<abstract xmlns="http://www.w3.org/1999/xhtml"> 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%).</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%).

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

International Journal of Computer Science in Sport

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

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...

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

Article Category: Special Issue on Modeling in Endurance Sports Movement and Sport Science, Editors: Chris R. Abbiss, Dietmar Saupe

Publié en ligne: 30 nov. 2017

Pages: 130 - 148

DOI: https://doi.org/10.1515/ijcss-2017-0011

Mots clés
adaptation, hr responses, monoexponential equation

© 2017

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

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

Article Category: Special Issue on Modeling in Endurance Sports Movement and Sport Science, Editors: Chris R. Abbiss, Dietmar Saupe

Publié en ligne: 30 nov. 2017

Pages: 130 - 148

DOI: https://doi.org/10.1515/ijcss-2017-0011

Mots clésadaptation, hr responses, monoexponential equation

© 2017

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Mots clés
adaptation, hr responses, monoexponential equation