Physical Activity Exchange, Research Institute for Sport and Exercise Sciences, Liverpool John Moores University, UK
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Comparability of children’s sedentary time estimates derived from wrist worn GENEActiv and hip worn ActiGraph accelerometer thresholds
- Published on Apr 2018
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.
Methods: 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.
- Lynne M. Boddy 1
- Robert J. Noonan 1, 2
- Youngwon Kim 3, 4
- Alex V. Rowlands 5, 6, 7
- Greg J. Welk 8
- Zoe R. Knowles 1
- Stuart J. Fairclough 2, 9
Sciences, Liverpool John Moores University, UK
Department of Health, Kinesiology and Recreation, College of Health, University of Utah, United States
MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Cambridge Biomedical Campus, UK
Diabetes Research Centre, University of Leicester, Leicester General Hospital, UK
NIHR Leicester Biomedical Research Centre, UK
Alliance for Research in Exercise, Nutrition and Activity (ARENA), Sansom Institute for Health Research, Division of Health Sciences, University of South Australia, Australia
Department of Kinesiology, College of Human Sciences, Iowa State University, United States
Department of Physical Education and Sport Sciences, University of Limerick, Ireland
JSAMS - Journal of Science and Medicine in Sports