Recurrence Quantification Analysis Based Methodology in Automatic Aerobic Threshold Detection: Applicability and Accuracy across Age Groups, Exercise Protocols and Health Conditions

• Published in: Applied sciences Vol. 14; no. 20; p. 9216
• Main Authors: Zimatore, Giovanna, Serantoni, Cassandra, Gallotta, Maria Chiara, Meucci, Marco, Mourot, Laurent, Ferrari, Dafne, Baldari, Carlo, De Spirito, Marco, Maulucci, Giuseppe, Guidetti, Laura
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.10.2024
• Subjects: aerobic transition; Algorithms; Anaerobic threshold; Athletes; Beta blockers; Cardiac patients; Ethics; Exercise intensity; Exercise therapy; Health aspects; Heart beat; Heart rate; heart rate variability; Metabolism; Methods; Nervous system; nonlinear time series analysis; Obesity; Physical fitness; Physiology; python code; Refuse disposal industry; Time series
• ISSN: 2076-3417; 2076-3417
• DOI: 10.3390/app14209216

A new method based on the Recurrence Quantification Analysis (RQA) of the heart rate (HR) offers an objective, efficient alternative to traditional methods for Aerobic Threshold (AerT) identification that have practical limitations due to the complexity of equipment and interpretation. This study aims to validate the RQA-based method’s applicability across varied demographics, exercise protocols, and health status. Data from 123 cardiopulmonary exercise tests were analyzed, and participants were categorized into four groups: athletes, young athletes, obese individuals, and cardiac patients. Each participant’s AerT was assessed using both traditional ventilatory equivalent methods and the automatic RQA-based method. Ordinary Least Products (OLP) regression analysis revealed strong correlations (r > 0.77) between the RQA-based and traditional methods in both oxygen consumption (VO2) and HR at the AerT. Mean percentage differences in HR were below 2.5%, and the Technical Error for HR at AerT was under 8%. The study validates the RQA-based method, directly applied to HR time series, as a reliable tool for the automatic detection of the AerT, demonstrating its accuracy across diverse age groups and fitness levels. These findings suggest a versatile, cost-effective, non-invasive, and objective tool for personalized exercise prescription and health risk stratification, thereby fulfilling the study’s goal of broadening the method’s applicability.