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Open accessJournal ArticleDOI: 10.3390/IJERPH18052538

University Students' Self-Rated Health in Relation to Perceived Acoustic Environment during the COVID-19 Home Quarantine.

04 Mar 2021-International Journal of Environmental Research and Public Health (MDPI AG)-Vol. 18, Iss: 5, pp 2538
Abstract: Background: Online education became mandatory for many students during the Coronavirus disease 2019 (COVID-19) pandemic and blurred the distinction between settings where processes of stress and restoration used to take place. The lockdown also likely changed perceptions of the indoor acoustic environment (i.e., soundscape) and raised its importance. In the present study, we seek to understand how indoor soundscape related to university students’ self-rated health in Bulgaria around the time that the country was under a state of emergency declaration caused by the COVID-19 pandemic. Methods: Between 17 May and 10 June 2020, we conducted a cross-sectional online survey among 323 students (median age 21 years; 31% male) from two universities in the city of Plovdiv, Bulgaria. Self-rated health (SRH) was measured with a single-item. Participants were asked how frequently they heard different types of sounds while at home and how pleasant they considered each of those sounds to be. Restorative quality of the home (the “being away” dimension of the Perceived Restorativeness Scale) was measured with a single-item. A priori confounders and effect modifiers included sociodemographics, house-related characteristics, general sensitivity to environmental influences, and mental health. Our analysis strategy involved sequential exploratory factor analysis (EFA), multivariate linear and ordinal regressions, effect modification tests, and structural equation modeling (SEM). Results: EFA supported grouping perceived sounds into three distinct factors—mechanical, human, and nature sounds. Regression analyses revealed that greater exposure to mechanical sounds was consistently associated with worse SRH, whereas no significant associations were found for human and nature sounds. In SEM, exposure to mechanical sounds related to lower restorative quality of the home, and then to poorer SRH, whereas nature sounds correlated with higher restorative quality, and in turn with better SRH. Conclusions: These findings suggest a role of positive indoor soundscape and restorative quality for promoting self-rated health in times of social distancing.

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Open accessJournal ArticleDOI: 10.1016/J.APACOUST.2021.108305
01 Dec 2021-Applied Acoustics
Abstract: Since the outbreak of the COVID-19 pandemic, as a result of the adoption of worldwide lockdown measures, the home environment has become the place where all the daily activities are taking place for many people. In these changed social and acoustical contexts, we wanted to evaluate the perception of the indoor acoustic environment in relation to traditional and new activities performed at home, i.e., relaxation, and working from home (WFH). Taking London as a case study, the present paper presents the results of an online survey administered to 464 home workers in January 2021. The survey utilized a previously developed model for the assessment of indoor soundscapes to describe the affective responses to the acoustic environments in a perceptual space defined by comfort (i.e. how comfortable or annoying the environment was judged) and content (i.e., how saturated the environment is with events and sounds) dimensions. A mixed-method approach was adopted to reinforce result validity by triangulating data from questionnaires and spontaneous descriptions given by participants. In this first part of the study, the main objectives were: (1) evaluating differences in soundscape evaluation, in terms of comfort and content dimensions, based on the activity performed at home, (2) identifying appropriate conditions for WFH and relaxation, and (3) investigating associations between psychological well-being and indoor soundscapes. The results showed that the environments were perceived as more comfortable and slightly fuller of content when rated in relation to relaxation than for WFH, thus suggesting a stricter evaluation of the acoustic environment in the latter case. As regards the second objective, spaces that were more appropriate for relaxation had high comfort, whereas spaces appropriate for WFH resulted more private and under control, i.e. with high comfort and low content scores. Lastly, better psychological well-being was associated with more comfortable soundscapes, both for WFH (rs = 0.346, p <.0005), and relaxation (rs = 0.353, p <.0005), and with lower content while WFH (rs = −0.133, p = .004). The discussion points out the need of considering the implications of changed working patterns to rethink the design of soundscapes in residential buildings, also in relation to potential well-being outcomes that will be further investigated in the Part II of the study. © 2021 The Author(s)

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9 Citations


Open accessJournal ArticleDOI: 10.1016/J.SCITOTENV.2021.147213
Huan Tong1, Francesco Aletta1, Andrew R. J. Mitchell1, Tin Oberman1  +1 moreInstitutions (1)
Abstract: Many cities around the world have claimed that the enforcement of lockdown measures to contain the spread of COVID-19 and the corresponding limitations of human activities led to reduced environmental noise levels. However, noise complaints reported by many local authorities were on the rise soon after the local lockdowns came into force. This research took Greater London in the UK as a case study. The overall aim was examining how noise complaints changed during the first stages of the lockdown implementation, during Spring 2020, both locally and at city scale, and how urban factors may have been influencing them. Noise complaint and urban factor datasets from the Government's publicly available data warehouse were used. The results show that during the COVID-19 lockdown the number of noise complaints increased by 48%, compared with the same period during Spring 2019. In terms of noise sources, complaints about construction (36%) and neighbourhood (50%) noise showed significant increases. Urban factors, including housing and demographic factors, played a more significant role than the actual noise exposure to road and rail traffic noise, as derived from the London noise maps. In detail, the change rate of noise complaints was higher in areas with higher unemployment rates, more residents with no qualifications, and lower house price. It is expected that this study could help government with allocating resources more effectively and achieve a better urban environment.

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Topics: Noise regulation (61%), Environmental noise (60%), Noise (53%)

7 Citations


Open accessJournal ArticleDOI: 10.1121/10.0005131
Abstract: The COVID-19 pandemic has significantly modified the behavior of societies. The application of isolation measures during the crisis resulted in changes in the acoustic environment. The aim of this work was to characterize the perception of the acoustic environment during the COVID-19 lockdown of people residing in Argentina in 2020. A descriptive cross-sectional correlational study was carried out. A virtual survey was conducted from April 14 to 26, 2020, and was answered mainly by social network users. During this period, Argentina was in a strict lockdown. The sample was finally composed of 1371 people between 18 and 79 years old. It was observed that most of the participants preferred the new acoustic environment. Mainly in the larger cities, before the isolation, mechanical sounds predominated, accompanied by the perception of irritation. Confinement brought a decrease in mechanical sounds and an increase in biological sounds, associated with feelings of tranquility and happiness. The time window opened by the lockdown offered an interesting scenario to assess the effect of anthropogenic noise pollution on the urban environment. This result offers a subjective approach, which contributes to understanding the link between individuals and communities with the environment.

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3 Citations


Open accessJournal ArticleDOI: 10.1177/01436244211054443
Abstract: Results of an online survey conducted during the COVID-19 lockdown among 848 home workers living in London (United Kingdom) and in Italy are reported with a focus on (1) the impacts of building ser...

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Journal ArticleDOI: 10.1016/J.ENVRES.2021.112254
Abstract: Background Excessive environmental noise exposure and noise annoyance have been linked to adverse physical and mental health outcomes. Although socioeconomic disparities in acoustically measured and geospatially estimated noise have been established, less is known about disparities in noise complaints, one of the most common sources of distress reported to local municipalities. Furthermore, although some studies have posited urban quieting during the COVID-19 pandemic, little empirical work has probed this and probed noise complaints during the pandemic. Objectives Using over 4 million noise complaints from the New York City (NYC) 311 database, we quantified census tract-level socioeconomic disparities in noise complaints since 2010 and examined how such disparities changed during the COVID-19 pandemic. Methods Using data from January 2010 through February 2020, we fit linear mixed-effects models, estimating monthly tract-level noise complaints by the proportion of residents who were low-income, time in months since January 2010, categorical month, their interactions, and potential confounds, such as total population and population density. To estimate COVID-19 pandemic effects, we included additional data from March 2020 through February 2021 and additional interactions between proportion low-income, month of year, and an indicator variable for COVID-19 pandemic onset in March 2020. Results Census tracts with a higher proportion of low-income residents reported more monthly noise complaints and this increased over time (time × month × proportion low-income interaction p-values Discussion Since 2010, noise complaints have increased the most in the most economically distressed communities, particularly in warmer seasons. This disparity was particularly exacerbated during the COVID-19 pandemic, contrary to some theories of urban quieting. Community-based interventions to ameliorate noise and noise annoyance, both public health hazards, are needed in underserved communities.

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Topics: Environmental noise (53%), Noise regulation (52%)

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Journal ArticleDOI: 10.1080/10705519909540118
Li-tze Hu, Peter M. Bentler1Institutions (1)
Abstract: This article examines the adequacy of the “rules of thumb” conventional cutoff criteria and several new alternatives for various fit indexes used to evaluate model fit in practice. Using a 2‐index presentation strategy, which includes using the maximum likelihood (ML)‐based standardized root mean squared residual (SRMR) and supplementing it with either Tucker‐Lewis Index (TLI), Bollen's (1989) Fit Index (BL89), Relative Noncentrality Index (RNI), Comparative Fit Index (CFI), Gamma Hat, McDonald's Centrality Index (Mc), or root mean squared error of approximation (RMSEA), various combinations of cutoff values from selected ranges of cutoff criteria for the ML‐based SRMR and a given supplemental fit index were used to calculate rejection rates for various types of true‐population and misspecified models; that is, models with misspecified factor covariance(s) and models with misspecified factor loading(s). The results suggest that, for the ML method, a cutoff value close to .95 for TLI, BL89, CFI, RNI, and G...

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Topics: Cutoff (52%), Goodness of fit (51%)

63,509 Citations


Open accessBook
Andrew F. Hayes1Institutions (1)
06 May 2013-
Abstract: I. FUNDAMENTAL CONCEPTS 1. Introduction 1.1. A Scientist in Training 1.2. Questions of Whether, If, How, and When 1.3. Conditional Process Analysis 1.4. Correlation, Causality, and Statistical Modeling 1.5. Statistical Software 1.6. Overview of this Book 1.7. Chapter Summary 2. Simple Linear Regression 2.1. Correlation and Prediction 2.2. The Simple Linear Regression Equation 2.3. Statistical Inference 2.4. Assumptions for Interpretation and Statistical Inference 2.5. Chapter Summary 3. Multiple Linear Regression 3.1. The Multiple Linear Regression Equation 3.2. Partial Association and Statistical Control 3.3. Statistical Inference in Multiple Regression 3.4. Statistical and Conceptual Diagrams 3.5. Chapter Summary II. MEDIATION ANALYSIS 4. The Simple Mediation Model 4.1. The Simple Mediation Model 4.2. Estimation of the Direct, Indirect, and Total Effects of X 4.3. Example with Dichotomous X: The Influence of Presumed Media Influence 4.4. Statistical Inference 4.5. An Example with Continuous X: Economic Stress among Small Business Owners 4.6. Chapter Summary 5. Multiple Mediator Models 5.1. The Parallel Multiple Mediator Model 5.2. Example Using the Presumed Media Influence Study 5.3. Statistical Inference 5.4. The Serial Multiple Mediator Model 5.5. Complementarity and Competition among Mediators 5.6. OLS Regression versus Structural Equation Modeling 5.7. Chapter Summary III. MODERATION ANALYSIS 6. Miscellaneous Topics in Mediation Analysis 6.1. What About Baron and Kenny? 6.2. Confounding and Causal Order 6.3. Effect Size 6.4. Multiple Xs or Ys: Analyze Separately or Simultaneously? 6.5. Reporting a Mediation Analysis 6.6. Chapter Summary 7. Fundamentals of Moderation Analysis 7.1. Conditional and Unconditional Effects 7.2. An Example: Sex Discrimination in the Workplace 7.3. Visualizing Moderation 7.4. Probing an Interaction 7.5. Chapter Summary 8. Extending Moderation Analysis Principles 8.1. Moderation Involving a Dichotomous Moderator 8.2. Interaction between Two Quantitative Variables 8.3. Hierarchical versus Simultaneous Variable Entry 8.4. The Equivalence between Moderated Regression Analysis and a 2 x 2 Factorial Analysis of Variance 8.5. Chapter Summary 9. Miscellaneous Topics in Moderation Analysis 9.1. Truths and Myths about Mean Centering 9.2. The Estimation and Interpretation of Standardized Regression Coefficients in a Moderation Analysis 9.3. Artificial Categorization and Subgroups Analysis 9.4. More Than One Moderator 9.5. Reporting a Moderation Analysis 9.6. Chapter Summary IV. CONDITIONAL PROCESS ANALYSIS 10. Conditional Process Analysis 10.1. Examples of Conditional Process Models in the Literature 10.2. Conditional Direct and Indirect Effects 10.3. Example: Hiding Your Feelings from Your Work Team 10.4. Statistical Inference 10.5. Conditional Process Analysis in PROCESS 10.6. Chapter Summary 11. Further Examples of Conditional Process Analysis 11.1. Revisiting the Sexual Discrimination Study 11.2. Moderation of the Direct and Indirect Effects in a Conditional Process Model 11.3. Visualizing the Direct and Indirect Effects 11.4. Mediated Moderation 11.5. Chapter Summary 12. Miscellaneous Topics in Conditional Process Analysis 12.1. A Strategy for Approaching Your Analysis 12.2. Can a Variable Simultaneously Mediate and Moderate Another Variable's Effect? 12.3. Comparing Conditional Indirect Effects and a Formal Test of Moderated Mediation 12.4. The Pitfalls of Subgroups Analysis 12.5. Writing about Conditional Process Modeling 12.6. Chapter Summary Appendix A. Using PROCESS Appendix B. Monte Carlo Confidence Intervals in SPSS and SAS

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Topics: Moderated mediation (62%), Regression analysis (56.99%), Mediation (statistics) (56.99%) ... show more

26,130 Citations


Open accessJournal ArticleDOI: 10.1046/J.1525-1497.2001.016009606.X
Abstract: OBJECTIVE: While considerable attention has focused on improving the detection of depression, assessment of severity is also important in guiding treatment decisions. Therefore, we examined the validity of a brief, new measure of depression severity.

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Topics: Criterion validity (57.99%), Construct validity (57.99%), Test validity (56.99%) ... show more

19,000 Citations


Open accessJournal ArticleDOI: 10.1001/ARCHINTE.166.10.1092
Abstract: Background Generalized anxiety disorder (GAD) is one of the most common mental disorders; however, there is no brief clinical measure for assessing GAD. The objective of this study was to develop a brief self-report scale to identify probable cases of GAD and evaluate its reliability and validity. Methods A criterion-standard study was performed in 15 primary care clinics in the United States from November 2004 through June 2005. Of a total of 2740 adult patients completing a study questionnaire, 965 patients had a telephone interview with a mental health professional within 1 week. For criterion and construct validity, GAD self-report scale diagnoses were compared with independent diagnoses made by mental health professionals; functional status measures; disability days; and health care use. Results A 7-item anxiety scale (GAD-7) had good reliability, as well as criterion, construct, factorial, and procedural validity. A cut point was identified that optimized sensitivity (89%) and specificity (82%). Increasing scores on the scale were strongly associated with multiple domains of functional impairment (all 6 Medical Outcomes Study Short-Form General Health Survey scales and disability days). Although GAD and depression symptoms frequently co-occurred, factor analysis confirmed them as distinct dimensions. Moreover, GAD and depression symptoms had differing but independent effects on functional impairment and disability. There was good agreement between self-report and interviewer-administered versions of the scale. Conclusion The GAD-7 is a valid and efficient tool for screening for GAD and assessing its severity in clinical practice and research.

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9,723 Citations


Open accessJournal ArticleDOI: 10.18637/JSS.V048.I02
Abstract: Structural equation modeling (SEM) is a vast field and widely used by many applied researchers in the social and behavioral sciences. Over the years, many software packages for structural equation modeling have been developed, both free and commercial. However, perhaps the best state-of-the-art software packages in this field are still closed-source and/or commercial. The R package lavaan has been developed to provide applied researchers, teachers, and statisticians, a free, fully open-source, but commercial-quality package for latent variable modeling. This paper explains the aims behind the development of the package, gives an overview of its most important features, and provides some examples to illustrate how lavaan works in practice.

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Topics: LISREL (55%), Structural equation modeling (50%)

9,469 Citations