scholarly journals Body composition assessment using bioelectrical impedance analysis (BIA) in a wide cohort of patients affected with mild to severe obesity

2021 ◽  
Author(s):  
Amelia Brunani ◽  
Simone Perna ◽  
Davide Soranna ◽  
Mariangela Rondanelli ◽  
Antonella Zambon ◽  
...  
Keyword(s):  
Body Composition ◽  
Bioelectrical Impedance Analysis ◽  
Severe Obesity ◽  
Bioelectrical Impedance ◽  
Impedance Analysis ◽  
Body Composition Assessment

Techniques For Assessment Of Body Composition In Health

2018 ◽  
Author(s):  
Carla M Prado ◽  
Camila LP Oliveira ◽  
M Cristina Gonzalez ◽  
Steven B Heymsfield
Keyword(s):  
Body Composition ◽  
Nutritional Status ◽  
Bioelectrical Impedance Analysis ◽  
Bioelectrical Impedance ◽  
Impedance Analysis ◽  
Dual Energy ◽  
Composition Analysis ◽  
Body Composition Assessment ◽  
Air Displacement Plethysmography ◽  
X Ray

Body composition assessment is an important tool in both clinical and research settings able to characterize the nutritional status of individuals in various physiologic and pathologic conditions. Health care professionals can use the information acquired by body composition analysis for the prevention and treatment of diseases, ultimately improving health status. Here we describe commonly used techniques to assess body composition in healthy individuals, including dual-energy x-ray absorptiometry, bioelectrical impedance analysis, air displacement plethysmography, and ultrasonography. Understanding the key underlying concept(s) of each assessment method, as well as its advantages and limitations, facilitates selection of the method of choice and the method of the compartment of interest. This review contains 5 figures, 3 tables and 52 references Key words: air displacement plethysmography, bioelectrical impedance analysis, body composition, disease, dual-energy x-ray absorptiometry, health, muscle mass, nutritional status, obesity, sarcopenia, ultrasound fat mass

scholarly journals Bioelectrical impedance analysis of body composition: influence of a newly implanted cardiac device

2019 ◽  
Vol 8 (1) ◽  
pp. 60-65
Author(s):  
Luiz Wellington Pinto ◽  
Silvia Veloso Gandra ◽  
Matheus de Carvalho Alves ◽  
Isabel Gomes ◽  
Eduardo Back Sternick
Keyword(s):  
Body Composition ◽  
Real Time ◽  
Electromagnetic Interference ◽  
Bioelectrical Impedance Analysis ◽  
Bioelectrical Impedance ◽  
Impedance Analysis ◽  
Body Composition Assessment ◽  
Cardiac Device ◽  
Device Implantation ◽  
Cardiac Devices

Abstract Current guidelines do not recommend bioelectrical impedance analysis (BIA) in patients with implanted cardiac devices. There is no data on the influence of such devices over the parameters assessed by BIA. We aimed to assess the influence of cardiac devices on the parameters assessed by BIA as well as to evaluate the likelihood of electromagnetic interference of BIA in patients with implanted cardiac devices. Sixty-two consecutive patients over 18 years of age who underwent single (PM) or multisite (CRT) pacemaker or defibrillator (ICD) implantation were included. Body composition assessment was done using a single frequency device, on both right and left sides, before and after cardiac device implantation. During BIA analysis after device implantation, we did real-time telemetry to assess electromagnetic interference. Patients were 67+14 years old and 51.6% male. PM was implanted in 52 patients (83.9%), ICD in 7 (11.3%), ICD with CRT in 2 (3.2%) and CRT in 1 (1.6%). During real-time telemetry, there was no electromagnetic interference including interruption of telemetry. Default device programming did not change after BIA assessment. After surgery, resistance and fat mass were smaller, while cellular mass, fat-free mass, metabolic rate and total body water/ body weight increased, on right and left sides measurements. We concluded that decreased resistance and related parameters after device implantation were probably influenced to a change in hydration status, regardless of the implanted device. Bioimpedance analysis is safe in patients with an implanted cardiac device.

scholarly journals Development and Validation of Bioelectrical Impedance Analysis Equations in Adolescents with Severe Obesity

2019 ◽  
Vol 149 (7) ◽  
pp. 1288-1293 ◽  
Author(s):  
Alissa Steinberg ◽  
Cedric Manlhiot ◽  
Ping Li ◽  
Emma Metivier ◽  
Paul B Pencharz ◽  
...  
Keyword(s):  
Body Composition ◽  
Bioelectrical Impedance Analysis ◽  
Severe Obesity ◽  
Validation Cohort ◽  
Bioelectrical Impedance ◽  
Impedance Analysis ◽  
Fat Free Mass ◽  
Predictive Equation ◽  
Predictive Equations ◽  
Squared Prediction Error

ABSTRACT Background Body mass index measures excess weight for size, and does not differentiate between fat mass (FM) and fat-free mass (FFM). Bioelectrical impedance analysis (BIA) is most commonly used to assess FM and FFM as it is simple and inexpensive. Variables from BIA measurements are used in predictive equations to estimate FM and FFM. To date, these equations have not been validated for use in adolescents with severe obesity. Objectives In a cohort of adolescents with severe obesity (SO), a BMI ≥ 120% of the 95th percentile, this study aimed to 1) derive a BIA predictive equation data from air displacement plethysmography (ADP) measurements; 2) reassess the equation in a second validation cohort; and 3) compare the accuracy of existing body composition equations. Methods Adolescents with SO were assessed using ADP and BIA. FM values derived from ADP measurements from the first cohort (n = 27) were used to develop a BIA predictive equation (i.e., Hamilton). A second cohort (n = 65) was used to cross-validate the new and 9 existing BIA predictive equations. Results Ninety-two adolescents (15.8 ± 1.9 y; BMI: 46.1 ± 9.9 kg/m2) participated. Compared with measured FFM using ADP: 1) the Lazzer, Hamilton, Gray, and Kyle equations were without significant bias; 2) the Hamilton and Gray equations had the smallest absolute and relative differences; 3) the Kyle and Gray equations showed the strongest correlation; 4) the Hamilton equation most accurately predicted FFM within ± 5% of measured FFM; and 5) 8 out of 9 equations had similar root mean squared prediction error values (6.03–6.64 kg). Conclusion The Hamilton BIA equation developed in this study best predicted body composition values for groups of adolescents with severe obesity in a validation cohort.

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