Related Experiment Video
Updated: Jul 9, 2025
Calculating Heart Rate Variability from ECG Data from Youth with Cerebral Palsy During Active Video Game Sessions
Published on: June 5, 2019
Inter-day reliability of heart rate complexity and variability metrics in healthy highly active younger and older
Christopher R J Fennell1, Alexis R Mauger2, James G Hopker2
1School of Sport and Exercise Sciences, University of Kent, Chipperfield Building, Canterbury, Kent, CT2 7PE, UK. crjf3@kent.ac.uk.
Purpose:
To investigate the inter-day reliability of time-domain, frequency-domain, and nonlinear HRV metrics in healthy highly active younger and older adults. The study also assessed the effect of age on the HRV metrics.
Methods:
Forty-four older adults (34 M, 10F; 59 ± 5 years; = 40.9 ± 7.6 ml kg-1 min-1) and twenty-two younger adults (16 M, 6F; 22 ± 4 years; = 47.2 ± 12.8 ml kg-1 min-1) attended the laboratory. Visit one assessed aerobic fitness through an exercise test. In visits two and three, participants completed a 30-min supine RR interval measurement to derive the HRV metrics.
Results:
The younger group (YG) and older group (OG) demonstrated poor to good day-to-day relative and absolute reliability for all HRV metrics (OG, ICCs = 0.33 to 0.69 and between day CVs = 3.8 to 29.2%; YG, ICCs = 0.37 to 0.93 and between day CVs = 3.5 to 36.5%). There was a significant reduction in ApEn (P < 0.001), SampEn (P = 0.031), RMSSD (P < 0.001), SDNN (P < 0.001), LF power (P < 0.001) and HF power (P < 0.001), HRV metrics with ageing. There was no significant effect of age the complexity metrics DFA α1 (P = 0.107), α2 (P = 0.147) and CI-8 (P = 0.493).
Conclusion:
HRV metrics are reproducible between days in both healthy highly active younger and older adults. There is a decline in linear and nonlinear HRV metrics with age, albeit there being no age-related change in the nonlinear metrics, DFA α1, α2 and CI-8.
More Related Videos
05:48Autonomic Function Following Concussion in Youth Athletes: An Exploration of Heart Rate Variability Using 24-hour Recording Methodology
Published on: September 21, 2018
05:51Assessing the Accuracy of Fitness Smartwatch Data for Cardiovascular and Physical Activity Monitoring: A Validation Study in Digital Health
Published on: February 21, 2025
Related Concept Videos
01:30Factors Influencing Heart Rate
Let us explore the significant factors affecting heart rate, including age, body temperature, posture, acute pain, chemical influences,...
01:25Correlation 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...
01:31Regulation 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...
01:19Cardiac Output I:Effect of Heart Rate on Cardiac Output
Cardiac output (CO) refers to the total amount of blood ejected by one of the ventricles in liters per minute (L/min). In a resting adult, CO ranges from 5 to 6 L/min, adjusting according to the body's metabolic requirements.
Effect of Heart Rate on Cardiac Output
Cardiac output adapts to metabolic demands during stress, physical activity, or illness. The autonomic nervous system regulates heart rate via the sinoatrial node. The parasympathetic nervous system decreases heart...
01:19Guidelines For Measuring Vital Signs
Before taking a patient's vital signs, a nurse would consider and assess the patient's comfort level and ensure appropriate equipment is available.
01:13Special considerations while measuring pulse