Table 2.

Prediction equation for G₁ and G₂ subjects

G1 subjects (n = 277)
Measured FFMDXA	50.54 ± 11.58 kg
Prediction FFMBIA	12.518 + 0.215 w + 0.397 h2/ ZF-F – 0.143 y + 7.843 S (Female = 0, Male = 1), (r2 = 0.92, SE = 3.23 kg, CV = 6.3 %) (1.a)
Prediction FFM
Cross-validation using G2 subjects FFM	50.48 ± 10.97 kg, r = 0.96, bias ± SD = −0.06 ± 3.22 kg, PE = 3.22 kg, RMSE = 2.31 kg, LOA = −6.46 to 6.38 kg
G2 subjects (n = 277)
Measured FFMDXA	49.81 ± 10.91 kg
Prediction FFMBIA	13.639 + 0.192 w + 0.392 h2/ ZF-F – 0.129 y + 8.355 S (Female = 0, Male = 1), (r2 = 0.92, SEE = 3.13 kg, CV = 6.1 %) (1.b)
Prediction FFM
Cross-validation using G1 subjects FFM	49.86 ± 10.60 kg, r = 0.96, bias ± SD = 0.05 ± 3.13 kg, PE = 3.12 kg, RMSE = 2.18 kg, LOA = −6.21 to 6.31 kg

FFM, fat free mass; Regression coefficient estimate ± SE; FFM_DXA, DXA measurement of FFM; FFM_BIA, BIA prediction of FFM; h²/Z_F-F, height ²/impedance; SEE, standard error of estimate; LOA, limits of agreement

RMSE, Root mean square error= $\sqrt{{\displaystyle \sum }\frac{{\left({\mathrm{y}}_{\mathrm{i}}^{\text{'}}-{\mathrm{y}}_{\mathrm{i}}\right)}^2}{\mathrm{n}}-\mathrm{p}-1}$, where y’ the predicted FFM, y is the observed; n is the number of subjects, and p is the number of predictor variables; PE, Pure errors= $\sqrt{{\displaystyle \sum }\frac{{\left({\mathrm{y}}_{\mathrm{i}}^{\text{'}}-{\mathrm{y}}_{\mathrm{i}}\right)}^2}{\mathrm{n}}}$; r, correlation coefficient between FFM_BIA and FFM_DXA