Faculty of Education, Health and Wellbeing, University of Wolverhampton, Walsall Campus, Walsall WS1 3BD, UK.
Division of Cardiology, VA Palo Alto Healthcare System, Palo Alto, CA, United States of America; Stanford University, Stanford, CA, United States of America.
Prog Cardiovasc Dis. 2019 Nov-Dec;62(6):515-521. doi: 10.1016/j.pcad.2019.11.011. Epub 2019 Nov 22.
New improved reference equations for cardiorespiratory fitness have recently been published, using Data from the Fitness Registry and the Importance of Exercise National Database (FRIEND Registry). The new linear equation for VO (ml.kg.min) was additive, derived using multiple-linear regression. An alternative multiplicative allometric model has also been published recently, thought to improve further the quality of fit. The purpose of the current study was to compare the accuracy and quality/goodness-of-fit of the linear, additive model with the multiplicative allometric model using the FRIEND database. The results identified that the allometric model out performs the linear model based on all model-comparison criteria. The allometric model demonstrates; 1) greater explained variance (R = 0.645; R = 0.803) vs. (R = 0.62; R = 0.79), 2) residuals that were more normally distributed, 3) residuals that yielded less evidence of curvature, 4) superior goodness-of-fit statistics i.e., greater maximum log-likelihood (MLL) and smaller Akaike Information Criterion (AIC) statistics, 5) less systematic bias together with smaller unexplained standard error of estimates. The Bland and Altman plots also confirmed little or no evidence of curvature with the allometric model, but systematic curvature (lack-of-fit) in the linear model. The multiplicative allometric model to predict VO was; VO (ml.kg.min) = M · H · exp. (0.424-0.346 · (sex) -0.011.age), where M = body mass and H = height (R = 0.645; R = 0.803) and sex is entered as a [0,1] indicator variable (male = 0 and female = 1). Another new insight obtained from the allometric model (providing construct validity) is that the height-to-body-mass ratio is similar to inverse body mass index or the lean body mass index, both associated with leanness when predicting VO. In conclusion adopting allometric models will provide more accurate predictions of VO (ml.kg.min) using more plausible, biologically sound and interpretable models.
最近发表了新的、经过改进的心肺健康参考方程,这些方程使用了来自健身注册和运动重要性国家数据库(FRIEND 注册)的数据。新的 VO(毫升/千克/分钟)线性方程是加性的,是通过多元线性回归推导出来的。最近还发表了一种替代的乘法比例模型,被认为可以进一步提高拟合质量。本研究的目的是使用 FRIEND 数据库比较线性、加法模型与乘法比例模型的准确性和拟合质量/优度。结果表明,基于所有模型比较标准,比例模型优于线性模型。比例模型具有以下特点:1)解释方差更大(R=0.645;R=0.803),而线性模型为(R=0.62;R=0.79);2)残差的正态分布更好;3)残差的曲率证据更少;4)拟合优度统计数据更好,即最大对数似然(MLL)更大,Akaike 信息准则(AIC)更小;5)系统偏差更小,未解释的估计标准误差更小。Bland 和 Altman 图也证实,比例模型几乎没有或没有曲率证据,但线性模型存在系统偏差(拟合不良)。预测 VO 的乘法比例模型为:VO(毫升/千克/分钟)=M·H·exp(0.424-0.346·(性别)-0.011·年龄),其中 M=体重,H=身高(R=0.645;R=0.803),性别以[0,1]指示变量输入(男性=0,女性=1)。从比例模型中获得的另一个新见解(提供结构有效性)是,身高与体重的比值与逆体重指数或瘦体重指数相似,这两者在预测 VO 时都与瘦体有关。总之,采用比例模型将使用更合理、更有生物学意义且更易解释的模型,提供更准确的 VO(毫升/千克/分钟)预测。