A comprehensive evaluation of commonly used accelerometer energy expenditure and MET prediction equations

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. The purpose of this study was to evaluate the validity of nine published and two proprietary EE prediction equations for three different accelerometers. Two hundred and seventy-seven participants completed an average of six treadmill (TRD) (1.34, 1.56, 2.23 ms(-1) each at 0 and 3% grade) and five self-paced activities of daily living (ADLs). EE estimates were compared with 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. Across all activities, each equation underestimated EE (bias -0.1 to -1.4 METs and -0.5 to -1.3 kcal, 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 kcal, 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. 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.