Assessing cardiorespiratory fitness without performing exercise testing
Assessing cardiorespiratory fitness without performing exercise testing
复制标题
DOI：
10.1016/j.amepre.2005.06.004
复制
发表时间：
2005-10-01
影响因子：
5.5
通讯作者：
Laukkanen, R
中科院分区：
文献类型：
--
作者：
Jurca, R;Jackson, AS;Laukkanen, R
背景：心肺适能（CRF）低下与慢性疾病风险增加和死亡率上升相关；然而，在许多医疗环境中通常不进行CRF评估。本研究的目的是扩展先前关于非运动测试模型的工作，以便从易于获取的健康指标中预测CRF。
方法：参与者为年龄在20至70岁的男性和女性，其CRF水平通过最大或次最大运动试验进行量化，这些试验是美国国家航空航天局/约翰逊航天中心（NASA，n = 1863）、有氧运动中心纵向研究（ACLS，n = 46190）或联合邓巴国民体质调查（ADNFS，n = 1706）的一部分。其他变量包括性别、年龄、体重指数、静息心率和自我报告的身体活动水平。
结果：多元线性回归模型中使用的所有变量在每个研究队列中都与CRF独立相关。在NASA、ACLS和ADNFS参与者中分别获得的多元相关系数为0.81、0.77和0.76。估计标准误差（SEE）分别为1.45、1.50和1.97代谢当量（METs）（1 MET = 3.5毫升O₂摄取量·千克体重⁻¹·分钟⁻¹），分别对应于NASA、ACLS和ADNFS回归模型。所有回归模型都显示出较高的交叉效度（0.72 < R < 0.80）。当将NASA回归模型应用于ACLS和ADNFS队列时，交叉验证系数最高（分别为R = 0.76和R = 0.75）。
结论：本研究表明，通过包括性别、年龄、体重指数、静息心率和自我报告的身体活动在内的非运动测试模型，可以准确估计成年人的CRF。
Background: Low cardiorespiratory fitness (CRF) is associated with increased risk of chronic diseases and mortality; however, CRF assessment is usually not performed in many healthcare settings. The purpose of this study is to extend previous work on a non-exercise test model to predict CRF from health indicators that are easily obtained.Methods: Participants were men and women aged 20 to 70 years whose CRF level was quantified with a maximal or submaximal exercise test as part of the National Aeronautics and Space Administration/Johnson Space Center (NASA, n = 1863), Aerobics Center Longitudinal Study (ACLS, n = 46,190), or Allied Dunbar National Fitness Survey (ADNFS, n = 1706). Other variables included gender, age, body mass index, resting heart rate, and self-reported physical activity levels.Results: All variables used in the multiple linear regression models were independently related to the CRF in each of the study cohorts. The multiple correlation coefficients obtained within NASA, ACLS, and ADNFS participants, respectively, were 0.81, 0.77, and 0.76. The standard error of estimate (SEE) was 1:45, 1.50, and 1.97 metabolic equivalents (METs) (1 MET = 3.5 ml O-2 uptake (.) kilograms of body mass(-1 .) minutes(-1)), respectively, for the NASA, ACLS, and ADNFS regression models. All regression models demonstrated a high level of cross-validity (0.72 < R < 0.80). The highest cross-validation coefficients were seen when the NASA regression model was applied to the ACLS and ADNFS cohorts (R = 0.76 and R = 0.75, respectively). Conclusions: This study suggests that CRF may be accurately estimated in adults from a non-exercise test model including gender, age, body mass index, resting heart rate, and self-reported physical activity.Conclusions: This study suggests that CRF may be accurately estimated in adults from a non-exercise test model including gender, age, body mass index, resting heart rate, and self-reported physical activity.