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Related Experiment Video

Updated: Nov 18, 2025

A Rapidly Incremented Tethered-Swimming Maximal Protocol for Cardiorespiratory Assessment of Swimmers
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Monitoring the Heart Rate Variability Responses to Training Loads in Competitive Swimmers Using a Smartphone

Eva Piatrikova, Nicholas J Willsmer, Marco Altini

    International Journal of Sports Physiology and Performance
    |February 9, 2021
    PubMed
    Summary

    Heart rate variability (HRV) can be effectively modeled using training load and well-being data. Seasonal HRV changes correlate with critical speed in swimmers, supporting HRV-guided training.

    Keywords:
    athlete’s statuscardiac parasympathetic functionmodelingperformanceswimming

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    Area of Science:

    • Sports Science
    • Exercise Physiology
    • Biotechnology

    Background:

    • Heart rate variability (HRV) is a key indicator of physiological stress and recovery.
    • The Banister impulse-response model is a common tool for quantifying training load effects.
    • Integrating subjective well-being measures with objective training data can enhance physiological monitoring.

    Purpose of the Study:

    • To assess the effectiveness of the Banister impulse-response model in predicting HRV using session rating of perceived exertion (sRPE) and well-being data.
    • To describe seasonal trends in HRV and their relationship with critical speed (CS) in competitive swimmers.

    Main Methods:

    • 10 highly trained swimmers provided daily HRV recordings, sRPE, and well-being scores for 15 weeks via a smartphone app.
    • The impulse-response model was applied to root mean square of successive differences (rMSSD) using sRPE and well-being as inputs.
    • Critical speed was determined via a 3-minute all-out test at the beginning and end of the study.

    Main Results:

    • The model showed moderate agreement (R² = .66) with HRV data using sRPE alone, improving by 4-21% with added well-being measures.
    • No significant differences in weekly average rMSSD or its variability were found, but small-to-large changes occurred seasonally.
    • Strong correlations were observed between seasonal HRV changes and critical speed.

    Conclusions:

    • The impulse-response model, combined with smartphone-collected HRV and well-being data, effectively models physiological responses to training and stressors.
    • Significant associations between seasonal HRV and critical speed highlight the value of HRV-guided training strategies for swimmers.