Why the study?
Aerobic and anaerobic thresholds are conventionally estimated in laboratory settings during incremental exercise tests, prompting evaluation of dynamical detrended fluctuation analysis of RR intervals as an alternative.
Does dynamical detrended fluctuation analysis (DDFA) of RR intervals accurately estimate aerobic and anaerobic thresholds compared to lactate thresholds in subjects undergoing incremental treadmill running?
Does dynamical detrended fluctuation analysis (DDFA) of RR intervals accurately estimate aerobic and anaerobic thresholds compared to lactate thresholds in subjects undergoing incremental treadmill running?
DDFA of RR intervals provides a simple, accurate alternative to lactate testing for estimating aerobic and anaerobic thresholds, with potential for integration into wearable devices.
Supports DDFA for noninvasive threshold estimation; leaves open prospective validation before clinical adoption.
We study the estimation of aerobic threshold (AeT) and anaerobic threshold (AnT) using dynamical detrended fluctuation analysis (DDFA). Conventionally, the thresholds are estimated in laboratory settings, where the subject performs an incremental exercise test on a cycloergometer or treadmill. We compared DDFA-based thresholds (DDFAT₁ and DDFAT₂) with lactate thresholds (LT₁ and LT₂) and examined thresholds derived from theoretical and measured maximal heart rates (HR). The analysis was conducted on 58 subjects undergoing an incremental treadmill running test. Our findings indicate significant discrepancies between thresholds derived from theoretical and measured maximal HRs compared to lactate thresholds. Specifically, theoretical maximal HR thresholds consistently underestimated lactate thresholds, exhibiting systematic bias. Measured maximal HR thresholds also showed a consistent underestimation, though with improved alignment to lactate thresholds. In contrast, the DDFA-based method demonstrated reasonable agreement with lactate thresholds and lacked systematic bias. The DDFA-based approach offers a simple and accurate alternative for estimating AeT and AnT. Its potential for continuous monitoring makes it suitable for integration into wearable devices such as smartwatches and heart rate monitors.
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Kanniainen et al. (2025) studied this question.
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