Development of nonexercise prediction models of maximal oxygen uptake in healthy Japanese young men View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2006-11-07

AUTHORS

Kiyoshi Sanada, Taishi Midorikawa, Tomohiro Yasuda, Charles F. Kearns, Takashi Abe

ABSTRACT

The present study developed nonexercise models for predicting maximal oxygen uptake \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$({\dot{{V}}}\hbox{O}_{\rm{2max}})$$\end{document} using skeletal muscle (SM) mass and cardiac dimensions and to investigate the validity of these equations in healthy Japanese young men. Sixty healthy Japanese men were randomly separated into two groups: 40 in the development group and 20 in the validation group. \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\dot{{V}}}\hbox{O}_{\rm{2max}}$$\end{document} during treadmill running was measured using an automated breath-by-breath mass spectrometry system. Left ventricular internal dimensions at end-diastole (LVIDD) and at end-systole (LVIDS) were measured using M-mode ultrasound with a 2.5 MHz transducer. Stroke volume (SV) was calculated based on the Pombo rule. SM mass was predicted by B-mode ultrasound muscle thickness. Correlations were observed between \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\dot{{V}}}\hbox{O}_{\rm{2max}}$$\end{document} and predicted thigh (r = 0.74, P < 0.001) and lower leg SM mass (r = 0.55, P < 0.001). Furthermore, there were correlations between \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\dot{{V}}}\hbox{O}_{\rm{2max}}$$\end{document} and LVIDD (r = 0.74, P < 0.001) and SV (r = 0.72, P < 0.001). Stepwise regression analysis was applied to thigh SM mass and SV for prediction of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\dot{{V}}}\hbox{O}_{\rm{2max}}$$\end{document} in the development group, and these parameters were closely correlated with absolute measured \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\dot{{V}}}\hbox{O}_{\rm{2max}}\ ({R}^{2}=0.72, P < 0.001)$$\end{document} by multiple regression analysis. When the \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\dot{{V}}}\hbox{O}_{\rm{2max}}$$\end{document} prediction equations were applied to the validation group, significant correlations were also observed between the measured and predicted \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\dot{{V}}}\hbox{O}_{\rm{2max}}\ ({R}^{2}=0.83, P < 0.001).$$\end{document} These results suggested that nonexercise prediction of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\dot{{V}}}\hbox{O}_{\rm{2max}}$$\end{document} using thigh SM mass and cardiac dimension is a valid method to predict \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\dot{{V}}}\hbox{O}_{\rm{2max}}$$\end{document} in young Japanese adults. More... »

PAGES

143-148

References to SciGraph publications

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s00421-006-0325-3

DOI

http://dx.doi.org/10.1007/s00421-006-0325-3

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1041058342

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/17115179


Indexing Status Check whether this publication has been indexed by Scopus and Web Of Science using the SN Indexing Status Tool
Incoming Citations Browse incoming citations for this publication using opencitations.net

JSON-LD is the canonical representation for SciGraph data.

TIP: You can open this SciGraph record using an external JSON-LD service: JSON-LD Playground Google SDTT

[
  {
    "@context": "https://springernature.github.io/scigraph/jsonld/sgcontext.json", 
    "about": [
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/11", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Medical and Health Sciences", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/1103", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Clinical Sciences", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Adult", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Asians", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Exercise Test", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Heart", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Heart Ventricles", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Humans", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Japan", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Male", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Models, Biological", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Muscle, Skeletal", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Organ Size", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Oxygen Consumption", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Predictive Value of Tests", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Reference Values", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Reproducibility of Results", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Stroke Volume", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Ultrasonography", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "Tokyo Metropolitan University, 1-1, Minami-Ohsawa, Hachioji, 192-0397, Tokyo, Japan", 
          "id": "http://www.grid.ac/institutes/grid.265074.2", 
          "name": [
            "Consolidated Research Institute for Advanced Science and Medical Care, Waseda University, 513 Wasedatsurumaki-cho, Shinjuku-ku, 162-0041, Tokyo, Japan", 
            "Tokyo Metropolitan University, 1-1, Minami-Ohsawa, Hachioji, 192-0397, Tokyo, Japan"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Sanada", 
        "givenName": "Kiyoshi", 
        "id": "sg:person.01100642414.49", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01100642414.49"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Tokyo Metropolitan University, 1-1, Minami-Ohsawa, Hachioji, 192-0397, Tokyo, Japan", 
          "id": "http://www.grid.ac/institutes/grid.265074.2", 
          "name": [
            "Tokyo Metropolitan University, 1-1, Minami-Ohsawa, Hachioji, 192-0397, Tokyo, Japan"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Midorikawa", 
        "givenName": "Taishi", 
        "id": "sg:person.0762567575.13", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0762567575.13"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Tokyo Metropolitan University, 1-1, Minami-Ohsawa, Hachioji, 192-0397, Tokyo, Japan", 
          "id": "http://www.grid.ac/institutes/grid.265074.2", 
          "name": [
            "Tokyo Metropolitan University, 1-1, Minami-Ohsawa, Hachioji, 192-0397, Tokyo, Japan"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Yasuda", 
        "givenName": "Tomohiro", 
        "id": "sg:person.01351450727.44", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01351450727.44"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Tokyo Metropolitan University, 1-1, Minami-Ohsawa, Hachioji, 192-0397, Tokyo, Japan", 
          "id": "http://www.grid.ac/institutes/grid.265074.2", 
          "name": [
            "Tokyo Metropolitan University, 1-1, Minami-Ohsawa, Hachioji, 192-0397, Tokyo, Japan"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Kearns", 
        "givenName": "Charles F.", 
        "id": "sg:person.01051074015.77", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01051074015.77"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Tokyo Metropolitan University, 1-1, Minami-Ohsawa, Hachioji, 192-0397, Tokyo, Japan", 
          "id": "http://www.grid.ac/institutes/grid.265074.2", 
          "name": [
            "Tokyo Metropolitan University, 1-1, Minami-Ohsawa, Hachioji, 192-0397, Tokyo, Japan"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Abe", 
        "givenName": "Takashi", 
        "id": "sg:person.013620221043.16", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013620221043.16"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "sg:pub.10.1007/bf00933320", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1049290734", 
          "https://doi.org/10.1007/bf00933320"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s00421-004-1250-y", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1041252638", 
          "https://doi.org/10.1007/s00421-004-1250-y"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s00421-005-0061-0", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1002240691", 
          "https://doi.org/10.1007/s00421-005-0061-0"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2006-11-07", 
    "datePublishedReg": "2006-11-07", 
    "description": "The present study developed nonexercise models for predicting maximal oxygen uptake \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym}\n\t\t\t\t\\usepackage{amsfonts}\n\t\t\t\t\\usepackage{amssymb}\n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$({\\dot{{V}}}\\hbox{O}_{\\rm{2max}})$$\\end{document} using skeletal muscle (SM) mass and cardiac dimensions and to investigate the validity of these equations in healthy Japanese young men. Sixty healthy Japanese men were randomly separated into two groups: 40 in the development group and 20 in the validation group. \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym}\n\t\t\t\t\\usepackage{amsfonts}\n\t\t\t\t\\usepackage{amssymb}\n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${\\dot{{V}}}\\hbox{O}_{\\rm{2max}}$$\\end{document} during treadmill running was measured using an automated breath-by-breath mass spectrometry system. Left ventricular internal dimensions at end-diastole (LVIDD) and at end-systole (LVIDS) were measured using M-mode ultrasound with a 2.5\u00a0MHz transducer. Stroke volume (SV) was calculated based on the Pombo rule. SM mass was predicted by B-mode ultrasound muscle thickness. Correlations were observed between \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym}\n\t\t\t\t\\usepackage{amsfonts}\n\t\t\t\t\\usepackage{amssymb}\n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${\\dot{{V}}}\\hbox{O}_{\\rm{2max}}$$\\end{document} and predicted thigh (r\u00a0=\u00a00.74, P <\u00a00.001) and lower leg SM mass (r\u00a0=\u00a00.55, P <\u00a00.001). Furthermore, there were correlations between \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym}\n\t\t\t\t\\usepackage{amsfonts}\n\t\t\t\t\\usepackage{amssymb}\n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${\\dot{{V}}}\\hbox{O}_{\\rm{2max}}$$\\end{document} and LVIDD (r\u00a0=\u00a00.74, P <\u00a00.001) and SV (r\u00a0=\u00a00.72, P <\u00a00.001). Stepwise regression analysis was applied to thigh SM mass and SV for prediction of \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym}\n\t\t\t\t\\usepackage{amsfonts}\n\t\t\t\t\\usepackage{amssymb}\n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${\\dot{{V}}}\\hbox{O}_{\\rm{2max}}$$\\end{document} in the development group, and these parameters were closely correlated with absolute measured \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym}\n\t\t\t\t\\usepackage{amsfonts}\n\t\t\t\t\\usepackage{amssymb}\n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${\\dot{{V}}}\\hbox{O}_{\\rm{2max}}\\ ({R}^{2}=0.72, P < 0.001)$$\\end{document} by multiple regression analysis. When the \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym}\n\t\t\t\t\\usepackage{amsfonts}\n\t\t\t\t\\usepackage{amssymb}\n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${\\dot{{V}}}\\hbox{O}_{\\rm{2max}}$$\\end{document} prediction equations were applied to the validation group, significant correlations were also observed between the measured and predicted \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym}\n\t\t\t\t\\usepackage{amsfonts}\n\t\t\t\t\\usepackage{amssymb}\n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${\\dot{{V}}}\\hbox{O}_{\\rm{2max}}\\ ({R}^{2}=0.83, P < 0.001).$$\\end{document} These results suggested that nonexercise prediction of \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym}\n\t\t\t\t\\usepackage{amsfonts}\n\t\t\t\t\\usepackage{amssymb}\n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${\\dot{{V}}}\\hbox{O}_{\\rm{2max}}$$\\end{document} using thigh SM mass and cardiac dimension is a valid method to predict \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym}\n\t\t\t\t\\usepackage{amsfonts}\n\t\t\t\t\\usepackage{amssymb}\n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${\\dot{{V}}}\\hbox{O}_{\\rm{2max}}$$\\end{document} in young Japanese adults.", 
    "genre": "article", 
    "id": "sg:pub.10.1007/s00421-006-0325-3", 
    "inLanguage": "en", 
    "isAccessibleForFree": false, 
    "isPartOf": [
      {
        "id": "sg:journal.1319730", 
        "issn": [
          "1439-6319", 
          "1432-1025"
        ], 
        "name": "European Journal of Applied Physiology", 
        "publisher": "Springer Nature", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "2", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "99"
      }
    ], 
    "keywords": [
      "stroke volume", 
      "maximal oxygen uptake", 
      "SM mass", 
      "cardiac dimensions", 
      "validation group", 
      "left ventricular internal dimension", 
      "ventricular internal dimension", 
      "healthy Japanese men", 
      "oxygen uptake", 
      "young men", 
      "skeletal muscle mass", 
      "regression analysis", 
      "M-mode ultrasound", 
      "Japanese young men", 
      "nonexercise models", 
      "ultrasound muscle thickness", 
      "young Japanese adults", 
      "Japanese men", 
      "muscle thickness", 
      "muscle mass", 
      "treadmill running", 
      "Japanese adults", 
      "stepwise regression analysis", 
      "end diastole", 
      "development group", 
      "significant correlation", 
      "multiple regression analysis", 
      "men", 
      "present study", 
      "MHz transducer", 
      "group", 
      "valid method", 
      "internal dimensions", 
      "LVIDd", 
      "thigh", 
      "uptake", 
      "breath", 
      "ultrasound", 
      "adults", 
      "correlation", 
      "mass", 
      "mass spectrometry system", 
      "study", 
      "prediction equations", 
      "analysis", 
      "volume", 
      "running", 
      "prediction model", 
      "spectrometry system", 
      "development", 
      "validity", 
      "model", 
      "results", 
      "transducer", 
      "method", 
      "thickness", 
      "parameters", 
      "system", 
      "prediction", 
      "dimensions", 
      "rules", 
      "equations"
    ], 
    "name": "Development of nonexercise prediction models of maximal oxygen uptake in healthy Japanese young men", 
    "pagination": "143-148", 
    "productId": [
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1041058342"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/s00421-006-0325-3"
        ]
      }, 
      {
        "name": "pubmed_id", 
        "type": "PropertyValue", 
        "value": [
          "17115179"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1007/s00421-006-0325-3", 
      "https://app.dimensions.ai/details/publication/pub.1041058342"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2022-06-01T22:04", 
    "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
    "sdPublisher": {
      "name": "Springer Nature - SN SciGraph project", 
      "type": "Organization"
    }, 
    "sdSource": "s3://com-springernature-scigraph/baseset/20220601/entities/gbq_results/article/article_432.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "https://doi.org/10.1007/s00421-006-0325-3"
  }
]
 

Download the RDF metadata as:  json-ld nt turtle xml License info

HOW TO GET THIS DATA PROGRAMMATICALLY:

JSON-LD is a popular format for linked data which is fully compatible with JSON.

curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1007/s00421-006-0325-3'

N-Triples is a line-based linked data format ideal for batch operations.

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/s00421-006-0325-3'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s00421-006-0325-3'

RDF/XML is a standard XML format for linked data.

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s00421-006-0325-3'


 

This table displays all metadata directly associated to this object as RDF triples.

233 TRIPLES      22 PREDICATES      108 URIs      97 LITERALS      24 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/s00421-006-0325-3 schema:about N0ee91246813b46b895f5746a7668cf09
2 N0f51bd8c8aee4651886eaad183bc2f43
3 N155aded5bc4149ccb496997ab65dc816
4 N16bd46f450f84900abef554dbe378d39
5 N63ccc13dabed4c3580c5ea8f970c7c16
6 N728071d190554289910e5d268c6281c3
7 N7720b95e2ca848b3af4fd9ee812b4124
8 N84966cb0d5d64575b36632d36f83a365
9 N97dcd9569f6d449fb04b1b3bdcb24045
10 N9a9ca300b1d34c39912501f55548dd72
11 Na7374ce1ffb04e3fa87bd8181d53885d
12 Nb61a44c6a34e405a8c258326180e3014
13 Nc4bc41f88c6840a28ae5a519402ed209
14 Nd32f08f894a545bf80d40715aa4022bc
15 Ned4688433f5842ca8a735adf92c64375
16 Nf65a04cc67474152b0fa9fdc5258b0e6
17 Nf8178e819b744c0399314b772dcc333b
18 anzsrc-for:11
19 anzsrc-for:1103
20 schema:author N32fdfb477aa24269a82478493d6fcd52
21 schema:citation sg:pub.10.1007/bf00933320
22 sg:pub.10.1007/s00421-004-1250-y
23 sg:pub.10.1007/s00421-005-0061-0
24 schema:datePublished 2006-11-07
25 schema:datePublishedReg 2006-11-07
26 schema:description The present study developed nonexercise models for predicting maximal oxygen uptake \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$({\dot{{V}}}\hbox{O}_{\rm{2max}})$$\end{document} using skeletal muscle (SM) mass and cardiac dimensions and to investigate the validity of these equations in healthy Japanese young men. Sixty healthy Japanese men were randomly separated into two groups: 40 in the development group and 20 in the validation group. \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\dot{{V}}}\hbox{O}_{\rm{2max}}$$\end{document} during treadmill running was measured using an automated breath-by-breath mass spectrometry system. Left ventricular internal dimensions at end-diastole (LVIDD) and at end-systole (LVIDS) were measured using M-mode ultrasound with a 2.5 MHz transducer. Stroke volume (SV) was calculated based on the Pombo rule. SM mass was predicted by B-mode ultrasound muscle thickness. Correlations were observed between \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\dot{{V}}}\hbox{O}_{\rm{2max}}$$\end{document} and predicted thigh (r = 0.74, P < 0.001) and lower leg SM mass (r = 0.55, P < 0.001). Furthermore, there were correlations between \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\dot{{V}}}\hbox{O}_{\rm{2max}}$$\end{document} and LVIDD (r = 0.74, P < 0.001) and SV (r = 0.72, P < 0.001). Stepwise regression analysis was applied to thigh SM mass and SV for prediction of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\dot{{V}}}\hbox{O}_{\rm{2max}}$$\end{document} in the development group, and these parameters were closely correlated with absolute measured \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\dot{{V}}}\hbox{O}_{\rm{2max}}\ ({R}^{2}=0.72, P < 0.001)$$\end{document} by multiple regression analysis. When the \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\dot{{V}}}\hbox{O}_{\rm{2max}}$$\end{document} prediction equations were applied to the validation group, significant correlations were also observed between the measured and predicted \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\dot{{V}}}\hbox{O}_{\rm{2max}}\ ({R}^{2}=0.83, P < 0.001).$$\end{document} These results suggested that nonexercise prediction of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\dot{{V}}}\hbox{O}_{\rm{2max}}$$\end{document} using thigh SM mass and cardiac dimension is a valid method to predict \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\dot{{V}}}\hbox{O}_{\rm{2max}}$$\end{document} in young Japanese adults.
27 schema:genre article
28 schema:inLanguage en
29 schema:isAccessibleForFree false
30 schema:isPartOf N983771cfd1ee406192525eafca75a4b0
31 Nca04e0bda7bf49d8940de068f3f7fe94
32 sg:journal.1319730
33 schema:keywords Japanese adults
34 Japanese men
35 Japanese young men
36 LVIDd
37 M-mode ultrasound
38 MHz transducer
39 SM mass
40 adults
41 analysis
42 breath
43 cardiac dimensions
44 correlation
45 development
46 development group
47 dimensions
48 end diastole
49 equations
50 group
51 healthy Japanese men
52 internal dimensions
53 left ventricular internal dimension
54 mass
55 mass spectrometry system
56 maximal oxygen uptake
57 men
58 method
59 model
60 multiple regression analysis
61 muscle mass
62 muscle thickness
63 nonexercise models
64 oxygen uptake
65 parameters
66 prediction
67 prediction equations
68 prediction model
69 present study
70 regression analysis
71 results
72 rules
73 running
74 significant correlation
75 skeletal muscle mass
76 spectrometry system
77 stepwise regression analysis
78 stroke volume
79 study
80 system
81 thickness
82 thigh
83 transducer
84 treadmill running
85 ultrasound
86 ultrasound muscle thickness
87 uptake
88 valid method
89 validation group
90 validity
91 ventricular internal dimension
92 volume
93 young Japanese adults
94 young men
95 schema:name Development of nonexercise prediction models of maximal oxygen uptake in healthy Japanese young men
96 schema:pagination 143-148
97 schema:productId N5b7ee2ab7b554f16b3c125420894c6fc
98 N67cea2f561184fabb81997ec76db6abb
99 Nb27965d9bcc4473f8d7dec956812a6ce
100 schema:sameAs https://app.dimensions.ai/details/publication/pub.1041058342
101 https://doi.org/10.1007/s00421-006-0325-3
102 schema:sdDatePublished 2022-06-01T22:04
103 schema:sdLicense https://scigraph.springernature.com/explorer/license/
104 schema:sdPublisher N8e30ec38229d4df98fcdb75fa50afcce
105 schema:url https://doi.org/10.1007/s00421-006-0325-3
106 sgo:license sg:explorer/license/
107 sgo:sdDataset articles
108 rdf:type schema:ScholarlyArticle
109 N001d4a5d50d5406fb97a8eafcb388ff4 rdf:first sg:person.0762567575.13
110 rdf:rest N8c2af4e121ad43a9a0588a078172655c
111 N0ee91246813b46b895f5746a7668cf09 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
112 schema:name Adult
113 rdf:type schema:DefinedTerm
114 N0f51bd8c8aee4651886eaad183bc2f43 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
115 schema:name Reproducibility of Results
116 rdf:type schema:DefinedTerm
117 N155aded5bc4149ccb496997ab65dc816 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
118 schema:name Muscle, Skeletal
119 rdf:type schema:DefinedTerm
120 N16bd46f450f84900abef554dbe378d39 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
121 schema:name Stroke Volume
122 rdf:type schema:DefinedTerm
123 N227c412d68214969a05197297b43069b rdf:first sg:person.013620221043.16
124 rdf:rest rdf:nil
125 N32fdfb477aa24269a82478493d6fcd52 rdf:first sg:person.01100642414.49
126 rdf:rest N001d4a5d50d5406fb97a8eafcb388ff4
127 N5b7ee2ab7b554f16b3c125420894c6fc schema:name pubmed_id
128 schema:value 17115179
129 rdf:type schema:PropertyValue
130 N63ccc13dabed4c3580c5ea8f970c7c16 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
131 schema:name Heart
132 rdf:type schema:DefinedTerm
133 N67cea2f561184fabb81997ec76db6abb schema:name doi
134 schema:value 10.1007/s00421-006-0325-3
135 rdf:type schema:PropertyValue
136 N728071d190554289910e5d268c6281c3 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
137 schema:name Oxygen Consumption
138 rdf:type schema:DefinedTerm
139 N7720b95e2ca848b3af4fd9ee812b4124 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
140 schema:name Reference Values
141 rdf:type schema:DefinedTerm
142 N84966cb0d5d64575b36632d36f83a365 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
143 schema:name Humans
144 rdf:type schema:DefinedTerm
145 N8c2af4e121ad43a9a0588a078172655c rdf:first sg:person.01351450727.44
146 rdf:rest Na0ddd23db8c9491b826413052158503e
147 N8e30ec38229d4df98fcdb75fa50afcce schema:name Springer Nature - SN SciGraph project
148 rdf:type schema:Organization
149 N97dcd9569f6d449fb04b1b3bdcb24045 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
150 schema:name Ultrasonography
151 rdf:type schema:DefinedTerm
152 N983771cfd1ee406192525eafca75a4b0 schema:volumeNumber 99
153 rdf:type schema:PublicationVolume
154 N9a9ca300b1d34c39912501f55548dd72 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
155 schema:name Models, Biological
156 rdf:type schema:DefinedTerm
157 Na0ddd23db8c9491b826413052158503e rdf:first sg:person.01051074015.77
158 rdf:rest N227c412d68214969a05197297b43069b
159 Na7374ce1ffb04e3fa87bd8181d53885d schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
160 schema:name Asians
161 rdf:type schema:DefinedTerm
162 Nb27965d9bcc4473f8d7dec956812a6ce schema:name dimensions_id
163 schema:value pub.1041058342
164 rdf:type schema:PropertyValue
165 Nb61a44c6a34e405a8c258326180e3014 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
166 schema:name Predictive Value of Tests
167 rdf:type schema:DefinedTerm
168 Nc4bc41f88c6840a28ae5a519402ed209 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
169 schema:name Japan
170 rdf:type schema:DefinedTerm
171 Nca04e0bda7bf49d8940de068f3f7fe94 schema:issueNumber 2
172 rdf:type schema:PublicationIssue
173 Nd32f08f894a545bf80d40715aa4022bc schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
174 schema:name Organ Size
175 rdf:type schema:DefinedTerm
176 Ned4688433f5842ca8a735adf92c64375 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
177 schema:name Exercise Test
178 rdf:type schema:DefinedTerm
179 Nf65a04cc67474152b0fa9fdc5258b0e6 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
180 schema:name Heart Ventricles
181 rdf:type schema:DefinedTerm
182 Nf8178e819b744c0399314b772dcc333b schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
183 schema:name Male
184 rdf:type schema:DefinedTerm
185 anzsrc-for:11 schema:inDefinedTermSet anzsrc-for:
186 schema:name Medical and Health Sciences
187 rdf:type schema:DefinedTerm
188 anzsrc-for:1103 schema:inDefinedTermSet anzsrc-for:
189 schema:name Clinical Sciences
190 rdf:type schema:DefinedTerm
191 sg:journal.1319730 schema:issn 1432-1025
192 1439-6319
193 schema:name European Journal of Applied Physiology
194 schema:publisher Springer Nature
195 rdf:type schema:Periodical
196 sg:person.01051074015.77 schema:affiliation grid-institutes:grid.265074.2
197 schema:familyName Kearns
198 schema:givenName Charles F.
199 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01051074015.77
200 rdf:type schema:Person
201 sg:person.01100642414.49 schema:affiliation grid-institutes:grid.265074.2
202 schema:familyName Sanada
203 schema:givenName Kiyoshi
204 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01100642414.49
205 rdf:type schema:Person
206 sg:person.01351450727.44 schema:affiliation grid-institutes:grid.265074.2
207 schema:familyName Yasuda
208 schema:givenName Tomohiro
209 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01351450727.44
210 rdf:type schema:Person
211 sg:person.013620221043.16 schema:affiliation grid-institutes:grid.265074.2
212 schema:familyName Abe
213 schema:givenName Takashi
214 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013620221043.16
215 rdf:type schema:Person
216 sg:person.0762567575.13 schema:affiliation grid-institutes:grid.265074.2
217 schema:familyName Midorikawa
218 schema:givenName Taishi
219 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0762567575.13
220 rdf:type schema:Person
221 sg:pub.10.1007/bf00933320 schema:sameAs https://app.dimensions.ai/details/publication/pub.1049290734
222 https://doi.org/10.1007/bf00933320
223 rdf:type schema:CreativeWork
224 sg:pub.10.1007/s00421-004-1250-y schema:sameAs https://app.dimensions.ai/details/publication/pub.1041252638
225 https://doi.org/10.1007/s00421-004-1250-y
226 rdf:type schema:CreativeWork
227 sg:pub.10.1007/s00421-005-0061-0 schema:sameAs https://app.dimensions.ai/details/publication/pub.1002240691
228 https://doi.org/10.1007/s00421-005-0061-0
229 rdf:type schema:CreativeWork
230 grid-institutes:grid.265074.2 schema:alternateName Tokyo Metropolitan University, 1-1, Minami-Ohsawa, Hachioji, 192-0397, Tokyo, Japan
231 schema:name Consolidated Research Institute for Advanced Science and Medical Care, Waseda University, 513 Wasedatsurumaki-cho, Shinjuku-ku, 162-0041, Tokyo, Japan
232 Tokyo Metropolitan University, 1-1, Minami-Ohsawa, Hachioji, 192-0397, Tokyo, Japan
233 rdf:type schema:Organization
 




Preview window. Press ESC to close (or click here)


...