Fig 1.
Principal scheme for the analytic models in the study.
Two models are used for the HR method to estimate the oxygen uptake in cycle ergometer exercise versus treadmill exercise conditions, respectively. Model 1 is based on three submaximal workloads, whereas model 2 is based on the same three submaximal workloads as well as a workload corresponding to maximal exercise. The figure shows a conceivable example in which model 1 and model 2 in the treadmill exercise results in two different regression equations, whereas in the cycle ergometer exercise, model 1 and model 2 results in the same regression equation. The last step in the HR method is applied here in terms of oxygen uptake being estimated from various percentages of HR reserve and the different regression equations.
Table 1.
Descriptive characteristics of the participants (means ± standard deviations (SD)).
Table 2.
, HR,
and RPE during submaximal and maximal cycle ergometer exercise (means ± SD).
Table 3.
, HR,
and RPE during submaximal (walking) and maximal (running) treadmill exercise (means ± SD).
Table 4.
Regression equations of model 1, and the absolute and relative exercise mode differences (n = 34, means ± SD, (95% CI) and P-values).
Table 5.
Regression equations of model 2, and the absolute and relative exercise mode differences (n = 34, means ± SD, (95% CI) and P-values).
Table 6.
Estimation of in model 1, and the absolute and relative exercise mode differences (n = 34, means ± SD, (95% CI) and P-values).
Table 7.
Estimation of in model 2, and the absolute and relative exercise mode differences (n = 34, means ± SD, (95% CI) and P-values).
Fig 2.
Estimated mean levels between 25–85% of HRR in cycle ergometer exercise (CEE) and treadmill exercise (TE) in model 1 and model 2.
Based on all participants (n = 34) individual values. CEE: blue solid line. TE: red dashed line. The linear regression equations with 95% CI and r-coefficients were: Model 1 (A) y(CEE) = 0.210(0.042─0.377) + 0.0279(0.0251─0.0308) * x(%HRR), r-coefficient = 0.781, y(
TE) = 0.178(-0.030─0.386) + 0.0271(0.0236─0.0307) * x(%HRR) and r-coefficient = 0.700. Model 2 (B) y(
CEE) = 0.222(0.064─0.380) + 0.0276(0.0249─0.0303) * x(%HRR), r-coefficient = 0.795, y(
TE) = 0.084(-0.072─0.241) + 0.0304(0.0277─0.0331) * x(%HRR) and r-coefficient = 0.825.
Fig 3.
Comparison of the estimated individual values between cycle ergometer exercise (CEE) and treadmill exercise (TE) in model 1 and model 2.
Based on all participants (n = 34), ranging between 25–85% of HRR. The overall linear regression equations (red dashed lines) with 95% CI and r-coefficients were: Model 1 (A) y(TE) = -0.033(-0.148─0.082) + 0.9753(0.9144─1.0362) * x(
CEE) and r-coefficient = 0.899. Model 2 (B) y(
TE) = -0.004(-0.081─0.073) + 1.0125(0.9714─1.0536) * x(
CEE) and r-coefficient = 0.953.
Fig 4.
Comparison of the estimated individual values between model 1 and model 2 in cycle ergometer exercise (CEE) and treadmill exercise (TE).
Based on all participants (n = 34), ranging between 25–85% of HRR. The overall linear regression equations (red dashed lines) with 95% CI and r-coefficients were: CEE (A) y(Model 2) = 0.056(0.028─0.084) + 0.9632(0.9483─0.9780) * x(
Model 1) and r-coefficient = 0.993. TE (B) y(
Model 2) = 0.318(0.223─0.413) + 0.8606(0.8091─0.9122) * x(
Model 1) and r-coefficient = 0.906.