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Aircraft noise can disrupt sleep and impair recuperation. The last U.S. investigation into the effects of aircraft noise on sleep dates back more than 20 years. Since then, traffic patterns and the noise levels produced by single aircraft have changed substantially. It is therefore important to acquire current data on sleep disturbance relative to varying degrees of aircraft noise exposure in the U.S. that can be used to check and potentially update the existing noise policy. This manuscript describes the design, procedures, and analytical approaches of the FAA's National Sleep Study. Seventy-seven U.S. airports with relevant nighttime air traffic from 39 states are included in the sampling frame. Based on simulation-based power calculations, the field study aims to recruit 400 participants from four noise strata and record an electrocardiogram (ECG), body movement, and sound pressure levels in the bedroom for five consecutive nights. The primary outcome of the study is an exposure-response function between the instantaneous, maximum A-weighted sound pressure levels (dBA) of individual aircraft measured in the bedroom and awakening probability inferred from changes in heart rate and body movement. Self-reported sleep disturbance due to aircraft noise is the secondary outcome that will be associated with long-term average noise exposure metrics such as the Day-Night Average Sound Level (DNL) and the Nighttime Equivalent Sound Level (). The effect of aircraft noise on several other physiological and self-report outcomes will also be investigated. This study will provide key insights into the effects of aircraft noise on objectively and subjectively assessed sleep disturbance.
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10650692 | PMC |
http://dx.doi.org/10.3390/ijerph20217024 | DOI Listing |
Eur J Prev Cardiol
September 2025
Department of Internal Medicine, Augusta Health Fishersville, VA, USA.
Anatol J Cardiol
September 2025
Danish Cancer Institute, Danish Cancer Society, Denmark;Department of Natural Science and Environment, Roskilde University, Roskilde, Denmark.
Environmental noise, particularly from road, rail, and aircraft traffic, is now firmly recognized as a widespread risk factor for cardiovascular disease. About 1 in 3 Europeans is exposed to chronic noise exposure above the guideline thresholds recommended by the World Health Organization (WHO), thus contributing substantially to cardiovascular morbidity and mortality. Robust evidence from recent meta-analyses links transportation noise to ischemic heart disease, heart failure, stroke, hypertension, and type 2 diabetes mellitus.
View Article and Find Full Text PDFBlood Press
December 2025
1st Department of Cardiology, Interventional Electrocardiology and Arterial Hypertension, Jagiellonian University Medical College, Kraków, Poland.
Background: Transportation noise seems to be inherent in modern urban living. However, many studies indicate that it can unfavorably affect human health, especially by influencing the cardiovascular outcome. The large number of people exposed to noise in the European Union becomes relevant to public health.
View Article and Find Full Text PDFEur J Prev Cardiol
September 2025
Department of Social Work, Shanghai Municipal Hospital of Traditional Chinese Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China.
Sensors (Basel)
August 2025
School of Air Traffic Management, Civil Aviation Flight University of China, Guanghan 618307, China.
Driven by the increasing global population and rapid urbanization, aircraft noise pollution has emerged as a significant environmental challenge, impeding the sustainable development of the aviation industry. Traditional noise prediction methods are limited by incomplete datasets, insufficient spatiotemporal consistency, and poor adaptability to complex meteorological conditions, making it difficult to achieve precise noise management. To address these limitations, this study proposes a novel noise prediction framework based on a hybrid Convolutional Neural Network-Bidirectional Long Short-Term Memory-Attention (CNN-BiLSTM-Attention) model.
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