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

## TL;DR

This study compares heart rate and heart rate reserve to predict energy expenditure accurately during treadmill exercise, offering a practical alternative to lab-based methods.

## Contribution

The study introduces HRres-based models that improve EE prediction accuracy, especially at submaximal intensities and across diverse populations.

## Key findings

- HRres-based models showed higher accuracy than other models at submaximal exercise intensities.
- All models achieved high accuracy with R2 values between 0.80 and 0.89.
- Predicted and measured energy expenditure showed no significant differences.

## Abstract

This study developed and validated simplified, individualized heart rate (HR)-based regression models to predict energy expenditure (EE) during treadmill exercise without direct VO2 calibration, addressing the need for more practical and accurate methods that overcome limitations of existing predictions and facilitate precise EE estimation outside specialized laboratory conditions. Energy expenditure was measured by assessing oxygen uptake (VO2) using a portable gas analyzer and predicted across three treadmill protocols: Bruce, Modified Bruce, and Progressive Speed. These protocols were selected to capture a wide range of exercise intensities and improve the accuracy of heart rate-based EE predictions. The six models combined heart rate, heart rate reserve (HRres), and demographic variables (sex, age, BMI, resting HR) using the Enter method of multiple regression, where all variables were included simultaneously to enhance the real-world applicability of the energy expenditure predictions. All models showed high accuracy with R2 values between 0.80 and 0.89, and there were no significant differences between measured and predicted energy expenditure (p ≥ 0.05). HRres-based models outperformed others at submaximal intensities and remained consistent across sex, weight, BMI, and resting HR variations. By incorporating individual resting and maximal HR values, HRres models offer a personalized, physiologically relevant estimation method. These results support integrating HRres-based EE prediction into wearable devices to improve accessible and precise monitoring of physiological energy metabolism.