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.

Purpose This study compared the ActiGraph accelerometer model 7164 (AM1) to the ActiGraph GT1M (AM2) during self-paced locomotion. Methods Participants n = 116, 18–73y, mean BMI = 26.1) walked at self-selected slow, medium, and fast speeds around an indoor circular hallway (0.47km). Both activity monitors were attached to a belt secured to the hip and simultaneously collected data in 60 second epochs. To compare differences between monitors, the average difference (bias) in count output and steps output were computed at each speed. Time spent in different activity intensities (light, moderate, vigorous) based on the Freedson et al. cut-points was compared for each minute. Results The average walking speed (mean ± SD) was 0.7 ± 0.22 m·s−1 for the slow speed, 1.3 ± 0.17 m·s−1 for medium, and 2.1 ± 0.61 m·s−1 for fast speeds. Ninety-five percent confidence intervals (CI) were used to determine significance. Across all speeds, step output was significantly higher for the AM1 (bias = 19.8% CI: −23.2, −16.4), due to large differences in step output at slow speed. The count output from AM2 was a significantly higher 2.7% (CI = 0.8, 4.7) than AM1. Overall, 96.1% of the minutes were classified into the same MET intensity category by both monitors. Conclusion The step output between models was not comparable at slow speeds and comparisons of step data collected with both models should be interpreted with caution. The count output from AM2 was slightly, but significantly higher than AM1 during self-paced locomotion, but these differences did not result in meaningful differences in activity intensity classifications. Thus, data collected with AM1 should be comparable to AM2 across studies for estimating habitual activity levels.

Purpose:To compare intensity misclassification and activity MET values using measured RMR (measMET) compared with 3.5 ml·kg−1·min−1 (standMET) and corrected METs [corrMET = mean standMET × (3.5 ÷ Harris-Benedict RMR)] in subgroups.Methods:RMR was measured for 252 subjects following a 4-hr fast and before completion of 11 activities. VO2 was measured during activity using indirect calorimetry (n = 2555 activities). Subjects were classified by BMI category (normal-weight or overweight/obese), sex, age (decade 20, 30, 40, or 50 y), and fitness quintiles (low to high). Activities were classified into low, moderate, and vigorous intensity categories.Results:The (mean ± SD) measMET was 6.1 ± 2.64 METs. StandMET [mean (95% CI)] was (0.51(0.42, 0.59) METs) less than measMET. CorrMET was not statistically different from measMET (−0.02 (−0.11, 0.06) METs). 12.2% of the activities were misclassified using standMETs compared with an 8.6% misclassification rate for METs based on predicted RMR (P < .0001). StandMET differences and misclassification rates were highest for low fit, overweight, and older individuals while there were no differences when corrMETs were used.Conclusion:Using 3.5 ml·kg−1·min−1 to calculate activity METs causes higher misclassification of activities and inaccurate point estimates of METs than a corrected baseline which considers individual height, weight, and age. These errors disproportionally impact subgroups of the population with the lowest activity levels.

Purpose This paper 1) provides the calibration procedures and methods for metabolic and activity monitor data collection, 2) compares measured MET values to the MET values from the Compendium of Physical Activities, and 3) examines the relationship between accelerometer output and METs for a range of physical activities Methods Participants (n=277) completed 11 activities for seven minutes each from a menu of 23 physical activities. Oxygen consumption (VO2) was measured using a portable metabolic system and an accelerometer was worn. MET values were defined as follows; measuredMETs (VO2/measured RMR) and standardMETs (VO2/3.5ml·kg·min−1). For the total sample and by sub-group (age [young <40y], sex and BMI [normal-weight <25 kg·m2]), measuredMETs and standardMETs were compared to the Compendium, using 95% confidence intervals to determine statistical significance (α=0.05). Average count·min−1 for each activity and the linear association between count·min−1 and METs are presented. Results Compendium METs were different than measured METs for 17/21 activities (81%). The number of activities different than the Compendium were similar between sub-groups or when standard METs were used. The average counts for the activities ranged from 11 counts·min−1(dishes) to 7490 counts·min−1 (2.23m·s−1, 3%) The r2 between counts and METs was 0.65. Conclusions This study provides valuable information about data collection, metabolic responses, and accelerometer output for common physical activities in a diverse participant sample. The Compendium should be updated with additional empirical data and linear regression models are inappropriate for accurately predicting METs from accelerometer output.