van der Ploeg G E, Gunn S M, Withers R T, Modra A C
Exercise Physiology Laboratory, School of Education, Flinders University, Adelaide, Australia.
Eur J Clin Nutr. 2003 Aug;57(8):1009-16. doi: 10.1038/sj.ejcn.1601636.
To generate equations for the prediction of percent body fat (% BF) via a four-compartment criterion body composition model from anthropometric variables and age.
Multiple regression analyses were used to predict % BF from the best-weighted combinations of independent variables.
In all 79 healthy males (X+/-s.d.: 35.0+/-12.2 y; 84.24+/-12.53 kg; 179.8+/-6.8 cm) aged 19-59 y were recruited from advertisements placed in a university newsletter and on community centres' noticeboards.
The following measurements were conducted: % BF using a four-compartment (water, bone mineral mass, fat and residual) model and a restricted anthropometric profile (nine skinfolds, five girths and two bone breadths).
Stepwise multiple regression selected six (subscapular, biceps, abdominal, thigh, calf and mid-axilla) of the nine skinfold measurements to predict % BF and using the sum of these six produced a quadratic equation with a standard error of estimate (SEE) and R(2) of 2.5% BF and 0.89, respectively. The inclusion of age as a predictor further improved the equation (% BF=-0.00057 x ( summation operator 6SF)(2)+0.298 x summation operator 6SF+0.078 x age - 1.13; SEE=2.2% BF, R(2)=0.91). However, the best equation used only the sum of three skinfold thicknesses (mid-axilla, calf and thigh) and age but also included waist girth and biepicondylar femur breadth as predictors (% BF=-0.00258 x ( summation operator 3SF)(2)+0.558 x summation operator 3SF+0.118 x age+0.282 x waist girth - 2.100 x femur breadth - 2.34; SEE=1.8% BF, R(2)=0.94). Analyses of two age groups, <30 and >/=30 y, demonstrated that for the same % BF, the former exhibited a higher sum of skinfold thicknesses.
Equations were generated for the prediction of % BF via the four-compartment criterion body composition model from anthropometric variables and age. Agewise differences for the sum of skinfold thicknesses may be related to an increase in internal fat for the older subjects.
通过基于人体测量变量和年龄的四成分标准身体成分模型生成预测体脂百分比(%BF)的方程。
采用多元回归分析,根据自变量的最佳加权组合预测%BF。
从大学时事通讯和社区中心布告栏上刊登的广告中招募了79名19至59岁的健康男性(X±标准差:35.0±12.2岁;84.24±12.53千克;179.8±6.8厘米)。
进行了以下测量:使用四成分(水、骨矿物质质量、脂肪和残余物)模型和受限人体测量指标(九处皮褶厚度、五处围度和两处骨宽度)测量%BF。
逐步多元回归从九处皮褶厚度测量中选择了六处(肩胛下、肱二头肌、腹部、大腿、小腿和腋窝中部)来预测%BF,将这六处皮褶厚度之和代入得到一个二次方程,估计标准误差(SEE)为2.5%BF,R²为0.89。将年龄作为预测变量纳入方程可进一步改善方程(%BF = -0.00057×(六处皮褶厚度总和)² + 0.298×六处皮褶厚度总和 + 0.078×年龄 - 1.13;SEE = 2.2%BF,R² = 0.91)。然而,最佳方程仅使用了三处皮褶厚度(腋窝中部、小腿和大腿)之和以及年龄,但还纳入了腰围和股骨双髁宽度作为预测变量(%BF = -0.00258×(三处皮褶厚度总和)² + 0.558×三处皮褶厚度总和 + 0.118×年龄 + 0.282×腰围 - 2.100×股骨宽度 - 2.34;SEE = 1.8%BF,R² = 0.94)。对两个年龄组(<30岁和≥30岁)的分析表明,对于相同的%BF,前者的皮褶厚度总和更高。
通过基于人体测量变量和年龄的四成分标准身体成分模型生成了预测%BF的方程。皮褶厚度总和的年龄差异可能与老年受试者体内脂肪增加有关。