OBJECTIVE: To generate equations for the prediction of percent body fat (% BF) via a four-compartment criterion body composition model from anthropometric variables and age. DESIGN: Multiple regression analyses were used to predict % BF from the best-weighted combinations of independent variables. SUBJECTS: 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. INTERVENTIONS: 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). RESULTS: 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. CONCLUSIONS: 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.
OBJECTIVE: To generate equations for the prediction of percent body fat (% BF) via a four-compartment criterion body composition model from anthropometric variables and age. DESIGN: Multiple regression analyses were used to predict % BF from the best-weighted combinations of independent variables. SUBJECTS: 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. INTERVENTIONS: 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). RESULTS: 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. CONCLUSIONS: 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.
Authors: Zackary S Cicone; Brett S Nickerson; Youn-Jeng Choi; Clifton J Holmes; Bjoern Hornikel; Michael V Fedewa; Michael R Esco Journal: Med Sci Sports Exerc Date: 2021-12-01 Impact factor: 5.411
Authors: Bharati Kulkarni; Hannah Kuper; Amy Taylor; Jonathan C Wells; K V Radhakrishna; Sanjay Kinra; Yoav Ben-Shlomo; George Davey Smith; Shah Ebrahim; Nuala M Byrne; Andrew P Hills Journal: J Appl Physiol (1985) Date: 2013-08-15
Authors: Juan R López-Taylor; Roberto G González-Mendoza; Alejandro Gaytán-González; Juan Antonio Jiménez-Alvarado; Marisol Villegas-Balcázar; Edtna E Jáuregui-Ulloa; Francisco Torres-Naranjo Journal: J Sports Med (Hindawi Publ Corp) Date: 2018-03-14