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dc.contributor.authorNevill, Alan M.
dc.contributor.authorRamsbottom, Roger
dc.contributor.authorWilliams, Clyde
dc.date.accessioned2007-01-31T14:29:14Z
dc.date.available2007-01-31T14:29:14Z
dc.date.issued1992
dc.date.submitted2007-01-31
dc.identifier.citationEuropean Journal of Applied Physiology, 65(2): 110-117
dc.identifier.issn0301-5548
dc.identifier.pmid1396632
dc.identifier.doi10.1007/BF00705066
dc.identifier.urihttp://hdl.handle.net/2436/8005
dc.description.abstractThis paper examines how selected physiological performance variables, such as maximal oxygen uptake, strength and power, might best be scaled for subject differences in body size. The apparent dilemma between using either ratio standards or a linear adjustment method to scale was investigated by considering how maximal oxygen uptake (l.min-1), peak and mean power output (W) might best be adjusted for differences in body mass (kg). A curvilinear power function model was shown to be theoretically, physiologically and empirically superior to the linear models. Based on the fitted power functions, the best method of scaling maximum oxygen uptake, peak and mean power output, required these variables to be divided by body mass, recorded in the units kg 2/3. Hence, the power function ratio standards (ml.kg-2/3.min-1) and (W.kg-2/3) were best able to describe a wide range of subjects in terms of their physiological capacity, i.e. their ability to utilise oxygen or record power maximally, independent of body size. The simple ratio standards (ml.kg-1.min-1) and (W.kg-1) were found to best describe the same subjects according to their performance capacities or ability to run which are highly dependent on body size. The appropriate model to explain the experimental design effects on such ratio standards was shown to be log-normal rather than normal. Simply by taking logarithms of the power function ratio standard, identical solutions for the design effects are obtained using either ANOVA or, by taking the unscaled physiological variable as the dependent variable and the body size variable as the covariate, ANCOVA methods.
dc.format.extent830295 bytes
dc.format.mimetypeapplication/pdf
dc.language.isoen
dc.publisherSpringer Verlag
dc.relation.urlhttp://www.springerlink.com/content/g34726g217887553/
dc.subjectRatio standards
dc.subjectPhysiological capacity
dc.subjectPerformance capacity
dc.subjectExperimental design effects
dc.subjectLog-linear models
dc.titleScaling physiological measurements for individuals of different body size.
dc.typeJournal article
dc.format.digYES
refterms.dateFOA2018-08-20T16:29:19Z
html.description.abstractThis paper examines how selected physiological performance variables, such as maximal oxygen uptake, strength and power, might best be scaled for subject differences in body size. The apparent dilemma between using either ratio standards or a linear adjustment method to scale was investigated by considering how maximal oxygen uptake (l.min-1), peak and mean power output (W) might best be adjusted for differences in body mass (kg). A curvilinear power function model was shown to be theoretically, physiologically and empirically superior to the linear models. Based on the fitted power functions, the best method of scaling maximum oxygen uptake, peak and mean power output, required these variables to be divided by body mass, recorded in the units kg 2/3. Hence, the power function ratio standards (ml.kg-2/3.min-1) and (W.kg-2/3) were best able to describe a wide range of subjects in terms of their physiological capacity, i.e. their ability to utilise oxygen or record power maximally, independent of body size. The simple ratio standards (ml.kg-1.min-1) and (W.kg-1) were found to best describe the same subjects according to their performance capacities or ability to run which are highly dependent on body size. The appropriate model to explain the experimental design effects on such ratio standards was shown to be log-normal rather than normal. Simply by taking logarithms of the power function ratio standard, identical solutions for the design effects are obtained using either ANOVA or, by taking the unscaled physiological variable as the dependent variable and the body size variable as the covariate, ANCOVA methods.


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