S C Hui1, A S Jackson, L T Wier. 1. Department of Sports Science and Physical Education, The Chinese University of Hong Kong, Shatin. hui2162@cuhk.edu.hk
Abstract
PURPOSE: The purpose of this study was to develop multivariate models to quantify resting, submaximal, and maximal rate pressure products (RPP). METHODS: A validation sample (N = 1623) was randomly selected from a clinically healthy population, and four cross-validation samples were randomly selected from a clinical cohort. The cross-validation samples were patients who had a negative exercise ECG with (Neg-Med, N = 179) and without cardiovascular drug (Neg-NoMed, N = 350), and patients who had a positive exercise ECG with (Pos-Med, N = 60) and without cardiovascular drug (Pos-NoMed, N = 75). Men made up 83% of the validation sample (mean age = 44.2+/-8.7) and women 17% (mean age = 39.7+/-10.1). The validation sample was used to develop multiple regression equations to quantify resting, submaximal, and maximal RPP. RESULTS: Results indicated that gender, body mass index (BMI), and physical activity level (Ex-code) were significantly related with resting RPP. Gender, age, BMI, and Ex-code were significantly related with maximal RPP. Gender, age, BMI, Ex-code, and percent of maximal heart rate at submaximal exercise (%HRmax) were significantly related with submaximal RPP. The multiple correlations for the resting, submaximal, and maximal models were 0.29 (SE = 16.75 beats x min(-1) x mm Hg), 0.87 (SE = 29.04 beats x min(-1) x mm Hg), and 0.31 (SE = 42.41 beats x min(-1) x mm Hg), respectively. The accuracy of the models was confirmed when applied to the Neg-NoMed and Pos-NoMed samples but not the Neg-Med and Pos-Med samples. This result suggest that the regression models developed from this study can be generalized to other populations where patients were not taking cardiovascular medication. Microcomputer programs were suggested to evaluate RPP at rest, maximal exercise, and submaximal exercise. CONCLUSION: Normative RPP for resting and exercise relies on multiple fitness parameters. Practical regression models are developed and can be applied to patients without cardiovascular medication.
PURPOSE: The purpose of this study was to develop multivariate models to quantify resting, submaximal, and maximal rate pressure products (RPP). METHODS: A validation sample (N = 1623) was randomly selected from a clinically healthy population, and four cross-validation samples were randomly selected from a clinical cohort. The cross-validation samples were patients who had a negative exercise ECG with (Neg-Med, N = 179) and without cardiovascular drug (Neg-NoMed, N = 350), and patients who had a positive exercise ECG with (Pos-Med, N = 60) and without cardiovascular drug (Pos-NoMed, N = 75). Men made up 83% of the validation sample (mean age = 44.2+/-8.7) and women 17% (mean age = 39.7+/-10.1). The validation sample was used to develop multiple regression equations to quantify resting, submaximal, and maximal RPP. RESULTS: Results indicated that gender, body mass index (BMI), and physical activity level (Ex-code) were significantly related with resting RPP. Gender, age, BMI, and Ex-code were significantly related with maximal RPP. Gender, age, BMI, Ex-code, and percent of maximal heart rate at submaximal exercise (%HRmax) were significantly related with submaximal RPP. The multiple correlations for the resting, submaximal, and maximal models were 0.29 (SE = 16.75 beats x min(-1) x mm Hg), 0.87 (SE = 29.04 beats x min(-1) x mm Hg), and 0.31 (SE = 42.41 beats x min(-1) x mm Hg), respectively. The accuracy of the models was confirmed when applied to the Neg-NoMed and Pos-NoMed samples but not the Neg-Med and Pos-Med samples. This result suggest that the regression models developed from this study can be generalized to other populations where patients were not taking cardiovascular medication. Microcomputer programs were suggested to evaluate RPP at rest, maximal exercise, and submaximal exercise. CONCLUSION: Normative RPP for resting and exercise relies on multiple fitness parameters. Practical regression models are developed and can be applied to patients without cardiovascular medication.
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