The application of repeated testing and monoexponential regressions to classify individual cardiorespiratory fitness responses to exercise training

Abstract

Purpose

We tested the hypothesis that monoexponential regressions will increase the certainty in response estimates and confidence in classification of cardiorespiratory fitness (CRF) responses compared to a recently proposed linear regression approach.

Methods

We used data from a previously published RCT that involved 24 weeks of training at high amount–high intensity (HAHI; N = 28), high amount–low intensity (HALI; N = 48), or low amount–low intensity (LALI; N = 33). CRF was measured at 0, 4, 8, 16, and 24 weeks. We fit the repeated CRF measures with monoexponential and linear regressions, and calculated individual response estimates, the error in these estimates (TE_MONOEXP and TE_SLOPE, respectively), and 95% confidence intervals (CIs). Individuals were classified as responders, uncertain, or non-responders based on where their CI lay relative to a minimum clinically important difference. Additionally, responses were classified using observed pre–post-changes and the typical error of measurement.

Results

Comparing the error in response estimates revealed that monoexponential regressions were a better fit than linear regressions for the majority of individual responses (N = 81/109) and mean CRF data (mean TE_MONOEXP:TE_SLOPE; HAHI = 2.00:2.58, HALI = 1.91:2.46, LALI = 1.63:2.18; all p < 0.01). Fewer individuals were confidently classified as responders with linear regressions (N = 29/109) compared to monoexponential (N = 55/109). Additionally, response estimates were highly correlated across all three approaches (all r > 0.92).

Conclusions

Future studies should determine the type of regression that best fits their data prior to classifying responses. The similarity in response estimates and classification from regressions and observed pre–post-changes questions the purported benefit of using repeated measures to characterize CRF responses to training.

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