984 resultados para Bioelectrical Impedance Analysis
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Objective: To observe the behavior of the plotted vectors on the RXc (R - resistance - and Xc - reactance corrected for body height/length) graph through bioelectrical impedance analysis (BIVA) and phase angle (PA) values in stable premature infants, considering the hypothesis that preterm infants present vector behavior on BIVA suggestive of less total body water and soft tissues, compared to reference data for term infants. Methods: Cross-sectional study, including preterm neonates of both genders, in-patients admitted to an intermediate care unit at a tertiary care hospital. Data on delivery, diet and bioelectrical impedance (800 mA, 50 kHz) were collected. The graphs and vector analysis were performed with the BIVA software. Results: A total of 108 preterm infants were studied, separated according to age (< 7 days and >= 7 days). Most of the premature babies were without the normal range (above the 95% tolerance intervals) existing in literature for term newborn infants and there was a tendency to dispersion of the points in the upper right quadrant, RXc plan. The PA was 4.92 degrees (+/- 2.18) for newborns < 7 days and 4.34 degrees (+/- 2.37) for newborns >= 7 days. Conclusion: Premature infants behave similarly in terms of BIVA and most of them have less absolute body water, presenting less fat free mass and fat mass in absolute values, compared to term newborn infants.
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.:Abstract-Objective: Bioelectrical impedance analysis (BIA) is widely used as bedside assessment of body composition. Body cell mass (BCM) and intracellular water (ICW) are clinically important body compartments. Estimates of ICW obtained from BIA by different calculation approaches were compared to a reference method in male HIV-infected patients. Patients: Representative subsample of clinically stable HIV-infected outpatients, consisting of 42 men with a body mass index of 22.4 +/- 3.8 kg/m(2) (range, 13-31 kg/m(2)). Methods: Total body potassium was assessed in a whole body counter, and compared to 50 kHz mono-frequency BIA and multifrequency bioelectrical impedance spectroscopy. Six different prediction equations for ICW from BIA data were applied. Methods were compared by the Bland-Altman method. Results: BIA-derived ICW estimates explained 58% to 73% of the observed variance in ICW (TBK), but limits of confidence were wide (-16.6 to +18.2% for the best method). BIA overestimated low ICW (TBK) and underestimated high ICW (TBK) when normalized for weight or height. Mono- and multifrequency BIA were not different in precision but population-specific equations tended to narrower confidence limits. Conclusion: BIA is an unreliable method to estimate ICW in this population, in contrast to the better established estimation of total body water and extracellular water. Potassium depletion in severe malnutrition may contribute to this finding but a major part of the residual between methods remains unexplained. (C) 2000 Harcourt Publishers Ltd.
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We have compared the use of bioelectrical impedance analysis (BIA) with anthropometry for the prediction of changes in total body potassium (TBK) in a group (n = 31) of children with cystic fibrosis. Linear regression analysis showed that TBK was highly correlated (r > 0.93) with height(2)/impedance, weight, height, and fat-free mass (FFM) estimated from skin-fold measurements. Changes in TBK were also correlated, but less well, with changes in height(2)/impedance, weight, height, and FFM (r = 0.69, 0.59, 0.44, and 0.40, respectively). The children were divided into two groups: those who had normal accretion of TBK (> 5%/y) and those who had suboptimal accretion of TBK (< 5%/y). Analysis of variance showed that the significant difference in the change in TBK between the groups was detectable by concomitant changes in impedance and weight but not by changes in height, FFM, or weight and height Z scores. The results of this study suggest that serial BIA measures may be useful as a predictor of progressive undernutrition and poor growth in children with cystic fibrosis. (C) Elsevier Science Inc. 1997.
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Introduction: Human immunodeficiency virus (HIV)associated lipodystrophy syndrome (LS) includes body composition and metabolic alterations. Lack of validated criteria and tools make difficult to evaluate body composition in this group. Objective: The aim of the study was to compare different methods to evaluate body composition between Brazilians HIV subjects with (HIV+LIPO+) or without LS (HIV+LIPO-) and healthy subjects (Control). Methods: in a cross-sectional analyses, body composition was measured by bioelectrical impedance analysis (BIA), skinfold thickness (SF) and dual-energy x-ray absorptiometry (DXA) in 10 subjects from HIV+LIPO+ group; 22 subjects from HIV+LIPO- group and 12 from Control group. Results: There were no differences in age and body mass index (BMI) between groups. The fat mass (FM) (%) estimated by SF did not correlate with DXA in HIV+LIPO+ group (r = 0,46/p >0,05) and had fair agreement in both HIV groups (HIV+LIPO+ =0,35/ HIV+ LIPO- = 0,40). BIA had significant correlation in all groups (p < 0,05) and strong agreement, meanly in HIV groups, for FM (HIV+LIPO+ = 0,79/ HIV+LIPO- = 0,85/Control = 0,60) and for fat free mass (FFM) (HIV+LIPO+ = 0,93/ HIV+LIPO- = 0,92 / Control = 0,73). Discussion: Total fat mass can be measured by BIA with good precision, but not by SF in HIV-infected patients with LS. Segmental BIA, triciptal SF, circumferences of arms, waist and legs maybe alternatives that need more studies.
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Bioelectrical impedance vector analysis (BIVA) is a new method that is used for the routine monitoring of the variation in body fluids and nutritional status with assumptions regarding body composition values. The aim of the present study was to determine bivariate tolerance intervals of the whole-body impedance vector and to describe phase angle (PA) values for healthy term newborns aged 7-28 d. This descriptive cross-sectional study was conducted on healthy term neonates born at a low-risk public maternity. General and anthropometric neonatal data and bioelectrical impedance data (800 mu A-50 kHz) were obtained. Bivariate vector analysis was conducted with the resistance-reactance (RXc) graph method. The BIVA software was used to construct the graphs. The study was conducted on 109 neonates (52.3% females) who were born at term, adequate for gestational age, exclusively breast-fed and aged 13 (SD 3.6) d. We constructed one standard, reference, RXc-score graph and RXc-tolerance ellipses (50, 75 and 95 %) that can be used with any analyser. Mean PA was 3.14 (SD 0.43)degrees (3.12 (SD 0.39)degrees for males and 3.17 (SD 0.48)degrees for females). Considering the overlapping of ellipses of males and females with the general distribution, a graph for newborns aged 7-28 d with the same reference tolerance ellipse was defined for boys and girls. The results differ from those reported in the literature probably, in part, due to the ethnic differences in body composition. BIVA and PA permit an assessment without the need to know body weight and the prediction error of conventional impedance formulas.
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Purpose: This study was designed to investigate the immediate effect of exercise intensity and duration on body fluid volumes in rats throughout a 3-wk exercise program. Methods: Changes in the extracellular water (ECW) and total body water (TBW) volumes of rats were measured preexercise and postexercise using multiple frequency bioelectrical impedance analysis. Groups of rats were exercised at two intensities (6 m.min(-1) and 12 m.min(-1)) for two exercise times (60 min and 90 min) 5 d.wk(-1) during a 3-wk period. Changes in plasma electrolytes, glucose, and lactate resulting from the exercise were also measured on 3 d of each week. Results: Each group of animals showed significant losses in ECW and TBW as a direct result of daily exercise. The magnitude of fluid loss was directly related to the intensity of the exercise, bur not to exercise duration; although the magnitude of daily fluid loss at the higher intensity exercise (12 m.min(-1)) decreased as the study progressed, possibly indicating a training effect. Conclusion: At low-intensity exercise, there is a small bur significant loss in both TBW and ECW fluids, and the magnitude of these losses does not change throughout a 3-wk exercise program. At moderate levels of exercise intensity, there is a greater loss of both TBW and ECW fluids. However, the magnitudes of these losses decrease significantly during the 3-wk exercise program, thus demonstrating a training effect.
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Purpose: Malnutrition and fluid overload contribute to the poor cardiovascular prognosis of dialysis patients. Since bioelectrical impedance analysis is an option for the evaluation of body composition and for the monitoring of hydration state, it may assist in the identification of subjects at high cardiovascular risk. The objective of this study was to evaluate the association between bioelectrical impedance parameters and cardiovascular events. Methods: The association between bioelectrical impedance parameters and fatal and non-fatal cardiovascular outcome was evaluated in 145 dialysis patients. Results: The mean age of the population studied was 54.9 ± 15.4 years, 49.7 % were males, and 35.9 % had diabetes. Forty (27.6 %) patients developed cardiovascular events during the 16 months (8; 32) of follow-up. Comparison of patients with and without cardiovascular events revealed higher extracellular mass/body cell mass (ECM/BCM) and extracellular water/total body water ratios and higher C-reactive protein levels in the former. Survival analysis showed that an ECM/BCM ratio >1.2 and a phase angle <6° were associated with poor cardiovascular prognosis. Among nondiabetic patients, these parameters and capacitance were independently associated with cardiovascular events, suggesting that poor nutritional status and fluid overload are associated with the occurrence of these events. Conclusions: Phase angle, capacitance and ECM/BCM ratio are valuable parameters for the evaluation of cardiovascular prognosis, supporting the use of bioelectrical impedance for the clinical assessment of dialysis patients. © 2012 Springer Science+Business Media Dordrecht.
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Detailed analysis of body composition in children has helped to understand changes that occur in growth and disease. Bioelectrical impedance analysis (BIA) has gained popularity as a simple, non-invasive and inexpensive tool of body composition assessment. Being an indirect technique, prediction equations have to be used in the assessment of body composition. There are many prediction equations available in the literature for the assessment of body composition from BIA. This study aims to cross-validate some of those prediction equations to determine the suitability of their use on Australian children of white Caucasian and Sri Lankan origins. Height, weight and BIA were measured. Total body water was measured using the isotope dilution method (D2O). Fat-mass (FM) and %FM were estimated from BIA using ten prediction equations described in the literature. Five to 14.99-year-old healthy, 96 white Caucasians and 42 Sri Lankan children were studied. The equation of Schaefer et al was the most suitable prediction equation for this group with the lowest mean bias for %FM assessment in both Caucasian (–1.0±9.6%) and Sri Lankan (1.6±5.2%) children and the fat content of the individuals did not influence the predictions by this equation. Impedance index (height2/impedance) explained for 80% of TBW in white Caucasians and 93% in Sri Lankans and figures were similar for the prediction of FFM. We conclude that BIA can be used effectively in the assessment of body composition in children. However, for the assessment of body composition using BIA, either prediction equations should be derived to suit the local populations or existing equations should be cross-validated to determine their suitability before their application.
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Backgrounds Ea aims: The boundaries between the categories of body composition provided by vectorial analysis of bioimpedance are not well defined. In this paper, fuzzy sets theory was used for modeling such uncertainty. Methods: An Italian database with 179 cases 18-70 years was divided randomly into developing (n = 20) and testing samples (n = 159). From the 159 registries of the testing sample, 99 contributed with unequivocal diagnosis. Resistance/height and reactance/height were the input variables in the model. Output variables were the seven categories of body composition of vectorial analysis. For each case the linguistic model estimated the membership degree of each impedance category. To compare such results to the previously established diagnoses Kappa statistics was used. This demanded singling out one among the output set of seven categories of membership degrees. This procedure (defuzzification rule) established that the category with the highest membership degree should be the most likely category for the case. Results: The fuzzy model showed a good fit to the development sample. Excellent agreement was achieved between the defuzzified impedance diagnoses and the clinical diagnoses in the testing sample (Kappa = 0.85, p < 0.001). Conclusions: fuzzy linguistic model was found in good agreement with clinical diagnoses. If the whole model output is considered, information on to which extent each BIVA category is present does better advise clinical practice with an enlarged nosological framework and diverse therapeutic strategies. (C) 2012 Elsevier Ltd and European Society for Clinical Nutrition and Metabolism. All rights reserved.
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Multifrequency bioimpedance analysis has the potential to provide a non-invasive technique for determining body composition in live cattle. A bioimpedance meter developed for use in clinical medicine was adapted and evaluated in 2 experiments using a total of 31 cattle. Prediction equations were obtained for total body water, extracellular body water, intracellular body water, carcass water and carcass protein. There were strong correlations between the results obtained through chemical markers and bioimpedance analysis when determined in cattle that had a wide range of liveweights and conditions. The r(2) values obtained were 0.87 and 0.91 for total body water and extracellular body water respectively. Bioimpedance also correlated with carcass water, measured by chemical analysis (r(2) = 0.72), but less well with carcass protein (r(2) = 0.46). These correlations were improved by inclusion of liveweight and sex as variables in multiple regression analysis. However, the resultant equations were poor predictors of protein and water content in the carcasses of a group of small underfed beef cattle, that had a narrow range of liveweights. In this case, although there was no statistical difference between the predicted and measured values overall, bioimpedance analysis did not detect the differences in carcass protein between the 2 groups that were apparent following chemical analysis. Further work is required to determine the sensitivity of the technique in small underfed cattle, and its potential use in heavier well fed cattle close to slaughter weight.
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The objective of the present study was to evaluate the performance of a new bioelectrical impedance instrument, the Soft Tissue Analyzer (STA), which predicts a subject's body composition. A cross-sectional population study in which the impedance of 205 healthy adult subjects was measured using the STA. Extracellular water (ECW) volume (as a percentage of total body water, TBW) and fat-free mass (FFM) were predicted by both the STA and a compartmental model, and compared according to correlation and limits of agreement analysis, with the equivalent data obtained by independent reference methods of measurement (TBW measured by D2O dilution, and FFM measured by dual-energy X-ray absorptiometry). There was a small (2.0 kg) but significant (P < 0.02) difference in mean FFM predicted by the STA, compared with the reference technique in the males, but not in the females (-0.4 kg) or in the combined group (0.8 kg). Both methods were highly correlated. Similarly, small but significant differences for predicted mean ECW volume were observed. The limits of agreement for FFM and ECW were -7.5-9.9 and -4.1-3.0 kg, respectively. Both FFM and ECW (as a percentage of TBW) are well predicted by the STA on a population basis, but the magnitude of the limits of agreement with reference methods may preclude its usefulness for predicting body composition in an individual. In addition, the theoretical basis of an impedance method that does not include a measure of conductor length requires further validation. (C) Elsevier Science Inc. 2000.
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The prevention and treatment of diseases related to changes in body composition require accurate methods for the measurement of body composition. However, few studies have dealt specifically with the assessment of body composition of undernourished older subjects by different methodologies. To assess the body composition of undernourished older subjects by two different methods, dual energy x-ray absorptiometry (DXA) and bioelectric impedance (BIA), and to compare results with those of an eutrophic group. The study model was cross-sectional; the study was performed at the University Hospital of the School of Medicine of Ribeiro Preto, University of So Paulo, Brazil. Forty-one male volunteers aged 62 to 91 years. The groups were selected on the basis of anamnesis, physical examination and nutritional assessment according to the Mini Nutritional Assessment (MNA) score. Body composition was assessed by DXA and BIA. Body weight, arm and calf circumference, body mass index (BMI), fat free mass (FFM) and fat mass (FM) were significantly lower in the undernourished group as compared to the eutrophic group. There were no significant differences between FFM and FM mean values determined by DXA and BIA in both groups, but the agreement between methods in the undernourished group was less strong. Our results suggest caution when BIA is to be applied in studies including undernourished older subjects. This study does not support BIA as an accurate method for the individual assessment of body composition.
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Objective: To compare percentage body fat (%BF) for a given body mass index (BMI) among New Zealand European, Maori and Pacific Island children. To develop prediction equations based on bioimpedance measurements for the estimation of fat-free mass (FFM) appropriate to children in these three ethnic groups. Design: Cross-sectional study. Purposive sampling of schoolchildren aimed at recruiting three children of each sex and ethnicity for each year of age. Double cross-validation of FFM prediction equations developed by multiple regression. Setting: Local schools in Auckland. Subjects: Healthy European, Maori and Pacific Island children (n = 172, 83 M, 89 F, mean age 9.4 +/- 2.8(s. d.), range 5 - 14 y). Measurements: Height, weight, age, sex and ethnicity were recorded. FFM was derived from measurements of total body water by deuterium dilution and resistance and reactance were measured by bioimpedance analysis. Results: For fixed BMI, the Maori and Pacific Island girls averaged 3.7% lower % BF than European girls. For boys a similar relation was not found since BMI did not significantly influence % BF of European boys ( P = 0.18). Based on bioimpedance measurements a single prediction equation was developed for all children: FFM (kg) = 0.622 height (cm)(2)/ resistance +0.234 weight (kg)+1.166, R-2 = 0.96, s. e. e. = 2.44 kg. Ethnicity, age and sex were not significant predictors. Conclusions: A robust equation for estimation of FFM in New Zealand European, Maori and Pacific Island children in the 5 - 14 y age range that is more suitable than BMI for the determination of body fatness in field studies has been developed. Sponsorship: Maurice and Phyllis Paykel Trust, Auckland University of Technology Contestable Grants Fund and the Ministry of Health.
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BACKGROUND AND AIMS: little is known regarding the reproducibility of body fat measuring devices; hence, we assessed the between and within-device reproducibility, and the within-day variability of body fat measurements. METHODS: body fat percentage was measured twice on seventeen female students aged between 18 and 20 with a body mass index of 21.9 ± 2.5 kg/m2 (mean ± SD) using seven bipolar bioelectrical impedance devices. Each participant was also measured each hour between 7:00 and 22:00. RESULTS: the correlation between first and second measurements was very high (Spearman r between 0.985 and 1.000, p<0.001), as well as between devices (Spearman r between 0.916 and 0.991, p<0.001). Repeated measurements analysis showed no differences were between devices (p=0.59) or readings (first vs. second: p=0.74). Conversely, significant differences were found between assessment periods throughout the day, measurements made in the morning being lower than those made in the afternoon (F test for repeated values= 6.58, p<0.001). CONCLUSIONS: the between and within-device reproducibility for measuring body fat is high, enabling the use of multiple devices in a single study. Conversely, small but significant changes in body fat measurements occur during the day, urging body fat measurements to be performed at fixed times.
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Objective: to assess the between and within-device reproducibility, as well as within-day variability of body fat measurements. Methods: body fat percentage (%BF) was measured twice on seventeen female students aged between 18 and 20 with a body mass index of 21.9 22.6 kg/m2 (mean SD) using seven bipolar bioelectrical impedance devices (BF-306) according to the manufacturer's recommendations. Each student was also measured each hour between 7:00 and 22:00. Statistical analysis was conducted using a general linear model for repeated measurements. Results: the correlation between first and second measurements was very high (Pearson r between 0.985 and 1.000, p<0.001), as well as the correlation between devices (Pearson r between 0.986 and 0.999, all p<0.001). Repeated measurements analysis showed no differences were between devices (F test=0.83, p=0.59) or readings (first vs. second: F test=0.12, p=0.74). Conversely, significant differences were found between assessment periods throughout the day, measurements made in the morning being lower than those made in the afternoon. Assuming an overall daily average of 100 (based on all measurements), the values were 95.8 3.2 (mean SD) at 8:00 versus 101.3 3.0 at 20:00, corresponding to a mean change of 2.2 1.1 in %BF (F test for repeated values=6.58, p<0.001). Conclusions: the between and within-device reproducibility for measuring body fat is high, enabling the use of multiple devices in a single study. Conversely, small but significant changes in body fat measurements occur during the day, urging body fat measurements to be performed at fixed times.