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Zhang X, Wang X, Liu T, Ha H, Dong J, Ma S. Effect of Ward Noise Management Combined with Meditation Training on Stroke Rehabilitation Patients. Noise Health 2024; 26:107-113. [PMID: 38904809 DOI: 10.4103/nah.nah_128_23] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2023] [Accepted: 03/06/2024] [Indexed: 06/22/2024] Open
Abstract
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.
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Affiliation(s)
- Xiujuan Zhang
- Department of Geriatrics, Tangshan Central Hospital, Tangshan, Hebei, 063000, China
| | - Xiaofang Wang
- Outpatient Department, Tangshan Central Hospital, Tangshan, Hebei, 063000, China
| | - Ting Liu
- Department of Infectious Diseases, Tangshan Central Hospital, Tangshan, Hebei, 063000, China
| | - Hongwei Ha
- Burns and Plastic Surgery Department I, Tangshan Workers' Hospital, Tangshan, Hebei, 063003, China
| | - Jianxiu Dong
- Nursing Department, Tangshan Workers' Hospital, Tangshan, Hebei, 063003, China
| | - Suhui Ma
- North China University of Science and Technology of Nursing and Rehabilitation, Tangshan, Hebei, 063210, China
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Dzhambov AM, Lercher P, Botteldooren D. Childhood sound disturbance and sleep problems in Alpine valleys with high levels of traffic exposures and greenspace. ENVIRONMENTAL RESEARCH 2024; 242:117642. [PMID: 37996006 DOI: 10.1016/j.envres.2023.117642] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/08/2023] [Accepted: 11/09/2023] [Indexed: 11/25/2023]
Abstract
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.
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Affiliation(s)
- Angel M Dzhambov
- Environmental Health Division, Research Institute at Medical University of Plovdiv, Bulgaria; Institute of Highway Engineering and Transport Planning, Graz University of Technology, Austria; Research Group "Health and Quality of Life in a Green and Sustainable Environment", Strategic Research and Innovation Program for the Development of MU - Plovdiv, Medical University of Plovdiv, Bulgaria; Department of Hygiene, Faculty of Public Health, Medical University of Plovdiv, Bulgaria.
| | - Peter Lercher
- Institute of Highway Engineering and Transport Planning, Graz University of Technology, Austria
| | - Dick Botteldooren
- Department of Information Technology, Research Group WAVES, Ghent University, Belgium
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Veber T, Pyko A, Carlsen HK, Holm M, Gislason T, Janson C, Johannessen A, Sommar JN, Modig L, Lindberg E, Schlünssen V, Toompere K, Orru H. 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. BMC Public Health 2023; 23:1246. [PMID: 37370100 DOI: 10.1186/s12889-023-16128-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2023] [Accepted: 06/15/2023] [Indexed: 06/29/2023] Open
Abstract
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.
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Affiliation(s)
- Triin Veber
- Institute of Family Medicine and Public Health, University of Tartu, Tartu, Estonia
| | - Andrei Pyko
- Centre for Occupational and Environmental Medicine, Region Stockholm, Stockholm, Sweden
- Institute of Environmental Medicine, Karolinska Institute, Stockholm, Sweden
| | - Hanne Krage Carlsen
- Department of Occupational and Environmental Medicine, School of Public Health and Community Medicine, Institute of medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Mathias Holm
- Department of Occupational and Environmental Medicine, School of Public Health and Community Medicine, Institute of medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | | | - Christer Janson
- Department of Medical Sciences: Respiratory, Allergy and Sleep Research, Uppsala University, Uppsala, Sweden
| | - Ane Johannessen
- Department of Global Public Health and Primary Care, University of Bergen, Bergen, Norway
| | - Johan Nilsson Sommar
- Section of Sustainable Health, Department of Public Health and Clinical Medicine, Umeå University, Umeå, Sweden
| | - Lars Modig
- Section of Sustainable Health, Department of Public Health and Clinical Medicine, Umeå University, Umeå, Sweden
| | - Eva Lindberg
- Department of Medical Sciences: Respiratory, Allergy and Sleep Research, Uppsala University, Uppsala, Sweden
| | - Vivi Schlünssen
- Research Unit for Environment, Occupation and Health, Danish Ramazzini Centre, Aarhus University, Aarhus, Denmark
| | - Karolin Toompere
- Institute of Family Medicine and Public Health, University of Tartu, Tartu, Estonia
| | - Hans Orru
- Institute of Family Medicine and Public Health, University of Tartu, Tartu, Estonia.
- Section of Sustainable Health, Department of Public Health and Clinical Medicine, Umeå University, Umeå, Sweden.
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Leone MJ, Dashti HS, Coughlin B, Tesh RA, Quadri SA, Bucklin AA, Adra N, Krishnamurthy PV, Ye EM, Hemmige A, Rajan S, Panneerselvam E, Higgins J, Ayub MA, Ganglberger W, Paixao L, Houle TT, Thompson BT, Johnson-Akeju O, Saxena R, Kimchi E, Cash SS, Thomas RJ, Westover MB. Sound and light levels in intensive care units in a large urban hospital in the United States. Chronobiol Int 2023; 40:759-768. [PMID: 37144470 PMCID: PMC10524721 DOI: 10.1080/07420528.2023.2207647] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2022] [Revised: 11/18/2022] [Accepted: 04/21/2023] [Indexed: 05/06/2023]
Abstract
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.
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Affiliation(s)
- Michael J Leone
- Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA
- Clinical Data Animation Center, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Hassan S Dashti
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA
- Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA
- Brain Data Science Platform, Broad Institute, Cambridge, Massachusetts, USA
- Division of Sleep Medicine, Harvard Medical School, Boston, Massachusetts, USA
| | - Brian Coughlin
- Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Ryan A Tesh
- Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA
- Clinical Data Animation Center, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Syed A Quadri
- Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA
- Clinical Data Animation Center, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Abigail A Bucklin
- Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA
- Clinical Data Animation Center, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Noor Adra
- Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA
- Clinical Data Animation Center, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Parimala Velpula Krishnamurthy
- Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA
- Clinical Data Animation Center, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Elissa M Ye
- Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA
- Clinical Data Animation Center, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Aashritha Hemmige
- Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA
- Clinical Data Animation Center, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Subapriya Rajan
- Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA
- Clinical Data Animation Center, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Ezhil Panneerselvam
- Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA
- Clinical Data Animation Center, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Jasmine Higgins
- Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA
- Clinical Data Animation Center, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Muhammad Abubakar Ayub
- Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA
- Clinical Data Animation Center, Massachusetts General Hospital, Boston, Massachusetts, USA
- Division of Pulmonary and Critical Care, Department of Medicine, Massachusetts General Hospital, and Harvard Medical School, Boston, Massachusetts, USA
| | - Wolfgang Ganglberger
- Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA
- Clinical Data Animation Center, Massachusetts General Hospital, Boston, Massachusetts, USA
- Sleep & Health Zurich, University of Zurich, Zurich, Switzerland
- Henry and Allison McCance Center for Brain Health, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Luis Paixao
- Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA
- Clinical Data Animation Center, Massachusetts General Hospital, Boston, Massachusetts, USA
- Department of Neurology, Washington University School of Medicine in St. Louis, St. Louis, Missouri, USA
| | - Timothy T Houle
- Sleep & Health Zurich, University of Zurich, Zurich, Switzerland
- Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - B Taylor Thompson
- Division of Pulmonary and Critical Care, Department of Medicine, Massachusetts General Hospital, and Harvard Medical School, Boston, Massachusetts, USA
| | - Oluwaseun Johnson-Akeju
- Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Richa Saxena
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA
- Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA
- Brain Data Science Platform, Broad Institute, Cambridge, Massachusetts, USA
- Division of Sleep Medicine, Harvard Medical School, Boston, Massachusetts, USA
| | - Eyal Kimchi
- Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Sydney S Cash
- Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA
- Clinical Data Animation Center, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Robert J Thomas
- Clinical Data Animation Center, Massachusetts General Hospital, Boston, Massachusetts, USA
- Department of Medicine, Division of Pulmonary, Critical Care & Sleep, Beth Israel Deaconess Medical Center, Boston, Massachusetts, USA
| | - M Brandon Westover
- Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA
- Clinical Data Animation Center, Massachusetts General Hospital, Boston, Massachusetts, USA
- Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA
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