Sports Orthopedics

Sports Orthopedics

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Background: Different methods for heart rate (HR)-determination are used in routine performance diagnostics. Aim of the study was to compare different HR measurement methods during treadmill performance diagnostics.Methods: 76 athletes (28.614.7 years, 38% female) performed a treadmill lactate thres...

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Autores principales: Schulz SVW, Enders K, Rolser N, Steinacker JM, Laszlo R
Formato: article
Lenguaje:DE
EN
Publicado: Dynamic Media Sales Verlag 2019
Materias:
Sports medicine
RC1200-1245
Acceso en línea:https://doaj.org/article/6d183d871c4244d1812c1ae807e14987
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spelling oai:doaj.org-article:6d183d871c4244d1812c1ae807e149872021-11-16T19:01:41ZSports Orthopedics0344-59252510-526410.5960/dzsm.2019.387https://doaj.org/article/6d183d871c4244d1812c1ae807e149872019-07-01T00:00:00Zhttps://www.germanjournalsportsmedicine.com/archive/archiv-2019/issue-7-8/validation-and-comparison-of-three-different-heart-rate-measuring-methods-during-treadmill-performance-diagnostics/https://doaj.org/toc/0344-5925https://doaj.org/toc/2510-5264Background: Different methods for heart rate (HR)-determination are used in routine performance diagnostics. Aim of the study was to compare different HR measurement methods during treadmill performance diagnostics.Methods: 76 athletes (28.614.7 years, 38% female) performed a treadmill lactate threshold test. HR during testing was simultaneously assessed by analysis of a 12-lead electrocardiogram (ECG) both automatically (aECG) and manually (mECG) and a heart rate monitor (HRM). ECGs and HRM measurements were analyzed by two diagnosticians and finally, three different HR curves (aECG, mECG, HRM) were generated and compared at different time points.Results: ECG-based HR detection revealed excellent reproducibility and reliability. Concerning HRM/aECG, faulty measurements were detected in 14.5%/36.8% of all athletes. However, constructions of HR/lactate curves were still possible in 84.6%/73.7% of all athletes. HR at different corresponding time points did not differ significantly between mECG and HRM/aECG (intraclass correlation coefficient >0.9/0.8 and coefficient of variation <5%/5%). In Bland-Altman analysis HRM/mECG and aECG/mECG, mean differences were usually low (3-5 bpm). Limits of agreement were relatively high (approx.10 bpm).Conclusions: Training areas defined by mECG may be used for home training control with HRM. If HRM measurements are used for the athletes training recommendations, HRs determined should be checked for plausibility and comparability with corresponding ECG measurements by physicians with appropriate expertise. Due to comparably high error susceptibility, aECG HR detection should not be used in performance diagnostics. KEY WORDS: Heart Rate Detection, Heart Rate Monitors, Lactate Curve, Performance DiagnosisSchulz SVWEnders KRolser NSteinacker JMLaszlo RDynamic Media Sales VerlagarticleSports medicineRC1200-1245DEENDeutsche Zeitschrift für Sportmedizin, Vol 2019, Iss 7 (2019)
institution DOAJ
collection DOAJ
language DE
EN
topic Sports medicine
RC1200-1245
spellingShingle Sports medicine
RC1200-1245
Schulz SVW
Enders K
Rolser N
Steinacker JM
Laszlo R
Sports Orthopedics
description Background: Different methods for heart rate (HR)-determination are used in routine performance diagnostics. Aim of the study was to compare different HR measurement methods during treadmill performance diagnostics.Methods: 76 athletes (28.614.7 years, 38% female) performed a treadmill lactate threshold test. HR during testing was simultaneously assessed by analysis of a 12-lead electrocardiogram (ECG) both automatically (aECG) and manually (mECG) and a heart rate monitor (HRM). ECGs and HRM measurements were analyzed by two diagnosticians and finally, three different HR curves (aECG, mECG, HRM) were generated and compared at different time points.Results: ECG-based HR detection revealed excellent reproducibility and reliability. Concerning HRM/aECG, faulty measurements were detected in 14.5%/36.8% of all athletes. However, constructions of HR/lactate curves were still possible in 84.6%/73.7% of all athletes. HR at different corresponding time points did not differ significantly between mECG and HRM/aECG (intraclass correlation coefficient >0.9/0.8 and coefficient of variation <5%/5%). In Bland-Altman analysis HRM/mECG and aECG/mECG, mean differences were usually low (3-5 bpm). Limits of agreement were relatively high (approx.10 bpm).Conclusions: Training areas defined by mECG may be used for home training control with HRM. If HRM measurements are used for the athletes training recommendations, HRs determined should be checked for plausibility and comparability with corresponding ECG measurements by physicians with appropriate expertise. Due to comparably high error susceptibility, aECG HR detection should not be used in performance diagnostics. KEY WORDS: Heart Rate Detection, Heart Rate Monitors, Lactate Curve, Performance Diagnosis
format article
author Schulz SVW
Enders K
Rolser N
Steinacker JM
Laszlo R
author_facet Schulz SVW
Enders K
Rolser N
Steinacker JM
Laszlo R
author_sort Schulz SVW
title Sports Orthopedics
title_short Sports Orthopedics
title_full Sports Orthopedics
title_fullStr Sports Orthopedics
title_full_unstemmed Sports Orthopedics
title_sort sports orthopedics
publisher Dynamic Media Sales Verlag
publishDate 2019
url https://doaj.org/article/6d183d871c4244d1812c1ae807e14987
work_keys_str_mv AT schulzsvw sportsorthopedics
AT endersk sportsorthopedics
AT rolsern sportsorthopedics
AT steinackerjm sportsorthopedics
AT laszlor sportsorthopedics
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