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

• Published in: PloS one 2016-01, Vol.11 (1), p.e0147578-e0147578
• Main Authors: Greenwald, Roby, Hayat, Matthew J, Barton, Jerusha, Lopukhin, Anastasia
• Format: Article
• Language: English
• Subjects: Adolescent; Adolescents; Air Pollutants - analysis; Air Pollutants - toxicity; Air pollution; Air pollution measurements; Analysis; Archives & records; Asthma; Bicycling; Biology and Life Sciences; Breathing; Consent; Dosage; Drug dosages; Engineering and Technology; Environmental aspects; Environmental health; Epidemiology; Errors; Exercise; Exercise - physiology; Female; Heart rate; Heart Rate - drug effects; Humans; Lung volume measurement; Male; Mathematical models; Medical equipment; Medicine and Health Sciences; Methods; Models, Theoretical; Outdoor air quality; Parameter estimation; Pedestrians; People and Places; Performance prediction; Physical activity; Physical fitness; Physical Sciences; Pollutants; Public health; Respiration; Respiration - drug effects; Studies; Systematic review; Tidal Volume - drug effects; Ventilation; Ventilators
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0147578

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 (fB), and forced vital capacity (FVC). We recruited healthy adolescents to use a treadmill while we continuously measured HR, fB, and the tidal volume (VT) 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 ([Formula: see text]) was normalized by FVC. We used general linear mixed models with a random effect for subject and identified nine potential predictor variables that influence either [Formula: see text] or 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 fB to have the best predictive performance ([Formula: see text]/FVC = -4.247+0.0595HR+0.226fB, mean percent error = 8.1±29%); however, given the ubiquity of HR measurements, a one-predictor model including HR may also be useful ([Formula: see text]/FVC = -3.859+0.101HR, mean percent error = 11.3±36%).