What Mathematical Models Are Accurate for Prescribing Aerobic Exercise in Women with Fibromyalgia?

SIMPLE SUMMARY: Intensity prescription for cardiorespiratory exercises is crucial for achieving health/fitness benefits. However, not all of the population can access a cardiopulmonary exercise test, either for economic reasons or location resources, to determine their ventilatory thresholds. Therefore, different mathematical models can predict the intensity based on the maximum or reserve heart rate. Exercise prescription guidelines indicate that people with fibromyalgia should exercise at 60% of their VO(2)max. However, people with fibromyalgia suffer from dysautonomia, which could lead to chronotropic incompetence, the inability to increase heart rate with increasing exercise intensities. Therefore, this study aimed to investigate the relationship and level of agreement between different mathematical models and the heart rate obtained from a cardiopulmonary exercise test at their ventilatory threshold 1. The results showed that the well-known “220 − age” at 76% and the mathematical model designed for people with fibromyalgia “209 − 0.85 × age” at 76% showed a significant level of agreement. However, Tanaka and Karvonen’s formula did not show a significant level of agreement. Thus, the “220 − age” at 76% and “209 − 0.85 × age” at 76% can be used in people with FM to prescribe aerobic exercise. ABSTRACT: Objectives: This article aims to verify the agreement between the standard method to determine the heart rate achieved in the ventilatory threshold 1 in the cardiopulmonary exercise testing (VT1) and the mathematical models with exercise intensities suggested by the literature in order to check the most precise for fibromyalgia (FM) patients. Methods: Seventeen women with FM were included in this study. The VT1 was used as the standard method to compare four mathematical models applied in the literature to calculate the exercise intensity in FM patients: the well-known “220 − age” at 76%, Tanaka predictive equation “208 − 0.7 × age” at 76%, the FM model HRMax “209 – 0.85 × age” at 76%, and Karvonen Formula at 60%. Bland–Altman analysis and correlation analyses were used to explore agreement and correlation between the standard method and the mathematical models. Results: Significant correlations between the heart rate at the VT1 and the four mathematical estimation models were observed. However, the Bland-Altman analysis only showed agreement between VT1 and “220 − age” (bias = −114.83 + 0.868 × x; 95% LOA = −114.83 + 0.868 × x + 1.96 × 7.46 to −114.83 + 0.868 × x − 1.96 × 7.46, where x is the average between the heart rate obtained in the CPET at VT1 and “220 − age”, in this case 129.15; p = 0.519) and “209 − 0.85 × age”(bias = −129.58 + 1.024 × x; 95% LOA = −129.58 + 1.024 × x + 1.96 × 6.619 to −129.58 + 1.024 × x − 1.96 × 6.619, where x is the average between the heart rate obtained in the CPET at VT1 and “209 − 0.85 × age”, in this case 127.30; p = 0.403). Conclusions: The well-known predictive equation “220 − age” and the FM model HRMax (“209 − 0.85 × age”) showed agreement with the standard method (VT1), revealing that it is a precise model to calculate the exercise intensity in sedentary FM patients. However, proportional bias has been detected in all the mathematical models, with a higher heart rate obtained in CPET than obtained in the mathematical model. The chronotropic incompetence observed in people with FM (inability to increase heart rate with increasing exercise intensities) could explain why methods that tend to underestimate the HRmax in the general population fit better in this population.