Boddy, LM, Noonan, RJ, Kim, Y, Rowlands, A, Welk, G, Knowles, ZR and Fairclough, S (2018) Comparability of children’s sedentary time estimates derived from wrist worn GENEActiv and hip worn ActiGraph accelerometer thresholds. Journal of Science and Medicine in Sport, 21 (10). pp. 1045-1049. ISSN 1440-2440

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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 51mg 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.

Item Type:	Article
Uncontrolled Keywords:	1106 Human Movement And Sports Science, 1117 Public Health And Health Services
Subjects:	R Medicine > RC Internal medicine > RC1200 Sports Medicine R Medicine > RJ Pediatrics > RJ101 Child Health. Child health services
Divisions:	Sport & Exercise Sciences
Publisher:	Elsevier
Date Deposited:	22 Mar 2018 11:56
Last Modified:	04 Sep 2021 10:37
DOI or ID number:	10.1016/j.jsams.2018.03.015
URI:	https://researchonline.ljmu.ac.uk/id/eprint/8357

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