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2018 | Supplement | Buchkapitel

Statistical Models for Predicting Short-Term HR Responses to Submaximal Interval Exercise

verfasst von : Katrin Hoffmann, Josef Wiemeyer

Erschienen in: Proceedings of the 11th International Symposium on Computer Science in Sport (IACSS 2017)

Verlag: Springer International Publishing

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Abstract

Aim of the study was to identify possible predictors influencing the variability of individual short-term heart rate (HR) responses to submaximal interval exercise using a probabilistic model. Short-term HR responses to the change of load bouts obtained in a twelve-week training intervention were analyzed. Questionnaires gathered preceding sport activity, sleep, nutrition, health and mood prior to each training session. Additionally, time of the day and number of interval was included in calculation. Multiple regression method was used to identify predictors for start heart rate (HR), steady state HR, and for the slope of the HR curve. Especially the number of the interval, physical and mental health, and negative mood were influencing these responses. The start heart rate was identified as predictor in five of eight response parameter. Time was a factor highly varying between participants. Future research need to validate the results in a wider sample and integrate more parameters in the analysis.

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Titel: Statistical Models for Predicting Short-Term HR Responses to Submaximal Interval Exercise
verfasst von: Katrin Hoffmann
Josef Wiemeyer
Verlag: Springer International Publishing
Buch: Proceedings of the 11th International Symposium on Computer Science in Sport (IACSS 2017)
Print ISBN: 978-3-319-67845-0

Electronic ISBN: 978-3-319-67846-7

Copyright-Jahr: 2018
DOI: https://doi.org/10.1007/978-3-319-67846-7_6

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