Validation of the ActiGraph Two-Regression Model for Predicting Energy Expenditure

Purpose To validate a two-regression model for predicting energy expenditure (EE) from ActiGraph GT1M accelerometer generated activity counts using a whole-room indirect calorimeter and the doubly-labeled water (DLW) technique. We also investigated if a low-pass filter (LPF) approach would improve the model’s accuracy in the minute-to-minute EE prediction.

Methods Thirty-four healthy volunteers (age 20-67 yrs, BMI-19.3-52.1 kg/m²) spent ~24-h in a room calorimeter while wearing a GT1M monitor and performed structured and self-selected activities followed by overnight sleep. The EE predicted by the models and expressed in metabolic equivalents (MET-min) during waking times was compared to the room calorimeter measured EE. A subset of volunteers (n=22) completed a 14-day DLW protocol in free-living while wearing an ActiGraph. The average daily EE predicted by the models (MET-min) was compared to the DLW.

Results Compared to the room calorimeter, the two-regression model over-predicted EE by 10.2±11.4% (1,282±125 and 1,174±152 MET-min, p<0.001) and time spent in moderate physical activity (PA) by 36.9 ± 46.0 min while underestimating the time spent in light PA by – 48.3±55.0 min (p<0.05). The LPF reduced the squared and mean absolute error in the EE prediction (p<0.05), but not the prediction error in time spent in moderate or light PA (both p>0.05). The EE measured by DLW (2,108±358 MET-min/day) and predicted by both filtered and unfiltered models (2,104±218 and 2,192±228 MET-min/day, respectively) were similar (p>0.05).

Conclusions The two-regression model with LPF showed good agreement with total EE measured using room calorimeter and DLW. However, the individual variability in assessing time spent in sedentary, low, and moderate PA intensities and related EE remains significant.

Link to Abstract: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2919650/