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] .
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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.