ArticlePloS one2019
Estimating minute ventilation and air pollution inhaled dose using heart rate, breath frequency, age, sex and forced vital capacity: A pooled-data analysis.
Article in PloS one, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed, 33 citations in OpenAlex.
- A Bayesian approach to estimate minute ventilation from heart rate during exercise for assessing environmental exposures of females.Physiological reports · 2026Article
- Predicting Minute Ventilation from Heart Rate in Adolescents: A Tool for Environmental Health Studies.Environments (Basel, Switzerland) · 2025Article
- A New Wearable System for Personal Air Pollution Exposure Estimation: Pilot Observational Study.JMIR mHealth and uHealth · 2025Observational
- TracMyAir smartphone application for modeling exposures to PMThe Science of the total environment · 2025Article
- Emerging concern on air pollution and health: Trade-off between air pollution exposure and physical activity.Eco-Environment & Health · 2024Article
- Personal airborne particulate matter exposure and intake dose, indoor air quality, biometric, and activity dataset from the city of Ljubljana, Slovenia.Data in brief · 2024Article
- Modeling the impact of indoor relative humidity on the infection risk of five respiratory airborne viruses.Scientific reports · 2022Article
- Airborne magnetic nanoparticles may contribute to COVID-19 outbreak: Relationships in Greece and Iran.Environmental research · 2022Article
- Comparing Airborne Particulate Matter Intake Dose Assessment Models Using Low-Cost Portable Sensor Data.Sensors (Basel, Switzerland) · 2020Article
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Authors and funding
7 authors at 6 institutions in 4 countries.
Funding
Abstract
Air pollution inhaled dose is the product of pollutant concentration and minute ventilation ([Formula: see text]). Previous studies have parameterized the relationship between [Formula: see text] and variables such as heart rate (HR) and have observed substantial inter-subject variability. In this paper, we evaluate a method to estimate [Formula: see text] with easy-to-measure variables in an analysis of pooled-data from eight independent studies. We compiled a large diverse data set that is balanced with respect to age, sex and fitness level. We used linear mixed models to estimate [Formula: see text] with HR, breath frequency (fB), age, sex, height, and forced vital capacity (FVC) as predictors. FVC was estimated using the Global Lung Function Initiative method. We log-transformed the dependent and independent variables to produce a model in the form of a power function and assessed model performance using a ten-fold cross-validation procedure. The best performing model using HR as the only field-measured parameter was [Formula: see text] = e-9.59HR2.39age0.274sex-0.204FVC0.520 with HR in beats per minute, age in years, sex is 1 for males and 2 for females, FVC in liters, and a median(IQR) cross-validated percent error of 0.664(45.4)%. The best performing model overall was [Formula: see text] = e-8.57HR1.72fB0.611age0.298sex-0.206FVC0.614, where fB is breaths per minute, and a median(IQR) percent error of 1.20(37.9)%. The performance of these models is substantially better than any previously-published model when evaluated using this large pooled-data set. We did not observe an independent effect of height on [Formula: see text], nor an effect of race, though this may have been due to insufficient numbers of non-white participants. We did observe an effect of FVC such that these models over- or under-predict [Formula: see text] in persons whose measured FVC was substantially lower or higher than estimated FVC, respectively. Although additional measurements are necessary to confirm this finding regarding FVC, we recommend using measured FVC when possible.
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