Body Composition Predictions From Bioelectric Impedance

The prediction of body composition variables from bioelectric impedance (BI) has considerable potential for use in surveys, because BI is reliable, and the equipment is portable (weight, 1.04 kg). The purpose of the present study was to determine if BI with selected anthropométrie variables predicte...

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Bibliographic Details
Published in:Human biology 1987-04, Vol.59 (2), p.221-233
Main Authors: GUO, SHUMEI, ROCHE, ALEX F., CHUMLEA, WM. CAMERON, POHLMAN, ROBERTA L., MILES, DANIEL S.
Format: Article
Language:English
Subjects:
Adolescent
Adult
Anthropometric measurements
Body Composition
Body density
Body fat
Circumferences
Coefficients
Electric Conductivity
Female
Forecasting
Humans
Male
Men
Modeling
Skinfold thickness
The Bioelectrical Estimation of Body Composition
White people
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Summary:The prediction of body composition variables from bioelectric impedance (BI) has considerable potential for use in surveys, because BI is reliable, and the equipment is portable (weight, 1.04 kg). The purpose of the present study was to determine if BI with selected anthropométrie variables predicted % BF (percent body fat) accurately. Two groups of subjects were used from whom accurate anthropométrie variables were obtained. The validation group of 148 healthy White adults (77 men; 71 women) aged 18 to 30 was used to formulate two parsimonious models for each sex to predict % BF from selected anthropometrie variables, one without and one with stature² divided by resistance (S²/R). The cross-validation group, aged 18 to 30 years (19 White men; 29 White women), was used to assess the stability of equations derived from S²/R and anthropometrie variables. Principal component analysis applied to 16 potential predictors showed five components explained most of the variation in % BF. All possible subsets regression procedure was employed to select the best equation on the basis of: (1) five predictors at most, (2) minimum root mean square error and (3) 0.1 level of significance. The multiple R² and r.m.s.e. were not changed by the inclusion of S²/R in men. However, the inclusion of S²/R changed the R² from 0.73 to 0.81 and the r.m.s.e. from 3.83% to 3.22% in women. Cross-validation of the equations that included S²/R showed the accuracy of prediction (coefficient of variation, 0.23 for men; 0.16 for women) was approximately the same as for the validation group. These findings indicated that the addition of S²/R to selected anthropometrie variables significantly improved the prediction of % BF for women, but not for men.
ISSN:0018-7143
1534-6617