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      Validity and Reliability of A-Mode Ultrasound for Body Composition Assessment of NCAA Division I Athletes

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          Abstract

          This study evaluated the validity and reliability of the BodyMetrix™ BX2000 A-mode ultrasound for estimating percent body fat (%BF) in athletes by comparing it to skinfolds and the BOD POD. Forty-five (22 males, 23 females) National Collegiate Athletic Association (NCAA) Division-I athletes volunteered for this study. Subjects were measured once in the BOD POD then twice by two technicians for skinfolds and ultrasound. A one-way repeated-measures ANOVA revealed significant differences between body composition methods ( F = 13.24, p < 0.01, η² = 0.24). This difference was further explained by a sex-specific effect such that the mean difference between ultrasound and BOD POD was large for females (~ 5% BF) but small for males (~ 1.5% BF). Linear regression using the %BF estimate from ultrasound to predict %BF from BOD POD resulted in an R 2 = 0.849, SEE = 2.6% BF and a TE = 4.4% BF. The inter-rater intraclass correlation (ICC) for skinfold was 0.966 with a large 95% confidence interval (CI) of 0.328 to 0.991. The inter-rater ICC for ultrasound was 0.987 with a much smaller 95% CI of 0.976 to 0.993. Both skinfolds and ultrasound had test-retest ICCs ≥ 0.996. The BX2000 ultrasound device had excellent test-retest reliability, and its inter-rater reliability was superior to the skinfold method. The validity of this method is questionable, particularly for female athletes. However, due to its excellent reliability, coaches and trainers should consider this portable and easy to use A-mode ultrasound to assess body composition changes in athletes.

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          Most cited references19

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          Statistical methods for assessing agreement between two methods of clinical measurement.

          In clinical measurement comparison of a new measurement technique with an established one is often needed to see whether they agree sufficiently for the new to replace the old. Such investigations are often analysed inappropriately, notably by using correlation coefficients. The use of correlation is misleading. An alternative approach, based on graphical techniques and simple calculations, is described, together with the relation between this analysis and the assessment of repeatability.
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            Quantifying test-retest reliability using the intraclass correlation coefficient and the SEM.

            Reliability, the consistency of a test or measurement, is frequently quantified in the movement sciences literature. A common metric is the intraclass correlation coefficient (ICC). In addition, the SEM, which can be calculated from the ICC, is also frequently reported in reliability studies. However, there are several versions of the ICC, and confusion exists in the movement sciences regarding which ICC to use. Further, the utility of the SEM is not fully appreciated. In this review, the basics of classic reliability theory are addressed in the context of choosing and interpreting an ICC. The primary distinction between ICC equations is argued to be one concerning the inclusion (equations 2,1 and 2,k) or exclusion (equations 3,1 and 3,k) of systematic error in the denominator of the ICC equation. Inferential tests of mean differences, which are performed in the process of deriving the necessary variance components for the calculation of ICC values, are useful to determine if systematic error is present. If so, the measurement schedule should be modified (removing trials where learning and/or fatigue effects are present) to remove systematic error, and ICC equations that only consider random error may be safely used. The use of ICC values is discussed in the context of estimating the effects of measurement error on sample size, statistical power, and correlation attenuation. Finally, calculation and application of the SEM are discussed. It is shown how the SEM and its variants can be used to construct confidence intervals for individual scores and to determine the minimal difference needed to be exhibited for one to be confident that a true change in performance of an individual has occurred.
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              Generalized equations for predicting body density of men.

              1. Skinfold thickness, body circumferences and body density were measured in samples of 308 and ninety-five adult men ranging in age from 18 to 61 years. 2. Using the sample of 308 men, multiple regression equations were calculated to estimate body density using either the quadratic or log form of the sum of skinfolds, in combination with age, waist and forearm circumference. 3. The multiple correlations for the equations exceeded 0.90 with standard errors of approximately +/- 0.0073 g/ml. 4. The regression equations were cross validated on the second sample of ninety-five men. The correlations between predicted and laboratory-determined body density exceeded 0.90 with standard errors of approximately 0.0077 g/ml. 5. The regression equations were shown to be valid for adult men varying in age and fatness.
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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                13 April 2016
                2016
                : 11
                : 4
                : e0153146
                Affiliations
                [001]Human Movement Science Program, Utah State University, Logan, Utah, United States of America
                Sonoma State University, UNITED STATES
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Conceived and designed the experiments: DRW DLC NWC. Performed the experiments: DRW DLC NWC. Analyzed the data: DRW DLC. Contributed reagents/materials/analysis tools: DRW. Wrote the paper: DRW DLC. Edits/revisions: DRW NWC.

                Article
                PONE-D-15-48563
                10.1371/journal.pone.0153146
                4830536
                27073854
                a177f94c-f86b-4cec-a0fc-1d1df38182f1
                © 2016 Wagner et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 6 November 2015
                : 24 March 2016
                Page count
                Figures: 3, Tables: 4, Pages: 12
                Funding
                The authors received no specific funding for this work.
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                Data are available at figshare.com. The data set can be found at http://dx.doi.org/10.6084/m9.figshare.1594815. The code (value labels) for this data set are available at http://dx.doi.org/10.6084/m9.figshare.1594816.

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