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Prediction of muscle mass in arms and legs based on 3D laser-based photonic body scans'standard dimensions in a homogenous sample of young men

机译:基于3D激光的光子体扫描的年轻男子均匀样本扫描的臂和腿部肌肉肿块预测

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Reliably identifying muscle mass from external anthropometric measurements can provide valuable information about a person's health conditions and related outcomes. A potential tool for easily predicting muscle mass is three-dimensional (3D) body scans, but accurate validation data are missing. The aim of our study was to predict skeletal muscle mass (SMM) as assessed by Bioelectrical Impedance Analysis (BIA) from 3D body scanner data. We aimed to examine which 3D body scan standard parameters of the upper and lower limbs best predict skeletal muscle mass measured by BIA in a cross-sectional and homogenous sample of N = 100 young men. In both arms and legs, Spearman's rank correlation coefficients with SMM were generally high for girths and volumes, and lower for lengths. The volumes of the forearm (R = 0.80-0.82) and calf (R = 0.87) correlated best with SMM. For the longitudinal follow-up of N = 45 young men, the Wilcoxon signed-rank test showed that, on average, the longitudinally followed-up increased in weight, height, BMI as well as relative/absolute fat mass. The best single predictors for individual differences in SMM were deltas for upper arm girth of both arms (adjusted R2 0.17 and 0.17) and deltas for calf girth of both legs (0.37 and 0.45). Although 3D body scan girth measures can predict SMM in upper and lower limbs satisfying, adding volumes and lengths to the equations increase the precision of the estimations fairly.
机译:可靠地识别来自外部人体测量测量的肌肉质量可以提供有关人员健康状况和相关结果的有价值的信息。容易预测肌肉质量的潜在工具是三维(3D)体扫描,但缺少准确的验证数据。我们的研究目的是预测来自3D Body扫描仪数据的生物电阻抗分析(BIA)评估的骨骼肌质量(SMM)。我们旨在检查上肢和下肢的3D体扫描标准参数最佳预测BIA在N = 100个年轻男性的横截面和均匀样品中测量的骨骼肌质量。在双臂和腿上,SPEARMAN的SMM等级相关系数通常为周长和卷,并且长度较低。前臂(R = 0.80-0.82)和小牛(R = 0.87)的体积最佳,SMM相关。对于N = 45次年轻男性的纵向后续,Wilcoxon签名级别测试显示,平均而言,纵向随访的重量,高度,BMI以及相对/绝对脂肪质量增加。 SMM中个体差异的最佳单个预测因子是用于双臂臂(调节R2 0.17和0.17)的上臂周长的醇,以及两条腿的牛犊(0.37和0.45)的δ。虽然3D体扫描环度措施可以在满足的上下肢体中预测SMM,向方程添加卷和长度,但相当增加估计的精度。

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