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Body composition and prediction equations using skinfold thickness for body fat percentage in Southern Brazilian adolescents

By Wagner Luis Ripka (3185850), Leandra Ulbricht (3185847) and Pedro Miguel Gewehr (3185844)

Abstract

<div><p>Objectives</p><p>The purpose of this study was to: a) determine the nutritional status of Brazilian adolescents, and; b) present a skinfold thickness model (ST) to estimate body fat developed with Brazilian samples, using dual energy x-ray absorptiometry (DXA) as reference method.</p><p>Methods</p><p>The main study group was composed of 374 adolescents, and further 42 adolescents for the validation group. Weight, height, waist circumference measurements, and body mass index (BMI) were collected, as well as nine ST–biceps (BI), triceps (TR), chest (CH), axillary (AX) subscapularis (SB), abdominal (AB), suprailiac (SI), medial thigh (TH), calf (CF), and fat percentage (¿) obtained by DXA.</p><p>Results</p><p>The prevalence of overweight in adolescents was 20.9%, and obesity 5.8%. Regression analysis through ordinary least square method (OLS) allowed obtainment of three equations with values of R<sup>2</sup> = 0.935, 0.912 and 0.850, standard error estimated = 1.79, 1.78 and 1.87, and bias = 0.06, 0.20 and 0.05, respectively.</p><p>Conclusion</p><p>the innovation of this study lies in presenting new regression equations for predicting body fat in Southern Brazilian adolescents based on a representative and heterogeneous sample from DXA.</p></div

Topics: Cell Biology, Biotechnology, Science Policy, Plant Biology, Biological Sciences not elsewhere classified, BF, TR, BI, adolescents Objectives, prediction equations, skinfold thickness model, waist circumference measurements, skinfold thickness, regression equations, BMI, Brazilian adolescents, R 2, study group, body composition, 374 adolescents, square method, OLS, CH, CF, 42 adolescents, ST, SI, SB, energy x-ray absorptiometry, Brazilian samples, TH, AB, validation group, AX, body mass index, estimate body, reference method, DXA
Year: 2017
DOI identifier: 10.1371/journal.pone.0184854
OAI identifier: oai:figshare.com:article/6098192
Provided by: FigShare
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