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.
Since the publishing of Nolan and Temple Lang's "Computing in the Statistics Curriculum" in 2010, the American Statistical Association issued new recommendations in the revised GAISE College report. To reflect modern practice and technologies, they emphasize giving students experience with multivariable thinking. Students develop multivariable thinking when they analyze real data in the context of investigating research questions of interest, which typically involve complex relationships between many variables. Proficiency in a statistical programming language facilitates the development of multivariable thinking by giving students tools to investigate complex data on their own. However, learning a programming language in an introductory course is difficult for many students. In this paper, we recommend a set of computational skills for introductory courses, demonstrate them using Rtidyverse , and describe a classroom activity to develop computational skills and multivariable thinking. We provide a tidyverse tutorial for introductory students, our course guide, and classroom activities.
Fetal trajectories characterizing growth rates in utero have relied primarily on goodness of fit rather than mechanistic properties exhibited in utero . Here, we use a validated fetal–placental allometric scaling law and a first principles differential equations model of placental volume growth to generate biologically meaningful fetal–placental growth curves. The growth curves form the foundation for understanding healthy versus at-risk fetal growth and for identifying the timing of key events in utero .
Bio‐impedance analysis (BIA) is a common technique used to estimate body composition. BIA measures the electrical impedance in water contained in a subject’s body and uses this measurement to predict body composition. Phase angle is one of the components of body composition and is a major indicator of body fat percentage. Although BIA is less accurate when predicting body composition compared to dual‐energy X‐ray absorptiometry (DXA) or bod pods, it is cheaper, more portable, and does not require a licensed operator. For those reasons, athletes commonly use BIA to estimate body fat composition. Current BIA techniques are shown to be inaccurate for very athletic individuals [1] since it is calibrated based on the general population. Previous studies have calculated phase angle distribution in elite athletes using DXA data, but none have examined BIA. Our research uses empirical Bayesian analysis with BIA data to create reference percentiles for phase angle by sport and gender among elite European athletes. Body Fat Series: Bioimpedance (BIA)SamWinterNaked
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