Comparing Heart Rate and Heart Rate Reserve for Accurate Energy Expenditure Prediction Against Direct Measurement.

Area of Science:

• Exercise Physiology
• Biomedical Engineering
• Sports Science

Background:

• Accurate energy expenditure (EE) estimation is crucial for exercise monitoring and health management.
• Existing EE prediction methods often require laboratory conditions (e.g., VO2 calibration), limiting real-world application.
• There is a need for simplified, individualized, and practical methods to predict EE during physical activity.

Purpose of the Study:

• To develop and validate simplified, individualized heart rate (HR)-based regression models for predicting energy expenditure (EE) during treadmill exercise.
• To assess the accuracy of these models without direct oxygen uptake (VO2) calibration.
• To enhance the practical applicability of EE estimation outside laboratory settings.

Main Methods:

• Measured EE using portable gas analyzers to determine oxygen uptake (VO2).
• Developed six multiple regression models incorporating HR, HR reserve (HRres), and demographic variables (sex, age, BMI, resting HR).
• Validated models across three treadmill protocols (Bruce, Modified Bruce, Progressive Speed) to cover diverse exercise intensities.

Main Results:

• All developed models demonstrated high accuracy, with R-squared values ranging from 0.80 to 0.89.
• No significant differences were found between measured and predicted EE (p ≥ 0.05).
• HRres-based models showed superior performance at submaximal intensities and consistency across various individual characteristics.

Conclusions:

• Simplified, individualized HR-based models, particularly HRres models, provide accurate and practical EE predictions.
• These models overcome limitations of traditional methods, enabling precise EE estimation in diverse settings.
• Integrating HRres-based EE prediction into wearable devices can improve accessible physiological energy metabolism monitoring.