Development of normative values for resting and exercise rate pressure product

Citation
Sc. Hui et al., Development of normative values for resting and exercise rate pressure product, MED SCI SPT, 32(8), 2000, pp. 1520-1527
Citations number
32
Categorie Soggetti
Medical Research General Topics
Journal title
MEDICINE AND SCIENCE IN SPORTS AND EXERCISE
ISSN journal
01959131 → ACNP
Volume
32
Issue
8
Year of publication
2000
Pages
1520 - 1527
Database
ISI
SICI code
0195-9131(200008)32:8<1520:DONVFR>2.0.ZU;2-O
Abstract
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.