Trend analysis of the % VO2 max-HR regression

Area of Science:

• Exercise Physiology
• Biomathematics
• Sports Science

Background:

• Assessing maximal oxygen consumption (VO2 max) is crucial for evaluating cardiorespiratory fitness.
• Heart rate (HR) is a commonly used physiological indicator during exercise.
• Developing accurate predictive models for VO2 max can enhance fitness assessments.

Purpose of the Study:

• To create a mathematical model predicting percent of maximal oxygen consumption (% VO2 max) based on relative heart rate (HR).
• To analyze the regression relationship between % VO2 max and relative HR across various exercise intensities.
• To investigate potential differences in this relationship across fitness levels.

Main Methods:

• Collected data from 26 subjects across high, medium, and low fitness levels.
• Subjects performed treadmill exercise from 30% to 100% of VO2 max.
• Utilized multiple regression analysis to model the relationship between % VO2 max and relative HR.

Main Results:

• High correlation coefficients (R) were found for linear (0.966) to quartic (0.977) models.
• Higher-order polynomial terms showed minor improvements in explaining variability.
• No significant differences in regression slopes or intercepts were observed between fitness subgroups.
• A bivariate equation (Y = 1.369X - 40.99) was established with a standard error of 5.67 % VO2 max.

Conclusions:

• A robust mathematical model effectively predicts % VO2 max from relative HR.
• The established regression equation is applicable across individuals with varying fitness levels.
• This model offers a practical tool for estimating cardiorespiratory fitness using heart rate data.