Threshold estimation in running using dynamical correlations of RR intervals

Threshold estimation in running using dynamical correlations of RR intervals

Abstract 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 treadmi...

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Bibliographic Details
Main Authors: Matias Kanniainen, Vesa Laatikainen‐Raussi, Teemu Pukkila, Krista Vohlakari, Esa Hynynen, Johanna K. Ihalainen, Esa Räsänen
Format: Article
Language:English
Published: Wiley 2025-05-01
Series:Physiological Reports
Subjects:
aerobic threshold
anaerobic threshold
exercise physiology
heart rate variability
Online Access:https://doi.org/10.14814/phy2.70241
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Summary:Abstract 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.
ISSN:2051-817X

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