Evaluation of the BOD POD for Estimating Percent Fat in Female College Athletes

Vescovi, Jason D.; Hildebrandt, Leslie Anne; Miller, Wayne C.; Hammer, Roger L.; Spiller, Amanda

doi:10.1519/00124278-200211000-00018

Cited by 4 publications

(16 citation statements)

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“…Compared with results from UWW, ADP underestimated % body fat (in absolute terms) by 8% in lean female athletes [35] , underestimated % body fat at lower fat values and overestimated at the higher fat values in boys [36] , and under-predicted % body fat by an average 2% in male college football players [37] . In summary, despite the popularity of densitometry techniques over many decades, both UWW and ADP techniques adopt the 2-component model that assumes density of FFM to be constant.…”

Section: Densitometrymentioning

confidence: 54%

Current Status of Body Composition Assessment in Sport

et al. 2012

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Dette er siste tekst-versjon av artikkelen, og den kan inneholde små forskjeller fra forlagets pdf-versjon. Forlagets pdf-versjon finner du på link.springer.com: http://dx.doi.org/10.2165/11597140-000000000-00000 This is the final text version of the article, and it may contain minor differences from the journal's pdf version. The original publication is available at link.springer.com: http://dx.doi.org/10.2165/11597140-000000000-00000 . Figure 3. Semi-automatic image evaluation: The edge detection algorithm for subcutaneous adipose tissue (SAT) thickness determination enables selecting areas of interest, distances (d US ) measurement series, color-coding of distance values, and statistical evaluations [48] . In this example of a SAT-layer above the triceps muscle, with the transducer held parallel to the humerus, 119 d US values ranging from 2.3 mm to 4.3 mm were automatically detected by the algorithm; the median was 3.4 mm (c = 1470 m/s). Layers and interfaces: A: gel, B: gel-epidermis, C: dermis, D: dermis-SAT, E: SAT, F: SAT-fascia of muscle, G: muscle. [77] . Abbreviations: mid dist = middle distance track runners; long dist = long distance runners; Scot long dist = Scottish long distance runners; SASI mid dist = South Australian Sports Institute middle distance track runners; SASI sprint = sprint runners; SASI jump = jumpers. Figure 6. Selected skinfold ratios in extremely lean male and female endurance athletes, and mean values from 106 male and 33 female athletes [80] . 4 ABSTRACTQuantifying human body composition has played an important role in monitoring all athlete performance and training regimens, but especially so in gravitational, weight class and aesthetic sports wherein the tissue composition of the body profoundly affects performance or adjudication. Over the past century, a myriad of techniques and equations have been proposed, but all have some inherent problems, whether in measurement methodology or in the assumptions they make. To date, there is no universally applicable criterion or "gold standard" methodology for body composition assessment.Having considered issues of accuracy, repeatability and utility, the multi-component model might be employed as a performance or selection criterion, provided the selected model accounts for variability in the density of FFM in its computation. However, when profiling change in interventions, single methods whose raw data are surrogates for body composition (with the notable exception of the BMI) remain useful.

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Section: Densitometrymentioning

confidence: 54%

Current Status of Body Composition Assessment in Sport

et al. 2012

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“…While water displacement is recognized as the gold standard, it is time consuming, extremely inconvenient, and not widely available. The validity of some of these techniques has also been questioned [17][18][19].…”

Section: Discussionmentioning

confidence: 99%

Rapid Automated Measurement of Body Fat Distribution from Whole-Body MRI

Brennan

Whelan

Robinson

et al. 2005

American Journal of Roentgenology

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Objective The purpose of this article is to determine the feasibility of using computer assisted diagnosis (CAD) techniques to automatically identify, localize, and measure body fat tissue from a rapid wholebody MRI examination Design Prospective investigative study.Setting Dublin, Ireland.Participants 42 volunteers (21 male, 21 female) aged 18 to 56. The study group consisted of healthy volunteers with a wide range of body weights, and included a cohort of high performance athletes. Main outcome measuresThe provision of an automated system, which assesses subjects' whole body MRI scans and which provides numerical and visual feedback to illustrate the findings. The system generates results in a matter of minutes allowing for an initial assessment to be performed immediately following the completion of an MRI scan. By highlighting areas where body fat is concentrated the system allows radiologists to quickly identify and examine regions of interest in the scan.Results Rapid, accurate, and reproducible delineation of body fat distribution can be obtained using whole body MRI techniques and Computer Aided Diagnosis.Conclusions Whole-body MRI in conjunction with CAD allows a fast, automatic, and accurate approach to body fat measurement and localization and can be a useful alternative to body mass index.Whole-body fat analysis can be achieved in less than 5 min.3

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“…Because it is a relatively new technique, the validity ADP for estimating %fat compared to a multi-compartment model has not been fully investigated, and the results of a few studies that have examined its validity in college female athletes have resulted in conflicting findings (Ballard et al 2004; Vescovi et al 2002). For example, Vescovi et al (2002) compared estimates of %fat from ADP to HW in female college athletes and reported that ADP over-predicted %fat by as much as 8% in subjects who were greater than 18.3%fat and over-predicted by as much as 16% in leaner subjects. In contrast, the data from the current study showed that ADP resulted in an overestimation of %fat as %fat increased, indicated by a significant trend (trend = -0.22, P \ 0.05; Table 2, Fig.…”

Section: Air-displacement Plethysmographymentioning

confidence: 99%

“…Conflicting findings regarding the validity of the equations associated with the laboratory methods mentioned above (Arngrimsson et al 2000;Ballard et al 2004;Clasey et al 1999;Economos et al 1997;Ellis 2000;Genton et al 2002;Oldroyd et al 2003;Silva et al 2006;Tylavsky et al 2003;Van Der Ploeg et al 2003;Vescovi et al 2002;Wang et al 1998;Williams et al 2006;Withers et al 1998) may also discourage coaches and trainers from using these techniques to assess the body composition of their athletes.…”

Section: Introductionmentioning

confidence: 99%

“…In the past, two compartment (2C) models, such as HW, served as the criterion method to validate various field and laboratory techniques (Vescovi et al 2002). More recently, however, multi-compartment models have become more widely accepted as the preferred criterion method, and equations have been developed using as many as six compartments of body composition.…”

Section: Introductionmentioning

confidence: 99%

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Estimating body fat in NCAA Division I female athletes: a five-compartment model validation of laboratory methods

et al. 2008

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The purpose of the present study was to determine the validity of various laboratory methods for estimating percent body fat (%fat) in NCAA Division I college female athletes (n = 29; 20 +/- 1 year). Body composition was assessed via hydrostatic weighing (HW), air displacement plethysmography (ADP), and dual-energy X-ray absorptiometry (DXA), and estimates of %fat derived using 4-compartment (C), 3C, and 2C models were compared to a criterion 5C model that included bone mineral content, body volume (BV), total body water, and soft tissue mineral. The Wang-4C and the Siri-3C models produced nearly identical values compared to the 5C model (r > 0.99, total error (TE) < 0.40%fat). For the remaining laboratory methods, constant error values (CE) ranged from -0.04%fat (HW-Siri) to -3.71%fat (DXA); r values ranged from 0.89 (ADP-Siri, ADP-Brozek) to 0.93 (DXA); standard error of estimate values ranged from 1.78%fat (DXA) to 2.19%fat (ADP-Siri, ADP-Brozek); and TE values ranged from 2.22%fat (HW-Brozek) to 4.90%fat (DXA). The limits of agreement for DXA (-10.10 to 2.68%fat) were the largest with a significant trend of -0.43 (P < 0.05). With the exception of DXA, all of the equations resulted in acceptable TE values (<3.08%fat). However, the results for individual estimates of %fat using the Brozek equation indicated that the 2C models that derived BV from ADP and HW overestimated (5.38, 3.65%) and underestimated (5.19, 4.88%) %fat, respectively. The acceptable TE values for both HW and ADP suggest that these methods are valid for estimating %fat in college female athletes; however, the Wang-4C and Siri-3C models should be used to identify individual estimates of %fat in this population.

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Evaluation of the BOD POD for Estimating Percent Fat in Female College Athletes

Cited by 4 publications

References 0 publications

Current Status of Body Composition Assessment in Sport

Current Status of Body Composition Assessment in Sport

Rapid Automated Measurement of Body Fat Distribution from Whole-Body MRI

Estimating body fat in NCAA Division I female athletes: a five-compartment model validation of laboratory methods

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