Preseason training optimises adaptations in the physical qualities required in rugby union athletes. Sleep can be compromised during periods of intensified training. Therefore, we investigated the ...relationship between sleep quantity and changes in physical performance over a preseason phase in professional rugby union athletes.
Twenty-nine professional rugby union athletes (Mean ± SD, age: 23 ± 3 years) had their sleep duration monitored for 3 weeks using wrist actigraphy. Strength and speed were assessed at baseline and at week 3. Aerobic capacity and body composition were assessed at baseline, at week 3 and at week 5. Participants were stratified into 2 groups for analysis: <7 h 30 min sleep per night (LOW,
= 15) and >7 h 30 min sleep per night (HIGH,
= 14).
A significant group x time interaction was determined for aerobic capacity (
= 0.02,
= 1.25) at week 3 and for skinfolds at week 3 (
< 0.01,
= 0.58) and at week 5 (
= 0.02,
= 0.92), in favour of the HIGH sleep group. No differences were evident between groups for strength or speed measures (
≥ 0.05).
This study highlights that longer sleep duration during the preseason may assist in enhancing physical qualities including aerobic capacity and body composition in elite rugby union athletes.
Acute coronavirus disease 2019 infection has been shown to negatively affect body composition among adult and malnourished or obesity children. Our aim is to longitudinally evaluate body composition ...in children affected by the Multisystem Inflammatory Syndrome (MIS-C).
In this cohort study, we recruited 40 patients affected by MIS-C, aged 2-18 years old, who were admitted in our clinic between December 2020 and February 2021. Physical examination for each participant included weight, height, body mass index (BMI) z score, circumferences, and skinfolds assessment. The same measurements were repeated during outpatient follow-up at 10 (T2), 30 (T3), 90 (T4), and 180 (T5) days after hospital discharge. Fat mass and fat free mass were calculated according to skinfolds predictive equations for children and adolescents. A control group was randomly selected among patients attending a pediatric nutritional outpatient clinic.
BMI z score significantly decrease between preadmission and hospital discharge. Similarly, arm circumference z score, arm muscular area z score, and arm fat area z score significantly decreased, during hospital stay. Fat mass index (FMI) significantly increased over time, peaking at T3. Fat free mass index decreased during hospitalization.
To the best of our knowledge, this is the first study to assess body composition in a numerically large pediatric MIS-C population from acute infection to 6 months after triggering event. FMI and anthropometric parameters linked to fat deposits were significantly higher 6 months after acute event. Thus, limiting physical activity and having sedentary lifestyle may lead to an accumulation of adipose tissue even in healthy children who experienced MIS-C and long hospitalization.
Background
This study aimed to provide reference values for body fat (BF) of basketball players considering sex, measurement method, and competitive level.
Methods
A systematic literature research ...was conducted using five electronic databases (PubMed, Web of Science, SPORTDiscus, CINAHL, Scopus). BF values were extracted, with analyses conducted using random-effects models and data reported as percentages with 95% confidence intervals (CI).
Results
After screening, 80 articles representing 4335 basketball players were selected. Pooled mean BF was 13.1% (95% CI 12.4–13.8%) for male players and 20.7% (95% CI 19.9–21.5%) for female players. Pooled mean BF was 21.4% (95% CI 18.4–24.3%) measured by dual-energy X-ray absorptiometry (DXA), 15.2% (95% CI 12.8–17.6%) via bioelectrical impedance analysis (BIA), 12.4% (95% CI 10.6–14.2%) via skinfolds and 20.0% (95% CI 13.4–26.6%) via air displacement plethysmography. Pooled mean BF across competitive levels were 13.5% (95% CI 11.6–15.3%) for international, 15.7% (95% CI 14.2–17.2%) for national and 15.1% (95% CI 13.5–16.7%) for regional-level players. As the meta-regression revealed significant effects of sex, measurement method and competitive level on BF, the meta-analysis was adjusted for these moderators. The final model revealed significant differences in BF between male and female players (
p
< 0.001). BF measured by DXA was significantly higher than that measured by BIA or skinfolds (
p
< 0.001). International-level players had significantly lower BF than national and regional-level players (
p
< 0.05).
Conclusions
Despite the limitations of published data, this meta-analysis provides reference values for BF of basketball players. Sex, measurement method and competitive level influence BF values, and therefore must be taken into account when interpreting results.
For male soccer players, the anthropometric profile and physical performance is already known but in women it is not well documented, the knowledge of these differences can qualify the training. ...Therefore, the aim of this study was two-fold: (i) to compare the anthropometric profile and physical fitness of Brazilian professional female and male soccer players; and (ii) analyze the relationships between anthropometric and physical fitness measures per sex. A cross-sectional study including 92 professional soccer players 44 women and 48 men was conducted. After one week of the end of the pre-season, the players from four different teams were assessed to anthropometric profile assessments and physical performance tests (e.g., jumping, linear sprint, change-of-direction and aerobic based test). Comparisons, correlation, and effect size analysis were performed between groups. Apart from the age and change-of-direction test, all other anthropometric and physical performance variables were significant different between women and men. Men were significantly faster than women in the 20 m linear sprint test (8.87%; p < .001; d = 2.382). The sum of skinfolds was correlated with some physical performance variables; however, body mass and body height were corrected with physical performance only for female soccer players. Thus, men performed better than women for most physical tests and presents differences in the anthropometric profile. Finally, coaches should consider that the anthropometric and performance profile are correlated differently between female and male soccer players. Thus, coaches should consider gender-specific parameters to create goals about the anthropometric and physical performance profile of players.
Prediction equations are commonly used to estimate body fat from anthropometric measurements, but are population specific. We aimed to establish and validate a body composition prediction formula for ...Asian newborns, and compared the performance of this formula with that of a published equation.
Among 262 neonates (174 from day 0, 88 from days 1-3 post delivery) from a prospective cohort study, body composition was measured using air-displacement plethysmography (PEA POD), with standard anthropometric measurements, including triceps and subscapular skinfolds. Using fat mass measurement by PEA POD as a reference, stepwise linear regression was utilized to develop a prediction equation in a randomly selected subgroup of 62 infants measured on days 1-3, which was then validated in another subgroup of 200 infants measured on days 0-3.
Regression analyses revealed subscapular skinfolds, weight, gender and gestational age were significant predictors of neonatal fat mass, explaining 81.1% of the variance, but not triceps skinfold or ethnicity. By Bland-Altman analyses, our prediction equation revealed a non-significant bias with limits of agreement (LOA) similar to those of a published equation for infants measured on days 1-3 (95% LOA: (-0.25, 0.26) kg vs (-0.23, 0.21) kg) and on day 0 (95% LOA: (-0.19, 0.17) kg vs (-0.17, 0.18) kg). The published equation, however, exhibited a systematic bias in our sample.
Our equation requires only one skinfold site measurement, which can significantly reduce time and effort. It does not require the input of ethnicity and, thus, aid its application to other Asian neonatal populations.
Purpose
This study aimed to investigate the effect of fluctuating female hormones during the menstrual cycle (MC) and oral contraceptive (OC) cycle on different measures of body composition.
Methods
...Twenty-two women with a natural MC and thirty women currently taking combined monophasic OC were assessed over three phases of the menstrual or oral contraceptive cycle. Body weight, skinfolds, bioelectric impedance analysis (BIA), ultrasound, dual-energy X-ray absorptiometry (DXA), and peripheral quantitative computed tomography (pQCT) measurements were performed to assess body composition. Urine specific gravity (USG) was measured as an indication of hydration, and serum oestradiol and progesterone were measured to confirm cycle phases.
Results
Five participants with a natural MC were excluded based on the hormone analysis. For the remaining participants, no significant changes over the MC and OC cycle were found for body weight, USG, skinfolds, BIA, ultrasound and pQCT measures. However, DXA body fat percentage and fat mass were lower in the late follicular phase compared to the mid-luteal phase of the MC, while for the OC cycle, DXA body fat percentage was higher and lean mass lower in the early hormone phase compared with the late hormone phase.
Conclusion
Our findings suggest that assessment of body fat percentage through BIA and skinfolds may be performed without considering the MC or OC cycle. Body adiposity assessment via DXA, however, may be affected by female hormone fluctuations and therefore, it may be advisable to perform repeat testing using DXA during the same phase of the MC or OC cycle.
The study of elite basketball players’ anthropometric characteristics alongside those of body composition contributes significantly to their profiling as professional athletes and plays an important ...role in the selection process, as these characteristics can have a significant impact on performance. In the current study, 110 professional basketball players from a series of Spanish professional Leagues (ACB, LEB and EBA) and youth level National Teams (U20 and U18) had their anthropometric profiles measured and compared to determine differences between them. Furthermore, all 110 players were divided into three different categories according to their playing position: guards, forwards and centres. The results obtained show no significant differences between players in different competitions in weight, height and the sum of skinfolds. Nonetheless, there were several differences related to body fat content (13.03% in ACB players and 10.52% in the lower categories and National Teams). There were also several differences found between the different playing positions amongst all playing levels in body mass (79.56 ± 2.41, 91.04 ± 1.51 and 104.56 ± 1.73 kg), height (182.28 ± 0.96, 195.65 ± 1.00 and 204.08 ± 0.67 cm), skinfold distribution and perimeters. However, there were no significant differences in body fat content between the different playing positions. The conclusions obtained from this study provide a better understanding to basketball specialists regarding the selection process of players at the elite level, especially on the transition from youth elite programs to men’s elite leagues.
Objective: In soccer players, body fat mass (FM) is commonly estimated by assessment methods such as skinfolds, bioelectrical impedance analysis (BIA) and dual-energy X-ray absorptiometry (DXA). The ...main aim was to cross-validate the 3 field methods for quantifying body fat against a recent model of DXA. Methods: This study involved a group of 18 international-level, elite male soccer players belonging to the squad of a Serie A club in Italy. Results: All skinfold equations (with the exception of Deuremberg) showed large-to-very large positive correlations (r from 0.61 to 0.82, p ≤ 0.01) with DXA, however, BIA %FM data showed unclear relationship. All the sum of skinfolds showed moderate-to-very large positive correlations with DXA (r from 0.46 to 0.79, p values from 0.061 to 0.000). The combination of triceps and iliac crest skinfolds selected by the stepwise procedure explained 78.6% variance in DXA total %FM. Conclusion: All different methods employed in the present study are likely to differ, which prevent their use interchangeably. Almost all the equations showed positive correlations, but with different values in comparison with a recent model of DXA. Finally, the sum of skinfolds appears to be a good alternative when limited time and budged is available.
The purpose of this study was to assess the agreement between a field-based three-compartment (3CFIELD) model and a laboratory-based three-compartment (3CLAB) model for tracking body composition ...changes over time.
Resistance-trained males completed a supervised nutrition and resistance training intervention. Before and after the intervention, assessments were performed via air displacement plethysmography (ADP), bioimpedance spectroscopy (BIS), portable ultrasonography (US), and bioelectrical impedance analysis (BIA). ADP body density and BIS body water were used within the reference 3CLAB model, whereas US-derived body density and BIA body water were used within the 3CFIELD model. Two-compartment model body composition estimates provided by US and BIA were also examined. Changes in fat-free mass and fat mass were analyzed using repeated-measures ANOVA, equivalence testing, Bland-Altman analysis, linear regression, and related validity analyses.
Significant increases in fat-free mass (3CLAB, 4.0 ± 4.5 kg; 3CFIELD, 3.9 ± 4.2 kg; US, 3.2 ± 4.3 kg; BIA, 3.9 ± 4.2 kg) and fat mass (3CLAB, 1.3 ± 2.2 kg; 3CFIELD, 1.4 ± 2.2 kg; US, 2.1 ± 2.6 kg; BIA, 1.4 ± 2.9 kg) were detected by all methods. However, only the 3CFIELD model demonstrated equivalence with the 3CLAB model. In addition, the 3CFIELD model exhibited superior performance to US and BIA individually, as indicated by the total error (3CFIELD, 1.0 kg; US, 1.8 kg; BIA, 1.6 kg), 95% limits of agreement (3CFIELD, ±2.1 kg; US, ±3.3 kg; BIA, ±3.1 kg), correlation coefficients (3CFIELD, 0.79-0.82; US, 0.49-0.55; BIA, 0.61-0.72), and additional metrics.
The present study demonstrated the potential usefulness of a 3CFIELD model incorporating US and BIA data for tracking body composition changes over time, as well as its superiority to US or BIA individually. As such, this accessible multicompartment model may be suitable for implementation in field or limited-resource settings.
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