Catarina N Matias

Bio: Catarina N Matias is an academic researcher from Universidade Lusófona. The author has contributed to research in topics: Bioelectrical impedance analysis & Lean body mass. The author has an hindex of 1, co-authored 6 publications receiving 6 citations.

Generalized bioelectric impedance-based equations underestimate body fluids in athletes.

Abstract: 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.

Prediction of Somatotype from Bioimpedance Analysis in Elite Youth Soccer Players.

Abstract: The accurate body composition assessment comprises several variables, causing it to be a time consuming evaluation as well as requiring different and sometimes costly measurement instruments. The aim of this study was to develop new equations for the somatotype prediction, reducing the number of normal measurements required by the Heath and Carter approach. A group of 173 male soccer players (age, 13.6 ± 2.2 years, mean ± standard deviation; body mass index, BMI, 19.9 ± 2.5 kg/m2), members of the academy of a professional Italian soccer team participating in the first division (Serie A), participated in this study. Bioelectrical impedance analysis (BIA) was performed using the single frequency of 50 kHz and fat-free mass (FFM) was calculated using a BIA specific, impedance based equation. Somatotype components were estimated according to the Heath-Carter method. The participants were randomly split into development (n = 117) and validation groups (n = 56). New anthropometric and BIA based models were developed (endomorphy = -1.953 - 0.011 × stature2/resistance + 0.135 × BMI + 0.232 × triceps skinfold, R2 = 0.86, SEE = 0.28; mesomorphy = 6.848 + 0.138 × phase angle + 0.232 × contracted arm circumference + 0.166 × calf circumference - 0.093 × stature, R2 = 0.87, SEE = 0.40; ectomorphy = -5.592 - 38.237 × FFM/stature + 0.123 × stature, R2 = 0.86, SEE = 0.37). Cross validation revealed R2 of 0.84, 0.80, and 0.87 for endomorphy, mesomorphy, and ectomorphy, respectively. The new proposed equations allow for the integration of the somatotype assessment into BIA, reducing the number of collected measurements, the instruments used, and the time normally required to obtain a complete body composition analysis.

Adaptive thermogenesis after moderate weight loss: magnitude and methodological issues.

Abstract: PURPOSE The aim of this study was (1) to assess AT through 13 different mathematical approaches and to compare their results; and (2) to understand if AT occurs after moderate WL. METHODS Ninety-four participants [mean (SD); BMI, 31.1 (4.3) kg/m2; age, 43.0 (9.4) years; 34% females] underwent a 1-year lifestyle intervention (clinicaltrials.gov ID: NCT03031951) and were randomized to intervention (IG, n = 49) or control groups (CG, n = 45), and all measurements were made at baseline and after 4 months. Fat mass (FM) and fat-free mass (FFM) were measured by dual-energy X-ray absorptiometry and REE by indirect calorimetry. AT was assessed through 13 different approaches, varying in how REE was predicted and/or how AT was assessed. RESULTS IG underwent a mean negative energy balance (EB) of 270 (289) kcal/day, p < 0.001), resulting in a WL of - 4.8 (4.9)% and an FM loss of - 11.3 (10.8)%. Regardless of approach, AT occurred in the IG, ranging from ~ - 65 to ~ - 230 kcal/day and three approaches showed significant AT in the CG. CONCLUSIONS Regardless of approach, AT occurred after moderate WL in the IG. AT assessment should be standardized and comparisons among studies with different methodologies to assess AT must be avoided.

Effectiveness of a lifestyle weight-loss intervention targeting inactive former elite athletes: the Champ4Life randomised controlled trial

Abstract: Objectives Many athletes struggle in managing the end of their career, often gaining weight and adopting unhealthy lifestyles. Lifestyle programmes targeting former athletes who have gained substantial fat mass (FM) postsports career are lacking. We studied the effects of the Champ4Life programme on body composition and other health-related outcomes in former elite athletes with overweight or obesity. Methods Ninety-four former athletes(42.4±7.3 y, 34.0% female) were recruited and randomly assigned to either an intervention group (IG; n=49) or a control group (CG; n=45). The IG attended 12 educational sessions addressing physical activity, weight management and nutrition. They also had a nutrition appointment aimed to prescribe a moderate caloric deficit(~300–500 kcal/day). Dual-energy X-ray absorptiometry was used to assess body composition. The Short-Form Health Survey-36 questionnaire was used to measure general health-related quality of life. Blood samples were collected to assess cardiometabolic health parameters. Results At 12 months, the IG lost more weight (estimated difference (ED)=−5.3 kg; −6.9 to −3.8), total FM (ED=−4.1 kg; −5.4 to −2.8) and abdominal FM (ED=−0.49 kg; −0.64 to −0.33) than did the CG (p’s Conclusions The Champ4Life programme was effective in substantially reducing total and abdominal FM while preserving fat-free mass and improving health-related markers. These findings will enable evidence-based decisions when implementing lifestyle interventions targeting retired elite athletes. Trial registeration number NCT03031951.

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

Abstract: 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.

Reference Percentiles for Bioelectrical Phase Angle in Athletes

Abstract: 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.

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

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 0.05), 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: s = −1.128, p = 0.011; “regular” season 2020/21: s = 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.