Key result
Recurrence quantification analysis of heart rate time series estimated aerobic threshold with strong correlation (r > 0.64) to standard cardiopulmonary exercise testing.
Why the study?
Aerobic threshold detection typically requires expensive equipment and invasive measurements, motivating a procedure to automatically identify it using only heart rate time series.
Does recurrence quantification analysis of heart rate time series accurately detect the aerobic threshold compared to standard cardiopulmonary exercise testing in young athletes?
Observational
Does recurrence quantification analysis of heart rate time series accurately detect the aerobic threshold compared to standard cardiopulmonary exercise testing in young athletes?
Recurrence quantification analysis of heart rate time series provides a valid, non-invasive method to estimate the aerobic threshold during exercise, closely correlating with standard CPET measurements.
Answers are grounded in this paper and cite their sources.
May enable wearable AerT detection without gas analysis; leaves open validation beyond young athletes before any practice change.
During exercise with increasing intensity, the human body transforms energy with mechanisms dependent upon actual requirements. Three phases of the body’s energy utilization are recognized, characterized by different metabolic processes, and separated by two threshold points, called aerobic (AerT) and anaerobic threshold (AnT). These thresholds occur at determined values of exercise intensity(workload) and can change among individuals. They are considered indicators of exercise capacities and are useful in the personalization of physical activity plans. They are usually detected by ventilatory or metabolic variables and require expensive equipment and invasive measurements. Recently, particular attention has focused on AerT, which is a parameter especially useful in the overweight and obese population to determine the best amount of exercise intensity for weight loss and increasing physical fitness. The aim of study is to propose a new procedure to automatically identify AerT using the analysis of recurrences (RQA) relying only on Heart rate time series, acquired from a cohort of young athletes during a sub-maximal incremental exercise test (Cardiopulmonary Exercise Test, CPET) on a cycle ergometer. We found that the minima of determinism, an RQA feature calculated from the Recurrence Quantification by Epochs (RQE) approach, identify the time points where generic metabolic transitions occur. Among these transitions, a criterion based on the maximum convexity of the determinism minima allows to detect the first metabolic threshold. The ordinary least products regression analysis shows that values of the oxygen consumption VO2, heart rate (HR), and Workload correspondent to the AerT estimated by RQA are strongly correlated with the one estimated by CPET (r > 0.64). Mean percentage differences are <2% for both HR and VO2 and <11% for Workload. The Technical Error for HR at AerT is <8%; intraclass correlation coefficients values are moderate (≥0.66) for all variables at AerT. This system thus represents a useful method to detect AerT relying only on heart rate time series, and once validated for different activities, in future, can be easily implemented in applications acquiring data from portable heart rate monitors.
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Zimatore et al. (2023) conducted an observational in Healthy young athletes. Recurrence quantification analysis (RQA) of heart rate time series vs. Cardiopulmonary Exercise Test (CPET) was evaluated on Correlation of oxygen consumption VO2, heart rate (HR), and Workload at AerT estimated by RQA vs CPET (r > 0.64). Recurrence quantification analysis of heart rate time series estimated aerobic threshold with strong correlation (r > 0.64) to standard cardiopulmonary exercise testing.
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