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The present systematic review aimed to compare the accuracy of Bioelectrical Impedance Analysis (BIA) and Bioelectrical Impedance Vector Analysis (BIVA) vs. reference methods for the assessment of body composition in athletes. Studies were identified based on a systematic search of internationally electronic databases (PubMed and Scopus) and hand s...
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Background
We aimed to explore the potential association of body composition parameters measured by bioelectrical impedance analysis (BIA) with the incidence of sarcopenia in patients with acute myeloid leukemia (AML) (non-M3) after chemotherapy.
Patients and Methods
This was a single-center observational study. Sixty-nine patients with newly diag...
Citations
... Based on the principles of human biomechanics, combined with the characteristics of athletes' training actions, the motion parameters and characteristic indicators of various muscle groups of the human body are determined, and the MSIF technology is used to establish the multi-group athletes' motion data model [9,10]. 3) The training plan and implementation of athletes' muscle strength control The athlete muscle strength control training program consists of two parts, namely, the athlete data collection system and the athlete MSIF model [11]. The athlete data collection system is composed of training database and athlete multisensor data collection platform. ...
With the booming development of competitive sports worldwide, athletic training is receiving increasing interest in the world. Major sports organizations and universities around the world have established their own athlete training centers to support sports training and scientific research activities in recent years. Data from strength training is crucial for controlling muscle strength. However, this key factor is often attacked by the network. As NS threats escalate, artificial intelligence-driven strength training systems encounter information security risks. Therefore, this paper proposed a new strength training method based on NS and Multi-Source Information Fusion (MSIF). This method evaluates athletes’ sports skills, speed quality and strength quality through data fusion algorithm to effectively monitor the activities related to muscle strength control training. The research results showed that under the same conditions, the P value of the indexes of sports skills, speed quality and strength quality of male and female athletes in Group X before and after the experiment was greater than 0.05, and there was no significant difference; the P value of Group Y was less than 0.05, showing a significant difference, and indicating that the relationship between NS and MSIF and athletes’ muscle strength control training was positive.
... In relation to BIA, it is important to mention that it can be conducted using four different technologies (e.g., leg-to-leg, handto-hand, foot-to-hand, and standing position) (16) and with the subject in different measurement positions (i.e., standing, supine, or sitting). This is relevant given that the fat mass results reported by the BIA may depend on these factors, although these aspects in relation to the technology and position are often not taken into account to assess their accuracy and validity (17). ...
... However, standing BIA in most cases does not provide electrical conductivity data, i.e., raw bioimpedance parameters (20). Therefore, the equations that applies this type of device to estimate fat mass and the reference values it uses are exclusively those included in the software of the specific BIA model being used (16,21). ...
Introduction
Assessment of fat mass has historically employed various methods like Dual-energy X-ray Absorptiometry (DXA), and bioelectrical impedance (BIA), and anthropometry with its set of formulas. However, doubts persist regarding their validity and interchangeability to evaluate fat mass. This research aimed to determine the validity of anthropometry, and BIA in estimating fat mass Vs DXA, considering the influence of sex and hydration status.
Methods
A descriptive, cross-sectional study included 265 young adults (161 males and 104 females), assessed through DXA, BIA in a standing position, and anthropometry. A fat mass estimation formula with DXA, a fat mass estimation formula with BIA and 10 fat mass estimation formulas with anthropometry were calculated.
Results
Significant differences were found across DXA, BIA and anthropometry in both kilograms and percentages for the overall sample (p<0.001), and when the covariable sex was included (p<0.001), with no significant effect of hydration status (p=0.332-0.527). Bonferroni-adjusted analyses revealed significant differences from DXA with anthropometry and BIA in most cases for the overall sample (p<0.001), as well as when stratified by sex (p<0.001–0.016). Lin’s coefficient indicated poor agreement between most of the formulas and methods both in percentage and kilograms of fat mass (CCC=0.135–0.892). In the Bland-Altman analysis, using the DXA fat mass values as a reference, lack of agreement was found in the general sample (p<0.001-0.007), except for Carter’s formula in kilograms (p=0.136) and percentage (p=0.929) and Forsyth for percentage (p=0.365). When separating the sample by sex, lack of agreement was found in males for all methods when compared with both percentage and kilograms calculated by DXA (p<0.001). In the female sample, all methods and formulas showed lack of agreement (p<0.001–0.020), except for Evans’s in percentage (p=0.058).
Conclusion
The formulas for fat mass assessment with anthropometry and BIA may not be valid with respect to the values reported with DXA, with the exception of Carter’s anthropometry formula for general sample and Evans’s anthropometry formula for female sample. BIA could also be an alternative if what is needed is to assess fat mass in women as a group.
... Starting from the relationships between resistance and reactance with TBW [19], numerous BIA-based predictive equations have been developed over the years [20]. Previous studies have demonstrated that the use of different BIA technologies (i.e., hand to hand, leg to leg, foot to hand, and segmental) and sampling frequencies results in different outputs, so that these equations cannot be interchangeable between different devices [21][22][23][24]. Additionally, the choice of the equation should be made considering the subjects' characteristics, such as chronological and biological age, geographical provenance, sex, health status, and level of physical activity [25][26][27]. ...
... Therefore, the choice of the appropriate predictive equation is crucial to ensure the validity of the body composition estimation. A further question is that various studies including those providing reference data based on BIA do not disclose the procedures used [24,28]. Many of these studies merely mention the type of software employed, making it impossible to discern which formulas were used to convert raw bioelectrical parameters into components of body mass. ...
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.
... This underscores how monitoring FFM alone can mask substantial changes, akin to the situation when observing overall body weight without dissecting it into its relevant components [25]. Advancements in BIA include the production of new device technologies enabling measurements in a standing position, or analyzers integrated into joysticks (hand-to-hand) and home scales (leg-to-leg) [27,28]. Unfortunately, the use of different technologies results in different outputs, thus preventing the comparison of data obtained from different devices. ...
... The list of procedures that delineate a proper BIA begins with an extensive description of device technology and the raw measured parameters. These procedures require the selection of specific predictive equations tailored to the subject under examination, and reporting them is crucial not only for ensuring the validity of the outputs but also for enabling future comparisons, given that each equation can yield different results [28,31]. For these reasons, evaluating raw parameters through BIVA, in conjunction with body composition estimations, can facilitate a more comprehensive and qualitative analysis [32••, 55]. ...
Purpose of the Review
The use of bioelectrical impedance analysis (BIA) for monitoring body composition during the ketogenic diet has experienced a rapid surge. This scoping review aimed to assess the validity of procedures applying BIA in the ketogenic diet and to suggest best practices for optimizing its utilization.
Recent Findings
We conducted a systematic scoping review of peer-reviewed literature involving BIA for assessing body composition in individuals adhering to a ketogenic diet. Searches of international databases yielded 1609 unique records, 72 of which met the inclusion criteria and were reviewed. Thirty-five studies used foot-to-hand technology, 34 used standing position technology, while 3 did not declare the technology used. Raw bioelectrical parameters were reported in 21 studies. A total of 196 body mass components were estimated, but predictive equations were reported in only four cases.
Summary
Most research on BIA during ketogenic diets did not report the equations used for predicting body composition, making it impossible to assess the validity of BIA outputs. Furthermore, the exceedingly low percentage of studies reporting and analyzing raw data makes it challenging to replicate methodologies in future studies, highlighting that BIA is not being utilized to its full potential. There is a need for more precise technology and device characteristics descriptions, full report of raw bioelectrical data, and predictive equations utilized. Moreover, evaluating raw data through vectorial analysis is strongly recommended. Eventually, we suggest best practices to enhance BIA outcomes during ketogenic diets.
... This bioimpedance system uses an electric current with a frequency of 50 kHz that measures the amount of intracellular and extracellular water. This system estimates values referring to total body fat and lean mass, appendicular lean mass, and body mass, subsequently for analysis [40,41]. ...
Encouraging healthy aging in postmenopausal women involves advocating for lifestyle modifications, including regular physical exercise like combined training (CT) and functional training (FT). Regarding this population, age-related alterations in body composition, such as decreased muscle mass and heightened adipose tissue, impact health. The aim of this study was to analyze the effects of FT and CT on body recomposition in postmenopausal women. About the methods, we randomly allocated 96 post-menopausal women to the FT, CT, or control group (CG). We measured body composition by bioimpedance and lower limb muscle strength by sit-to-stand test in five repetitions, respectively. The training protocol lasted 16 weeks, and we measured body composition and lower limb muscle strength every 4 weeks, totaling five assessments. Regarding results, we notice that both training groups increased lean mass from the 8th week of training. In addition, a reduction was observed in total fat percentage and an increase in appendicular lean mass from the 12th week of intervention. No differences were found for body mass. Furthermore, only the experimental groups increase muscle strength, starting from the 4th week of training. The conclusion was that FT and CT promote similar adaptations in body recomposition without affecting body mass in postmenopausal women.
... In the classic BIVA approach, adjustments are made for stature to reduce the effect of conductor length, representing a valid method for assessing body fluids. In contrast, in the specific BIVA approach, adjustments are made for stature and cross-sectional areas (arms, trunk, and legs) to reduce the effect of body volume, representing the percentage of fat mass (%FM) [11,12]. Therefore, the height-adjusted vector length (Z/H) is inversely related to total body water (TBW) [8], and specific vector length (Zsp) is positively correlated with %FM [13]. ...
Objectives: CrossFit® is a high-intensity sport characterized by various workouts that require strength, speed, endurance, or agility, impacting participants' body composition. This observational study aimed to determine the morphological (anthropometrical and bioelectrical) profile of CrossFit® athletes and to compare them with other athletic populations. Methods: Anthropometrical measurements and bioelectrical vector analysis (classic and specific approaches) were performed on 145 CrossFit® practitioners (107 men aged 30.7 ± 8.4 years and 38 women aged 28.1 ± 6.7 years). Each participant's relative somatotype was calculated and compared between sexes and with a Spanish CrossFit® athletes' group. Resistance-reactance graphs and Hotelling's T2 test were applied to characterize the sample, compare them with an athletes' reference population, and identify differences between somatotype groups. Results: The most represented somatotype in both groups was the balanced mesomorph (male 3.5-5.2-1.7 and female 4.4-4.5-1.8). Compared with Spanish CrossFit® athletes, significant differences were denoted for men but not women (SAD = 2.3). The bioelectrical graphs indicated that the distribution of CrossFit® athletes is quite heterogeneous and within average values for the athlete's reference. The mesomorphic and endomorphic components were associated with a higher phase angle. Conclusions: CrossFit® practitioners predominantly present a mesomorphic component and show a body type like other power athletes, although with less pronounced characteristics. The so-matotype may influence the vector's position in the RXc graphs. This study provided the bioelectrical tolerance ellipses for CrossFit® practitioners in classic and specific approaches for the first time.
... As mentioned, assessing FM has become critical to clinical practice because the excessive accumulation of body fat has been associated with cardiovascular disease and other chronic diseases, including dyslipidemia and type 2 diabetes [24][25][26] . However, according to a recent systematic review [19] , BIA frequently under-or overestimated FM percentage (%FM) in athletes, which led the authors to conclude that BIA should be used to assess body composition in athletes only with foot-tohand technology and using predictive equations specifically developed for athletes. ...
... A recent systematic review performed by Campa et al. [19] regarding the validation of BIA devices versus reference methods in the assessment of body composition in athletes drew the same conclusions: inbuilt equations of most BIA devices overestimated or underestimated %FM. This prompted the authors to conclude that, regardless of the BIA technology, the estimated %FM did not align with the reference methods. ...
... Bioimpedance analysis (BIA) is a safe, fast, non-invasive, and cost-effective technique used to estimate body composition in clinical practice and population studies [17]. The BIA operates on the principle of passing a low-intensity alternating electric current (approximately 800 µA) at 50 kHz through the human body, which travels at different speeds depending on its composition. ...
... In contrast, a higher percentage of fat mass is associated with conditions such as diabetes, cardiovascular disease, certain types of cancer, and physical disability [27][28][29][30]. A phase angle value lower than 4.9 • is associated with a condition known as sarcopenia [17,[31][32][33], characterized by a quantitative and qualitative deficiency in muscle tissue. This phenomenon has also been observed in the pre-transplant period, for example, in liver cirrhosis [34], kidney transplantation, and in heart failure [35]. ...
It is widely demonstrated that moderate-intensity exercise is associated with improved fitness in non-communicable chronic diseases. However, there are no specific guidelines available for transplant recipients. Body composition is closely linked to exercise capacity, typically estimated by cardiopulmonary testing, but its potential correlation with cardiovascular health outcomes has not been investigated yet. This study aims to evaluate and compare cardiorespiratory performance and body composition in two groups of liver and kidney transplant recipients. A mixed group of transplant recipients (10 kidney and 15 liver) participating in a lifestyle reconditioning program through unsupervised physical exercise prescription was examined. Both groups were assessed using bioimpedance analysis (BIA), lifestyle, and physical activity levels by IPAQ questionnaire and cardiopulmonary testing (CPET). The two groups differed by IPAQ examination: liver transplant patients practiced more physical activity. Statistically significant differences were found in peak VO 2 /HR (oxygen pulse), which was higher in the kidney group compared to the liver group (15.63 vs. 12.49 with p < 0.05). Body composition did not show significant differences in BMI and the percentage of FM/FFM (FFM: 78.04 ± 7.7 in Kidney T vs. 77.78 ± 7.2 in Liver T). Systolic pressure measured at the peak was significantly higher in the liver group (162.6 vs. 134 with p < 0.01). The correlation between the CPET and BIA parameters showed a positive VO 2 max and FFM mass trend. The results suggest differences in cardiorespiratory fitness between the two populations of solid organ transplant recipients despite not being related to the physical activity level. The data support the importance of body composition analysis in sports medicine and the prescription of physical exercise, especially considering the potential correlation with VO 2 max, even though home-based exercise does not seem to alter it substantially. BMI does not appear to be a determinant of cardiovascular performance. Other determinants should be investigated to understand the differences observed.
... New methods that provide hydration status safely, accurately, reliably, and feasibly are also needed. In this sense, bioelectrical impedance analysis (BIA) is a technique for this specific context that offers both whole-body and segmental analysis (24) . This method utilizes the components of impedance [resistance (R) and reactance (Xc)], also providing phase angle (PhA), a relevant indicator of cellular health and muscle functionality (25; 26; 27) . ...
We aim to understand the effects of hydration changes on athletes' neuromuscular performance, on body water compartments, fat-free mass hydration, and hydration biomarkers; and to test the effects of the intervention on the response of acute dehydration in the hydration indexes.
The H2OAthletes study (clinicaltrials.gov ID: NCT05380089) is a randomized controlled trial in 38 national/international athletes of both sexes with low total water intake (WI) (i.e., <35.0mL/kg/day). In the intervention participants will be randomly assigned to the control (CG, N=19) or experimental group (EG, N=19). During the 4-day intervention, WI will be maintained in the CG and increased in the EG (i.e., >45.0mL/kg/day). Exercise-induced dehydration protocols with thermal stress will be performed before and after the intervention. Neuromuscular performance (knee extension/flexion with electromyography and handgrip), hydration indexes (serum, urine, and saliva osmolality), body water compartments and water flux (dilution techniques, body composition (4-compartment model), and biochemical parameters (vasopressin and sodium) will be evaluated.
This trial will provide novel evidence about the effects of hydration changes on neuromuscular function and hydration status in athletes with low WI, providing useful information for athletes and sports-related professionals aiming to improve athletic performance.
... R, Xc, and Z were adjusted by height (R/H, Xc/H, Z/H). According to classic BIVA, Z/H is inversely related to total body water (TBW) [32]. In contrast, vector direction indicates cellular health and cell membrane integrity and is inversely related to the extracellular/intracellular water (ECW/ICW) ratio [33]. ...
1) Background: Cardiovascular disease is one of the leading causes of mortality after liver transplantation. Body composition and cardiovascular performance assessment represent a potential approach for modulating lifestyle correction and proper follow-up in chronic disease patients. This study aimed to verify the additional role of an unsupervised physical activity program in a sample of male liver transplant recipients who follow the Mediterranean diet. (2) Methods: Thirty-three male liver transplant recipients were enrolled. Sixteen subjects followed a moderate-intensity home exercise program in addition to nutritional support, and seventeen received advice on the Mediterranean diet. After six months, bioelectrical vector impedance analysis (BIVA) and cardiopulmonary exercise testing (CPET) were performed. (3) Results: No differences in CPET (VO2 peak: exercise 21.4 ± 4.1 vs. diet 23.5 ± 6.5 mL/kg/min; p = 0.283) and BIVA (Z/H: exercise 288.3 ± 33.9 vs. diet 310.5 ± 34.2 Ω/m; p = 0.071) were found. Furthermore, the BIVA values of resistance correlate with the submaximal performance of the Ve/VCO2 slope (R = 0.509; p < 0.05) and phase angle with the maximal effort of the VO2 peak (R = 0.557; p < 0.05). (4) Conclusions: Unsupervised physical exercise alone for six months does not substantially modify liver transplant recipients' cardiovascular performance and hydration status, despite their adherence to a Mediterranean diet. The body composition analysis is useful to stratify the risk profile, and it is potentially associated with better outcomes in transplanted subjects.
