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A Method to Estimate Free-Living Active and Sedentary Behavior from an Accelerometer
- Published on July 15, 2013
The purpose of this study was to develop and validate two novel machine-learning methods (soj-1x and soj-3x) in a free-living setting.
Participants were directly observed in their natural environment for ten consecutive hours on three separate occasions. PA and SB estimated from soj-1x, soj-3x and a neural network previously calibrated in the laboratory (lab-nnet) were compared to direct observation.
Compared to the lab-nnet, soj-1x and soj-3x improved estimates of MET-hours (lab-nnet: % bias (95% CI) = 33.1 (25.9, 40.4), rMSE = 5.4 (4.6, 6.2), soj-1x: % bias = 1.9 (-2.0, 5.9), rMSE = 1.0 (0.6, 1.3), soj-3x: % bias = 3.4 (0.0, 6.7), rMSE = 1.0 (0.6, 1.5)) and minutes in different intensity categories (lab-nnet: % bias = -8.2 (sedentary), -8.2 (light) and 72.8 (MVPA), soj-1x: % bias = 8.8 (sedentary), -18.5 (light) and -1.0 (MVPA), soj-3x: % bias = 0.5 (sedentary), -0.8 (light) and -1.0 (MVPA)). Soj-1x and soj-3x also produced accurate estimates of guideline minutes and breaks from sedentary time.
Compared to the lab-nnet algorithm, soj-1x and soj-3x improved the accuracy and precision in estimating free-living MET-hours, sedentary time, and time spent in light intensity activity and MVPA. Additionally, soj-3x is superior to soj-1x in differentiating sedentary behavior from light intensity activity.