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Oxygen Uptake Plateau Diagnosis Using a New Developed Segmented Regression Estimation Method for Autocorrelated Data.

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    This study introduces a new segmented regression algorithm for identifying oxygen uptake plateaus in autocorrelated data. The proposed method offers improved accuracy, especially with smaller sample sizes, and reliably detects oxygen consumption plateaus.

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    Area of Science:

    • Physiology
    • Biostatistics
    • Exercise Science

    Background:

    • Linear regression inadequately models nonlinear oxygen uptake dynamics.
    • Segmented regression offers a more satisfactory approximation for oxygen uptake.
    • Existing methods for plateau identification struggle with autocorrelated data.

    Purpose of the Study:

    • To extend segmented regression and Wald-type tests for oxygen uptake plateau identification to autocorrelated data.
    • To develop and evaluate an algorithm for estimating segmented regression models under autocorrelation.

    Main Methods:

    • Proposed an algorithm using generalized least squares for segmented regression with autocorrelation.
    • Developed a bootstrap method for resampling Wald's statistic null distribution.
    • Evaluated performance via Monte Carlo simulations and applied to real oxygen consumption data.

    Main Results:

    • The proposed estimator outperforms classic methods in autocorrelated scenarios, particularly with small sample sizes.
    • The plateau diagnosis test demonstrated coherent empirical Type 1 Error rates and good statistical power.
    • The method objectively identifies oxygen consumption plateaus based on a significance level.

    Conclusions:

    • The developed methods provide a robust approach for estimating parameters in segmented regression models for autocorrelated data.
    • The proposed bootstrap test effectively diagnoses oxygen consumption plateaus.
    • The methods show good performance in both simulated and real-world case studies.