Effects of Aircraft Noise Exposure on Heart Rate during Sleep in the Population Living Near Airports
Bibliographic record
Abstract
Background Noise in the vicinity of airports is a public health problem. Many laboratory studies have shown that heart rate is altered during sleep after exposure to road or railway noise. Fewer studies have looked at the effects of exposure to aircraft noise on heart rate during sleep in populations living near airports. Objective The aim of this study was to investigate the relationship between the sound pressure level (SPL) of aircraft noise and heart rate during sleep in populations living near airports in France. Methods In total, 92 people living near the Paris-Charles de Gaulle and Toulouse-Blagnac airports participated in this study. Heart rate was recorded every 15 s during one night, using an Actiheart monitor, with simultaneous measurements of SPL of aircraft noise inside the participants’ bedrooms. Energy and event-related indicators were then estimated. Mixed linear regression models were applied, taking into account potential confounding factors, to investigate the relationship between energy indicators and heart rate during sleep measured every 15 s. Event-related analyses were also carried out in order to study the effects of an acoustic event associated with aircraft noise on heart rate during sleep. Results The more the SPL from all sources (LAeq,15s) and the SPL exceeded for 90% of the measurement period (LA90,15s) increased, the more heart rate also increased. No significant associations were observed between the maximum 1-s equivalent SPL associated with aircraft overflight (LAmax,1s) and differences between the heart rate recorded during or 15 or 30 s after an aircraft noise event and that recorded before the event. On the other hand, a positive and significant association was found between LAmax,1s and the heart rate amplitude calculated during an aircraft noise event. Results were unchanged when analyses were limited to participants who had lived more than five years in their present dwelling. Conclusion Our study shows that exposure to the maximum SPL linked to aircraft overflight affect the heart rate during sleep of residents near airports. However, further studies on a larger number of participants over several nights are needed to confirm these results.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".