Morphology, body composition and maturity status of young Colombian athletes from the Urabá subregion: A k-Medoids and hierarchical clustering analysis

Abstract

The Urabá subregion is one of the most prominent cradles of Colombian elite athletes and, therefore, highly recognized within the "Land of Athletes" framework of the Colombian Ministry of Sports. In order to contribute to the young talent identification and selection of sports specialization, the aim of this STROBE-based cross-sectional study was to determine the morphological characteristics (MC), body composition (BC) and maturity status (MS) of U16 athletes from this subregion (7 municipalities). Eighty-one young athletes (66 weightlifters, 15 boxers) with at least one regional-competition of experience participated (33F; 48M; 14.9 ± 1.4 years; 62.28 ± 16.6 kg; 162.8 ± 9.9 cm). After parental informed consent, ISAKstandardized anthropometric data were collected during a youth sports championship. Athletes were subdivided in clusters using the PAM (k-Medoids clustering) and the bottom-up agglomerative (hierarchical clustering) algorithms. Both clustering methods were based on 55 variables that encompassed MC (raw variables, indices, somatotype), BC (five-compartment model, %BF-equations, ∑S) and MS (maturity offset, PHV, inter alia). The number of clusters was predefined as k = 2 since was the best solution according to 18 criterion-algorithms (100 bootstrap simulations). Non-parametric tests showed significant differences between sex, sports, municipalities and clusters for certain analysed variables. Internal validity of the clustering showed that sport type might explain the variation in the data; thus, it is noteworthy reasonable to recommend the implementation of unsupervised machine learning strategies along with other supervised methodologies in the identification and characterization of young talents and early sports specialization in Colombian athletes with Olympic projection but further research and support is needed.

Development and Validation of a Novel Waist Girth-Based Equation to Estimate Fat Mass in Young Colombian Elite Athletes (F20CA Equation): A STROSA-Based Study

“…Young athletes with projection to a high competitive level require early monitoring to understand individual responses to exercise programs, morphological changes [ 27 , 28 ] and the development of technical skills [ 46 , 47 ]. Therefore, the assessment of nutritional status and body composition is extremely important to avoid unwarranted nutrient deficiencies, chronic low energy availability (LEA) and eating disorders [ 48 ].…”

“…Importantly, so far no anthropometry-based equations for estimating body composition in Colombian child and adolescent athletes have been developed [ 23 ]. The few research studies in this population focused on the external validation of skinfold-based equations [ 24 , 25 ] or the generation of anthropometric profiles [ 26 , 27 , 28 ]. Only one study performed the concurrent validation of five equations (none including WG) to estimate the %BF in young Colombian athletes [ 25 ].…”

Body Composition Profiles of Applicants to a Physical Education and Sports Major in Southeastern Mexico

“…These statistical calculations were performed using the IBM SPSS v26 (IBM Corp., Armonk, NY, USA) by establishing a significance level of p < 0.05. A clustering analysis was performed as previous studies [ 25 , 26 ] to generate the profiles based on all measured variables within the free software environment for statistical computing and graphics R v4.0.2 [ 27 ]. The number of clusters was determined using the ‘NbClust’ function testing criterion algorithms that compared the two- to eight-cluster solutions.…”

Simple anthropometry-based calculations to monitor body composition in athletes: Scoping review and reference values

“…This must be applied specifically to the P S, as a valid proxy measurement of adiposity (Marfell-Jones, 2001). On the other hand, for reporting results of an investigation, it is advisable to give complete statistical information including statistics power, sample estimates, confidence intervals, test statistics P-values, and effect size (assuming that basic details such as sample size and standard errors have already been given in the text) (Gardner and Altman, 1986), whilst the public awareness of artificial intelligence's practical benefits in kinanthropometry is raised (Bonilla et al, 2020). See a comprehensive step-by-step explanation of these statistical calculations, with anthropometric data examples, for a personalized approach to evaluating progress and effectiveness of exercise and sports nutrition interventions in Swinton et al (2018).…”