#29 - COMPARISON OF LABORATORY MEASURES FOR ESTIMATION OF BODY FAT PERCENTAGE AGAINST CRITERION MULTI-COMPARTMENT MODELS

Abstract

COMPARISON OF LABORATORY MEASURES FOR ESTIMATION OF BODY FAT PERCENTAGE AGAINST CRITERION MULTI-COMPARTMENT MODELS

INTRODUCTION: Measurements of anthropometric data (i.e. body fat percentage [BF%]) are important to consider for health and performance in female athletes. Laboratory measures for determining body composition use algorithms based on the general population which could result in misinformation regarding BF% for athletic females. PURPOSE: The purpose of this study will be to compare laboratory measures (i.e., air displacement plethysmography (ADP) and dual-energy x-ray absorptiometry (DEXA)) to criterion 3-Compartment (3C) and 4-Compartment (4C) models for determining body composition in collegiate female athletes. METHODS: Approximately sixty Division-I collegiate female athletes between the ages of 18-22 from various sports will volunteer in this study. Participants will perform three laboratory measures to estimate BF% (i.e., bioelectrical impedance spectroscopy (BIS), air displacement plethysmography (ADP), and dual-energy x-ray absorptiometry (DEXA)) within a single testing session following a standardized protocol. Criterion 3-C and 4-C models of BF% estimation will be derived using body volume (ADP), total-body water (BIS), body weight (kg), and bone mineral density from DEXA (4-C only). All data will be analyzed using SPSS and tested for normality using skewness, kurtosis, and normality tests (e.g., Shapiro-Wilk). A repeated-measures analysis of variance (ANOVA) will be used to assess differences between measures with Bonferroni post-hoc procedures. The agreement between ADP, DEXA, 3-C, and 4-C will be based on r-values, standard error of the estimate, constant error, and proportional bias. Bland-Altman procedures will be used to identify the 95% limits of agreement. An a priori significance level will be set at an alpha level of 0.05. POTENTIAL OUTCOMES: Results of this study may demonstrate the need for the implementation of new algorithms based on various factors (e.g., athlete versus non-athlete) within laboratory criterion measures.