Body composition and prediction equations using skinfold thickness for body fat percentage in Southern Brazilian adolescents
The purpose of this study was to: a) determine the nutritional status of Brazilian adolescents, and; b) present a skinfold thickness model (ST) to estimate body fat developed with Brazilian samples, using dual energy x-ray absorptiometry (DXA) as reference method. The main study group was composed o...
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| creator | Ripka, Wagner Luis Ulbricht, Leandra Gewehr, Pedro Miguel |
| description | The purpose of this study was to: a) determine the nutritional status of Brazilian adolescents, and; b) present a skinfold thickness model (ST) to estimate body fat developed with Brazilian samples, using dual energy x-ray absorptiometry (DXA) as reference method.
The main study group was composed of 374 adolescents, and further 42 adolescents for the validation group. Weight, height, waist circumference measurements, and body mass index (BMI) were collected, as well as nine ST-biceps (BI), triceps (TR), chest (CH), axillary (AX) subscapularis (SB), abdominal (AB), suprailiac (SI), medial thigh (TH), calf (CF), and fat percentage (%BF) obtained by DXA.
The prevalence of overweight in adolescents was 20.9%, and obesity 5.8%. Regression analysis through ordinary least square method (OLS) allowed obtainment of three equations with values of R2 = 0.935, 0.912 and 0.850, standard error estimated = 1.79, 1.78 and 1.87, and bias = 0.06, 0.20 and 0.05, respectively.
the innovation of this study lies in presenting new regression equations for predicting body fat in Southern Brazilian adolescents based on a representative and heterogeneous sample from DXA. |
| doi_str_mv | 10.1371/journal.pone.0184854 |
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The main study group was composed of 374 adolescents, and further 42 adolescents for the validation group. Weight, height, waist circumference measurements, and body mass index (BMI) were collected, as well as nine ST-biceps (BI), triceps (TR), chest (CH), axillary (AX) subscapularis (SB), abdominal (AB), suprailiac (SI), medial thigh (TH), calf (CF), and fat percentage (%BF) obtained by DXA.
The prevalence of overweight in adolescents was 20.9%, and obesity 5.8%. Regression analysis through ordinary least square method (OLS) allowed obtainment of three equations with values of R2 = 0.935, 0.912 and 0.850, standard error estimated = 1.79, 1.78 and 1.87, and bias = 0.06, 0.20 and 0.05, respectively.
the innovation of this study lies in presenting new regression equations for predicting body fat in Southern Brazilian adolescents based on a representative and heterogeneous sample from DXA.</description><identifier>ISSN: 1932-6203</identifier><identifier>EISSN: 1932-6203</identifier><identifier>DOI: 10.1371/journal.pone.0184854</identifier><identifier>PMID: 28910398</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Abdomen ; Absorptiometry, Photon - methods ; Adipose tissue ; Adipose Tissue - diagnostic imaging ; Adolescent ; Adolescents ; Biology and Life Sciences ; Blood pressure ; Body Composition ; Body fat ; Body Height ; Body mass ; Body mass index ; Body measurements ; Body weight ; Brazil ; Computer engineering ; Dual energy X-ray absorptiometry ; Energy consumption ; Evaluation ; Female ; Health status indicators ; Human nutrition ; Humans ; Innovations ; Male ; Mathematical models ; Measurement ; Medical research ; Medicine and Health Sciences ; Nutritional Status ; Obesity ; Obesity - epidemiology ; Overweight ; Overweight - epidemiology ; Pediatrics ; People and Places ; Predictions ; Prevalence ; Regression analysis ; Skinfold Thickness ; Standard error ; Teenagers ; Thigh ; Values ; Variables</subject><ispartof>PloS one, 2017-09, Vol.12 (9), p.e0184854-e0184854</ispartof><rights>COPYRIGHT 2017 Public Library of Science</rights><rights>2017 Ripka et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>2017 Ripka et al 2017 Ripka et al</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c692t-bc99b05664e1b40d7921cf5054e2b6356129892035ab3e78507c54739f7aa2eb3</citedby><cites>FETCH-LOGICAL-c692t-bc99b05664e1b40d7921cf5054e2b6356129892035ab3e78507c54739f7aa2eb3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC5599014/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC5599014/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,315,728,781,785,865,886,2103,2929,23868,27926,27927,53793,53795</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/28910398$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><contributor>Kiechl, Stefan</contributor><creatorcontrib>Ripka, Wagner Luis</creatorcontrib><creatorcontrib>Ulbricht, Leandra</creatorcontrib><creatorcontrib>Gewehr, Pedro Miguel</creatorcontrib><title>Body composition and prediction equations using skinfold thickness for body fat percentage in Southern Brazilian adolescents</title><title>PloS one</title><addtitle>PLoS One</addtitle><description>The purpose of this study was to: a) determine the nutritional status of Brazilian adolescents, and; b) present a skinfold thickness model (ST) to estimate body fat developed with Brazilian samples, using dual energy x-ray absorptiometry (DXA) as reference method.
The main study group was composed of 374 adolescents, and further 42 adolescents for the validation group. Weight, height, waist circumference measurements, and body mass index (BMI) were collected, as well as nine ST-biceps (BI), triceps (TR), chest (CH), axillary (AX) subscapularis (SB), abdominal (AB), suprailiac (SI), medial thigh (TH), calf (CF), and fat percentage (%BF) obtained by DXA.
The prevalence of overweight in adolescents was 20.9%, and obesity 5.8%. Regression analysis through ordinary least square method (OLS) allowed obtainment of three equations with values of R2 = 0.935, 0.912 and 0.850, standard error estimated = 1.79, 1.78 and 1.87, and bias = 0.06, 0.20 and 0.05, respectively.
the innovation of this study lies in presenting new regression equations for predicting body fat in Southern Brazilian adolescents based on a representative and heterogeneous sample from DXA.</description><subject>Abdomen</subject><subject>Absorptiometry, Photon - methods</subject><subject>Adipose tissue</subject><subject>Adipose Tissue - diagnostic imaging</subject><subject>Adolescent</subject><subject>Adolescents</subject><subject>Biology and Life Sciences</subject><subject>Blood pressure</subject><subject>Body Composition</subject><subject>Body fat</subject><subject>Body Height</subject><subject>Body mass</subject><subject>Body mass index</subject><subject>Body measurements</subject><subject>Body weight</subject><subject>Brazil</subject><subject>Computer engineering</subject><subject>Dual energy X-ray absorptiometry</subject><subject>Energy consumption</subject><subject>Evaluation</subject><subject>Female</subject><subject>Health status indicators</subject><subject>Human nutrition</subject><subject>Humans</subject><subject>Innovations</subject><subject>Male</subject><subject>Mathematical models</subject><subject>Measurement</subject><subject>Medical research</subject><subject>Medicine and Health Sciences</subject><subject>Nutritional Status</subject><subject>Obesity</subject><subject>Obesity - epidemiology</subject><subject>Overweight</subject><subject>Overweight - epidemiology</subject><subject>Pediatrics</subject><subject>People and Places</subject><subject>Predictions</subject><subject>Prevalence</subject><subject>Regression analysis</subject><subject>Skinfold Thickness</subject><subject>Standard error</subject><subject>Teenagers</subject><subject>Thigh</subject><subject>Values</subject><subject>Variables</subject><issn>1932-6203</issn><issn>1932-6203</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2017</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><sourceid>DOA</sourceid><recordid>eNqNk01v1DAQhiMEoqXwDxBYQkJw2MWOP2JfKrUVHytVqkSBq-U4TtatN07tBFHEj8fZTasN6gHlEMd55p2Z154se4ngEuECfbjyQ2iVW3a-NUuIOOGUPMoOkcD5guUQP95bH2TPYryCkGLO2NPsIOcCQSz4Yfbn1Fe3QPtN56PtrW-BaivQBVNZvf00N4MaFxEM0bYNiNe2rb2rQL-2-ro1MYLaB1COMrXqQWeCNm2vGgNsCy790K9NaMFpUL-tsyrpV96ZODLxefakVi6aF9P7KPv-6eO3sy-L84vPq7OT84VmIu8XpRaihJQxYlBJYFWIHOmaQkpMXjJMGcoFF6lNqkpsCk5hoSkpsKgLpXJT4qPs9U63cz7Kybgokz2c5yRnJBGrHVF5dSW7YDcq3EqvrNxu-NBIFXqrnZGCl7CCRJWoSMUgwTViKuUjKlXLS5O0jqdsQ7kx1dhpUG4mOv_T2rVs_E9JqRAQjcW8mwSCvxlM7OXGJsOcU63xw1g3wQwVkOQJffMP-nB3E9Wo1MB4fimvHkXlCYWEUswETtTyASo9ldlYnW5ZbdP-LOD9LCAxvfnVN2qIUa4uv_4_e_Fjzr7dY9dGuX4dvRu213AOkh2og48xmPreZATlOCR3bshxSOQ0JCns1f4B3QfdTQX-C5_nDbQ</recordid><startdate>20170914</startdate><enddate>20170914</enddate><creator>Ripka, Wagner Luis</creator><creator>Ulbricht, Leandra</creator><creator>Gewehr, Pedro Miguel</creator><general>Public Library of Science</general><general>Public Library of Science (PLoS)</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>IOV</scope><scope>ISR</scope><scope>3V.</scope><scope>7QG</scope><scope>7QL</scope><scope>7QO</scope><scope>7RV</scope><scope>7SN</scope><scope>7SS</scope><scope>7T5</scope><scope>7TG</scope><scope>7TM</scope><scope>7U9</scope><scope>7X2</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8AO</scope><scope>8C1</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>8FH</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>ATCPS</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>BHPHI</scope><scope>C1K</scope><scope>CCPQU</scope><scope>D1I</scope><scope>DWQXO</scope><scope>FR3</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>H94</scope><scope>HCIFZ</scope><scope>K9.</scope><scope>KB.</scope><scope>KB0</scope><scope>KL.</scope><scope>L6V</scope><scope>LK8</scope><scope>M0K</scope><scope>M0S</scope><scope>M1P</scope><scope>M7N</scope><scope>M7P</scope><scope>M7S</scope><scope>NAPCQ</scope><scope>P5Z</scope><scope>P62</scope><scope>P64</scope><scope>PATMY</scope><scope>PDBOC</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PTHSS</scope><scope>PYCSY</scope><scope>RC3</scope><scope>7X8</scope><scope>5PM</scope><scope>DOA</scope></search><sort><creationdate>20170914</creationdate><title>Body composition and prediction equations using skinfold thickness for body fat percentage in Southern Brazilian adolescents</title><author>Ripka, Wagner Luis ; Ulbricht, Leandra ; Gewehr, Pedro Miguel</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c692t-bc99b05664e1b40d7921cf5054e2b6356129892035ab3e78507c54739f7aa2eb3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2017</creationdate><topic>Abdomen</topic><topic>Absorptiometry, Photon - methods</topic><topic>Adipose tissue</topic><topic>Adipose Tissue - diagnostic imaging</topic><topic>Adolescent</topic><topic>Adolescents</topic><topic>Biology and Life Sciences</topic><topic>Blood pressure</topic><topic>Body Composition</topic><topic>Body fat</topic><topic>Body Height</topic><topic>Body mass</topic><topic>Body mass index</topic><topic>Body measurements</topic><topic>Body weight</topic><topic>Brazil</topic><topic>Computer engineering</topic><topic>Dual energy X-ray absorptiometry</topic><topic>Energy consumption</topic><topic>Evaluation</topic><topic>Female</topic><topic>Health status indicators</topic><topic>Human nutrition</topic><topic>Humans</topic><topic>Innovations</topic><topic>Male</topic><topic>Mathematical models</topic><topic>Measurement</topic><topic>Medical research</topic><topic>Medicine and Health Sciences</topic><topic>Nutritional Status</topic><topic>Obesity</topic><topic>Obesity - epidemiology</topic><topic>Overweight</topic><topic>Overweight - epidemiology</topic><topic>Pediatrics</topic><topic>People and Places</topic><topic>Predictions</topic><topic>Prevalence</topic><topic>Regression analysis</topic><topic>Skinfold Thickness</topic><topic>Standard error</topic><topic>Teenagers</topic><topic>Thigh</topic><topic>Values</topic><topic>Variables</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Ripka, Wagner Luis</creatorcontrib><creatorcontrib>Ulbricht, Leandra</creatorcontrib><creatorcontrib>Gewehr, Pedro Miguel</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Gale In Context: Opposing Viewpoints</collection><collection>Gale In Context: Science</collection><collection>ProQuest Central (Corporate)</collection><collection>Animal Behavior Abstracts</collection><collection>Bacteriology Abstracts (Microbiology B)</collection><collection>Biotechnology Research Abstracts</collection><collection>Nursing & Allied Health Database</collection><collection>Ecology Abstracts</collection><collection>Entomology Abstracts (Full archive)</collection><collection>Immunology Abstracts</collection><collection>Meteorological & Geoastrophysical Abstracts</collection><collection>Nucleic Acids Abstracts</collection><collection>Virology and AIDS Abstracts</collection><collection>Agricultural Science Collection</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</collection><collection>ProQuest Pharma Collection</collection><collection>Public Health Database</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>Materials Science & Engineering Collection</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>Agricultural & Environmental Science Collection</collection><collection>ProQuest Central Essentials</collection><collection>Biological Science Collection</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>Natural Science Collection</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ProQuest One Community College</collection><collection>ProQuest Materials Science Collection</collection><collection>ProQuest Central Korea</collection><collection>Engineering Research Database</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Central Student</collection><collection>AIDS and Cancer Research Abstracts</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Materials Science Database</collection><collection>Nursing & Allied Health Database (Alumni Edition)</collection><collection>Meteorological & Geoastrophysical Abstracts - 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Academic</collection><collection>PubMed Central (Full Participant titles)</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>PloS one</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Ripka, Wagner Luis</au><au>Ulbricht, Leandra</au><au>Gewehr, Pedro Miguel</au><au>Kiechl, Stefan</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Body composition and prediction equations using skinfold thickness for body fat percentage in Southern Brazilian adolescents</atitle><jtitle>PloS one</jtitle><addtitle>PLoS One</addtitle><date>2017-09-14</date><risdate>2017</risdate><volume>12</volume><issue>9</issue><spage>e0184854</spage><epage>e0184854</epage><pages>e0184854-e0184854</pages><issn>1932-6203</issn><eissn>1932-6203</eissn><abstract>The purpose of this study was to: a) determine the nutritional status of Brazilian adolescents, and; b) present a skinfold thickness model (ST) to estimate body fat developed with Brazilian samples, using dual energy x-ray absorptiometry (DXA) as reference method.
The main study group was composed of 374 adolescents, and further 42 adolescents for the validation group. Weight, height, waist circumference measurements, and body mass index (BMI) were collected, as well as nine ST-biceps (BI), triceps (TR), chest (CH), axillary (AX) subscapularis (SB), abdominal (AB), suprailiac (SI), medial thigh (TH), calf (CF), and fat percentage (%BF) obtained by DXA.
The prevalence of overweight in adolescents was 20.9%, and obesity 5.8%. Regression analysis through ordinary least square method (OLS) allowed obtainment of three equations with values of R2 = 0.935, 0.912 and 0.850, standard error estimated = 1.79, 1.78 and 1.87, and bias = 0.06, 0.20 and 0.05, respectively.
the innovation of this study lies in presenting new regression equations for predicting body fat in Southern Brazilian adolescents based on a representative and heterogeneous sample from DXA.</abstract><cop>United States</cop><pub>Public Library of Science</pub><pmid>28910398</pmid><doi>10.1371/journal.pone.0184854</doi><tpages>e0184854</tpages><oa>free_for_read</oa></addata></record> |
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| source | MEDLINE; DOAJ Directory of Open Access Journals; Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals; Public Library of Science (PLoS) Journals Open Access; PubMed Central; Free Full-Text Journals in Chemistry |
| subjects | Abdomen Absorptiometry, Photon - methods Adipose tissue Adipose Tissue - diagnostic imaging Adolescent Adolescents Biology and Life Sciences Blood pressure Body Composition Body fat Body Height Body mass Body mass index Body measurements Body weight Brazil Computer engineering Dual energy X-ray absorptiometry Energy consumption Evaluation Female Health status indicators Human nutrition Humans Innovations Male Mathematical models Measurement Medical research Medicine and Health Sciences Nutritional Status Obesity Obesity - epidemiology Overweight Overweight - epidemiology Pediatrics People and Places Predictions Prevalence Regression analysis Skinfold Thickness Standard error Teenagers Thigh Values Variables |
| title | Body composition and prediction equations using skinfold thickness for body fat percentage in Southern Brazilian adolescents |
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