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Predicting Short-Term HR Response to Varying Training Loads Using Exponential Equations

Hoffmann, K. ; Wiemeyer, J. (2017)
Predicting Short-Term HR Response to Varying Training Loads Using Exponential Equations.
In: International Journal of Computer Science in Sport, 2017, 16 (2)
Article, Secondary publication

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Item Type: Article
Type of entry: Secondary publication
Title: Predicting Short-Term HR Response to Varying Training Loads Using Exponential Equations
Language: English
Date: 2017
Place of Publication: Darmstadt
Year of primary publication: 2017
Journal or Publication Title: International Journal of Computer Science in Sport
Volume of the journal: 16
Issue Number: 2
Corresponding Links:
Origin: Secondary publication via sponsored Golden Open Access
Abstract:

Aim of this study was to test whether a monoexponential formula is appropriate to analyze and predict individual responses to the change of load bouts online during training. Therefore, 234 heart rate (HR) data sets obtained from extensive interval protocols of four participants during a twelve-week training intervention on a bike ergometer were analyzed. First, HR for each interval was approximated using a monoexponential formula. HR at onset of exercise (HRstart), HR induced by load (HRsteady) and the slope of HR (c) were analyzed. Furthermore, a calculation routine incrementally predicted HRsteady using measured HR data after onset of exercise. Validity of original and approximated data sets were very high (r² =0.962, SD =0.025; Max =0.991, Min =0.702). HRstart was significantly different between all participants (one exception). HRsteady was similar in all participants. Parameter c was independent of the duration of intervention and intervals regarding one training session but was significantly different in all participants (one exception). Final HR was correctly predicted on average after 58.8 s (SD = 34.77, Max =150 s, Min =30 s) based on a difference criteria of less than 5 bpm. In 3 participants, HRsteady was predicted correctly in 142 out of 175 courses (81.1%).

URN: urn:nbn:de:tuda-tuprints-70376
Classification DDC: 700 Arts and recreation > 796 Sports
Divisions: 03 Department of Human Sciences > Institut für Sportwissenschaft
Date Deposited: 14 Dec 2017 12:40
Last Modified: 13 Dec 2022 10:44
URI: https://tuprints.ulb.tu-darmstadt.de/id/eprint/7037
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