ArticleJournal of exposure science & environmental epidemiology2019
Development and validation of models to predict personal ventilation rate for air pollution research.
Article in Journal of exposure science & environmental epidemiology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
6 citing papers in PubMed.
- Advancing Long-Term Outdoor Air Pollution Exposure Assessment for Health Studies: Lessons from a Multinational Research Initiative.Current environmental health reports · 2026Review
- Commuter exposure to smoke and particulate matter air pollution in Auckland during the 2019 New Zealand International Convention Centre fire.Journal of the Royal Society of New Zealand · 2025Article
- Emerging concern on air pollution and health: Trade-off between air pollution exposure and physical activity.Eco-Environment & Health · 2024Article
- Obtainment and Inoculation of Acinetobacter pittii Strain JJ-2, and Combined Action with Plants for Formaldehyde and COCurrent microbiology · 2023Article
- Comparing Airborne Particulate Matter Intake Dose Assessment Models Using Low-Cost Portable Sensor Data.Sensors (Basel, Switzerland) · 2020Article
- Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
Air pollution intake represents the amount of pollution inhaled into the body and may be calculated by multiplying an individual's ventilation rate with the concentration of pollutant present in their breathing zone. Ventilation rate is difficult to measure directly, and methods for estimating ventilation rate (and intake) are lacking. Therefore, the goal of this work was to examine how well linear models using heart rate and other basic physiologic data can predict personal ventilation rate. We measured personal ventilation and heart rate among a panel of subjects (n = 36) while they conducted a series of specified routine tasks of varying exertion levels. From these data, 136 candidate models were identified using a series of variable transformation and selection algorithms. A second "free‑living" validation study (n = 26) served as an independent validation dataset for these candidate models. The top‑performing model, which included heart rate (H
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Registered trials
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