"Objective: To develop and cross-validate predictive models for percentage body fat (%BF) from
anthropometric measurements [including BMI z-score (zBMI) and calf circumference
(CC)] excluding skinfold thickness.Methods:
A descriptive study was carried out in 3,084 pre-pubertal children. Regression models
and neural network were developed with %BF measured by Bioelectrical Impedance
Analysis (BIA) as the dependent variables and age, sex and anthropometric measurements
as independent predictors.Results:
All %BF grade predictive models presented a good global accuracy (≥91.3%) for obesity
discrimination. Both overfat/obese and obese prediction models presented respectively
good sensitivity (78.6% and 71.0%), specificity (98.0% and 99.2%) and reliability for
positive or negative test results (≥82% and ≥96%). For boys, the order of parameters,
by relative weight in the predictive model, was zBMI, height, waist-circumference-toheight-
ratio (WHtR) squared variable (_Q), age, weight, CC_Q and hip circumference
(HC)_Q (adjusted r2 = 0.847 and RMSE = 2.852); for girls it was zBMI, WHtR_Q, height,
age, HC_Q and CC_Q (adjusted r2 = 0.872 and RMSE = 2.171).Conclusion:
%BF can be graded and predicted with relative accuracy from anthropometric measurements
excluding skinfold thickness. Fitness and cross-validation results showed that our
multivariable regression model performed better in this population than did some previously
published models."info:eu-repo/semantics/publishedVersio
