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

Objective: Some proposals for oxygen uptake plateau identification are based on linear regression adaptations. However, linear regression does not adequately explain the oxygen uptake nonlinear dynamics. Recently, segmented regression was considered as an alternative to fit this dynamics, by performing an approximation by straight line segments, which provided a satisfactory fit. In this context, the non-plateau and plateau hypotheses were verified by means of a Wald-type test. This work aims to extend these proposals to scenarios with autocorrelated data. Methods: We propose an algorithm to estimate the segmented regression model under autocorrelation using generalized least squares and suggest a bootstrap method to resample from the null distribution of Wald’s statistic. The performance of the estimate and methods of the plateau diagnosis were evaluated via Monte Carlo experiments. Results: The empirical results show that, under autocorrelation, the proposed estimator performs better when compared to the classic method, mainly in scenarios with small sample sizes and moderate/strong autocorrelation structure. The simulations also showed that the plateau diagnosis test has a coherent empirical Type 1 Error probability and good power. Conclusion: We proposed an alternative to estimate the parameters of a segmented regression model for autocorrelated data and an oxygen consumption plateau bootstrap test, and concluded the methods present good performance under simulated and applied case studies. Significance: The proposed method was used to model real oxygen consumption data. Empirical evidence shows that the methods can be used to objectively identify the plateau in oxygen consumption only by specifying a tolerable significance level.