BackgroundBioelectrical impedance analysis (BIA) is a convenient and child-friendly method for longitudinal analysis of changes in body composition. However, most validation studies of BIA have been performed on adult Caucasians. The present cross-sectional study investigated the validity of two portable BIA devices, the Inbody 230 (BIA8MF) and the Tanita BC-418 (BIA8SF), in healthy Taiwanese children.MethodsChildren aged 7–12 years (72 boys and 78 girls) were recruited. Body composition was measured by the BIA8SF and the BIA8MF. Dual X-ray absorptiometry (DXA) was used as the reference method.ResultsThere were strong linear correlations in body composition measurements between the BIA8SF and DXA and between the BIA8MF and DXA. Both BIAs underestimated fat mass (FM) and percentage body fat (%BF) relative to DXA in both genders The degree of agreement in lean body mass (LBM), FM, and %BF estimates was higher between BIA8MF and DXA than between BIA8SF and DXA. The Lin’s concordance correlation coefficient (ρc) for LBM8MF met the criteria of substantial to perfect agreement whereas the ρc for FM8MF met the criteria of fair to substantial agreement. Bland-Altman analysis showed a clinically acceptable agreement between LBM measures by BIA8MF and DXA. The limit of agreement in %BF estimation by BIA and DXA were wide and the errors were clinically important. For the estimation of ALM, BIA8SF and BIA8MF both provided poor accuracy.ConclusionsFor all children, LBM measures were precise and accurate using the BIA8MF whereas clinically significant errors occurred in FM and %BF estimates. Both BIAs underestimated FM and %BF in children. Thus, the body composition results obtained using the inbuilt equations of the BIA8SF and BIA8MF should be interpreted with caution, and high quality validation studies for specific subgroups of children are required prior to field research.

Modern bioelectrical impedance analysis (BIA) provides a wide range of body composition estimates such as fat mass (FM), lean body mass (LBM), and body water, using specific algorithms. Assuming that the fat free mass (FFM) and LBM can be accurately estimated by the 8-electrode BIA analyzer (BIA8MF; InBody230, Biospace), the bone mineral content (BMC) may be calculated by subtracting the LBM from the FFM estimates based on the three-compartment (3C) model. In this cross-sectional study, 239 healthy Taiwanese adults (106 male and 133 female) aged 20–45 years were recruited for BIA and dual-energy X-ray absorptiometry (DXA) measurements of the whole body and body segments, with DXA as the reference. The results showed a high correlation between BIA8MF and DXA in estimating total and segmental LBM, FM and percentage body fat (r = 0.909–0.986, 0.757–0.964, and 0.837–0.936, respectively). For BMC estimates, moderate to high correlations (r = 0.425–0.829) between the two methods were noted. The percentage errors and pure errors for BMC estimates between the methods ranged from 33.9% to 93.0% and from 0.159 kg to 0.969 kg, respectively. This study validated that BIA8MF can accurately assesses LBM, FM and body fat percentage (BF%). However, the estimation of segmental BMC based on the difference between FFM and LBM in body segments may not be reliable by BIA8MF.

Bioelectrical impedance analysis (BIA) is a common method for assessing body composition in research and clinical trials. BIA is convenient but when compared with other reference methods, the results have been inconclusive. The level of obesity degree in subjects is considered to be an important factor affecting the accuracy of the measurements. A total of 711 participants were recruited in Taiwan and were sub-grouped by gender and levels of adiposity. Regression analysis and Bland-Altman analysis were used to evaluate the agreement of the measured body fat percentage (BF%) between BIA and DXA. The BF% measured by the DXA and BIA methods (Tanita BC-418) were expressed as BF%DXA and BF%BIA8, respectively. A one-way ANOVA was used to test the differences in BF% measurements by gender and levels of adiposity. The estimated BF%BIA8 and BF%DXA in the all subjects, male and female groups were all highly correlated (r = 0.934, 0.901, 0.916, all P< 0.001). The average estimated BF%BIA8 (22.54 ± 9.48%) was significantly lower than the average BF%DXA (26.26 ± 11.18%). The BF%BIA8 was overestimated in the male subgroup (BF%DXA< 15%), compared to BF%DXA by 0.45%, respectively. In the other subgroups, the BF%BIA8 values were all underestimated. Standing BIA estimating body fat percentage in Chinese participants have a high correlation, but underestimated on normal and high obesity degree in both male and female subjects.

In this paper, we describe an imprint method for the fabrication of bi-directionally tunable surface plasmon resonance (SPR) filters. A periodic metal/ferroelectric film stack exhibiting SPR phenomena was directly imprinted using a sharp mold without the need for a polymer-based resist. Both the refractive index of the surrounding lead zirconate titanate (PZT) films and the period of the textured PZT/metal/PZT structure were dependent upon both the absolute value and sign of the applied potential. The SPR wavelength of the PZT/gold/PZT-based tunable filter varied over a range of greater than 100 nm when applying potentials ranging from 0 to -15 V. This imprinting method has great potential for use in the fabrication of tunable optical filters without the need for complicated processes or specific materials.

BackgroundAbdominal visceral fat affects the metabolic processes, and is an important risk factor for morbidity and mortality. The purpose of the study was to develop a quick and accurate estimate in the visceral fat area (VFA) of the L4-L5 vertebrae using anthropometric predictor variables that can be measured conveniently.MethodsA total of 227 individuals participated in this study and were further divided into a Modeling group (MG) and a Validation group (VG). Anthropometrics measurements (height, weight, waist circumference, hip circumference, age, and subcutaneous fat thickness) and VFACT were measured using computer assisted tomography for all participants. Multivariate linear regression analysis was applied to the MG to construct a VFA estimator using anthropometric predictor variables and to evaluate its performance using the VG.ResultsThe estimate equation obtained from the MG were VFAANT = -144.66 + 1.84X1 + 1.35X2 + 0.52X3 (r = 0.92, SEE =14.58 cm2, P < 0.001, n = 152). The X1, X2, and X3 variables in the equation were denoted as waist circumference (WC), age, and abdomen subcutaneous fat thickness (AS). In addition, the correlation between VFAANT and VFACT showed a high correlation (r = 0.92).ConclusionA rapid and accurate VFA estimation can be achieved by using only age, WC, and AS. The approach in the present study provides an easy and reliable estimate that can be applied widely in health and epidemiology studies.