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Background Bioelectrical impedance analysis (BIA) is a rapid and user-friendly technique for assessing body composition in sports. Currently, no sport-specific predictive equations are available, and the utilization of generalized formulas can introduce systematic bias. The objectives of this study were as follows: (i) to develop and validate new predictive models for estimating fat-free mass (FFM) components in male elite soccer players; (ii) to evaluate the accuracy of existing predictive equations. Methods A total of 102 male elite soccer players (mean age 24.7 ± 5.7 years), participating in the Italian first league, underwent assessments during the first half of the in-season period and were randomly divided into development and validation groups. Bioelectrical resistance (R) and reactance (Xc), representing the bioimpedance components, were measured using a foot-to-hand BIA device at a single frequency of 50 kHz. Dual-energy X-ray absorptiometry was employed to acquire reference data for FFM, lean soft tissue (LST), and appendicular lean soft tissue (ALST). The validation of the newly developed predictive equations was conducted through regression analysis, Bland–Altman tests, and the area under the curves (AUC) of regression receiver operating characteristic (RROC) curves. Results Developed models were: FFM = − 7.729 + (body mass × 0.686) + (stature 2 /R × 0.227) + (Xc × 0.086) + (age × 0.058), R 2  = 0.97, Standard error of estimation (SEE) = 1.0 kg; LST = − 8.929 + (body mass × 0.635) + (stature 2 /R × 0.244) + (Xc × 0.093) + (age × 0.048), R 2  = 0.96, SEE = 0.9 kg; ALST = − 24.068 + (body mass × 0.347) + (stature 2 /R × 0.308) + (Xc × 0.152), R 2  = 0.88, SEE = 1.4 kg. Train-test validation, performed on the validation group, revealed that generalized formulas for athletes underestimated all the predicted FFM components (p < 0.01), while the new predictive models showed no mean bias (p > 0.05), with R 2 values ranging from 0.83 to 0.91, and no trend (p > 0.05). The AUC scores of the RROC curves indicated an accuracy of 0.92, 0.92, and 0.74 for FFM, LST, and ALST, respectively. Conclusions The utilization of generalized predictive equations leads to an underestimation of FFM and ALST in elite soccer players. The newly developed soccer-specific formulas enable valid estimations of body composition while preserving the portability of a field-based method.

Background/Objectives: Bioelectrical impedance vector analysis (BIVA) is increasingly used in clinical research to assess soft tissue hydration. It is known that physical characteristics of electrodes, such as low intrinsic impedance, low electrode/skin contact impedance and type of gel, affect the reliability of noninvasive bioimpedance assessments. The aim of this study was to determine the effect of intrinsic impedance of electrode on the bioimpedance vector displacement in RXc graph. Subjects/Methods: The intrinsic impedance is measured in nine pregelled disposable Ag/AgCl electrodes usually used for bioimpedance measures. The BIVA method is performed on 35 healthy volunteers using a 50 kHz phase-sensitive bioimpedance analyzer (BIA 101 Anniversary) with the lowest intrinsic impedance electrode and highest. The individual bioimpedance vector is plotted on the bivariate normal interval of reference population. The differences in the mean bioimpedance vectors obtained with each electrode are plotted, with their 95% confidence ellipses, on the dRXc graph. The paired one-sample Hotelling's T 2 -test is used to compare the differences of the mean bioimpedance vectors. Results: We found large variability in intrinsic resistance (11–665 Ω) and reactance (0.25–2.5 Ω) values of the electrodes analyzed and significant displacement ( P <0.05) of bioimpedance vector positions in healthy adults according to the paired one-sample Hotelling's T 2 -test. Conclusions: A robust study of all physical characteristics of commercial Ag/AgCl electrodes is necessary to reach consensus on pregelled Ag/AgCl electrodes valid for bioimpedance measurement. This information will enable BIVA users to avoid systemic errors when performing BIVA assessments, specifically when these measurements are used for clinical interpretations.

Background and Objectives: South Asians are known to have excess adiposity at a lower body mass index, with truncal fat accumulation. Whether this confers higher risk to develop severe COVID-19 is not known. This study evaluated body mass index, body fat mass and waist circumference as risk factors for COVID-19 severity and its progression, in South Asian adults. Methods and Study Design: Details of COVID-19 patients (19-90 years) were obtained prospectively, along with weight, height, waist circumference and body fat mass assessed by bioelectrical impedance analysis. Binomial logistic and Poisson regression were performed to test associations between waist circumference, body fat mass and body mass index to evaluate the adjusted OR or relative risk for disease severity at admission and length of stay. Results: After adjusting for age, sex, height and co-morbidities, body mass index >23 kg/m2 (adjusted OR 2.758, 95% CI 1.025, 7.427), waist circumference (adjusted OR 1.047, 95% CI 1.002, 1.093) and body fat mass (adjusted OR 1.111, 95% CI 1.013, 1.219) were associated with a significant risk for disease severity at admission, while only waist circumference (adjusted relative risk 1.004, 95% CI 1.001, 1.008), and body fat mass (adjusted relative risk 1.011, 95% CI 1.003, 1.018), were associated with a significantly longer length of stay. Conclusions: Body mass index, at a lower cut-off of >23 kg/m2, is a significant risk factor for COVID-19 disease severity in the group of patients studied. The waist circumference and body fat mass are also good indicators for both severity at admission and length of stay.

Background/Objectives: Bioelectrical impedance analysis (BIA) is one of the most affordable and feasible body composition assessment techniques for clinical and field settings. However, it is important to use an equation appropriate for the study population. This study aimed to propose and validate prediction equations to estimate body composition using BIA for Indonesian men. Subjects/Methods: Total body water (TBW), fat-free mass (FFM) and fat mass (FM) were determined using the deuterium dilution technique in 292 Indonesian males. Participants were divided equally into development and validation groups to develop prediction equations and to cross-validate the proposed prediction equations, respectively. In addition, selected prediction equations using BIA were cross-validated. Results: The proposed BIA equations were valid in our cross-validation samples. The best performance equations obtained from the absolute measure of body composition (TBW, FFM and FM) showed that r ranged between 0.89 and 0.91 and standard error of the estimate=1.8–2.6 kg. Cross-validation analysis indicated that the proposed equations had a bias of 0.1–0.3 kg, pure error of 1.3–1.8 kg and limits of agreement (mean difference±1.96 s.d.) of −0.26 to 0.13±4.09 to 5.59 kg. Among existing prediction equations examined, those by Deurenberg et al. (1989) and Lukaski et al. (1987) significantly overestimated FM by 4.0 and 3.2 kg, respectively, whereas the equation by Deurenberg et al. (1991) significantly ( P <0.001) underestimated FFM by 5.0 kg compared with the reference FFM. Conclusions: The new BIA prediction equations may provide more precise and accurate estimation of body composition in Indonesian men than the existing equations.

Introduction. The height-weight difference index (HWDI) is a new indicator for evaluating obesity status. While body-fat percentage (BF%) is considered to be the most accurate obesity evaluation tool, it is a more expensive method and more difficult to measure than the others. Objective. Our objectives were to find the relationship between HWDI and BF% and to find a BF% prediction model from HWDI in relation to age and gender. Method. Bioelectrical impedance analysis was used to measure BF% in 2,771 healthy adult Thais. HWDI was calculated as the difference between height and weight. Pearson’s correlation coefficient was used to assess the relationship between HWDI and BF%. Multiple linear and nonlinear regression analysis were used to construct the BF% prediction model. Results. HWDI and BF% were found to be inverse which related to a tendency toward a linear relationship. Results of a multivariate linear regression analysis, which included HWDI and age as variables in the model, predicted BF% to be 34.508 − 0.159 (HWDI) + 0.161 (age) for men and 53.35 − 0.265 (HWDI) + 0.132 (age) for women. Conclusions. The prediction model provides an easy-to-use obesity evaluation tool that should help awareness of underweight and obesity conditions.

Body composition is acknowledged as a determinant of athletic health and performance. Its assessment is crucial in evaluating the efficiency of a diet or aspects related to the nutritional status of the athlete. Despite the methods traditionally used to assess body composition, bioelectric impedance analysis (BIA) and bioelectric impedance vector analysis (BIVA) have recently gained attention in sports, as well as in a research context. Only until recently have specific regression equations and reference tolerance ellipses for athletes become available, while specific recommendations for measurement procedures still remain scarce. Therefore, the present narrative review summarizes the current literature regarding body composition analysis, with a special focus on BIA and BIVA. The use of specific technologies and sampling frequencies is described, and recommendations for the assessment of body composition in athletes are provided. Additionally, the estimation of body composition parameters (i.e., quantitative analysis) and the interpretation of the raw bioelectrical data (i.e., qualitative analysis) are examined, highlighting the innovations now available in athletes. Lastly, it should be noted that, up until 2020, the use of BIA and BIVA in athletes failed to provide accurate results due to unspecific equations and references; however, new perspectives are now unfolding for researchers and practitioners. In light of this, BIA and especially BIVA can be utilized to monitor the nutritional status and the seasonal changes in body composition in athletes, as well as provide accurate within- and between-athlete comparisons.

Background. Bariatric surgery is considered an effective option for the management of morbid obesity. The incidence of obesity has been gradually increasing all over the world reaching epidemic proportions in some regions of the world. Obesity can cause a reduction of up to 22% in the life expectancy of morbidly obese patients. Objective. The objective of this paper is to assess the weight loss associated with the first 6 months after bariatric surgery using bioelectric impedance analysis (BIA) for the evaluation of fat mass and fat-free mass. Method. A total of 36 morbidly obese patients were subjected to open gastric bypass surgery. The patients weight was monitored before and after the procedure using the bioelectric impedance analysis. Results. Bariatric surgery resulted in an average percentage of weight loss of 28.6% (40 kg) as determined 6 months after the procedure was performed. Analysis of the different components of body weight indicated an undesirable loss of fat-free mass along with the reduction of total body weight. Conclusion. Open gastric bypass induced a significant loss of total weight and loss of fat-free mass in patients six months after the surgery. The use of bioelectric impedance analysis resulted in an appropriate estimation of the total weight components in individuals subjected to bariatric surgery allowing a more real analysis of the variation of weight after the surgery.

This study aims to compare the lesion characteristics of v-HPSD and conventional PC ablation modes and to quantitatively evaluate their dependence on impedance variation. The study was performed using an ex vivo porcine heart model. Ablations in the v-HPSD group were carried out with a QDOT-MICRO[TM] catheter, whereas conventional power-control (PC) ablations employed a Thermocool-STSF® catheter, set at a fixed power of 40 W and terminated upon reaching an ablation index of 500. To study the effect of baseline impedance, it was dynamically adjusted to low, medium, and high preset levels by incorporating a sliding rheostat in series with the ablation circuit. Ablation lesions in the PC group were characterized by significantly smaller superficial and maximum diameters but greater depth compared to the v-HPSD group (Superficial diameter: 5.19±0.71 mm vs. 5.83±0.73 mm, p<0.01; Maximum diameter: 7.04±0.95 mm vs. 7.79±0.96 mm, p<0.01; Depth: 4.67±0.22 mm vs. 3.50±0.40 mm, p<0.01). Although the mean lesion volume was larger in the PC group, this difference did not reach statistical significance (74.08±30.02 mm³ vs. 65.98±21.99 mm³, p=0.097). The v-HPSD mode exhibits superior adaptability, achieving comparable overall lesion volume in high-impedance settings by generating wider and depth-stable lesions, which underscores its enhanced performance predictability.

Objective This study aims to compare the lesion characteristics of v-HPSD and conventional PC ablation modes and to quantitatively evaluate their dependence on impedance variation. Methods The study was performed using an ex vivo porcine heart model. Ablations in the v-HPSD group were carried out with a QDOT-MICRO[TM] catheter, whereas conventional power-control (PC) ablations employed a Thermocool-STSF® catheter, set at a fixed power of 40 W and terminated upon reaching an ablation index of 500. To study the effect of baseline impedance, it was dynamically adjusted to low, medium, and high preset levels by incorporating a sliding rheostat in series with the ablation circuit. Results Ablation lesions in the PC group were characterized by significantly smaller superficial and maximum diameters but greater depth compared to the v-HPSD group (Superficial diameter: 5.19±0.71 mm vs. 5.83±0.73 mm, p<0.01; Maximum diameter: 7.04±0.95 mm vs. 7.79±0.96 mm, p<0.01; Depth: 4.67±0.22 mm vs. 3.50±0.40 mm, p<0.01). Although the mean lesion volume was larger in the PC group, this difference did not reach statistical significance (74.08±30.02 mm³ vs. 65.98±21.99 mm³, p=0.097). The ablation mode and impedance level demonstrated a significant interaction, which differentially affected the lesion parameters: highly significant for maximum depth and mean volume (P < 0.001), significant for superficial diameter (P < 0.05), and non-significant for maximum diameter (P = 0.587). Conclusion The v-HPSD mode exhibits superior adaptability, achieving comparable overall lesion volume in high-impedance settings by generating wider and depth-stable lesions, which underscores its enhanced performance predictability. Keywords: Radiofrequency ablation, Power control(PC), Very high Power short duration(v-HPSD), Baseline impedance, Lesion size

The appropriate use of predictive equations in estimating body composition through bioelectrical impedance analysis (BIA) depends on the device used and the subject’s age, geographical ancestry, healthy status, physical activity level and sex. However, the presence of many isolated predictive equations in the literature makes the correct choice challenging, since the user may not distinguish its appropriateness. Therefore, the present systematic review aimed to classify each predictive equation in accordance with the independent parameters used. Sixty-four studies published between 1988 and 2023 were identified through a systematic search of international electronic databases. We included studies providing predictive equations derived from criterion methods, such as multi-compartment models for fat, fat-free and lean soft mass, dilution techniques for total-body water and extracellular water, total-body potassium for body cell mass, and magnetic resonance imaging or computerized tomography for skeletal muscle mass. The studies were excluded if non-criterion methods were employed or if the developed predictive equations involved mixed populations without specific codes or variables in the regression model. A total of 106 predictive equations were retrieved; 86 predictive equations were based on foot-to-hand and 20 on segmental technology, with no equations used the hand-to-hand and leg-to-leg. Classifying the subject’s characteristics, 19 were for underaged, 26 for adults, 19 for athletes, 26 for elderly and 16 for individuals with diseases, encompassing both sexes. Practitioners now have an updated list of predictive equations for assessing body composition using BIA. Researchers are encouraged to generate novel predictive equations for scenarios not covered by the current literature. Registration code in PROSPERO : CRD42023467894.