a Department of Physical Education, Sports and Recreation ; Universidad de La Frontera , Temuco , Chile.
b Global Brain Health Institute , University of California San Francisco | Trinity College Dublin , Dublin , Ireland.
J Sports Sci. 2019 Jan;37(2):188-195. doi: 10.1080/02640414.2018.1488437. Epub 2018 Jun 18.
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.
准确估计加速度计输出的能量消耗 (EE) 仍然是老年人面临的挑战。本研究旨在验证不同的 ActiGraph (AG) 方程在预测老年人 EE 方面的有效性。40 名老年人(年龄 = 77.4 ± 8.1 岁)在居住的家中/花园中完成了一系列活动,同时在臀部佩戴 AG(GT3X+)和便携式热量计(MetaMax 3B-标准)。使用五个方程(Freedson、 refined Crouter、Sasaki 和 Santos-Lozano(垂直轴、矢量幅度))计算 AG 预测的 EE。使用均方根误差 (RMSE) 和平均偏差评估方程的准确性。Sasaki 方程在所有活动(0.47 METs)和所有身体活动强度 (PAI)(范围为 0.18-0.48 METs)中均表现出最低的 RMSE。Freedson 和 Santos-Lozano 方程倾向于高估久坐活动的 EE(范围:0.48 至 0.97 METs),而中高强度活动 (MVPA) 的 EE 则被低估(范围:-1.02 至-0.64 METs)。refined Crouter 和 Sasaki 方程没有显示出系统偏差,但它们分别在整个 PAI 范围内高估和低估了 EE。总之,没有一个方程能够完全准确地预测整个 PAI 范围内的 EE。然而,与其他方法相比,refined Crouter 和 Sasaki 方程在整体准确性和精度方面表现更好。