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.

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.

The bioelectrical impedance vector analysis (BIVA) represents a qualitative analysis of body composition. The vector, defined by resistance (R) and reactance (Xc) standardized by stature, can be evaluated compared to the 50%,75%, and 95% tolerance ellipses representative of the reference populations. The tolerance ellipses for healthy adults have been provided in 1995 and were developed by mixing underage, adult, and elderly subjects, possibly misrepresenting the actual adult population. The current multicentric, cross-sectional study aimed to provide new tolerance ellipses specific for the general adult population and as a secondary aim to present centile curves for the bioelectrical phase angle. R, Xc, and phase angle were measured in 2137 and 2230 males and females using phase-sensitive foot-to-hand analyzers at 50 kHz. A minimum of 35 subjects were included for each sex and age category from 18 to 65 years. The new mean vectors showed a leftward shift on the R-Xc graph with respect to the former reference values (males: F = 75.3; p < 0.001; females: F = 36.6, p < 0.001). The results provided new 3rd, 5th, 10th, 25th, 50th, 75th, 90th, 95th, and 97th percentile curves for phase angle, identifying time point phases of decrement (males: -0.03° per year at 33.0-51.0 years and -0.05° per year after 51 years; females: -0.03° per year from 37.2 to 57.9 years). Compared to the original references, the new data are characterized by a different distribution within the R-Xc graph with a higher phase angle. Thirty years after the BIVA invention, the current study presents new tolerance ellipses and phase angle reference values for the adult population.

The aims of this study were to analyze the usefulness of raw bioelectrical impedance (BI) parameters in assessing water compartments and fluid distribution in athletes. A total of 202 men and 71 female athletes were analyzed. Total body water (TBW) and extracellular water (ECW) were determined by dilution techniques, while intracellular water (ICW) was calculated. Fluid distribution was calculated as the ECW/ICW ratio (E:I). Phase angle (PhA), resistance (R) and reactance (Xc) were obtained through BI spectroscopy using frequency 50kHz. Fat (FM) and fat-free mass (FFM) were assessed by dual-energy X-ray absorptiometry. After adjusting for height, FM, FFM, age and sports category we observed that: PhA predicted ICW (females: β = 1.62, p < 0.01; males: β = 2.70, p < 0.01) and E:I (males and females: β = −0.08; p < 0.01); R explained TBW (females: β = −0.03; p < 0.01; males: β = −0.06; p < 0.01) and ECW (females: β = –0.02, p < 0.01; males: β = −0.03, p < 0.01) and ICW (females: β = –0.01, p < 0.053; males: β = –0.03 p < 0.01); and Xc predicted ECW (females: β = −0.06, p < 0.01; males: β = −0.12, p < 0.01). A higher PhA is a good predictor of a larger ICW pool and a lower E:I, regardless of body composition, age, height, and sports category. Lower R is associated with higher water pools whereas ECW expansion is explained by lower Xc. Raw BI parameters are useful predictors of total and extracellular pools, cellular hydration and fluid distribution in athletes.