A Bayesian approach to estimate minute ventilation from heart rate during exercise for assessing environmental exposures of females

Estimating minute ventilation (V̇_E) is essential for assessing the health impacts of environmental exposures during exercise field-studies. Predictive equations using heart rate (HR) are commonly used, but overlook exercise intensity domains, and reduced accuracy is shown, particularly for females. Thus, we developed predictive equations for females' V̇_E based on HR responses at different exercise intensity domains using a Bayesian approach. Nineteen physically active females performed an incremental running test with breath-by-breath measurements of V̇_E, metabolic rate, and HR. The first and second ventilatory thresholds were identified by measurement of the ventilatory equivalent for oxygen and carbon dioxide, respectively. The Bayesian framework showed that the model fit for estimating V̇_E by HR was improved when the incremental running test and its intensity domains were considered. An exponential model provided the best fit (V̇_E = 2.86 × exp.(0.019 × HR)) for the full incremental running test (R² = 0.957), whereas linear models yielded superior fits when analyzing individual moderate (V̇_E = -32.92 + (HR × 0.19)), heavy (V̇_E = -101.94 + (HR × 0.99)) and severe (V̇_E = -268.81 + (HR × 1.98)) exercise intensity domains (R² = 0.977). Accurate estimates of V̇_E from HR measurements must consider the exercise intensity domain and the linear regression model for better biomonitoring of human exposures.