The AP was more precise and more sensitive to reductions in sitting time than the AG, and thus, studies designed to assess SB should consider using the AP. When the AG monitor is used, 150 counts per minute may be the most appropriate cut point to define SB.

Investigations employing wearable monitors have begun to examine how sedentary time behaviors influence health. Purpose To demonstrate the utility of a measure of sedentary behavior and to validate the activPAL and ActiGraph GT3X for estimating measures of sedentary behavior: absolute number of breaks and break-rate. Methods Thirteen participants completed two, 10-hour conditions. During the baseline condition, participants performed normal daily activity and during the treatment condition, participants were asked to reduce and break-up their sedentary time. In each condition, participants wore two ActiGraph GT3X monitors and one activPAL. The ActiGraph was tested using the low frequency extension filter (AG-LFE) and the normal filter (AG-Norm). For both ActiGraph monitors two count cut-points to estimate sedentary time were examined: 100 and 150 counts∙min−1. Direct observation served as the criterion measure of total sedentary time, absolute number of breaks from sedentary time and break-rate (number of breaks per sedentary hour [brks.sed-hr−1]). Results Break-rate was the only metric sensitive to changes in behavior between baseline (5.1 [3.3 to 6.8] brks.sed-hr−1) and treatment conditions (7.3 [4.7 to 9.8] brks.sed-hr−1) (mean [95% CI]). The activPAL produced valid estimates of all sedentary behavior measures and was sensitive to changes in break-rate between conditions (baseline: 5.1 [2.8 to 7.1] brks.sed-hr−1, treatment: 8.0 [5.8 to 10.2] brks.sed-hr−1). In general, the AG-LFE and AG-Norm were not accurate in estimating break-rate or absolute number of breaks and were not sensitive to changes between conditions. Conclusion This study demonstrates the utility of expressing breaks from sedentary time as a rate per sedentary hour, a metric specifically relevant to free-living behavior, and provides further evidence that the activPAL is a valid tool to measure components of sedentary behavior in free-living environments.

Numerous accelerometers and prediction methods are used to estimate energy expenditure (EE). Validation studies have been limited to small sample sizes in which participants complete a narrow range of activities and typically validate only one or two prediction models for one particular accelerometer. Purpose To evaluate the validity of nine published and two proprietary EE prediction equations for three different accelerometers. Methods 277 participants completed an average of 6 treadmill (TRD) (1.34, 1.56, 2.23 m·sec−1 each at 0% and 3% grade) and 5 self-paced activities of daily living (ADLs). EE estimates were compared to indirect calorimetry. Accelerometers were worn while EE was measured using a portable metabolic unit. To estimate EE, 4 ActiGraph prediction models were used, 5 Actical models, and 2 RT3 proprietary models. Results Across all activities, each equation underestimated EE (bias −0.1 to −1.4 METs and −0.5 to −1.3 kcals, respectively). For ADLs EE was underestimated by all prediction models (bias −0.2 to −2.0 and −0.2 to −2.8, respectively), while TRD activities were underestimated by seven equations, and overestimated by four equations (bias −0.8 to 0.2 METs and −0.4 to 0.5 kcals, respectively). Misclassification rates ranged from 21.7% (95% CI 20.4%, 24.2%) to 34.3% (95% CI 32.3%, 36.3%), with vigorous intensity activities being most often misclassified. Discussion The prediction equations did not yield accurate point estimates of EE across a broad range of activities, nor were they accurate at classifying activities across a range of intensities (light < 3 METs, moderate 3–5.99 METs, vigorous ≥ 6 METs). Current prediction techniques have many limitations when translating accelerometer counts to EE.

Methods to estimate physical activity (PA) and sedentary behavior (SB) from wearable monitors need to be validated in free-living settings. PURPOSE 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. METHODS 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. RESULTS 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. CONCLUSION 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.