Discussion
This study reported results from a large sample of CR patients, in which we found that different guideline-based exercise intensity domains were elicited for the same relative level of exercise, whether at VT1 or VT2 (figure 1). Considering Brazilian, European and American guidelines, the results corresponded to low to moderate exercise intensity domains for VT1 and to moderate to very high-intensity exercise domains for VT2. The results showed that the relations between %HRpeak at VTs and HRpeak are not constant (figure 3), especially for VT1, which mathematically limits the applicability of the prescription methods based on a percentage of peak effort variables. We also offer greater insight into improving CR exercise prescription when CPET is unavailable by providing VT1 and VT2 equations tailored to patients with CVD using variables assessed during an ergometry test. Moreover, MAPE analyses revealed that the novel equations have greater accuracy than other methods generally used in CR to prescribe the exercise intensity targets based on %HRpeak, indicating that our proposal is the closest to metabolic thresholds of CPET (figure 4).
VT1 and VT2 in relation to guideline-based exercise intensity domains
The correspondence of different exercise intensity parameters has been studied. Considerable variation is found in recommendation targets for prescription,13 16 27–30 since the same relative %HRpeak, %HRR and/or %VO2peak elicited different guideline-based exercise intensity domains (which could be considered low to moderate intensity for VT1 and moderate to high intensity for VT2). Consequently, an important percentage of subjects will be trained below VT1 or above VT2 when HR-based methods are used, as demonstrated by the high dispersion found in the individual responses of the patients (figure 1). Hansen et al16 also found considerable variation in guideline-based exercise intensity domains at the same relative level of exercise, and Díaz-Buschmann et al13 pointed out the substantial interpatient variance among patients with CVD about HR-based prescription. These findings could partially be explained by the non-linearity and high variability of the relation between VO2 and workload in patients with CVD,31 32 chronotropic incompetence and the use of beta blockers, which influence the relation between HR and VO2.33 Additionally, Iannetta et al34 recently reported that the intensity of exercise training during CR predicts the increase of MET peak, highlighting that the heterogeneity in the metabolic stimulus of each exercise session can generate individual variation in training adaptations.34–36
Novel equations for patients with CVD
This study revealed a simple but crucial mathematic limitation inherent to the method for seeking a fixed percentage of peak parameters for prescribing exercise intensity. For example, if we consider 69% of HRpeak as the lower limit of exercise prescription (value for HR at VT1 in our data), we will assume that the equation follows a linear equation (Y=A*X +B), in which Y=HR at VT1, A=0.69, X=HRpeak and B=0 (HR at VT1=0.69 * HRpeak+0). Thus, the mathematical concept suggests that the relation between HR at VT1 and HRpeak (or Y/X) is constant when plotted against HRpeak (or X). In other words, when using the %HRpeak method, we assume that intercept (B) equals zero without considering any correction for the data dispersion.
However, when we analyse the plotted curve of our data (figure 3A), we observe that the intercept (B) is 30.62, strongly different from zero. Moreover, the coefficient (A) is 0.4707, which is also very different from the value considered for moderate-intensity exercise in the guidelines (Brazilian: 0.70; European: 0.55; American: 0.64). We also noticed a significant association between higher values of %HRpeak at VT1 and lower HRpeak values (figure 3B). Regarding VT2, our intercept (B) is much more like previously assumed values (4.397 vs 0), and the same is true for the coefficient (A) (0.8544 vs Brazilian: 0.85; European: 0.7; American: 0.76).
Thus, applying this simple math problem to the real world, we can see that the widely used method of prescribing exercise intensity according to percentages of HRpeak has an important limitation. The greater observed error can be related to the method itself and not the percentage values, as we cannot assume that the intercept of the equation is zero, especially concerning VT1, which is considered the lower limit for a moderate-intensity exercise prescription.
Accuracy of prescription methods
Another important contribution of this study is the accuracy approach by MAPE, in which lower values indicate greater forecasting accuracy of a model. MAPE of the VT1 equation was 6.0%, lower than guideline-based prescription methods (9.5%–23.8%). Moreover, the MAPE for the VT2 equation was 4.3%, which was also lower than guideline-based methods (5.8%–19.3%). These results suggested that the novel equations can be used as an alternative for patients with CVD, defining an intensity closer to the parameter determined by the CPET. The approach by MAPE was recently also used in a study comparing the accuracy of different predictive equations applied to CR.32 The reported MAPE for VT1 estimation ranged from 11.3 to 16.5% in patients with heart failure.
Clinical implications
Similarly to others,13 16 17 our study has demonstrated that currently employed methods using percentages of peak exercise (‘range-based’ approach) can be inaccurate for exercise intensity prescription, which may influence clinical outcomes. Hence, the HR predictive equations proposed in this study, primarily developed for VTs identification and using parameters available on the ergometry test, recognised as a minimum standard, demonstrated higher r2 and a lower error measured by MAPE than previously adopted indirect methods.
Thus, this new approach has great clinical applicability and may be a useful alternative when only an ergometry test is available, providing an indirect prescription closer to VTs of the CPET, which remains the gold-standard method.
Limitations
First, although including a large sample, the data were provided from a single centre. However, our sample is from Brasilia (the capital of Brazil). It may constitute casual pooled data from different Brazilian regions as the area received intense migration in the mid-1960s due to the relocation of the capital, as previously reported.24 External validity in other population samples remains to be tested to assure international applicability. Lastly, our study only included data from CPET performed on a treadmill; thus, our equations may not apply to exercise tests obtained from cycle ergometers.