Research Study Abstract

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


  • 1

    Physical Activity Exchange, Research Institute for Sport and Exercise Sciences, Liverpool John Moores University, UK

  • 2

    Sciences, Liverpool John Moores University, UK

  • 3

    Department of Health, Kinesiology and Recreation, College of Health, University of Utah, United States

  • 4

    MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Cambridge Biomedical Campus, UK

  • 5

    Diabetes Research Centre, University of Leicester, Leicester General Hospital, UK

  • 6

    NIHR Leicester Biomedical Research Centre, UK

  • 7

    Alliance for Research in Exercise, Nutrition and Activity (ARENA), Sansom Institute for Health Research, Division of Health Sciences, University of South Australia, Australia

  • 8

    Department of Kinesiology, College of Human Sciences, Iowa State University, United States

  • 9

    Department of Physical Education and Sport Sciences, University of Limerick, Ireland


JSAMS - Journal of Science and Medicine in Sports