Purpose: The purpose of this study was to develop multivariate models to qu
antify resting, submaximal, and maximal rate pressure products (RPP). Metho
ds: A validation sample (N = 1623) was randomly selected from a clinically
healthy population, and four cross-validation samples were randomly selecte
d from a clinical cohort. The cross-validation samples were patients who ha
d a negative exercise ECG with (Neg-Med, N = 179) and without cardiovascula
r drug (Neg-NoMed, N = 350), and patients who had a positive exercise ECG w
ith (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 wome
n 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 ph
ysical 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 submaxima
l exercise (%HRmax) were significantly related with submaximal RPP. The mul
tiple correlations for the resting, submaximal, and maximal models were 0.2
9 (SE = 16.75 beats.min(-1).mm Hg), 0.87 (SE = 29.04 beats.min(-1).mm Hg),
and 0.31 (SE = 42.41 beats.min(-1).mm Hg), respectively. The accuracy of th
e models was confirmed when applied to the Neg-NoMed and Pos-Noh led sample
s but not the Neg-Med and Pos-Med samples. This result suggest that the reg
ression models developed from this study can be generalized to other popula
tions where patients were not taking cardiovascular medication. Microcomput
er programs were suggested to evaluate RPP at rest, maximal exercise, and s
ubmaximal exercise. Conclusion: Nonnative RPP for resting and exercise reli
es on multiple fitness parameters. Practical regression models are develope
d and can be applied to patients without cardiovascular medication.