scholarly journals Development of Population-Specific Prediction Equations for Bioelectrical Impedance Analyses in Vietnamese Children

2020 ◽  
Vol 4 (Supplement_2) ◽  
pp. 875-875
Author(s):  
Phuong Nguyen ◽  
Melissa Young ◽  
Long Khuong ◽  
Reynaldo Martorell ◽  
Usha Ramakrishnan ◽  
...  
Keyword(s):  
Body Composition ◽  
Mean Squared Error ◽  
Resistance Index ◽  
Bioelectrical Impedance ◽  
Overweight And Obesity ◽  
Fat Free Mass ◽  
Future Research ◽  
Prediction Equations ◽  
Low Middle Income Countries ◽  
Specific Prediction

Abstract Objectives Bioelectrical impedance analysis (BIA) is an accurate, inexpensive and field-friendly methods to assess body composition, but there is limited information on its use and validity for children in low-middle income countries. Our aim was to develop and validate population-specific prediction equations for estimating total fat mass (FM) and fat free-mass (FFM) in Vietnamese children using reactance and resistance from BIA and anthropometric variables. Methods We conducted a cross-sectional survey of 120 children in Thai Nguyen, Vietnam. Body composition was measured using dual energy x-ray absorptiometry (DXA), BIA and anthropometry (height, weight, triceps and subscapular skinfolds, and waist, hip, and mid upper arm circumferences). To develop prediction equations, we split the sample into development (70%) and validation datasets (30%). The model performance was evaluated using PRESS (Predicted residual error sum of squares), RMSE (Root mean squared error), MEA (Mean absolute error) and R,2. Results %MCEPASTEBIN% The development of prediction equations for total FM resulted in seven models. We identified a top performing model with the least number of parameters (age, sex, weight and resistance index), low RMSE (178 and 164 for FM and FFM, respectively), low MAE (136 and 141 for FM and FFM, respectively), high R2 (>.90), and the least difference between predicted and actual values (FM 25 0.03 g and FFM 8 0.01 g). Conclusions We developed valid and highly predictive equations to estimate FM and FFM in Vietnamese children using BIA. These findings have important implications for future research examining the risks associated with overweight and obesity in young children in resource-poor settings. Funding Sources Rollins School of Public Health (RSPH) Dean's Pilot and Innovation Grant, the New Jersey Institute for Food, Nutrition, and Health, and the Nestle Foundation.

Development of population-specific prediction equations for bioelectrical impedance analyses in Vietnamese children

2020 ◽  
Vol 124 (12) ◽  
pp. 1345-1352 ◽  
Author(s):  
Phuong Hong Nguyen ◽  
Melissa F. Young ◽  
Long Quynh Khuong ◽  
Usha Ramakrishnan ◽  
Reynaldo Martorell ◽  
...  
Keyword(s):  
Body Composition ◽  
Young Children ◽  
Mean Squared Error ◽  
Resistance Index ◽  
Bioelectrical Impedance ◽  
Overweight And Obesity ◽  
Fat Free Mass ◽  
Future Research ◽  
Prediction Equations ◽  
Specific Prediction

AbstractThere is a need for accurate, inexpensive and field-friendly methods to assess body composition in children. Bioelectrical impedance analysis (BIA) is a promising approach; however, there have been limited validation and use among young children in resource-poor settings. We aim to develop and validate population-specific prediction equations for estimating total fat mass (FM), fat free-mass (FFM) and percentage body fat (PBF) in Vietnamese children (4–7 years) using reactance and resistance from BIA, anthropometric variables and demographic information. We conducted a cross-sectional survey of 120 children. Body composition was measured using dual-energy X-ray absorptiometry (DXA), BIA and anthropometry. To develop prediction equations, we split all data into development (70 %) and validation datasets (30 %). The model performance was evaluated using predicted residual error sum of squares, root mean squared error (RMSE), mean absolute error (MAE) and R2. We identified a top performing model with the least number of parameters (age, sex, weight and resistance index or resistance and height), low RMSE (FM 0·70, FFM 0·74, PBF 3·10), low MAE (FM 0·55, FFM 0·62, PBF 2·49), high R2 (FM 0·95, FFM 0·92, PBF 0·82) and the least difference between predicted values and actual values from DXA (FM 0·03 kg or 0·01 sd, FFM 0·06 kg or 0·02 sd, PBF 0·27 % or 0·04 sd). In conclusion, we developed the first valid and highly predictive equations to estimate FM, FFM and PBF in Vietnamese children using BIA. These findings have important implications for future research on the double burden of disease and risks associated with overweight and obesity in young children.

scholarly journals Developing an Impedance Based Equation for Fat-Free Mass of Black Preadolescent South African Children

Nutrients ◽  
2019 ◽  
Vol 11 (9) ◽  
pp. 2021
Author(s):  
Amanda van Zyl ◽  
Zelda White ◽  
Johan Ferreira ◽  
Friedeburg A. M. Wenhold
Keyword(s):  
Body Composition ◽  
South African ◽  
Bioelectrical Impedance ◽  
Fat Free Mass ◽  
Prediction Equations ◽  
X Ray ◽  
Black South African ◽  
African Children ◽  
New Equation ◽  
South African Children

Bioelectrical impedance analysis (BIA) is a practical alternative to dual-energy X-ray absorptiometry (DXA) for determining body composition in children. Currently, there are no population specific equations available for predicting fat-free mass (FFM) in South African populations. We determined agreement between fat-free mass measured by DXA (FFMDXA) and FFM calculated from published multi-frequency bioelectrical impedance prediction equations (FFMBIA); and developed a new equation for predicting FFM for preadolescent black South African children. Cross-sectional data on a convenience sample of 84 children (mean age 8.5 ± 1.4 years; 44 {52%} girls) included body composition assessed using Dual X-ray Absorptiometry (FFMDXA) and impedance values obtained from the Seca mBCA 514 Medical Body Composition analyzer used to calculate FFM using 17 published prediction equations (FFMBIA). Only two equations yielded FFM estimates that were similar to the DXA readings (p > 0.05). According to the Bland–Altman analysis, the mean differences in FFM (kg) were 0.15 (LOA: −2.68; 2.37) and 0.01 (LOA: −2.68; 2.66). Our new prediction equation, F F M = 105.20 + 0.807 × S e x + 0.174 × W e i g h t + 0.01 × R e a c t a n c e + 15.71 × log ( R I ) , yielded an adjusted R2 = 0.9544. No statistical shrinkage was observed during cross-validation. A new equation enables the BIA-based prediction of FFM in the assessment of preadolescent black South African children.

Assessment of Body Composition in Youths and Relationship to Sport

1996 ◽  
Vol 6 (2) ◽  
pp. 146-164 ◽  
Author(s):  
Linda B. Houtkooper
Keyword(s):  
Body Composition ◽  
Measurement Techniques ◽  
Bioelectrical Impedance ◽  
Fat Free Mass ◽  
Prediction Errors ◽  
Prediction Equations ◽  
Body Composition Assessment ◽  
Mineral Components ◽  
Assessment Techniques ◽  
Multiple Component

Body composition assessment techniques provide estimates of percent body fat (%BF), fat mass (FM), and fat-free mass (FFM) based on indirect assessment models and methods. Prediction equations for %BF developed using a two-component model based on adult body composition constants will overestimate %BF in youths, especially prepubescent youths. Body composition prediction equations that have been validated and cross-validated using multiple-component criterion models which include measurements of body density and the water and mineral components of FFM provide the most accurate means for assessment of body composition in youths. Use of appropriate prediction equations and proper measurement techniques, for either bioelectrical impedance or skinfolds, results in body composition estimates with standard errors of estimate (prediction errors) of 3 to 4% BF and 2.0 to 2.5 kg of FFM. Poor measurement technique and inappropriate prediction equations will result in much larger prediction errors.

Validation of a Multielectrode Bioelectrical Impedance Analyzer With a Dual-Energy X-Ray Absorptiometer for the Assessment of Body Composition in Older Adults

2020 ◽  
Vol 28 (4) ◽  
pp. 598-604
Author(s):  
Nathan F. Meier ◽  
Yang Bai ◽  
Chong Wang ◽  
Duck-chul Lee
Keyword(s):  
Older Adults ◽  
Body Composition ◽  
Bioelectrical Impedance ◽  
Dual Energy ◽  
Fat Free Mass ◽  
Percentage Error ◽  
Prediction Equations ◽  
X Ray ◽  
History Of ◽  
The Mean

Changes in body composition are related to mobility, fall risk, and mortality, especially in older adults. Various devices and methods exist to measure body composition, but bioelectrical impedance analysis (BIA) has several advantages. The purpose of this study was to validate a common BIA device with a dual-energy X-ray absorptiometer (DXA) in older adults and develop prediction equations to improve the accuracy of the BIA measurements. The participants were 277 older adults (162 women and 115 men; age 73.9 ± 5.8 years) without a history of cancer and without a history of severe medical or mental conditions. Individuals fasted 12 hr before BIA and DXA measurement. The correlations between the two methods for appendicular lean mass (ALM), fat-free mass (FFM), and percentage body fat (%BF) were .86, .93, and .92, respectively, adjusting for age and sex. The mean percentage error (DXA—InBody) and mean absolute percentage error were −12% and 13% for ALM, −13% and 13% for FFM, and 16% and 17% for %BF. The prediction equations estimated ALM, FFM, and %BF; sex was coded as 1 for male and 0 for female: Although highly correlated, BIA overestimated FFM, and ALM and underestimated %BF compared with DXA. An application of prediction equations eliminated the mean error and reduced the range of individual error across the sample. Prediction equations may improve BIA accuracy sufficiently to substitute for DXA in some cases.

Practical Body Composition Assessment for Children, Adults, and Older Adults

1998 ◽  
Vol 8 (3) ◽  
pp. 285-307 ◽  
Author(s):  
Vivian H. Heyward
Keyword(s):  
Older Adults ◽  
Body Composition ◽  
Standardized Testing ◽  
Bioelectrical Impedance ◽  
Impedance Analysis ◽  
Fat Free Mass ◽  
Clinical Settings ◽  
Prediction Equations ◽  
Body Composition Assessment ◽  
Testing Procedures

This paper provides an overview of practical methods for assessing body composition of children, adults, and older adults. Three methods commonly used in field and clinical settings are skinfolds, bioelectrical impedance analysis, and anthropometry. For each method, standardized testing procedures, sources of measurement error, recommendations for technicians, and selected prediction equations for each age category are presented. The skinfold method is appropriate for estimating body fat of children (6–17 years) and body density of adults (18–60 years) from diverse ethnic groups. Likewise, bioimpedance is well suited tor estimating the fat-free mass of children (10-19 years) as well as American Indian, black, Hispanic, and white adults. Anthropometric prediction equations that use a combination of circumferences and bony diameters are recommended for older adults (up to 79 years of age), as well as obese men and women.

scholarly journals Relationship between BMI and adiposity among different ethnic groups in 2-year-old New Zealand children

2019 ◽  
Vol 121 (6) ◽  
pp. 670-677 ◽  
Author(s):  
Mariam J. Buksh ◽  
Joanne E. Hegarty ◽  
Rebecca Griffith ◽  
Jane M. Alsweiler ◽  
Chris J. McKinlay ◽  
...  
Keyword(s):  
Body Composition ◽  
Body Size ◽  
New Zealand ◽  
Ethnic Groups ◽  
Bioelectrical Impedance ◽  
Overweight And Obesity ◽  
Fat Free Mass ◽  
Indian Children ◽  
The Relationship ◽  
Size At Birth

AbstractAge- and sex-based BMI cut-offs are used to define overweight and obesity, but the relationship between BMI and body composition has not been very well studied in children or compared between children of different ethnic groups. Body size and composition in childhood are also influenced by size at birth. Our aim was to compare body size and composition at 2 years in children with different ethnicity and size at birth. We prospectively followed a multi-ethnic cohort of 300 children born with risk factors for neonatal hypoglycaemia (infants of diabetics, large or small at birth or late preterm) to 2 years corrected age. Complete data on weight, height and head circumference and body composition using bioelectrical impedance 24±1 months corrected age were available in 209 children. At birth, compared with European children, Chinese, Indian and other ethnicity children were lighter, and Indian children had smaller head circumferences, but birth lengths were similar in all ethnic groups. At 2 years, Pacific children were heavier and had higher BMI z scores, and Indian children had smaller head circumferences and lower BMI z scores than those from other ethnic groups. However, fat mass and fat-free mass indices were similar in all groups. At median BMI, fat mass:fat-free mass ratio was 23 % lower in Pacific than in Indian children (0·22 v. 0·27, P=0·03). BMI is not a good indicator of adiposity in this multi-ethnic cohort of 2-year-old New Zealand children.

scholarly journals Muscular Strength, Power, and Endurance Adaptations after Two Different University Fitness Classes

Sports ◽  
2021 ◽  
Vol 9 (8) ◽  
pp. 107
Author(s):  
Brittany S. Hollerbach ◽  
Sarah J. Cosgrove ◽  
Justin A. DeBlauw ◽  
Nattinee Jitnarin ◽  
Walker S. C. Poston ◽  
...  
Keyword(s):  
College Students ◽  
Body Composition ◽  
Muscular Strength ◽  
Vertical Jump ◽  
Bioelectrical Impedance ◽  
Impedance Analysis ◽  
Future Research ◽  
Significant Group ◽  
Muscle Strengthening ◽  
Maintain Body Weight

Physical activity (PA) classes help college students add weekly PA, which can help improve health and maintain body weight. Traditional weight training (TWT) can improve strength and aerobic capacity. High-intensity functional training such as CrossFit® (CF) provides time-efficient workouts with both muscle strengthening and aerobic exercises. Limited research has compared these classes for college students. We examined changes in muscular strength, power, and endurance as well as body composition. Participants were 85 healthy college students enrolled in TWT (n = 36, age 22.6 ± 4.1 years, 72.2% male) or CF (n = 49, age 21.8 ± 3.2 years, 55.1% male) classes meeting twice/wk for 8 weeks between October 2017 and May 2018. Baseline and posttest measurements included a vertical jump, grip strength, a 2 min push-up test, a 1 min squat test, height, weight, and a bioelectrical impedance analysis. Although no significant group × time interactions were found, there was a significant main effect of time for push-ups and squats (both p < 0.001). Participants enjoyed the classes and most planned to continue. Both classes improved muscular endurance although no significant differences were found between them. Activity classes provide college students with an option for increasing their weekly PA and help maintain body composition. Future research should examine the benefits from longer or more frequent classes.

Comparison of energy expenditure, body composition, metabolic disorders, and energy intake between obese children with a history of craniopharyngioma and children with multifactorial obesity

2015 ◽  
Vol 28 (11-12) ◽  
Author(s):  
Ilanit Bomer ◽  
Carola Saure ◽  
Carolina Caminiti ◽  
Javier Gonzales Ramos ◽  
Graciela Zuccaro ◽  
...  
Keyword(s):  
Body Composition ◽  
Energy Expenditure ◽  
Energy Intake ◽  
Positive Association ◽  
Bioelectrical Impedance ◽  
Fat Free Mass ◽  
Control Group ◽  
Model Assessment ◽  
Obese Children ◽  
Mass Percentage

AbstractCraniopharyngioma is a histologically benign brain malformation with a fundamental role in satiety modulation, causing obesity in up to 52% of patients.To evaluate cardiovascular risk factors, body composition, resting energy expenditure (REE), and energy intake in craniopharyngioma patients and to compare the data with those from children with multifactorial obesity.All obese children and adolescents who underwent craniopharyngioma resection and a control group of children with multifactorial obesity in follow-up between May 2012 and April 2013.Anthropometric measurements, bioelectrical impedance, indirect calorimetry, energy intake, homeostatic model assessment insulin resistance (HOMA-IR), and dyslipidemia were evaluated.Twenty-three patients with craniopharyngioma and 43 controls were included. Children with craniopharyngioma-related obesity had a lower fat-free mass percentage (62.4 vs. 67.5; p=0.01) and a higher fat mass percentage (37.5 vs. 32.5; p=0.01) compared to those with multifactorial obesity. A positive association was found between %REE and %fat-free mass in subjects with multifactorial obesity (68±1% in normal REE vs. 62.6±1% in low REE; p=0.04), but not in craniopharyngioma patients (62±2.7 in normal REE vs. 61.2±1.8% in low REE; p=0.8). No differences were found in metabolic involvement or energy intake.REE was lower in craniopharyngioma patients compared to children with multifactorial obesity regardless of the amount of fat-free mass, suggesting that other factors may be responsible for the lower REE.

scholarly journals A specific bioelectrical impedance equation to predict body composition in Turner's syndrome

2010 ◽  
Vol 54 (1) ◽  
pp. 24-29 ◽  
Author(s):  
Alexis D. Guedes ◽  
Bianca Bianco ◽  
Mônica V. N. Lipay ◽  
Emmanuela Q. Callou ◽  
Marise L. Castro ◽  
...  
Keyword(s):  
Body Composition ◽  
Linear Regression Analysis ◽  
Bioelectrical Impedance ◽  
Impedance Analysis ◽  
Fat Free Mass ◽  
X Ray ◽  
Great Consistency ◽  
Total Fat ◽  
Specific Equation ◽  
Standard Error Estimate

INTRODUCTION: Cardiovascular disease is one of the main causes for Turner syndrome (TS) mortality and the evaluation of its risk factors such as excess body fat and its distribution is considered one of the major aspects of the adult patient care. OBJECTIVE: To develop and validate a specific bioelectrical impedance analysis (BIA) equation to predict body composition in TS patients. SUBJECTS AND METHODS: Clinical and anthropometric data, dual-energy X-ray absorptiometry (DXA) for total fat-free mass (FFM) and BIA for resistance and reactance were obtained from 50 adult TS patients. Linear regression analysis was performed with multiple clinical and BIA data to obtain a predicting equation. RESULTS: The equation developed to estimate FFM in adult TS patients showed great consistency with DXA, elevated correlation (r = 0. 974) and determination (r² = 0. 948) coefficients and an adequate standard error estimate (SEE = 1.52 kg). CONCLUSIONS: The specific equation developed here allowed making an adequate FFM estimate in adult TS patients.

scholarly journals Obesity, smoking status and their relationships in selected population groups

2021 ◽  
pp. 291-299
Keyword(s):  
Body Composition ◽  
Lipid Profile ◽  
Fat Mass ◽  
Severe Obesity ◽  
Overweight And Obesity ◽  
Fat Free Mass ◽  
Chronic Obstructive ◽  
Average Value ◽  
Copd Patients ◽  
The Mean

Background. Overweight and obesity are defined as abnormal or excessive fat accumulation that may impair health. Obesity is associated with many chronic diseases, including cardiovascular disease and diabetes, and recently the role of overweight and obesity in lung disease has received new interest. Chronic obstructive lung disease is the third-leading cause of death globally, and both obesity and diet appear to play roles in its pathophysiology. Cross-sectional studies have demonstrated an inverse association between obesity and the prevalence of chronic obstructive pulmonary disease (COPD). Objective. This study aims to evaluate the relationship between smoking, lipid profile and obesity (body composition changes) in a selected groups of population (30 non-smokers, 30 smokers and 60 COPD patients). Material and Methods. We evaluated fat mass, fat free mass, body mass index and lipid profile in a group of 120 randomly selected probands (60 COPD patients; 30 smokers without COPD; 30 non-smokers without COPD) to identify possible negative relationships of smoking to body composition. To the measurement of fat mass (FM) and fat free mass (FFM) was used a device Bodystat Quadscan 4000 (Bodystat Ltd, British Isles). The device works by using four-frequency bioelectrical impedance analysis. Laboratory parameters as total cholesterol (T-C), high-density cholesterol (HDL-C), low-density cholesterol (LDL-C) and triacylglycerols (TG) were investigated by automated clinical chemistry analyzer LISA 200th. The measured values were statistically processed and evaluated in a statistical program STATISTICA Cz. Version 7.1. (Kruskall-Wallis test). Results. A comparison of the mean fat mass we found statistically highly significant differences between the group of COPD patients and non-smokers (P <0.001) and insignificant differences (P ≥ 0.05) between the other groups of our experiment. A comparison of the mean fat mass values of all three groups of the experiment shows a steady increase in fat from non-smokers (17.66 ± 10.04 kg) to COPD patients with the highest mean value (25.08 ± 10.14 kg). In the group of COPD patients we recorded the lowest average value of FFM (51.76 ± 13.84 kg), in group of smokers the middle (56.06 ± 10.76 kg) and in non-smokers the highest average value of FFM (59.91 ± 9.90 kg) at relatively the same body weight in the groups. Based on calculated body mass index (BMI), we found in group of COPD patients overweight in 15 cases (25%), obesity in 7 patients (11.67%), severe obesity in 14 patients (23.3%) and morbid obesity in 2 patients (3.33%); in the group of smokers overweight in 16 cases (53.33%), obesity in 5 cases (16.6%) and severe obesity in 1 case (3.33%); in non-smokers we recording overweight in 14 cases (46.67%), obesity in 5 cases (16.67%) and severe obesity in 2 cases (6.67%). In the lipid profile of the monitored groups of probands, we observed statistically significant differences only for LDL cholesterol (LDL-C). There was a statistically significant difference (P <0.001) between the group with COPD and smokers, as well as between the group of smokers and non-smokers (P < 0.05). Conclusions. In the vast majority of patients with COPD, the lung damage that leads to COPD is caused by long-term cigarette smoking. The presence and absence of risk factors such as smoking, inappropriate lipid profile and obesity (amount of fat mass) in selected population groups were observed. Additional studies to explore both the quantitative and qualitative changes in body composition with disease process of COPD are required.

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