Road traffic noise impacts sleep continuity in suburban residents: Exposure-response quantification of noise-induced awakenings from vehicle pass-bys at night

Effect of Ward Noise Management Combined with Meditation Training on Stroke Rehabilitation Patients

OBJECTIVE

To study the value of ward noise management combined with meditation training in stroke rehabilitation patients.

METHODS

According to the retrospective analysis method, 150 stroke patients hospitalized in the rehabilitation center of a Tangshan Workers' Hospital from July 2020 to December 2023 were selected as study objects. They were divided into three groups, namely the control group (routine rehabilitation care, n = 50), observation group A (meditation training, n = 50), and observation group B (meditation training and ward noise management, n = 50) according to whether they received ward noise management and meditation training. The general demographic data, Fatigue Severity Scale (FSS), Pittsburgh Sleep Quality Index (PSQI), and the Short Form 36 (SF-36) were collected. Chi-square test and analysis of variance were used to analyse the data.

RESULTS

The baseline data of the patients in each group were not statistically significant (P > 0.05). Before treatment, no difference in the FSS, PSQI, SF-36 scores and environmental noise level between the groups (P > 0.05) was observed. After management, the scores of SF-36 in observation group B were higher than those in the control group and observation group A (P < 0.05) except for somatic pain. Other indicators in observation group B were lower than those in the control and observation group A (P < 0.001).

CONCLUSIONS

Ward noise management and meditation training can effectively reduce patients' fatigue, significantly reducing ambient noise levels, promoting the improvement of life quality, and improving sleep quality.

Childhood sound disturbance and sleep problems in Alpine valleys with high levels of traffic exposures and greenspace

Sound disturbance and sleep problems are regarded as the most common adverse effects of environmental noise but evidence of the role of air pollution and greenspace is scant. This is especially true for children who find themselves in a sensitive developmental period and experience their environment differently than adults. This study examined the joint effects of traffic exposures and residential greenspace on child sound disturbance and sleep problems via perceptions of neighborhood quality. We used cross-sectional data for 1251 schoolchildren (8-12 years) in the Tyrol region of Austria/Italy. Questionnaires provided information on sociodemographic and housing factors, perceived neighborhood quality, sound disturbance in different situations, and sleep problems. Modelled acoustic indicators included day-evening-night sound levels and the highest percentile level, and night-time sound level and a bespoke sleep disturbance index. Nitrogen dioxide served as a proxy for traffic-related air pollution. The normalized difference vegetation index was calculated as a measure of residential greenspace, and presence of a domestic garden was self-reported. Results showed that higher level of traffic-related exposures was positively associated with sound disturbance and sleep problems, while living in a greener area, especially in a house with a garden, was associated with lower sound disturbance and less sleep problems even in the presence of traffic. Traffic exposures contributed to more unfavorable, and greenspace to more positive perceptions in terms of traffic-related stressors, opportunities for outdoor recreation, and general satisfaction with the neighborhood. This indirect path seemed more important for greenspace than for traffic exposures. In conclusion, it seems advantageous to combine traffic-related mitigation with improving access to greenspace in interventions for supporting the acoustic comfort of children during day and nighttime. Even highly nature-dominated environments could still benefit from proximal green infrastructure, especially from domestic gardens.

Traffic noise in the bedroom in association with markers of obesity: a cross-sectional study and mediation analysis of the respiratory health in Northern Europe cohort

BACKGROUND

Previous research suggests an association between road traffic noise and obesity, but current evidence is inconclusive. The aim of this study was to assess the association between nocturnal noise exposure and markers of obesity and to assess whether sleep disturbance might be a mediator in this association.

METHODS

We applied data from the Respiratory Health in Northern Europe (RHINE) cohort. We used self-measured waist circumference (WC) and body mass index (BMI) as outcome values. Noise exposure was assessed as perceived traffic noise in the bedroom and/or the bedroom window's location towards the street. We applied adjusted linear, and logistic regression models, evaluated effect modifications and conducted mediation analysis.

RESULTS

Based on fully adjusted models we found that women, who reported very high traffic noise levels in bedroom, had 1.30 (95% CI 0.24-2.37) kg/m² higher BMI and 3.30 (95% CI 0.39-6.20) cm higher WC compared to women, who reported no traffic noise in the bedroom. Women who reported higher exposure to road traffic noise had statistically significant higher odds of being overweight and have abdominal obesity with OR varying from 1.15 to 1.26 compared to women, who reported no traffic noise in the bedroom. For men, the associations were rather opposite, although mostly statistically insignificant. Furthermore, men, who reported much or very much traffic noise in the bedroom, had a statistically significantly lower risk of abdominal obesity. Sleep disturbance fully or partially mediated the association between noise in bedroom and obesity markers among women.

CONCLUSION

Our results suggest that self-reported traffic noise in the bedroom may be associated to being overweight or obese trough sleep disturbance among women, but associations were inconclusive among men.

Sound and light levels in intensive care units in a large urban hospital in the United States

Intensive care units (ICUs) may disrupt sleep. Quantitative ICU studies of concurrent and continuous sound and light levels and timings remain sparse in part due to the lack of ICU equipment that monitors sound and light. Here, we describe sound and light levels across three adult ICUs in a large urban United States tertiary care hospital using a novel sensor. The novel sound and light sensor is composed of a Gravity Sound Level Meter for sound level measurements and an Adafruit TSL2561 digital luminosity sensor for light levels. Sound and light levels were continuously monitored in the room of 136 patients (mean age = 67.0 (8.7) years, 44.9% female) enrolled in the Investigation of Sleep in the Intensive Care Unit study (ICU-SLEEP; Clinicaltrials.gov: #NCT03355053), at the Massachusetts General Hospital. The hours of available sound and light data ranged from 24.0 to 72.2 hours. Average sound and light levels oscillated throughout the day and night. On average, the loudest hour was 17:00 and the quietest hour was 02:00. Average light levels were brightest at 09:00 and dimmest at 04:00. For all participants, average nightly sound levels exceeded the WHO guideline of < 35 decibels. Similarly, mean nightly light levels varied across participants (minimum: 1.00 lux, maximum: 577.05 lux). Sound and light events were more frequent between 08:00 and 20:00 than between 20:00 and 08:00 and were largely similar on weekdays and weekend days. Peaks in distinct alarm frequencies (Alarm 1) occurred at 01:00, 06:00, and at 20:00. Alarms at other frequencies (Alarm 2) were relatively consistent throughout the day and night, with a small peak at 20:00. In conclusion, we present a sound and light data collection method and results from a cohort of critically ill patients, demonstrating excess sound and light levels across multiple ICUs in a large tertiary care hospital in the United States. ClinicalTrials.gov, #NCT03355053. Registered 28 November 2017, https://clinicaltrials.gov/ct2/show/NCT03355053.

Automated identification and assessment of environmental noise sources

Noise pollution is one of the major health risks in urban life. The approach to measurement and identification of noise sources needs to be improved and enhanced to reduce high costs. Long measurement times and the need for expensive equipment and trained personnel must be automated. Simplifying the identification of main noise sources and excluding residual and background noise allows more effective measures. By spatially filtering the acoustic scene and combining unsupervised learning with psychoacoustic features, this paper presents a prototype system capable of automated calculation of the contribution of individual noise sources to the total noise level. Pilot measurements were performed at three different locations in the city of Ljubljana, Slovenia. Equivalent sound pressure levels obtained with the device were compared to the results obtained by manually marking individual parts of each of the three measurements. The proposed approach correctly identified the main noise sources in the vicinity of the measurement points.