Anthropometric multicompartmental model to predict body composition In Brazilian girls

Detalhes bibliográficos
Autor(a) principal: Machado, Dalmo
Data de Publicação: 2017
Outros Autores: Silva, Analiza, Gobbo, Luis [UNESP], Elias, Paula, Paula, Francisco J. A. de, Ramos, Nilo
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Institucional da UNESP
Texto Completo: http://dx.doi.org/10.1186/s13102-017-0088-7
http://hdl.handle.net/11449/159987
Resumo: Background: Anthropometric models remain appropriate alternatives to estimate body composition of peripubertal populations. However, these traditional models do not consider other body components that undergo major changes during peripubertal growth spurt, with restrictions to a multicompartimental approach as a quantitative growth. DXA has great potential to determine pediatric body composition in more than one component (3-C), but has limited use in field settings. Thus, the aim of this study was to propose and validate an anthropometric model for simultaneous estimation of lean soft tissue (LST), bone mineral content (BMC) and fat mass (FM) in healthy girls, from a multivariate approach of densitometric technique, as the criterion method. Methods: A sample of 84 Brazilian girls (7-17 years) was defined by chronological age and maturity offset. Whole total and regional DXA body scan were performed and, the components were defined (LST, BMC and FM) and considered as dependent variables. Twenty-one anthropometric measures were recorded as independent variables. From a multivariate regression, an anthropometric multicompartmental model was obtained. Results: It was possible to predict DXA body components with only four predictive measurements: body weight (BW); supra-iliac skinfold (SiSk); horizontal abdominal skinfold (HaSk) and contracted arm circumference (CaCi) with high coefficients of determination and low estimation errors (LST = 0.6662657 BW -0. 2157279 SiSk -0.2069373 HaSk + 0.3411678 CaCi -1.8504187; BMC = 0.0222185 BW -0.1001097 SiSk -0.0064539 HaSk -0.0084785 CaCi + 0.3733974 and FM = 0.3645630 BW + 0.1000325 SiSk -0.2888978 HaSk -0.4752146 CaCi + 2.8461916). The cross-validation was confirmed through the sum of squares of residuals (PRESS) method, presenting accurate coefficients (Q(PRESS)(2) from 0.81 to 0.93) and reduced error reliability (S-PRESS from 0.01 to 0.30). Conclusions: When sophisticated instruments are not available, this model provides valid estimates of multicompartmental body composition of girls in healthy Brazilian pediatric populations.
id UNSP_a869053846ef05fcbff8838111517a33
oai_identifier_str oai:repositorio.unesp.br:11449/159987
network_acronym_str UNSP
network_name_str Repositório Institucional da UNESP
repository_id_str 2946
spelling Anthropometric multicompartmental model to predict body composition In Brazilian girlsMulticompartmental analysisChildrenAdolescentsEquationDXABackground: Anthropometric models remain appropriate alternatives to estimate body composition of peripubertal populations. However, these traditional models do not consider other body components that undergo major changes during peripubertal growth spurt, with restrictions to a multicompartimental approach as a quantitative growth. DXA has great potential to determine pediatric body composition in more than one component (3-C), but has limited use in field settings. Thus, the aim of this study was to propose and validate an anthropometric model for simultaneous estimation of lean soft tissue (LST), bone mineral content (BMC) and fat mass (FM) in healthy girls, from a multivariate approach of densitometric technique, as the criterion method. Methods: A sample of 84 Brazilian girls (7-17 years) was defined by chronological age and maturity offset. Whole total and regional DXA body scan were performed and, the components were defined (LST, BMC and FM) and considered as dependent variables. Twenty-one anthropometric measures were recorded as independent variables. From a multivariate regression, an anthropometric multicompartmental model was obtained. Results: It was possible to predict DXA body components with only four predictive measurements: body weight (BW); supra-iliac skinfold (SiSk); horizontal abdominal skinfold (HaSk) and contracted arm circumference (CaCi) with high coefficients of determination and low estimation errors (LST = 0.6662657 BW -0. 2157279 SiSk -0.2069373 HaSk + 0.3411678 CaCi -1.8504187; BMC = 0.0222185 BW -0.1001097 SiSk -0.0064539 HaSk -0.0084785 CaCi + 0.3733974 and FM = 0.3645630 BW + 0.1000325 SiSk -0.2888978 HaSk -0.4752146 CaCi + 2.8461916). The cross-validation was confirmed through the sum of squares of residuals (PRESS) method, presenting accurate coefficients (Q(PRESS)(2) from 0.81 to 0.93) and reduced error reliability (S-PRESS from 0.01 to 0.30). Conclusions: When sophisticated instruments are not available, this model provides valid estimates of multicompartmental body composition of girls in healthy Brazilian pediatric populations.Univ Sao Paulo, Sch Phys Educ & Sport Ribeirao Preto, Bandeirantes Ave 3900, BR-14040900 Ribeirao Preto, SP, BrazilUniv Lisbon, Exercise & Hlth Lab, CIPER, Fac Motricidade Humana, Lisbon, PortugalUniv Estadual Paulista, Dept Phys Educ, Ribeirao Preto, BrazilUniv Sao Paulo, Ribeirao Preto Med Sch, Dept Internal Med, Ribeirao Preto, SP, BrazilCoastal Carolina Univ, Dept Grad & Specialty Studies, Conway, SC USAUniv Estadual Paulista, Dept Phys Educ, Ribeirao Preto, BrazilBiomed Central LtdUniversidade de São Paulo (USP)Univ LisbonUniversidade Estadual Paulista (Unesp)Coastal Carolina UnivMachado, DalmoSilva, AnalizaGobbo, Luis [UNESP]Elias, PaulaPaula, Francisco J. A. deRamos, Nilo2018-11-26T15:46:01Z2018-11-26T15:46:01Z2017-12-21info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article8application/pdfhttp://dx.doi.org/10.1186/s13102-017-0088-7Bmc Sports Science Medicine And Rehabilitation. London: Biomed Central Ltd, v. 9, 8 p., 2017.2052-1847http://hdl.handle.net/11449/15998710.1186/s13102-017-0088-7WOS:000418952900001WOS000418952900001.pdfWeb of Sciencereponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengBmc Sports Science Medicine And Rehabilitation0,926info:eu-repo/semantics/openAccess2024-01-01T06:16:28Zoai:repositorio.unesp.br:11449/159987Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T21:49:56.251253Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false
dc.title.none.fl_str_mv Anthropometric multicompartmental model to predict body composition In Brazilian girls
title Anthropometric multicompartmental model to predict body composition In Brazilian girls
spellingShingle Anthropometric multicompartmental model to predict body composition In Brazilian girls
Machado, Dalmo
Multicompartmental analysis
Children
Adolescents
Equation
DXA
title_short Anthropometric multicompartmental model to predict body composition In Brazilian girls
title_full Anthropometric multicompartmental model to predict body composition In Brazilian girls
title_fullStr Anthropometric multicompartmental model to predict body composition In Brazilian girls
title_full_unstemmed Anthropometric multicompartmental model to predict body composition In Brazilian girls
title_sort Anthropometric multicompartmental model to predict body composition In Brazilian girls
author Machado, Dalmo
author_facet Machado, Dalmo
Silva, Analiza
Gobbo, Luis [UNESP]
Elias, Paula
Paula, Francisco J. A. de
Ramos, Nilo
author_role author
author2 Silva, Analiza
Gobbo, Luis [UNESP]
Elias, Paula
Paula, Francisco J. A. de
Ramos, Nilo
author2_role author
author
author
author
author
dc.contributor.none.fl_str_mv Universidade de São Paulo (USP)
Univ Lisbon
Universidade Estadual Paulista (Unesp)
Coastal Carolina Univ
dc.contributor.author.fl_str_mv Machado, Dalmo
Silva, Analiza
Gobbo, Luis [UNESP]
Elias, Paula
Paula, Francisco J. A. de
Ramos, Nilo
dc.subject.por.fl_str_mv Multicompartmental analysis
Children
Adolescents
Equation
DXA
topic Multicompartmental analysis
Children
Adolescents
Equation
DXA
description Background: Anthropometric models remain appropriate alternatives to estimate body composition of peripubertal populations. However, these traditional models do not consider other body components that undergo major changes during peripubertal growth spurt, with restrictions to a multicompartimental approach as a quantitative growth. DXA has great potential to determine pediatric body composition in more than one component (3-C), but has limited use in field settings. Thus, the aim of this study was to propose and validate an anthropometric model for simultaneous estimation of lean soft tissue (LST), bone mineral content (BMC) and fat mass (FM) in healthy girls, from a multivariate approach of densitometric technique, as the criterion method. Methods: A sample of 84 Brazilian girls (7-17 years) was defined by chronological age and maturity offset. Whole total and regional DXA body scan were performed and, the components were defined (LST, BMC and FM) and considered as dependent variables. Twenty-one anthropometric measures were recorded as independent variables. From a multivariate regression, an anthropometric multicompartmental model was obtained. Results: It was possible to predict DXA body components with only four predictive measurements: body weight (BW); supra-iliac skinfold (SiSk); horizontal abdominal skinfold (HaSk) and contracted arm circumference (CaCi) with high coefficients of determination and low estimation errors (LST = 0.6662657 BW -0. 2157279 SiSk -0.2069373 HaSk + 0.3411678 CaCi -1.8504187; BMC = 0.0222185 BW -0.1001097 SiSk -0.0064539 HaSk -0.0084785 CaCi + 0.3733974 and FM = 0.3645630 BW + 0.1000325 SiSk -0.2888978 HaSk -0.4752146 CaCi + 2.8461916). The cross-validation was confirmed through the sum of squares of residuals (PRESS) method, presenting accurate coefficients (Q(PRESS)(2) from 0.81 to 0.93) and reduced error reliability (S-PRESS from 0.01 to 0.30). Conclusions: When sophisticated instruments are not available, this model provides valid estimates of multicompartmental body composition of girls in healthy Brazilian pediatric populations.
publishDate 2017
dc.date.none.fl_str_mv 2017-12-21
2018-11-26T15:46:01Z
2018-11-26T15:46:01Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://dx.doi.org/10.1186/s13102-017-0088-7
Bmc Sports Science Medicine And Rehabilitation. London: Biomed Central Ltd, v. 9, 8 p., 2017.
2052-1847
http://hdl.handle.net/11449/159987
10.1186/s13102-017-0088-7
WOS:000418952900001
WOS000418952900001.pdf
url http://dx.doi.org/10.1186/s13102-017-0088-7
http://hdl.handle.net/11449/159987
identifier_str_mv Bmc Sports Science Medicine And Rehabilitation. London: Biomed Central Ltd, v. 9, 8 p., 2017.
2052-1847
10.1186/s13102-017-0088-7
WOS:000418952900001
WOS000418952900001.pdf
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv Bmc Sports Science Medicine And Rehabilitation
0,926
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 8
application/pdf
dc.publisher.none.fl_str_mv Biomed Central Ltd
publisher.none.fl_str_mv Biomed Central Ltd
dc.source.none.fl_str_mv Web of Science
reponame:Repositório Institucional da UNESP
instname:Universidade Estadual Paulista (UNESP)
instacron:UNESP
instname_str Universidade Estadual Paulista (UNESP)
instacron_str UNESP
institution UNESP
reponame_str Repositório Institucional da UNESP
collection Repositório Institucional da UNESP
repository.name.fl_str_mv Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)
repository.mail.fl_str_mv
_version_ 1808129363144605696