGraph 100 count · min −1 threshold. GENEActiv data were also classified using three empirical wrist thresholds and equivalence testing was completed. Results: Analysis indicated that a GENEActiv SVM value of 51 mg demonstrated fair to moderate agreement (Kappa: 0.32–0.41) with the 100 count · min −1 threshold. However, the generated and empirical thresholds for GENEActiv devices were not significantly equivalent to ActiGraph 100 count · min −1 . GENEActiv data classified using the 35.6 mg threshold intended for ActiGraph devices generated significantly equivalent ST estimates as the ActiGraph 100 count · min −1 . Conclusions: The newly generated and empirical GENEActiv wrist thresholds do not provide equivalent estimates of ST to the ActiGraph 100 count · min −1 approach. More investigation is required to assess the validity of applying ActiGraph cutpoints to GENEActiv data. Future studies are needed to examine the backward compatibility of ST data and to produce a robust method of classifying SVM-derived ST.