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To better understand the interaction of physical activity and air pollution exposure, it is important to quantify the change in ventilation rate incurred by activity. In this paper, we describe a method for estimating ventilation using easily-measured variables such as heart rate (HR), breathing rate (f B ), and forced vital capacity (FVC). We recruited healthy adolescents to use a treadmill while we continuously measured HR, f B , and the tidal volume (V T ) of each breath. Participants began at rest then walked and ran at increasing speed until HR was 160–180 beats per minute followed by a cool down period. The novel feature of this method is that minute ventilation (V  ˙   E) was normalized by FVC. We used general linear mixed models with a random effect for subject and identified nine potential predictor variables that influence eitherV  ˙   Eor FVC. We assessed predictive performance with a five-fold cross-validation procedure. We used a brute force selection process to identify the best performing models based on cross-validation percent error, the Akaike Information Criterion and the p-value of parameter estimates. We found a two-predictor model including HR and f B  to have the best predictive performance (V  ˙   E/FVC = -4.247+0.0595HR+0.226f B , mean percent error = 8.1±29%); however, given the ubiquity of HR measurements, a one-predictor model including HR may also be useful (V  ˙   E/FVC = -3.859+0.101HR, mean percent error = 11.3±36%).

A Novel Method for Quantifying the Inhaled Dose of Air Pollutants Based on Heart Rate, Breathing Rate and Forced Vital Capacity