Comparison of Some Secondary Body Composition Algorithms.
Body composition measurements vary greatly in degree of measurement difficulty and accuracy. Hydrostatic weighing, chemical dilution or their equivalents were the accepted "gold" standards for assessing fat mass. Dual Energy X-ray Absorptiometry (DEXA) is fast replacing these techniques as the preferred standard. However, these direct measurement techniques require expensive and precise equipment and must be operated by highly trained personnel. For this reason the "Body Mass Index" or "BMI" is being widely used to indicate body composition. The BMI is thought to be reasonably accurate and simply requires body weight and height measurements. Between these two extremes lie the many other approaches and their corresponding algorithms. The discrepancies inherent in these algorithms are discussed. The prediction accuracy of the more popular empirical algorithms is evaluated. These evaluations indicate that a promising approach to body composition measurements is a combination of both BMI and bioelectric impedance measurements. ********** Body composition such as the ratio of fat mass to total body mass cannot be measured. However, parameters used to predict body composition can be measured. These parameters are classed as either primary or secondary. Primary parameters are used in analytical algorithms to represent body composition. Secondary parameters are used in empirical algorithms to predict primary parameters. Secondary parameters are needed since primary parameters are difficult to measure. However, prediction errors are inherent in the use of these secondary parameters. This paper evaluates the accuracy of skin fold, body mass index and three bioelectrical secondary algorithms. The bioelectrical algorithms (presented by Kushner, 1992, and Thomas, Cornis and Ward, 1992) are implemented by the Tanita Corporation. The accuracy of these algorithms is indirectly compared to the well accepted hydrostatic weighing technique (as first described by Garrow, Stalley, Diethelm et al, 1979). Method In order to establish a common basis of understanding, a model of body composition measurement techniques and terminology is first presented. With this background, the measurement techniques to be evaluated are selected and described. Also, a suitable data base for testing these measurement techniques is developed. Finally, performance criteria and procedures for testing this performance are established. A Body Composition Model Figure 1 shows a model of the components of body composition technology. Body composition can be represented in either the volumetric (plethysmography) or hydrometric domain. The two domains are equivalent. Equivalency is insured by domain transforms. In both domains there are primary algorithms that operate on primary measurements to represent body composition and there are secondary algorithms that operate on secondary measurements to predict primary measurements. The primary algorithms are well accepted by the industry since they are analytical and use well accepted assumptions for the domain constants. The secondary algorithms are not yet well accepted by the industry. One reason for this lack of acceptance is that the techniques have either been demonstrated to be inaccurate or have not yet been fully evaluated for accuracy. The latter is the case for the body mass index and bioelectric techniques. Inaccuracy is inherent in the secondary algorithms since they must be empirically derived by statistically fitting the secondary parameters to a finite set of measured primary parameters. [FIGURE 1 OMITTED] Primary Algorithms In the volumetric domain the de facto standard primary algorithm for representing body composition is: f(D) = [DfDo / (Df - [D.sub.o])] (D-1 - Do-1) (1) where: [D.sub.f] = Density of body fat, [D.sub.o] = Density of other lean body tissue, D = Composite body density and f(D) = Ratio (as a function of D) of body fat mass to total body mass. …