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

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 (HR_start), HR induced by load (HR_steady) and the slope of HR (c) were analyzed. Furthermore, a calculation routine incrementally predicted HR_steady 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). HR_start was significantly different between all participants (one exception). HR_steady 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, HR_steady was predicted correctly in 142 out of 175 courses (81.1%).