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  • Title: Comparability of children's sedentary time estimates derived from wrist worn GENEActiv and hip worn ActiGraph accelerometer thresholds.
    Author: Boddy LM, Noonan RJ, Kim Y, Rowlands AV, Welk GJ, Knowles ZR, Fairclough SJ.
    Journal: J Sci Med Sport; 2018 Oct; 21(10):1045-1049. PubMed ID: 29650338.
    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 100count·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 100count·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 100count·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 100count·min-1 threshold. However, the generated and empirical thresholds for GENEActiv devices were not significantly equivalent to ActiGraph 100count·min-1. GENEActiv data classified using the 35.6mg threshold intended for ActiGraph devices generated significantly equivalent ST estimates as the ActiGraph 100count·min-1. CONCLUSIONS: The newly generated and empirical GENEActiv wrist thresholds do not provide equivalent estimates of ST to the ActiGraph 100count·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.
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