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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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
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Online Access:https://doi.org/10.14814/phy2.70241
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author Matias Kanniainen
Vesa Laatikainen‐Raussi
Teemu Pukkila
Krista Vohlakari
Esa Hynynen
Johanna K. Ihalainen
Esa Räsänen
author_facet Matias Kanniainen
Vesa Laatikainen‐Raussi
Teemu Pukkila
Krista Vohlakari
Esa Hynynen
Johanna K. Ihalainen
Esa Räsänen
author_sort Matias Kanniainen
collection DOAJ
description 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.
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spelling doaj-art-c239c2fcdde547d88bac82c760bb8e1c2025-08-20T03:10:06ZengWileyPhysiological Reports2051-817X2025-05-01139n/an/a10.14814/phy2.70241Threshold estimation in running using dynamical correlations of RR intervalsMatias Kanniainen0Vesa Laatikainen‐Raussi1Teemu Pukkila2Krista Vohlakari3Esa Hynynen4Johanna K. Ihalainen5Esa Räsänen6Computational Physics Laboratory Tampere University Tampere FinlandFaculty of Sport and Health Sciences University of Jyväskylä Jyväskylä FinlandComputational Physics Laboratory Tampere University Tampere FinlandFaculty of Sport and Health Sciences University of Jyväskylä Jyväskylä FinlandFinnish Institute of High Performance Sport KIHU Jyväskylä FinlandFaculty of Sport and Health Sciences University of Jyväskylä Jyväskylä FinlandComputational Physics Laboratory Tampere University Tampere FinlandAbstract 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.https://doi.org/10.14814/phy2.70241aerobic thresholdanaerobic thresholdexercise physiologyheart rate variability
spellingShingle Matias Kanniainen
Vesa Laatikainen‐Raussi
Teemu Pukkila
Krista Vohlakari
Esa Hynynen
Johanna K. Ihalainen
Esa Räsänen
Threshold estimation in running using dynamical correlations of RR intervals
Physiological Reports
aerobic threshold
anaerobic threshold
exercise physiology
heart rate variability
title Threshold estimation in running using dynamical correlations of RR intervals
title_full Threshold estimation in running using dynamical correlations of RR intervals
title_fullStr Threshold estimation in running using dynamical correlations of RR intervals
title_full_unstemmed Threshold estimation in running using dynamical correlations of RR intervals
title_short Threshold estimation in running using dynamical correlations of RR intervals
title_sort threshold estimation in running using dynamical correlations of rr intervals
topic aerobic threshold
anaerobic threshold
exercise physiology
heart rate variability
url https://doi.org/10.14814/phy2.70241
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AT kristavohlakari thresholdestimationinrunningusingdynamicalcorrelationsofrrintervals
AT esahynynen thresholdestimationinrunningusingdynamicalcorrelationsofrrintervals
AT johannakihalainen thresholdestimationinrunningusingdynamicalcorrelationsofrrintervals
AT esarasanen thresholdestimationinrunningusingdynamicalcorrelationsofrrintervals