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

Edgar I. Gasperín-Rodríguez, Julio A. Gómez-Figueroa, Luis M. Gómez-Miranda (), Paul T. Ríos-Gallardo, Carolina Palmeros-Exsome, Marco A. Hernández-Lepe, José Moncada-Jiménez and Diego A. Bonilla
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Edgar I. Gasperín-Rodríguez: Nutrition Faculty, Veracruzan University, Veracruz 91700, Mexico
Julio A. Gómez-Figueroa: Physical Education, Sport and Recreation School, Veracruzan University, Veracruz 94294, Mexico
Luis M. Gómez-Miranda: Sports Faculty, Autonomous University of Baja California, Tijuana 22390, Mexico
Paul T. Ríos-Gallardo: Nutrition Faculty, Veracruzan University, Veracruz 91700, Mexico
Carolina Palmeros-Exsome: Nutrition Faculty, Veracruzan University, Veracruz 91700, Mexico
Marco A. Hernández-Lepe: Medical and Psychology School, Autonomous University of Baja California, Tijuana 22390, Mexico
José Moncada-Jiménez: Human Movement Sciences Research Center (CIMOHU), University of Costa Rica, San José 11501, Costa Rica
Diego A. Bonilla: Research Division, Dynamical Business & Science Society–DBSS International SAS, Bogotá 110311, Colombia

IJERPH, 2022, vol. 19, issue 23, 1-10

Abstract: This study aimed to determine the body composition profile of candidates applying for a Physical Education and Sports major. 327 young adults (F: 87, M: 240) participated in this cross-sectional study. Nutritional status and body composition analysis were performed, and the profiles were generated using an unsupervised machine learning algorithm. Body mass index (BMI), percentage of fat mass (%FM), percentage of muscle mass (%MM), metabolic age (MA), basal metabolic rate (BMR), and visceral fat level (VFL) were used as input variables. BMI values were normal-weight although VFL was significantly higher in men (<0.001; η 2 = 0.104). MA was positively correlated with BMR (0.81 [0.77, 0.85]; p < 0.01), BMI (0.87 [0.84, 0.90]; p < 0.01), and VFL (0.77 [0.72, 0.81]; p < 0.01). The hierarchical clustering analysis revealed two significantly different age-independent profiles: Cluster 1 (n = 265), applicants of both sexes that were shorter, lighter, with lower adiposity and higher lean mass; and, Cluster 2 (n = 62), a group of overweight male applicants with higher VFL, taller, with lower %MM and estimated energy expended at rest. We identified two profiles that might help universities, counselors and teachers/lecturers to identify applicants in which is necessary to increase physical activity levels and improve dietary habits.

Keywords: body fat; public health students; physical education and sports major; university health services; unsupervised machine learning (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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