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


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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


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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.
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33 Japanese men
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35 LVIDd
36 M-mode ultrasound
37 MHz transducer
38 SM mass
39 adults
40 analysis
41 breath
42 cardiac dimensions
43 correlation
44 development
45 development group
46 dimensions
47 end diastole
48 equations
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50 healthy Japanese men
51 internal dimensions
52 left ventricular internal dimension
53 mass
54 mass spectrometry system
55 maximal oxygen uptake
56 men
57 method
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59 multiple regression analysis
60 muscle mass
61 muscle thickness
62 nonexercise models
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71 rules
72 running
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84 ultrasound
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