Bioelectrical impedance vector analysis (BIVA) is a general concept that includes all methodologies used in the analysis of the bioelectrical vector, whereas the "classic" BIVA is a patented methodology included among these methods of analysis. Once this was clarified, the systematic review of the literature provides a deeper insight into the scope and range of application of BIVA in sport and exercise. The main goal of this work was to systematically review the sources on the applications of BIVA in sport and exercise and to examine its usefulness and suitability as a technique for the evaluation of body composition, hydration status, and other physiological and clinical relevant characteristics, ultimately to trace future perspectives in this growing area, including a proposal for a research agenda. Systematic literature searches in PubMed, SPORTDiscus and Scopus databases up to July, 2017 were conducted on any empirical investigations using phase-sensitive bioimpedance instruments to perform BIVA within exercise and sport contexts. The search included healthy sedentary individuals, physically active subjects and athletes. Nineteen eligible papers were included and classified as sixteen original articles and three scientific conference communications. Three studies analysed short-term variations in the hydration status evoked by exercise/training through whole-body measurements, eleven assessed whole-body body composition changes induced by long-term exercise, four compared athletic groups or populations using the whole-body assessment, and two analysed bioelectrical patterns of athletic injuries or muscle damage through localised bioimpedance measurements. BIVA is a relatively new technique that has potential in sport and exercise, especially for the assessment of soft-tissue injury. On the other hand, the current tolerance ellipses of “classic” BIVA are not a valid method to identify dehydration in individual athletes and a new approach is needed. “Specific” BIVA, a method which proposes a correction of bioelectrical values for body geometry, emerges as the key to overcome “classic” BIVA limitations regarding the body composition assessment. Further research establishing standardised testing procedures and investigating the relationship between physiology and the bioelectrical signal in sport and exercise is needed.

The assessment of body hydration is a complex process, and no measurement is valid for all situations. Bioelectrical impedance vector analysis (BIVA) has emerged as a relatively novel technique for assessing hydration status in sports. We applied BIVA a) to determine hydration changes evoked by an intense synchronized swimming (SS) training session; b) to characterize the sample of young elite swimmers in relation with a nonathletic reference population; and c) to generate its 50%, 75% and 95% percentiles of the bioelectrical variables. Forty-nine elite SS female swimmers of two age categories, comen (C_o: 13.9 ± 0.9 years, n = 34) and junior (J_r: 16.3 ± 0.6 years, n = 15), performed a long, high intensity training session. Body mass (BM) and bioelectrical variables (R, resistance; Xc, reactance; PA, phase angle; and Z, impedance module) were assessed pre- and post-training. BIVA was used to characterize 1) the distribution pattern of the bioelectrical vector (BIA vector) for both age groups, and 2) pre- to post-training BIA vector migration. Bioelectrical variables were also correlated with BM change values. Most swimmers were mostly located outside the 75% and some beyond the 95% percentile of the bioelectrical tolerance ellipses of the general population. The BIA vector showed statistically significant differences in both C_o (T² = 134.7, p = 0.0001) and J_r (T² = 126.2, p < 0.001). Both groups were also bioelectrically different (T² = 17.6, p < 0.001). After the training session, a decrease in BM (p = 0.0001) and an increase in BIA variables (p = 0.01) was observed. BIVA also showed a significant pre-post vector migration both in C_o (T² = 82.1; p < 0.001) and J_r (T² = 41.8; p < 0.001). No correlations were observed between BM changes and bioelectrical variables. BIVA showed specific bioelectrical characteristics in young elite SS athletes. Considering the decrease in BM and the migration of the BIA vector, we conclude that the homeostatic hydration status of these young elite female swimmers was affected by the execution of intense training sessions. From a methodological perspective, BIVA appears to be sensitive enough to detect subtle hydration changes, but further research is needed to ensure its validity and reliability. Moreover, these findings highlight the importance of ensuring adequate fluid intake during training in young SS athletes.

This study compared subcutaneous adipose tissue (SAT) measurements using a skinfold caliper and Renco Lean-Meater Series 12 A-mode portable ultrasound scanner (A-US). It aimed to assess their inter- and intra-rater reliability and measure the agreement between both methods. Eighty-four volunteers of different fitness levels were divided into three groups by Ʃ6 skinfolds: G1 ≤ 55 mm (n = 33 males); G2 > 55 mm (n = 32 males); G3 = 98.0 ± 52.3 mm (n = 19 females). Triceps, subscapular, biceps, iliac crest, supraspinal, abdominal, front thigh and medial calf were assessed by ultrasound and skinfolds. Two technicians for both tools performed triplicate measures. Intraclass correlation (ICC), technical error of measurement (TEM) and coefficients of variation (CVs) were applied for test-retest and inter-rater reliability. Non-Parametric statistics were used in order to establish possible statistical differences and correlation between skinfolds thickness and uncompressed subcutaneous adipose tissue thickness from ultrasound. The amount of agreement between both methods was assessed with Lin’s coefficient and a scatterplot of all site locations. A Bland-Altman plot was constructed to establish limits of agreement between groups and regression analysis was employed to assess the ability of skinfolds to explain the variance of ultrasound. Test-retest ICC for skinfolds and ultrasound were higher than 0.989 and 0.793, respectively. Inter-rater ICC for skinfolds was 0.999 with a 95% CI of 0.995 to 0.999 and for ultrasound was 0.755 with a much larger 95% CI of 0.622 to 0.841. TEMs (> 8.50%) and CVs (> 6.72%) compromised ultrasound reliability. Statistical differences were found in most of the analysed anatomical sites (p < 0.001) except in biceps G2 (Z = -1.150, p = 0.25) and G3 (Z = -1.309, p = 0.19). Good correlations (r > 0.7, p ≤ 0.05) were reported at almost all anatomical sites and groups except for biceps (G1: Rho = 0.26, p = 0.140) and abdominal (G2: Rho = -0.16, p = 0.38; G3: Rho = 0.43, p = 0.068). Lin’s concordance correlation coefficient registered low values of agreement between skinfolds and A-mode ultrasound (ranged from—0.009–0.646). The scatterplot and the estimated regression line drawn through the midst of all anatomical sites of the whole sample had a slope of 0.51 and R² adjusted = 0.62 was obtained. The combined analysis between the Bland-Altman plot and the linear regression showed that specifically in the G2 and G3 groups, as the SAT increases the differences between skinfolds and ultrasounds measurements also increases. The Renco Lean-Meater ultrasound is not interchangeable with skinfold measures. Its utility is questionable, particularly for assessing SAT in active adult populations. Its poor test-retest and inter-rater reliability as well as the lack of agreement when compared to the skinfolds would exclude the free use of the A-mode ultrasound scanner in its hypothetical replacing of the classical calipers.

The monitoring of internal load in basketball can be used to understand the effects and potential physiological adaptations caused by external load. The main aim of this systematic review was to identify the methods and variables used to quantify internal load in female basketball. The studies included different populations and events: youth athletes, elite, and amateur players. Subjective methods included using the rating of perceived exertion (RPE) method, and sensor-based methods included monitoring the cardiac response to exercise, using heart rate (HR) as the primary metric. The results showed that the HRAvg exhibited a wider range of values during training than during competition, and different metrics were used to evaluate internal load, such as HRMax, HRmin, %HRMax, total time and % of time spent in different HR zones (2–8 zones), Banister’s TRIMP, and summated HR zones. RPE and HR metrics were the most commonly used methods. However, the use of multiple metrics with little standardization resulted in significant heterogeneity among studies, limiting meaningful comparisons. The review provides a reference for current research on female basketball. Future research could address this limitation by adopting more consistent measurement protocols standardizing the use of metrics.

Load monitoring has been identified as a valuable tool for optimizing training planning and minimizing injury risk. This study’s aim was divided into two main objectives: (1) to describe the physical demands during official competition through IMU (inertial movement unit) metrics and (2) to investigate the relationship between basketball statistics and these physical demands. Twelve female highly trained basketballers (26.5 ± 5.3 years, 180 ± 7.1 cm, and 73.6 ± 10.3 kg) were monitored during four official games. Our results indicate that games with more frequent possession changes, particularly those driven by steals and turnovers, exhibit higher physical demands. Additionally, longer game durations were associated with longer recovery time while maintaining similar active time and physical load. Players who assume prominent shooting roles face greater conditional demands, such as increased jumps and impacts, even with equal playing time. These findings suggest that IMUs provide valuable insights into high-intensity actions and patterns, indicating a direct association between physical load and player performance in professional female basketball. This study also highlights the potential for professionals to better manage workload and understand player demands using these insights, even in the absence of in-game sensor data. Our research underscores the importance of contextual analysis in sports performance studies, encouraging future investigations into game phases and their specific physical demands.