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Prediction of Relevant Training Control Parameters at Individual Anaerobic Threshold without Blood Lactate Measurement

Author

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  • Claudia Römer

    (Department of Sports Medicine, Charité—Universitätsmedizin Berlin, Humboldt-University of Berlin, 10117 Berlin, Germany)

  • Bernd Wolfarth

    (Department of Sports Medicine, Charité—Universitätsmedizin Berlin, Humboldt-University of Berlin, 10117 Berlin, Germany)

Abstract

Background: Active exercise therapy plays an essential role in tackling the global burden of obesity. Optimizing recommendations in individual training therapy requires that the essential parameters heart rate HR(IAT) and work load (W/kg(IAT) at individual anaerobic threshold (IAT) are known. Performance diagnostics with blood lactate is one of the most established methods for these kinds of diagnostics, yet it is also time consuming and expensive. Methods: To establish a regression model which allows HR(IAT) and (W/kg(IAT) to be predicted without measuring blood lactate, a total of 1234 performance protocols with blood lactate in cycle ergometry were analyzed. Multiple linear regression analyses were performed to predict the essential parameters (HR(IAT)) (W/kg(IAT)) by using routine parameters for ergometry without blood lactate. Results: HR(IAT) can be predicted with an RMSE of 8.77 bpm ( p < 0.001), R 2 = 0.799 (|R| = 0.798) without performing blood lactate diagnostics during cycle ergometry. In addition, it is possible to predict W/kg(IAT) with an RMSE (root mean square error) of 0.241 W/kg ( p < 0.001), R 2 = 0.897 (|R| = 0.897). Conclusions: It is possible to predict essential parameters for training management without measuring blood lactate. This model can easily be used in preventive medicine and results in an inexpensive yet better training management of the general population, which is essential for public health.

Suggested Citation

  • Claudia Römer & Bernd Wolfarth, 2023. "Prediction of Relevant Training Control Parameters at Individual Anaerobic Threshold without Blood Lactate Measurement," IJERPH, MDPI, vol. 20(5), pages 1-12, March.
    • Handle: RePEc:gam:jijerp:v:20:y:2023:i:5:p:4641-:d:1088812
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    References listed on IDEAS

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    1. Qian Li & Steven Blume & Joanna Huang & Mette Hammer & Thomas Graf, 2015. "The Economic Burden of Obesity by Glycemic Stage in the United States," PharmacoEconomics, Springer, vol. 33(7), pages 735-748, July.
    2. Claudia Römer & Bernd Wolfarth, 2022. "Heart Rate Recovery (HRR) Is Not a Singular Predictor for Physical Fitness," IJERPH, MDPI, vol. 20(1), pages 1-10, December.
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