Classification of cardiorespiratory fitness without exercise testing
Classification of cardiorespiratory fitness without exercise testing
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DOI：
10.1097/00005768-199903000-00019
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发表时间：
1999-03-01
期刊：
影响因子：
--
通讯作者：
Pastides, H
中科院分区：
文献类型：
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作者：
Matthews, CE;Heil, DP;Pastides, H
目的：我们研究了基于非运动的（V）对点O-2 max的预测模型对年龄在19-79岁之间的男性和女性人群（N = 799）的心肺适能（CRF）进行分类的能力。研究方法：采用多元线性回归法，以年龄、年龄（2）、性别、体力活动状态、身高和体质量为自变量，建立了研究组的（V）over dot O-2 max（mL.kg（-1）.min（-1））预测模型。该模型的分类准确性进行了检查交叉制表年龄和性别特定的五分之一的测量和预测的CRF。结果如下：该模型的总体分类准确性适中（36%）;然而，83%的受试者被正确分类或在测量的CRF的五分之一范围内。极端错误分类（例如，将低拟合个体误分类为高拟合）仅很少观察到（0.13%）。结论：目前的研究结果支持的概念，CRF预测模型可以用来合理地表征健康水平的队列使用的数据，可以从问卷调查中获得。因此，预测的CRF值可能是有用的，作为一个暴露变量在大型流行病学研究中，运动试验是不可行的。
Purpose: We examined the ability of a nonexercise based (V) over dot O-2max, prediction model to classify cardiorespiratory fitness (CRF) in a population of men and women aged 19-79 yr of age (N = 799). Methods: A (V) over dot O-2max (mL.kg(-1).min(-1)) prediction model was developed in the study group using multiple linear regression from the independent variables age, age(2), gender, physical activity status, height, and body mass. The classification accuracy of this model was examined by cross-tabulating age and gender specific quintiles of measured and predicted CRF. Results: Overall classification accuracy of the model was modest (36%); however, 83% of all subjects were either classified correctly or within one quintile of measured CRF. Extreme misclassification (e.g., misclassifying a low fit individual as high fit) was only rarely observed (0.13%). Conclusions: The present results support the concept that CRF prediction models can be used to reasonably characterize the fitness level of a cohort using data that can be obtained from a questionnaire. Accordingly, predicted CRF values may be useful as an exposure variable in large epidemiologic studies in which exercise testing is not feasible.