Raw and Count Data Comparability of Hip-Worn ActiGraph GT3X+ and Link Accelerometers

ABSTRACTPURPOSETo enable inter- and intra-study comparisons it is important to ascertain comparability among accelerometer models. This study compared raw and count data between hip-worn ActiGraph GT3X+ and GT9X Link accelerometers.METHODSAdults (n=26 [n=15 women]; aged 49.1±20.0 years) wore GT3X+ and Link accelerometers over the right hip for an 80-min protocol involving 12-21 sedentary, household, and ambulatory/exercise activities lasting 2-15 min each. For each accelerometer, mean and variance of the raw (60 Hz) data for each axis and vector magnitude (VM) were extracted in 30-s epochs. A machine learning model (Montoye 2015) was used to predict energy expenditure in METs from the raw data. Raw data were also processed into activity counts in 30-s epochs for each axis and VM, with Freedson 1998 and 2011 count-based regression models used to predict METs. Time spent in sedentary, light, moderate, and vigorous intensities were derived from predicted METs from each model. Correlations were calculated to compare raw and count data between accelerometers, and percent (%) agreement was used to compare epoch-by-epoch activity intensity.RESULTSFor raw data, correlations for mean acceleration were 0.96±0.05, 0.89±0.16, 0.71±0.33, and 0.80±0.28 and for variance 0.98±0.02, 0.98±0.03, 0.91±0.06, and 1.00±0.00 in the X, Y, and Z axes and VM, respectively. For count data, corresponding correlations were 1.00±0.01, 0.98±0.02, 0.96±0.04, and 1.00±0.00, respectively. Freedson 1998 and 2011 count-based models had significantly higher %agreement for activity intensity (95.1±5.6% and 95.5±4.0%) than the Montoye 2015 raw data model (61.5±27.6%; p

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