Bioimpedance Vector Patterns Changes in Response to Swimming Training: An Ecological Approach

Localized hamstring bioimpedance in marathon runners is related to muscle high-energy enzyme serum levels and predicts race time

Introduction: The aim was to analyze the response of serum levels of inflammatory, high-energy muscle biomarkers and hamstring localized bioimpedance (L-BIA) measurements to marathon running and to ascertain whether they correlate with each other or with race time. Methods: Blood samples and hamstrings tetra-polar L-BIA measurements from 14 Caucasian male recreational athletes at the Barcelona Marathon 2019 were collected at base line, immediately after and 48 h post-race. Serum C reactive protein (sCRP), creatinine kinase (sCK) and lactate dehydrogenase (sLDH) were determined using an AU-5800 chemistry analyzer. L-BIA was obtained at 50 kHz with a Quantum V Segmental phase-sensitive bioimpedance analyzer. Results: Median sCRP increased (4-fold) after 48 h post-race. Median sCK and sLDH levels increased immediately post-race (3-fold, 2-fold) and 48h post-race (5-fold, 1-fold). Left, right and combined hamstring reactance (Xc) and phase angle (PhA) increased immediately post-race. Xc combined hamstring pre- and immediately post-race correlated with race-time and with sCK and sLDH median levels pre-race. Xc combined hamstring pre- and immediately post-race > 15.6 Ω and 15.8 Ω, respectively, predicted the race time of 3:00:00 h. Conclusion: L-BIA reactance (Xc) is an objective direct, real time, easy, noninvasive bioelectrical parameter that may predict muscle and marathon athlete performance.

One-Year Changes in Bioelectrical Impedance Data in Adolescent Athletes

Raw bioelectrical impedance (BI) data and vector analysis (BIVA) have been used to evaluate fat-free mass (FFM) cross-sectionally in adolescent athletes; however, there have been no longitudinal studies about it. This study aimed to assess the magnitude of changes in raw BI data (resistance [R], reactance [Xc], and phase angle [PhA]), BIVA, and FFM in adolescent athletes (n = 137, 40% female). BI data were collected using a single-frequency device at baseline and after one year of sports practice. Baseline chronological age categorized the participants (11, 12, or 13 years [y]). In females, Xc/H increased (13 to 14 y, p = 0.04) while R/H decreased in all age groups (p = 0.001). PhA (11 to 12 y, p = 0.048) and FFM (11 to 12 y and 12 to 13 y groups p = 0.001) increased and showed the lowest magnitude of changes in the 13 to 14 y group (p = 0.05). In males, Xc/H decreased (11 to 12 and 12 to 13 y groups, p = 0.001) with a higher magnitude of changes in the 13 to 14 y group (p = 0.004); R/H decreased (p = 0.001); FFM increased in all groups (p = 0.001); however, no magnitude of changes was observed. PhA increased in the 13 to 14 y group (p = 0.004). BIVA showed no differences among ellipse distances in females. In males, a high distance was observed in the 11 to 12 y group. "Time interval" influenced PhA and Xc/H in the female group and R/H and Xc/H in the male group. "Initial age" and "time interval" influenced the increase in PhA in the male group. Raw BI data and BIVA patterns can detect the magnitude of the changes in a sex-dependent manner.

In-Season Longitudinal Hydration/Body Cell Mass Ratio Changes in Elite Rugby Players

BACKGROUND

Hydration status has a direct role in sports performance. Bioelectrical Impedance Vector Analysis (BIVA) and Urine Specific Gravity (USG) are commonly used to assess hydration. The study aims to identify the sensitivity and relationship between BIVA and USG in a field sports setting.

METHODS

BIVA and USG measurements were conducted five times throughout one rugby season. 34 elite male rugby players (25.1 ± 4.4 years; 184.0 ± 7.8 cm; 99.9 ± 13.4 kg) were enrolled. Differences over time were tested using one-way repeated measures ANOVA, and Bonferroni's post-hoc test was applied in pairwise comparisons. Resistance-reactance graphs and Hotelling's T2 test were used to characterize the sample and to identify bioelectrical changes. A repeated measures correlation test was conducted for BIVA-USG associations.

RESULTS

Two clear trends were seen: (1) from July to September, there was a vector shortening and an increase of the phase angle (p < 0.001); and (2) from December to April, there was a vector lengthening and a decrease of the phase angle (p < 0.001). USG reported neither changes nor correlation with BIVA longitudinally (p > 0.05). Vector variations indicated a body fluid gain (especially in the intracellular compartment) and a body cell mass increase during the preseason, suggesting a physical condition and performance improvement. During the last months of the season, the kinetic was the opposite (fluid loss and decreased body cell mass).

CONCLUSIONS

Results suggested that BIVA is sensitive to physiological changes and a better option than USG for assessing hydration changes during a rugby sports season.

Fat-free mass estimation in male elite futsal players: Development and validation of a new bioelectrical impedance-based predictive equation

OBJECTIVES

The present study aimed to develop and cross-validate a futsal-specific bioelectrical equation for estimating fat-free mass (FFM) in male players.

METHODS

A total of 66 futsal players (age 23.3 ± 5.4 years) from the Major Portuguese Futsal League "LIGA PLACARD" and from the 2nd and 3rd National Futsal Leagues were included in this cross-sectional, observational study. The participants underwent a foot-to-hand bioelectrical impedance analysis (BIA) at 50 kHz and completed a dual-energy X-ray absorptiometry (DXA) scan for reference body composition data. The sport-specific model was developed by stepwise multiple regression using bioelectrical raw parameters [resistance (R) and reactance (Xc)] as independent variables. Validation was performed using the PRESS approach, least squares regression, concordance correlation coefficient (CCC) and Bland-Altman analyses.

RESULTS

A BIA-based model was developed for FFM [FFM (kg) = -8.865 + 0.437 * Body Mass (kg) + 0.186 * Xc + 0.415 * stature (cm)²/R (R² = 0.89, standard error of estimation = 2.38 kg)]. Results showed a substantial strength of agreement (CCC = 0.953), an r² of 0.88 with a standard error of estimation equal to 2.31 kg, no mean bias (0.04 kg, p>0.05), low limits of agreement (ranged from -4.5 to 4.6 kg), and no trend (r = -0.170, p = 0.172).

CONCLUSIONS

The present equation is the first to allow for a valid, accurate, and sport-specific assessment of FFM in male futsal players.

Methods over Materials: The Need for Sport-Specific Equations to Accurately Predict Fat Mass Using Bioimpedance Analysis or Anthropometry

Bioelectrical impedance analysis (BIA) and anthropometry are considered alternatives to well-established reference techniques for assessing body composition. In team sports, the percentage of fat mass (FM%) is one of the most informative parameters, and a wide range of predictive equations allow for its estimation through both BIA and anthropometry. Although it is not clear which of these two techniques is more accurate for estimating FM%, the choice of the predictive equation could be a determining factor. The present study aimed to examine the validity of BIA and anthropometry in estimating FM% with different predictive equations, using dual X-ray absorptiometry (DXA) as a reference, in a group of futsal players. A total of 67 high-level male futsal players (age 23.7 ± 5.4 years) underwent BIA, anthropometric measurements, and DXA scanning. Four generalized, four athletic, and two sport-specific predictive equations were used for estimating FM% from raw bioelectric and anthropometric parameters. DXA-derived FM% was used as a reference. BIA-based generalized equations overestimated FM% (ranging from 1.13 to 2.69%, p < 0.05), whereas anthropometry-based generalized equations underestimated FM% in the futsal players (ranging from −1.72 to −2.04%, p < 0.05). Compared to DXA, no mean bias (p > 0.05) was observed using the athletic and sport-specific equations. Sport-specific equations allowed for more accurate and precise FM% estimations than did athletic predictive equations, with no trend (ranging from r = −0.217 to 0.235, p > 0.05). Regardless of the instrument, the choice of the equation determines the validity in FM% prediction. In conclusion, BIA and anthropometry can be used interchangeably, allowing for valid FM% estimations, provided that athletic and sport-specific equations are applied.

Editorial: New Training Strategies and Evaluation Methods for Improving Health and Physical Performance

Physical activity is among the most effective methods for improving health, body composition, and physical function, and its practice is suitable for every population [...].

Bioelectrical Impedance Vector Analysis Discriminates Aerobic Power in Futsal Players: The Role of Body Composition

Aims: The present study aimed to assess the ability of bioelectrical impedance vector analysis (BIVA) in discriminating fitness levels in futsal players, exploring the association of body composition and bioelectrical parameters with aerobic power. Methods: Forty-eight professional futsal players (age 23.8 ± 5.3 years) were involved in a cross-sectional study during their pre-season phase. Fat mass (FM) and muscle mass were determined by dual-energy X-ray absorptiometry. VO2max was obtained by indirect calorimetry through a graded exercise test performed on a treadmill. Bioelectrical resistance (R), reactance (Xc), and phase angle (PhA) were directly measured using a foot-to-hand bioimpedance technology at a 50 kHz frequency. Bioelectric R and Xc were standardized for the participants’ height and used to plot the bioimpedance vector in the R-Xc graph according to the BIVA approach. Results: The participants divided into groups of VO2max limited by tertiles showed significant differences in mean vector position in the R-Xc graph (p < 0.001), where a higher VO2max resulted in a longer vector and upper positioning. FM, muscle mass, and PhA differed (p < 0.01) among the athletes grouped by tertiles of VO2max, where athletes with a greater aerobic power showed a lower percentage of FM and a higher percentage of muscle mass and PhA. FM and PhA were associated with VO2max (FM: r = −0.658, p < 0.001; PhA: r = 0.493, p < 0.001). These relationships remained significant after adjusting for age and body mass (FM: ß = −0.335, p = 0.046; PhA: ß = 0.351, p = 0.003). Conclusions: Bioelectrical impedance vectors positioned on the lower pole of the R-Xc graph identified futsal players with a lower VO2max, while longer vectors corresponded to a greater aerobic power. Additionally, PhA, that describes the vector direction, was positively associated with VO2max, while a higher FM negatively affected VO2max in the futsal players. BIVA and PhA evaluation may represent a valid support for screening the aerobic fitness level in professional futsal players, when more sophisticated assessment methods are not available.

Reference Percentiles for Bioelectrical Phase Angle in Athletes

Simple Summary

The bioelectrical phase angle is a raw parameter that can be utilized as an indicator of performance, muscle quantity and hydration status of cells. However, sex- and sport-specific phase angle reference percentiles are lacking for the athletic population. For the first time, this study provides 5th, 15th, 50th, 85th, and 95th reference percentiles for phase angle in male and female athletes practicing different sports. These reference values can be used to track body composition and performance related-outcomes in sports practice, while leveraging the portability of bioelectric impedance analysis.

Abstract

The present study aimed to develop reference values for bioelectrical phase angle in male and female athletes from different sports. Overall, 2224 subjects participated in this study [1658 males (age 26.2 ± 8.9 y) and 566 females (age 26.9 ± 6.6 y)]. Participants were categorized by their sport discipline and sorted into three different sport modalities: endurance, velocity/power, and team sports. Phase angle was directly measured using a foot-to-hand bioimpedance technology at a 50 kHz frequency during the in-season period. Reference percentiles (5th, 15th, 50th, 85th, and 95th) were calculated and stratified by sex, sport discipline and modality using an empirical Bayesian analysis. This method allows for the sharing of information between different groups, creating reference percentiles, even for sports disciplines with few observations. Phase angle differed (men: p < 0.001; women: p = 0.003) among the three sport modalities, where endurance athletes showed a lower value than the other groups (men: vs. velocity/power: p = 0.010, 95% CI = −0.43 to −0.04; vs. team sports: p < 0.001, 95% CI = −0.48 to −0.02; women: vs. velocity/power: p = 0.002, 95% CI = −0.59 to −0.10; vs. team sports: p = 0.015, 95% CI = −0.52 to −0.04). Male athletes showed a higher phase angle than female athletes within each sport modality (endurance: p < 0.01, 95% CI = 0.63 to 1.14; velocity/power: p < 0.01, 95% CI = 0.68 to 1.07; team sports: p < 0.01, 95% CI = 0.98 to 1.23). We derived phase angle reference percentiles for endurance, velocity/power, and team sports athletes. Additionally, we calculated sex-specific references for a total of 22 and 19 sport disciplines for male and female athletes, respectively. This study provides sex- and sport-specific percentiles for phase angle that can track body composition and performance-related parameters in athletes.

The Influence of Menstrual Cycle on Bioimpedance Vector Patterns, Performance, and Flexibility in Elite Soccer Players

PURPOSE

To examine whether menstrual cycle affects body composition and bioimpedance vector analysis (BIVA) patterns, jumping and sprinting ability, and flexibility in elite soccer players.

METHODS

A total of 20 elite female soccer players (age: 23.8 [3.4] y, height: 1.63 [0.04] m, body mass: 61.4 [5.9] kg, and body mass index: 22.5 [2.4] kg/m2) were monitored during the early follicular and ovulatory phase across 2 consecutive menstrual cycles. Bioimpedance analysis was performed using foot-to-hand technology, and total body water and fat mass were determined by specific equations developed for athletes. Bioelectrical resistance and reactance were adjusted according to the BIVA procedures and plotted as a vector within the resistance-reactance graph. In addition, countermovement jump, 20-m sprint, and sit and reach were assessed.

RESULTS

A time effect (P < .05) was found for body mass, total body water, bioelectrical resistance and reactance, and flexibility. Specifically, body mass increased (P = .021) along with a gain in total body water (P = .001) from the ovulatory to the early follicular phase, while it decreased from the early follicular to the ovulatory phase during the second menstrual cycle. The BIVA vector shortened during the early follicular phases (P < .001). No change in jumping and sprinting capacity was observed (P > .05). Flexibility was impaired during the early follicular phases (P < .05).

CONCLUSIONS

Specific bioelectrical impedance analysis and BIVA procedures are able to detect menstrual cycle-induced changes in body composition in elite soccer players. The early follicular phase resulted in fluid accumulations and BIVA vector shortening. In addition, while menstrual cycle did not affect performance, a fluctuation in flexibility was observed.

Effects of the COVID-19 Lockdown on Body Composition and Bioelectrical Phase Angle in Serie A Soccer Players: A Comparison of Two Consecutive Seasons

Simple Summary

In 2020, the first Italian soccer league (Serie A) was canceled due to the COVID-19 pandemic. Consequently, a detraining process was triggered in soccer players, leading coaches and sports scientists to implement alternative training strategies to prevent a remodeling in body composition. This study tested the hypothesis that male elite soccer players, when confined to their home during the coronavirus disease 2019 pandemic, will display unfavorable trends in bioelectrical and body composition parameters. The results of the present study showed that reduction in phase angle and muscle mass occurred in soccer players during the coronavirus disease 2019 pandemic lockdown. Recognizing these adverse effects of a detraining period is critical in avoiding adverse effects on body composition in soccer players. In addition, the bioelectrical phase angle has been identified as a valid predictor of muscle mass changes during the competitive soccer season. Considerably, the phase angle represents a parameter that can be measured directly through bioelectrical impedance analysis, and it is independent of predictive equations such as those that quantify muscle mass.

Abstract

The present study compared changes in body composition during the COVID-19-associated lockdown with the same period of the following season in elite soccer players. Fifteen elite male soccer players (30.5 ± 3.6 years.) underwent a bioelectrical impedance analysis (BIA) before (end of February) and after (end of May) the lockdown, which occurred during the 2019/2020 season, and at the same period during the following competitive season in 2020/2021, when restrictions were lifted. Fat and muscle mass were estimated using predictive equations, while phase angle (PhA) and bioelectrical impedance vector analysis (BIVA) patterns were directly measured. After lockdown, fat mass remained unchanged (p > 0.05), while muscle mass (95%CI = −1.12/−0.64; ES = −2.04) and PhA (95%CI = 0.51/−0.24, ES = −1.56) decreased. A rightward displacement of the BIVA vector was also found (p < 0.001, ES = 1.50). After the same period during the regular season, FM% and muscle mass did not change (p > 0.05), while the PhA increased (95%CI = 0.01/0.22; ES = 0.63). A leftward vector displacement (p < 0.001, ES = 1.05) was also observed. The changes in muscle mass correlated with changes in PhA (“lockdown” season 2019/2020: ß = −1.128, p = 0.011; “regular” season 2020/21: ß = 1.963, p = 0.011). In conclusion, coaches and strength conditioners should monitor muscle mass in soccer players during detraining periods as this parameter appears to be mainly affected by changes in training plans.

Body composition changes during the lockdown-restart transition due to the SARS-CoV-2 pandemic in a group of professional football players

BACKGROUND

At the beginning of March 2020, because of the ongoing pandemic, all sport events were suspended in Italy. The Italian Serie A league abruptly interrupted all competitions and teams could not train in a group anymore. These containment measures were gradually eased in May. The aim of this study was to ascertain the impact of the lockdown-restart transition on the body composition of a group of Italian footballers of a Serie A team during the pandemic.

METHODS

The body composition of 18 male football players (age27.6 ± 4.5 yr.) from an Italian Serie A team was studied by bioelectrical impedance (BIA). Measures were conducted after lockdown, when they restarted to train in group with the ball (T0), and after the first regular match (T1).

RESULTS

Results show that there were no changes in body mass, body mass index, fat mass, fat-free mass, and total body water between T0 and T1. Differently, intracellular water, extracellular water, and phase angle were changed.

CONCLUSIONS

We concluded that the lockdown did not have any impact in athletes' body composition as they kept training at home. Moreover, the BIA method was able to detect subtle signals of shift in body water compartments (intra cellular and extra cellular water) likely due to the different kind of training conducted during and after the lockdown.

Generalized bioelectric impedance-based equations underestimate body fluids in athletes

The current study aimed: (i) to external validate total body water (TBW) and extracellular water (ECW) derived from athlete and non‐athlete predictive equations using radioisotope dilution techniques as a reference criterion in male and female athletes; (ii) in a larger sample, to determine the agreement between specific and generalized equations when estimating body fluids in male and female athletes practicing different sports. A total of 1371 athletes (men: n = 921, age 23.9 ± 1.4 y; women: n = 450, age 27.3 ± 6.8 y) participated in this study. All athletes underwent bioelectrical impedance analyses, while TBW and ECW were assessed with dilution techniques in a subgroup of 185 participants (men: n = 132, age 21.7 ± 5.1 y; women: n = 53, age 20.3 ± 4.5 y). Two specific and eight generalized predictive equations were tested. Compared to the criterion methods, no mean bias was observed using the athlete‐specific equations for TBW and ECW (−0.32 to 0.05, p > 0.05) and the coefficient of determination ranged from R² = 0.83 to 0.94. The majority of the generalized predictive equations underestimated TBW and ECW (p < 0.05); R² ranged from 0.66 to 0.89. In the larger sample, all the generalized equations showed lower TBW and ECW values (ranging from −6.58 to −0.19, p < 0.05) than specific predictive equations; except for TBW in female power/velocity (one equation) athletes and team sport (two equations). The use of generalized BIA‐based equations leads to an underestimation of TBW, and ECW compared to athlete‐specific predictive equations. Additionally, the larger sample indicates that generalized equations overall provided lower TBW and ECW compared to the athlete‐specific equations.

Bioelectrical Impedance Vector Analysis: A Valuable Tool to Monitor Daily Body Hydration Dynamics at Altitude

Bioelectrical impedance vector analysis (BIVA) is a method used to estimate variation in body hydration. We assessed the potential of BIVA for monitoring daily body hydration fluctuations in nine healthy, normally active males under matching normoxic (NX) and hypobaric hypoxic (HH) experimental conditions. Furthermore, we aimed to investigate whether changes in BIVA may correspond with the development of acute mountain sickness (AMS). Subjects were exposed in a hypobaric chamber to both NX (corresponding to an altitude of 262 m) and HH conditions corresponding to an altitude of 3500 m during two four-day sojourns within which food, water intake and physical activity were controlled. Bioimpedance and body weight measurements were performed three times a day and medical symptoms were assessed every morning using the Lake Louise score (LLS). Total body water (TBW) was also assessed on the last day of both sojourns using the deuterium dilution technique. We detected circadian changes in vector length, indicating circadian body water variations that did not differ between NX and HH conditions (ANOVA effects: time: p = 0.018, eta² = 0.149; interaction: p = 0.214, eta² = 0.083; condition: p = 0.920, eta² = 0.001). Even though none of the subjects developed AMS, four subjects showed clinical symptoms according to the LLS during the first 24 hours of HH conditions. These subjects showed a pronounced (Cohen’s d: 1.09), yet not statistically significant (p = 0.206) decrease in phase angle 6 hours after exposure, which may indicate fluid shift from the intracellular to the extracellular compartment. At the end of each sojourn, vector length correlated with deuterium dilution TBW “gold standard” measurements (linear regression: NX: p = 0.002 and r² = 0.756, HH: p < 0.001 and r² = 0.84). BIVA can be considered a valuable method for monitoring body hydration changes at altitude. Whether such changes are related to the development of clinical symptoms associated with AMS, as indicated in the present investigation, must be confirmed in future studies.

Assessment of Body Composition in Athletes: A Narrative Review of Available Methods with Special Reference to Quantitative and Qualitative Bioimpedance Analysis

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.

Psychological and Physiological Features Associated with Swimming Performance

BACKGROUND

The aim of the present research was to study the psychological and physiological features associated with aerobic and anaerobic performance in trained swimmers.

METHODS

A correlation and stepwise regression analyses were conducted with the data obtained in a RESTQ-76 sport questionnaire, a heart rate variability test, and an anaerobic and aerobic swimming performance efforts of 20 swimmers.

RESULTS

Aerobic performance correlated, principally, with parameters related to parasympathetic modulation measured in the frequency and time domains of the heart rate variability (LF/HF r: -0.806, p < 0.001; NN50 r: 0.937, p < 0.001). Swimmers' anaerobic performance correlated to psychological features (low stress r: 0.526, p: 0.025, and high fatigue r: -0.506, p: 0.032).

CONCLUSION

Swimming performance presented different psychological and physiological features depending on the probe characteristic. Specifically, swimmers' anaerobic performance was associated with psychological features (low stress and high fatigue perception) and aerobic performance with physiological features (high parasympathetic modulation). This information could help coaches to know the variables to control in their swimmers, depending on the probe in which they compete (anaerobic or aerobic).

Athletes' Self-Assessment of Urine Color Using Two Color Charts to Determine Urine Concentration

Our objective was to determine self-reported accuracy of an athletic population using two different urine color (Uc) charts (8-color vs. 7-color Uc chart). After approval by the Institutional Review Board, members of an athletic population (n = 189, 20 (19–22) year old student- or tactical athletes and coaches, with n = 99 males and n = 90 females) scored their Uc using two charts. To determine the diagnostic value of Uc, results were compared with urine concentration (osmolality and urine specific gravity, USG). Uc was scored slightly darker with the 8-color vs. 7-color Uc chart (2.2 ± 1.2 vs. 2.0 ± 1.2, respectively, p < 0.001), with a moderate correlation between charts (r = 0.76, 95% CI: 0.69–0.81). Bland-Altman analysis showed a weak reporting bias (r = 0.15, p = 0.04). The area under the curve for correct urine sample classification ranged between 0.74 and 0.86. Higher accuracy for both methods was found when Uc scores were compared to USG over osmolality, indicated by 4.8–14.8% range in difference between methods. The optimal Uc cut-off value to assess a low vs. a high urine concentration for both Uc charts varied in this study between 1 and ≤2 while accuracy for charts was similar up to 77% when compared to USG.

Evaluation of Fluid Loss and Customary Fluid Intake among a Selected Group of Young Swimmers: A Preliminary Field Study

This study aimed to assess fluid loss (FL) and customary fluid intake (FI) during a training session, and the relationship between FL and total body water (TBW) content in a selected group of young swimmers. The study involved 17 (seven females, 10 males) individuals whose anthropometric and body composition analyses and FI during training units were carried out. The total average FI and total actual FL oscillated around 531 mL and −513 mL for the whole study group (469 mL and −284 mL for females, 574 mL and −674 mL for males). The dependent and independent sample t-tests, the Cohen’s d effect size and Pearson’s correlation coefficient were analysed. Significant differences were observed between pre-workout and post-workout body weights after training without FI in the whole group (66.5 kg vs. 66.0 kg, p < 0.001, d = 0.06), in females (61.2 kg vs. 60.9 kg, p = 0.015, d = 0.04) and males (70.3 kg vs. 69.6 kg, p < 0.001, d = 0.9). For the TBW content and fat-free mass (FFM) before and after training, significant differences were observed only in males (TBW: 43.8 L vs. 43.2 L, p = 0.002, d = 0.14; and 62.4% vs. 61.7%, p < 0.001, d = 0.36; FFM: 59.8 kg vs. 59.1 kg, p = 0.002, d = 0.12). Moreover, the relationship between the actual FL and TBW before training was observed in the whole (mL vs. %: r = −0.64, p = 0.006; mL vs. L: r = −0.84, p < 0.001) and the male group (mL vs. L: r = −0.73, p = 0.017). These results indicated FL in young swimmers during training and the relationship between FL and pre-training TBW content, which suggests that it is important to also pay special attention to effective hydration procedures before and during training in aquatic environments.

Young Swimmers' Middle-Distance Performance Variation within a Training Season

The current study aimed to longitudinally evaluate anthropometric, physiological, and biomechanical variables related to middle-distance performance during a 45-week swimming training season. Thirty-four swimmers (age: 12.07 ± 1.14 years) performed a maximum of 400 m front crawl at the beginning (T1) and finish of the first macrocycle (T2, 15 weeks) and the finish of the second (T3, 18 weeks) and third macrocycles (T4, 12 weeks). Time-related variables, stroke rate (SR), stroke length (SL), and stroke index (SI) were recorded during the test, and blood lactate ([La]) and glucose ([Glu]) concentrations were measured post-exercise. The time of the 400 m effort decreased after each macrocycle (T2 vs. T1, 7.8 ± 5.6%; T3 vs. T2, 3.7 ± 3.1%; T4 vs. T3, 3.8 ± 3.4%; p < 0.01). Four hundred meter speed changes between T1 and T2 were positively related to variations in [La], [Glu], SL, and SI (r = 0.36–0.60, p < 0.05). Changes between T2 and T3 were related to SI only (r = 0.5, p < 0.05), and modifications between T3 and T4 were associated with SL and SI variations (r = 0.34 and 0.65, p < 0.05). These results indicate that a well-structured year plan including three macrocycles leads to a significant age-group swimming performance improvement, mostly connected with an increase in technical proficiency.

Leucine metabolites do not induce changes in phase angle, bioimpedance vector analysis patterns, and strength in resistance trained men

We aimed to assess the effects of off-the-shelf leucine metabolite supplements on phase angle (PhA), bioimpedance vector analysis (BIVA) patterns and strength during an 8-week resistance training protocol. Fifty-three male participants were allocated into 4 groups: α-hydroxyisocaproic acid (n = 12, age = 30.9 ± 9.3 years), β-hydroxy-β-methylbutyrate free acid (n = 12, age = 31.0 ± 9.3 years), calcium β-hydroxy-β-methylbutyrate (n = 15, age = 32.1 ± 5.2 years) or placebo (n = 14, age = 28.9 ± 6.6 years). Bioimpedance parameters and 1-repetition maximum (1RM) for back squat and bench press were assessed at baseline and at the end of weeks 4 and 8. Additionally, fat-free mass and fat mass were evaluated by dual-energy X-ray absorptiometry. No statistically group by time interactions were found, even adjusting for age. PhA and vector did not change over the training period, while time-dependent increases were observed for 1RM back squat and 1RM bench press. A direct association was observed between PhA and 1RM bench press changes (whole sample), while PhA and strength were correlated throughout the study, even when adjusting for fat-free mass and percentage of fat mass. Leucine metabolites have no effect on PhA, BIVA patterns or strength during an 8-week resistance training program, in resistance trained subjects. The trial was registered at ClincicalTrials.gov: NCT03511092. Novelty: Supplementation with leucine metabolites is not a supplementation strategy that improves bioelectrical phase angle, cellular health, and strength after an 8-week resistance training program. When consuming a high protein diet, none of the α-hydroxyisocaproic acid, β-hydroxy-β-methylbutyrate free acid, and calcium β-hydroxy-β-methylbutyrate metabolites resulted in an ergogenic effect in resistance trained men.

Bioimpedance Vector References Need to Be Period-Specific for Assessing Body Composition and Cellular Health in Elite Soccer Players: A Brief Report

PURPOSE

Bioimpedance data through bioimpedance vector analysis (BIVA) is used to evaluate cellular function and body fluid content. This study aimed to (i) identify whether BIVA patters differ according to the competitive period and (ii) provide specific references for assessing bioelectric properties at the start of the season in male elite soccer players.

METHODS

The study included 131 male soccer players (age: 25.1 ± 4.7 yr, height: 183.4 ± 6.1 cm, weight: 79.3 ± 6.6) registered in the first Italian soccer division (Serie A). Bioimpedance analysis was performed just before the start of the competitive season and BIVA was applied. In order to verify the need for period-specific references, bioelectrical values measured at the start of the season were compared to the reference values for the male elite soccer player population.

RESULTS

The results of the two-sample Hotelling T² tests showed that in the bivariate interpretation of the raw bioimpedance parameters (resistance (R) and reactance (Xc)) the bioelectric properties significantly (T² = 15.3, F = 7.6, p ≤ 0.001, Mahalanobis D = 0.45) differ between the two phases of the competition analyzed. In particular, the mean impedance vector is more displaced to the left into the R-Xc graph at the beginning of the season than in the first half of the championship.

CONCLUSIONS

For an accurate evaluation of body composition and cellular health, the tolerance ellipses displayed by BIVA approach into the R-Xc graph must be period-specific. This study provides new specific tolerance ellipses (R/H: 246 ± 32.1, Xc/H: 34.3 ± 5.1, r: 0.7) for performing BIVA at the beginning of the competitive season in male elite soccer players.

Body Water Content and Morphological Characteristics Modify Bioimpedance Vector Patterns in Volleyball, Soccer, and Rugby Players

Background: Bioimpedance vector analysis (BIVA) is a widely used method based on the interpretation of raw bioimpedance parameters to evaluate body composition and cellular health in athletes. However, several variables contribute to influencing BIVA patterns by militating against an optimal interpretation of the data. This study aims to explore the association of morphological characteristics with bioelectrical properties in volleyball, soccer, and rugby players. Methods: 164 athletes belonging to professional teams (age 26.2 ± 4.4 yrs; body mass index (BMI) 25.4 ± 2.4 kg/m²) underwent bioimpedance and anthropometric measurements. Bioelectric resistance (R) and reactance (Xc) were standardized for the athlete's height and used to plot the vector in the R-Xc graph according to the BIVA approach. Total body water (TBW), phase angle (PhA), and somatotype were determined from bioelectrical and anthropometric data. Results: No significant difference (p > 0.05) for age and for age at the start of competition among the athletes was found. Athletes divided into groups of TBW limited by quartiles showed significant differences in the mean vector position in the R-Xc graph (p < 0.001), where a higher content of body fluids resulted in a shorter vector and lower positioning in the graph. Furthermore, six categories of somatotypes were identified, and the results of bivariate and partial correlation analysis highlighted a direct association between PhA and mesomorphy (r = 0.401, p < 0.001) while showing an inverse correlation with ectomorphy (r = -0.416, p < 0.001), even adjusted for age. On the contrary, no association was observed between PhA and endomorphy (r = 0.100, p = 0.471). Conclusions: Body fluid content affects the vector length in the R-Xc graph. In addition, the lateral displacement of the vector, which determines the PhA, can be modified by the morphological characteristics of the athlete. In particular, higher PhA values are observed in subjects with a high mesomorphic component, whereas lower values are found when ectomorphy is dominant.

Interpopulation Similarity of Sex and Age-Related Body Composition Variations Among Older Adults

The aim of the study was to analyze sex and age-related body composition variations among older adults from the Brazilian, Italian, and Mexican population. A cross-sectional analysis was conducted in 1103 community-dwelling older adults (634 women and 469 men), aged 60 to 89 years, living in Brazil (n = 176), Italy (n = 554), and Mexico (n = 373). Anthropometric measurements were taken, BMI was calculated, and impedance measurements were obtained (resistance, R, reactance, Xc). Specific bioelectrical impedance vector analysis (specific BIVA) was applied, with the specific vector defined by impedance, or vector length (Z = (Rsp² + Xcsp)^0.5), and phase angle (PA = arctan Xc/R 180/π). Population, sex, and age differences in anthropometric and bioelectrical variables were evaluated by means of a two way ANOVA. The mean bioelectrical vectors were graphed by confidence ellipses and statistically compared by the Hotelling's T² test. The three population groups showed differences in body mass and composition (p < 0.001): the Brazilian sample was characterized by greater body dimensions, longer vectors (higher relative content of fat mass), and lower phase angles (lower skeletal muscle mass). Men were taller and heavier than women (p < 0.001) but had a similar BMI (p = 0.102). They also had higher phase angle (higher skeletal muscle mass) (p < 0.001) and lower vector length (lower %FM) (p < 0.001). In the three population groups, the oldest individuals showed lower anthropometric and phase angle values with respect to the youngest ones (p < 0.001), whereas the vector length did not change significantly with age (p = 0.665). Despite the differences between sexes and among populations, the trend of age-related variations was similar in the Brazilian, Italian, and Mexican older adults.