Body composition profiles of applicants to a Physical Education and Sports major in southeastern Mexico

 

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書誌詳細
著者: Gasperín Rodríguez, Edgar Ismael, Gómez Figueroa, Julio Alejandro, Gómez Miranda, Luis Mario, Ríos Gall, Paul Tadeo, Palmeros Exsome, Carolina, Hernández Lepe, Marco Antonio, Moncada Jiménez, José, Bonilla Ocampo, Diego Alexander
フォーマット: artículo original
出版日付:2022
その他の書誌記述: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; η2 = 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.
国:Kérwá
機関:Universidad de Costa Rica
Repositorio:Kérwá
言語:Inglés
OAI Identifier:oai:kerwa.ucr.ac.cr:10669/102810
オンライン・アクセス:https://hdl.handle.net/10669/102810
https://doi.org/10.3390/ijerph192315685
キーワード:body fat
public health students
Physical Education and Sports major
university health services
unsupervised machine learning
body composition