Open AccessAnthropometric predictors of body fat in a large population of 9‐year‐old school‐aged children
Author(s) -
Almeida Sílvia M,
Furtado José M,
Mascarenhas Paulo,
Ferraz Maria E,
Silva Luís R,
Ferreira José C,
Monteiro Mariana,
Vilanova Manuel,
Ferraz Fernando P
Publication year - 2016
Publication title -
obesity science and practice
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.654
H-Index - 14
ISSN - 2055-2238
DOI - 10.1002/osp4.51
Subject(s) - bioelectrical impedance analysis , anthropometry , medicine , waist , population , circumference , linear regression , demography , body mass index , body fat percentage , regression analysis , obesity , statistics , mathematics , geometry , environmental health , sociology
Summary 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‐to‐height‐ratio (WHtR) squared variable (_Q), age, weight, CC_Q and hip circumference (HC)_Q (adjusted r 2 = 0.847 and RMSE = 2.852); for girls it was zBMI, WHtR_Q, height, age, HC_Q and CC_Q (adjusted r 2 = 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.