Energy Balance & Body Composition Laboratory; Department of Kinesiology & Sport Management, Texas Tech University, Lubbock, TX, USA.
Size Stream LLC, Cary, NC, USA.
Eur J Clin Nutr. 2020 May;74(5):842-845. doi: 10.1038/s41430-020-0603-x. Epub 2020 Mar 16.
Estimates of body composition have been derived using 3-dimensional optical imaging (3DO), but no equations to date have been calibrated using a 4-component (4C) model criterion. This investigation reports the development of a novel body fat prediction formula using anthropometric data from 3DO imaging and a 4C model. Anthropometric characteristics and body composition of 179 participants were measured via 3DO (Size Stream SS20) and a 4C model. Machine learning was used to identify significant anthropometric predictors of body fat (BF%), and stepwise/lasso regression analyses were employed to develop new 3DO-derived BF% prediction equations. The combined equation was externally cross-validated using paired 3DO and DXA assessments (n = 158), producing a R value of 0.78 and a constant error of (X ± SD) 0.8 ± 4.5%. 3DO BF% estimates demonstrated equivalence with DXA based on equivalence testing with no proportional bias in the Bland-Altman analysis. Machine learning methods may hold potential for enhancing 3DO-derived BF% estimates.
已经使用三维光学成像（3DO）来估计身体成分，但迄今为止，还没有使用四分量（4C）模型标准校准的方程。本研究报告了一种使用 3DO 成像和 4C 模型的人体脂肪预测公式的新开发。通过 3DO（Size Stream SS20）和 4C 模型测量了 179 名参与者的人体测量特征和身体成分。使用机器学习来识别身体脂肪（BF%）的重要人体测量预测因子，并采用逐步/套索回归分析来开发新的 3DO 衍生 BF%预测方程。使用配对的 3DO 和 DXA 评估（n=158）对组合方程进行外部交叉验证，得出 R 值为 0.78，常数误差为（X±SD）0.8±4.5%。3DO BF%估计值与基于等价性测试的 DXA 相当，在 Bland-Altman 分析中没有比例偏差。机器学习方法可能有潜力增强 3DO 衍生的 BF%估计值。
