Cargando…
Overweight in Young Athletes: New Predictive Model of Overfat Condition
The aim of the study is to establish a simple and low-cost method that, associated with Body Mass Index (BMI), differentiates overweight conditions due to a prevalence of lean mass compared to an excess of fat mass during the evaluation of young athletes. 1046 young athletes (620 male, 426 female) a...
Autores principales: | , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2019
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6950678/ https://www.ncbi.nlm.nih.gov/pubmed/31888120 http://dx.doi.org/10.3390/ijerph16245128 |
_version_ | 1783486127550758912 |
---|---|
author | Mascherini, Gabriele Petri, Cristian Ermini, Elena Bini, Vittorio Calà, Piergiuseppe Galanti, Giorgio Modesti, Pietro Amedeo |
author_facet | Mascherini, Gabriele Petri, Cristian Ermini, Elena Bini, Vittorio Calà, Piergiuseppe Galanti, Giorgio Modesti, Pietro Amedeo |
author_sort | Mascherini, Gabriele |
collection | PubMed |
description | The aim of the study is to establish a simple and low-cost method that, associated with Body Mass Index (BMI), differentiates overweight conditions due to a prevalence of lean mass compared to an excess of fat mass during the evaluation of young athletes. 1046 young athletes (620 male, 426 female) aged between eight and 18 were enrolled. Body composition assessments were performed with anthropometry, circumferences, skinfold, and bioimpedance. Overweight was established with BMI, while overfat was established with the percentage of fat mass: 3.5% were underweight, 72.8% were normal weight, 20.1% were overweight, and 3.5% were obese according to BMI; according to the fat mass, 9.5% were under fat, 63.6% were normal fat, 16.2% were overfat, and 10.8% were obese. Differences in overfat prediction were found using BMI alone or with the addition of the triceps fold (area under the receiver operating characteristics curve (AUC) for BMI = 0.867 vs. AUC for BMI + TRICEPS = 0.955, p < 0.001). These results allowed the creation of a model factoring in age, sex, BMI, and triceps fold that could provide the probability that a young overweight athlete is also in an overfat condition. The calculated probability could reduce the risk of error in establishing the correct weight status of young athletes. |
format | Online Article Text |
id | pubmed-6950678 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-69506782020-01-16 Overweight in Young Athletes: New Predictive Model of Overfat Condition Mascherini, Gabriele Petri, Cristian Ermini, Elena Bini, Vittorio Calà, Piergiuseppe Galanti, Giorgio Modesti, Pietro Amedeo Int J Environ Res Public Health Article The aim of the study is to establish a simple and low-cost method that, associated with Body Mass Index (BMI), differentiates overweight conditions due to a prevalence of lean mass compared to an excess of fat mass during the evaluation of young athletes. 1046 young athletes (620 male, 426 female) aged between eight and 18 were enrolled. Body composition assessments were performed with anthropometry, circumferences, skinfold, and bioimpedance. Overweight was established with BMI, while overfat was established with the percentage of fat mass: 3.5% were underweight, 72.8% were normal weight, 20.1% were overweight, and 3.5% were obese according to BMI; according to the fat mass, 9.5% were under fat, 63.6% were normal fat, 16.2% were overfat, and 10.8% were obese. Differences in overfat prediction were found using BMI alone or with the addition of the triceps fold (area under the receiver operating characteristics curve (AUC) for BMI = 0.867 vs. AUC for BMI + TRICEPS = 0.955, p < 0.001). These results allowed the creation of a model factoring in age, sex, BMI, and triceps fold that could provide the probability that a young overweight athlete is also in an overfat condition. The calculated probability could reduce the risk of error in establishing the correct weight status of young athletes. MDPI 2019-12-16 2019-12 /pmc/articles/PMC6950678/ /pubmed/31888120 http://dx.doi.org/10.3390/ijerph16245128 Text en © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Mascherini, Gabriele Petri, Cristian Ermini, Elena Bini, Vittorio Calà, Piergiuseppe Galanti, Giorgio Modesti, Pietro Amedeo Overweight in Young Athletes: New Predictive Model of Overfat Condition |
title | Overweight in Young Athletes: New Predictive Model of Overfat Condition |
title_full | Overweight in Young Athletes: New Predictive Model of Overfat Condition |
title_fullStr | Overweight in Young Athletes: New Predictive Model of Overfat Condition |
title_full_unstemmed | Overweight in Young Athletes: New Predictive Model of Overfat Condition |
title_short | Overweight in Young Athletes: New Predictive Model of Overfat Condition |
title_sort | overweight in young athletes: new predictive model of overfat condition |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6950678/ https://www.ncbi.nlm.nih.gov/pubmed/31888120 http://dx.doi.org/10.3390/ijerph16245128 |
work_keys_str_mv | AT mascherinigabriele overweightinyoungathletesnewpredictivemodelofoverfatcondition AT petricristian overweightinyoungathletesnewpredictivemodelofoverfatcondition AT erminielena overweightinyoungathletesnewpredictivemodelofoverfatcondition AT binivittorio overweightinyoungathletesnewpredictivemodelofoverfatcondition AT calapiergiuseppe overweightinyoungathletesnewpredictivemodelofoverfatcondition AT galantigiorgio overweightinyoungathletesnewpredictivemodelofoverfatcondition AT modestipietroamedeo overweightinyoungathletesnewpredictivemodelofoverfatcondition |