Related Experiment Video
Updated: May 26, 2025

Calculating Heart Rate Variability from ECG Data from Youth with Cerebral Palsy During Active Video Game Sessions
Published on: June 5, 2019
Predicting Heart Rate Slow Component Dynamics: A Model Across Exercise Intensities, Age, and Sex
Massimo Teso1, Alessandro L Colosio2, Maura Loi3
1College of Health and Life Sciences, Hamad Bin Khalifa University, Doha P.O. Box 34110, Qatar.
Abstract:
The heart rate slow component (scHR) is an intensity-dependent HR increment that emerges during constant exercises, partially dissociated from metabolism (V˙O2). The scHR has been observed during constant-workload exercise in young and older adults. Unless this scHR is accounted for, exercise prescription using HR targets lead to an undesired reduction in metabolic intensity over time.
Purpose:
The purpose of this study is to characterize scHR across intensities, sex, and age to develop and validate a predictive equation able to maintain the desired metabolic stimulus over time in a constant aerobic exercise session.
Methods:
In our study, 66 individuals (35 females; 35 ± 13 yrs) performed the following: (i) a ramp-test for respiratory exercise threshold (GET and RCP) and maximal oxygen uptake (V˙O2max) detection, and (ii) 6 × 9-minute constant exercises at different intensities. The scHR was calculated by linear fitting from the fifth minute of exercise (bpm⋅min-1). A multiple-linear equation was developed to predict the scHR based on individual and exercise variables. The validity of the equation was tested on an independent sample by a Pearson correlation and Bland-Altman analysis between the measured and estimated HR during constant exercises.
Results:
The scHR increases with intensity and is larger in males (p < 0.05). A multiple-linear equation predicts the scHR based on the relative exercise intensity to RCP, age, and sex (r2 = 0.54, SEE = 0.61 bpm⋅min-1). scHR (bpm⋅min-1) = -0.0514 + (0.0240 × relative exercise intensity to RCP) - (0.0172 × age) - (0.347 × Sex (males = 0 and females score = 1)). In the independent sample, we found an excellent correlation between the measured and estimated HR (r2 = 0.98, p < 0.001) with no bias (-0.01 b·min-1, z-score= -0.04) and a fair precision (±4.09 b·min-1).
Conclusions:
The dynamic of the scHR can be predicted in a heterogeneous sample accounting for the combined effects of relative intensity, sex, and age. The above equation provides the means to dynamically adapt HR targets over time, avoiding an undesired reduction in the absolute and relative training load. This strategy would allow the maintenance of the desired metabolic stimulus (V˙O2) throughout an exercise session in a heterogeneous population.
Related Concept Videos
Factors Influencing Heart Rate
Let us explore the significant factors affecting heart rate, including age, body temperature, posture, acute pain, chemical influences,...
Pathophysiology of Cardiac Performance
Regulation of Heart Rates
The SNS increases heart rate through the release of norepinephrine and epinephrine, which act on beta-1 adrenergic receptors in the heart. This action increases the rate of depolarization in the sinoatrial (SA) node, the heart's...
Correlation between ECG and Cardiac Cycle
A cardiac action potential originates in the SA node and spreads throughout the atria and the AV node in approximately 0.03 seconds. This results in the P wave in an ECG and triggers atrial contraction. The action potential is then briefly slowed at the AV node, allowing the atria to contract and fill the ventricles with blood before...
Exercise and Cardiac Output
Sustained exercise increases the muscles' oxygen demand, which can be...
Exercise and Cardiovascular Response
Light to moderate physical activity initiates a series of interconnected responses in the body. The heart rate modestly increases in anticipation of the workout, followed by widespread vasodilation as oxygen consumption by skeletal muscles increases. This results in decreased peripheral resistance, increased capillary blood flow, and accelerated...

