Comparing ActiGraph equations for estimating energy expenditure in older adults |
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| Authors: | Nicolas Aguilar-Farias GMEE Peeters Robert J Brychta Kong Y Chen Wendy J Brown |
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| Institution: | 1. Department of Physical Education, Sports and Recreation;2. Universidad de La Frontera, Temuco, Chile;3. Global Brain Health Institute, University of California San Francisco | Trinity College Dublin, Dublin, Ireland;4. Diabetes, Endocrine, and Obesity Branch of the National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, MD, USA;5. The School of Human Movement and Nutrition Sciences, The University of Queensland, Brisbane, Australia |
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| Abstract: | Accurate estimation of energy expenditure (EE) from accelerometer outputs remains a challenge in older adults. The aim of this study was to validate different ActiGraph (AG) equations for predicting EE in older adults. Forty older adults (age = 77.4 ± 8.1 yrs) completed a set of household/gardening activities in their residence, while wearing an AG at the hip (GT3X+) and a portable calorimeter (MetaMax 3B – criterion). Predicted EEs from AG were calculated using five equations (Freedson, refined Crouter, Sasaki and Santos-Lozano (vertical-axis, vectormagnitude)). Accuracy of equations was assessed using root-mean-square error (RMSE) and mean bias. The Sasaki equation showed the lowest RMSE for all activities (0.47 METs) and across physical activity intensities (PAIs) (range 0.18–0.48 METs). The Freedson and Santos-Lozano equations tended to overestimate EE for sedentary activities (range: 0.48 to 0.97 METs), while EEs for moderate-to-vigorous activities (MVPA) were underestimated (range: ?1.02 to ?0.64 METs). The refined Crouter and Sasaki equations showed no systematic bias, but they respectively overestimated and underestimated EE across PAIs. In conclusion, none of the equations was completely accurate for predicting EE across the range of PAIs. However, the refined Crouter and Sasaki equations showed better overall accuracy and precision when compared with the other methods. |
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| Keywords: | Accelerometry aging measurement motion sensors physical activity |
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