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Background/Aim: Skinfold-based equations are widely used to evaluate body fat (BF), but over-/underestimation is often reported. We evaluate the capacity of improved skinfold-based equations to estimate BF changes during weight reduction and compare them against well-established equations. Methods: Overweight adults (n = 44) participated in a 4- month weight reduction intervention. Dual-energy X-ray absorptiometry (DXA) and anthropometric measurements were taken at baseline and after intervention. The BF% was calculated using Garcia, Peterson, and Durnin and Womersley (DW) equations. Results: Baseline and postintervention BF% measured by DXA correlated highest with BF% predicted according to Garcia (r = 0.934 and r = 0.948, respectively), followed by Peterson (r = 0.941 and r = 0.932, respectively) and DW (r = 0.557 and r = 0.402, respectively); only a slight systematic error in overestimating the BF% was observed in estimates according to Garcia (r = 0.147 and r = 0.104, respectively; p < 0.001), while increasing errors occurred using the Peterson (r = 0.624 and r = 0.712, respectively; p < 0.001) and DW (r = 0.767 and r = 0.769, respectively; p < 0.001) equations. Moderate correlations between BF changes (kg) measured by DXA and predicted by DW (r = 0.7211), Peterson (r = 0.697), and Garcia (r = 0.645) were observed. Conclusion: Improved skin-fold equations cannot accurately measure changes in BF after weight reduction
Improved prediction of body fat by measuring skinfold thickness, circumferences, and bone breadths
(2005)
Objective: To develop improved predictive regression equations for body fat content derived from common anthropometric measurements. Research Methods and Procedures: 117 healthy German subjects, 46 men and 71 women, 26 to 67 years of age, from two different studies were assigned to a validation and a cross-validation group. Common anthropornetric measurements and body composition by DXA were obtained. Equations using anthropometric measurements predicting body fat mass (BFM) with DXA as a reference method were developed using regression models. Results: The final best predictive sex-specific equations combining skinfold thicknesses (SF), circumferences, and bone breadth measurements were as follows: BFMNew (kg) for men = -40.750 + {(0.397 x waist circumference) + [6.568 x (log triceps SF + log subscapular SF + log abdominal SF)]} and BFMNew (kg) for women = -75.231 + {(0.512 x hip circumference) + [8.889 x (log chin SF + log triceps SF + log subscapular SF)] + (1.905 x knee breadth)}. The estimates of BFM from both validation and cross-validation had an excellent correlation, showed excellent correspondence to the DXA estimates, and showed a negligible tendency to underestimate percent body fat in subjects with higher BFM compared with equations using a two-compartment (Durnin and Womersley) or a four-compartment (Peterson) model as the reference method. Discussion: Combining skinfold thicknesses with circumference and/or bone breadth measures provide a more precise prediction of percent body fat in comparison with established SF equations. Our equations are recommended for use in clinical or epidemiological settings in populations with similar ethnic background