• Modeling physiological and anthropometric variables known to vary with body size and other confounding variables

      Nevill, Alan M.; Bate, Stuart; Holder, Roger L. (Wiley Interscience, 2005)
      This review explores the most appropriate methods of identifying population differences in physiological and anthropometric variables known to differ with body size and other confounding variables. We shall provide an overview of such problems from a historical point of view. We shall then give some guidelines as to the choice of body-size covariates as well as other confounding variables, and show how these might be incorporated into the model, depending on the physiological dependent variable and the nature of the population being studied. We shall also recommend appropriate goodness-of-fit statistics that will enable researchers to confirm the most appropriate choice of model, including, for example, how to compare proportional allometric models with the equivalent linear or additive polynomial models. We shall also discuss alternative body-size scaling variables (height, fat-free mass, body surface area, and projected area of skeletal bone), and whether empirical vs. theoretical scaling methodologies should be reported. We shall offer some cautionary advice (limitations) when interpreting the parameters obtained when fitting proportional power function or allometric models, due to the fact that human physiques are not geometrically similar to each other. In conclusion, a variety of different models will be identified to describe physiological and anthropometric variables known to vary with body size and other confounding variables. These include simple ratio standards (e.g., per body mass ratios), linear and additive polynomial models, and proportional allometric or power function models. Proportional allometric models are shown to be superior to either simple ratio standards or linear and additive polynomial models for a variety of different reasons. These include: 1) providing biologically interpretable models that yield sensible estimates within and beyond the range of data; and 2) providing a superior fit based on the Akaike information criterion (AIC), Bayes information criterion (BIC), or maximum log-likelihood criteria (resulting in a smaller error variance). As such, these models will also: 3) naturally lead to a more powerful analysis-of-covariance test of significance, which will 4) subsequently lead to more correct conclusions when investigating population (epidemiological) or experimental differences in physiological and anthropometric variables known to vary with body size.
    • Modelling the influence of age, body size and sex on maximum oxygen uptake in older humans.

      Johnson, Patrick J.; Winter, Edward M.; Paterson, Don H.; Koval, John J.; Nevill, Alan M.; Cunningham, David A.; Department of Exercise Physiology, De Montfort University Bedford, Bedford MK40 2BZ, UK. (The Physiological Society, 2000-03)
      The purpose of this study was to describe the influence of body size and sex on the decline in maximum oxygen uptake (O2,max) in older men and women. A stratified random sample of 152 men and 146 women, aged 55-86 years, was drawn from the study population. Influence of age on O2,max, independent of differences in body mass (BM) or fat-free mass (FFM), was investigated using the following allometric model: O2,max = BMb (or FFMb) exp(a + (c ' age) + (d ' sex)) [epsilon]. The model was linearised and parameters identified using standard multiple regression. The BM model explained 68.8 % of the variance in O2,max. The parameters (+/- s.e.e., standard error of the estimate) for lnBM (0.563 +/- 0.070), age (-0.0154 +/- 0.0012), sex (0.242 +/- 0.024) and the intercept (-1.09 +/- 0.32) were all significant (P < 0.001). The FFM model explained 69.3 % of the variance in O2,max, and the parameters (+/- s.e.e) lnFFM (0.772 +/- 0.090), age (-0.0159 +/- 0.0012) and the intercept (-1.57 +/- 0.36) were significant (P < 0.001), while sex (0.077 +/- 0.038) was significant at P = 0.0497. Regardless of the model used, the age-associated decline was similar, with a relative decline of 15 % per decade (0.984 exp(age)) in O2,max in older humans being estimated. The study has demonstrated that, for a randomly drawn sample, the age-related loss in O2,max is determined, in part, by the loss of fat-free body mass. When this factor is accounted for, the loss of O2,max across age is similar in older men and women.