4.6 Article

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

Journal

MEDICINE AND SCIENCE IN SPORTS AND EXERCISE
Volume 42, Issue 9, Pages 1785-1792

Publisher

LIPPINCOTT WILLIAMS & WILKINS
DOI: 10.1249/MSS.0b013e3181d5a984

Keywords

ACCELEROMETRY; INDIRECT CALORIMETRY; DOUBLY LABELED WATER; LOW-PASS FILTER

Categories

Funding

  1. NIH/NIDDK [R01 DK69465]
  2. NCRR/NIH [1UL1 RR024975]
  3. Vanderbilt Diabetes Research and Training Center [DK20593]
  4. NIH/NHLB [R01 HL082988]
  5. NIDDK/NIH [Z01-DK071044]

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ROTHNEY, M. P., R. J. BRYCHTA, N. N. MEADE, K. Y. CHEN, and M. S. BUCHOWSKI. Validation of the ActiGraph Two-Regression Model for Predicting Energy Expenditure. Med. Sci. Sports Exerc., Vol. 42, No. 9, pp. 1785-1792, 2010. Purpose: The purpose of this study was 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 yr, body mass index = 19.3-52.1 kg.m(-2)) spent approximately 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-minutes) during waking times was compared with the room calorimeter-measured EE. A subset of volunteers (n = 22) completed a 14-d DLW protocol in free living while wearing an ActiGraph. The average daily EE predicted by the models (MET-minutes) was compared with the DLW. Results: Compared with the room calorimeter, the two-regression model overpredicted EE by 10.2% +/- 11.4% (1282 +/- 125 and 1174 +/- 152 MET.min, P < 0.001) and time spent in moderate physical activity (PA) by 36.9 perpendicular to 46.0 min while underestimating the time spent in light PA by -48.3 perpendicular to 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 (2108 +/- 358 MET.min.d(-1)) and predicted by both filtered and unfiltered models (2104 +/- 218 and 2192 +/- 228 MET.min.d(-1), 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.

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