Next Article in Journal
Spatial Continuum in History: Pier Buildings on the Bosphorus and Golden Horn, Istanbul
Previous Article in Journal
A Study on the Visual Perception of Cultural Value Characteristics of Traditional Southern Fujian Architecture Based on Eye Tracking
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Buildings
Volume 14
Issue 11
10.3390/buildings14113530
buildings-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Qualitative Mechanisms of Perceived Indoor Environmental Quality on Anxiety Symptoms in University

by
Fei Guo
1,
Mingxuan Luo
1,
Hui Zhao
2,
Zekun Du
1,
Zhen Zhang
2,*,
Hongchi Zhang
1,
Jing Dong
1 and
Dongxu Zhang
3
1
School of Architecture and Fine Art, Dalian University of Technology, Dalian 116023, China
2
No. 4 Ward of Cardiology, The Second Affiliated Hospital of Dalian Medical University, Dalian 116023, China
3
School of Architecture and Urban Planning, Guangzhou University, Guangzhou 510006, China
*
Author to whom correspondence should be addressed.
Buildings 2024, 14(11), 3530; https://doi.org/10.3390/buildings14113530
Submission received: 10 September 2024 / Revised: 27 October 2024 / Accepted: 31 October 2024 / Published: 5 November 2024
(This article belongs to the Topic Advances in Low-Carbon, Climate-Resilient, and Sustainable Built Environment)
Browse Figures
Versions Notes

Abstract

:
The indoor environment is widely acknowledged as a non-pharmacological tool for regulating residents’ mental health. In dormitory environments with relatively high residential density, the mental health of university students requires particular attention. This study surveyed 445 students from a northern Chinese university and used structural equation modeling (SEM) to analyze the impact of perceived indoor environmental quality (IEQ)—including thermal, lighting, acoustics, indoor air quality, and overcrowding—on self-reported anxiety symptoms. The results indicated the following: (1) students’ perceptions of dormitory IEQ significantly affected anxiety symptoms, explaining 40% of the variance; (2) anxiety symptoms associated with the IEQ were mainly characterized by anxiety and panic (r = 0.91, p < 0.001); (3) subjective perceptions of the acoustic environment (r = −0.55, p < 0.001) and indoor air quality (r = −0.15, p < 0.05) were key predictors of anxiety, while thermal environment, lighting environment, and overcrowding were not significant. The findings enrich the IEQ system and provide directions for optimizing the dormitory indoor environment from the perspective of student mental health, with implications for other types of residential buildings.

1. Introduction

Mental disorders have a high prevalence worldwide, with approximately one in eight individuals being affected [1]. The World Health Organization (WHO) “Global Strategy on Health, Environment and Climate Change [2]” and the “Comprehensive Mental Health Action Plan 2013–2030 [3]” both emphasize the importance of mental health development. Over the past decade, the number of Chinese university students has been steadily increasing [4], and the detection rate of various mental disorders has risen significantly [5,6]. Focusing on the mental health of college students is an important responsibility for both schools and society.
Anxiety is an integral part of mental health. Anxiety symptoms are a series of physical and mental reactions, such as tension, increased heart rate, sweating, and gastrointestinal discomfort, which arise from both anxiety mood and anxiety disorder [7]. It is not limited to students with diagnosed anxiety disorders or other mental disorders, as even students without any mental disorders can experience temporary anxiety symptoms [8]. Anxiety symptoms can significantly impact students’ learning and daily life through various pathways [9,10]. It not only affects students during their time at university but may also affect their quality of life in the future [11]. The causes of anxiety symptoms are complex, with academic pressures, poor sleep quality, and a lack of physical activity being risk factors for university students [12,13]. Furthermore, the environment in which students reside can expose them to various factors that can interfere with brain structure and function, thereby affecting mental health [14,15].
It is well known that indoor environmental quality (IEQ) significantly impacts individual physical and mental health [16]. The American Society of Heating and Air-Conditioning Engineers (ASHRAE) defines IEQ as the indoor experience of building occupants, including energy efficiency, health, and comfort in building design, analysis, and operation [17]. Poor indoor environments can directly affect mental health [18,19], and indirectly impact it by influencing user comfort and physical health [20,21], with both short-term and long-term effects [22,23]. Improving the indoor environment can have a positive impact [24]. University dormitories in China, which combine multiple functions such as learning, dining, and resting, are primary living spaces for students and can inevitably influence their mental health [25]. Therefore, it is crucial to research the effects of the indoor environment of student dormitories on mental health.
Many studies commonly measured IEQ using thermal comfort, lighting comfort, acoustic comfort, and indoor air quality (IAQ) [26,27], and these factors are closely related to occupants’ mental health [28]. In terms of the thermal environment, heat exposure can affect metabolic levels, primarily by stimulating the nervous system, leading to anxiety [29]. Both excessively high and low indoor temperatures can impact mental health [19,30]. Thus, the likelihood of psychological distress increases when individuals have low subjective thermal comfort, subsequently raising the chances of anxiety [31]. Studies show that improving thermal environments can alleviate residents’ thermal comfort and mental health [32]. Regarding the lighting environment, lighting conditions affect individuals’ circadian rhythms and influence emotions, neuroendocrine functions, and cognitive abilities, producing anxiety symptoms [33,34]. Daylighting conditions in classrooms and bedrooms affect students’ comfort and mental well-being [35]. Research indicates that overly bright nighttime lighting is associated with self-reported anxiety [36]. Regarding the acoustic environment, noise exposure affects the endocrine and autonomic nervous systems, generating biological responses that lead to anxiety [37], and contribute to increased annoyance [38]. Nighttime noise is a primary environmental factor affecting sleep quality, and it can negatively impact mental health by disrupting circadian rhythms and causing sleep disturbances [39]. Improving the acoustic environment reduces anxiety levels [40], and soundscapes also affect mental health [41]. Additionally, volatile organic compounds, semi-volatile organic compounds, and mold in indoor environments pose threats to residents’ health, leading to decreased attention, memory decline, and increased risk of anxiety disorders [42,43]. Both short-term and long-term exposure to poor air conditions increase the risk of anxiety [44,45], and improving IAQ has been shown to benefit mental health [46]. Various IEQ factors in many residential and educational buildings have been shown to affect mental health, and improving IEQ can be beneficial for mental well-being. However, research on how IEQ specifically impacts university students’ mental health, particularly focusing on anxiety symptoms, remains limited.
Furthermore, Chinese dormitories are characterized by small individual spaces per capita, high residential density, and similar space designs, with many students expressing dissatisfaction [47]. In addition to the physical factors of the indoor environment, overcrowding is also an important factor affecting residents’ overall satisfaction with IEQ. Dong et al. [48] found that dormitory spatial design has the highest impact on IEQ satisfaction. Overcrowding can affect the sense of privacy and impact mental health [49]. Morganti et al. [50] incorporated overcrowding into IEQ and found a significant relationship between poorer indoor space quality and mental health. However, research incorporating this factor into the assessment of IEQ is limited, and the relationship between dormitory architectural space and anxiety symptoms remains to be explored.
While IEQ has a significant impact on mental health, quantifying their correlations remains challenging. Mao et al. [51] found that the subjective comfort of individual IEQ factors (temperature, humidity, sound, and lighting) is negatively correlated with university students’ anxiety symptoms. However, human perception of the environment is the result of complex interactions and integrations of various sensory stimuli [52]. Focusing on a single or partial IEQ factor makes it difficult to fully reflect the interactions. The specific mechanisms by which the subjective perceptions of the five factors under the overall indoor environment affect university students’ mental health are uncertain. Furthermore, existing research has mainly focused on the relationship between perceived IEQ and factors such as stress and well-being [49,53,54]. There is less focus on research specifically examining the subjective perceptions of overall IEQ and their correlation with anxiety symptoms.
Against this background, this study reflects college university students’ perception of the dormitory space from the perspective of architectural design and explores the operating mechanism of university students’ subjective perception of IEQ (thermal comfort, lighting comfort, acoustic comfort, IAQ, and overcrowding) on anxiety symptoms through questionnaires and structural equation modeling (SEM). This study aims to provide a basis for optimizing dormitory environments to enhance students’ quality of life and mental health. The objectives of this research are to address the following two questions:
1.
What is the relationship between the perceived IEQ and the anxiety symptoms of university students?
2.
Under the combined effects of indoor environmental factors, what are the primary factors significantly impacting students’ mental health? What are the main anxiety symptoms experienced by students?

2. Materials and Methods

Based on the theoretical foundations and research findings on the relationship between perceived IEQ and anxiety, this study conducted a questionnaire survey and proposed hypotheses. A structural equation modeling (SEM) approach was employed to construct and analyze the path model, investigating the influence mechanism of university students’ perceived IEQ on their anxiety symptoms. The summary of research procedures is presented in Figure 1.

2.1. Basic Information on the Study Site

To understand the impact of dormitory IEQ on students’ anxiety symptoms, a retrospective study was conducted on the regular online questionnaire survey of students in the Xishan living area of Dalian University of Technology in Liaoning from 6 to 22 June 2022. During the survey period, the school’s courses had entered the final stage, and students spent more time in their dormitories preparing for exams and final assignments, providing an ideal research opportunity.
The dormitory buildings were arranged in a double-sided layout with internal corridors, as depicted in Figure 2. The building adopts an internal corridor design with rooms arranged on both sides, each accommodating four students. The bathrooms are located indoors without external windows. The dormitories feature a practical “upper bed, lower table” layout, optimizing space utilization. This plan layout form is typical of college dormitory buildings in northern China.

2.2. Questionnaire Design and Hypothesis

The ASHRAE Guideline 10 and the WELL Building Standard state that indoor environmental factors are closely related to occupant health and well-being, identifying the combined effects of various environmental factors on physical and psychological health [55,56]. In China, the “Assessment standard for green building” (GB/T50378-2019) puts forward requirements for IEQ from the perspective of health [57]. From a theoretical perspective, R. Gifford proposed in the theory of environmental psychology that people and the environment interact in various ways, manifesting in actions, emotions, and well-being [58]. According to the “behavior-environment congruence” theory, a good environment that meets people’s needs can influence their quality of life and mental health [59,60]. However, in many related studies, not all IEQ factors impact mental health [23,36,61]. To investigate the impact of perceived IEQ on students’ anxiety symptoms, we formulated three hypotheses: (1) all factors of perceived IEQ have an impact on anxiety symptoms; (2) some factors of perceived IEQ have an impact on anxiety symptoms, while others are not significant; (3) perceived IEQ has no impact on anxiety symptoms.
In this study, the self-rating anxiety scale (SAS) from regular surveys was selected to measure the self-reported anxiety symptom levels of university students. This scale, designed by William W.K. Zung, is a widely used anxiety screening tool. It primarily reflects the severity of anxiety symptoms through 20 specific physiological and emotional questions, can effectively distinguish between patients with anxiety disorders and those with other diagnoses [62], and has high discriminant validity in non-clinical anxiety scales [63,64]. The questionnaire can be used for patients with anxiety disorders to express their anxiety symptoms, as well as for the assessment of the general population [65,66]. Participants self-reported similar levels of mental health. The Cronbach’s α value for the SAS in this study was 0.882. It has been widely used as a validated tool in research that requires participants to self-assess their anxiety [67,68].
The SAS questionnaire comprises 20 questions with response options varying from 1 (none, or a little of the time) to 4 (most, or all of the time). Certain questions (5, 9, 13, 17, and 19) are reverse-scored, and the questionnaire is provided in Table S1. The final score is obtained by summing all the question scores and then multiplying by a coefficient of 1.25. This score is then compared to the Chinese normative data to determine the level of anxiety symptoms (SAS < 50, normal; 50 ≤ SAS < 60, mild anxiety; 60 ≤ SAS < 70, moderate anxiety; 70 ≤ SAS ≤ 80, severe anxiety) [69]. To clarify the specific characteristics of anxiety symptoms, we referenced the study by Olatunji et al. [70] and used principal component analysis to divide the anxiety symptoms into four dimensions: “anxiety and panic” assessing complaints related to anxiety and panic, “somatic control” assessing composure and relaxation, “vestibular sensations” assessing bodily functions and feelings of calmness, and “ gut/muscle sensations” related to gastrointestinal and muscular discomfort.
In the IEQ section of the questionnaire, the perceived IEQ can reflect the satisfactory or unsatisfactory environmental factors that university students experience in their dormitories [71]. The IEQ section of the questionnaire includes five dimensions: thermal comfort, lighting comfort, acoustic comfort, IAQ, and overcrowding. The Likert scale is used to assess the overall comfort of thermal, acoustic, and lighting environments in subjective satisfaction questionnaires. This study evaluates these factors separately during the daytime and nighttime. In addition, noise source locations were also interrogated. Regarding IAQ, ventilation, odor, and air humidity are how individuals perceive to assess air quality, which is closely related to the human sensory system and health [72,73]. Therefore, the IAQ section set up ‘humidity’, ‘dryness’, ‘odor’, and ‘ventilation’. In terms of overcrowding, the length, width, height, and size of the bedroom space and the window size are evaluated from the perspective of architectural design [74,75].
Based on existing research results and actual conditions, we have prepared separate questions for the bathroom and bedroom [48]. The questionnaire uses a 5-point Likert scale, with higher scores indicating higher satisfaction with each factor [22,76]. Each section of the IEQ has “supplement” items for students to fill in specific feelings and complaints. The questionnaire is summarized in Table 1, and the specific questionnaire is shown in Table S2.
The questionnaire is distributed and collected online through the “WJX” platform, a widely recognized and used survey platform in China. Using “WJX” allowed for efficient distribution and retrieval of survey responses. Participants were over 18 years old, read the informed consent form, ticked “I Agree” through the electronic questionnaire, and voluntarily scanned the QR code of the questionnaire to participate in the anonymous survey. The study was approved by the Ethics Committee of the Dalian University of Technology (DUTSAFA231226-01).

2.3. Data Analysis Method

To analyze the results of the questionnaire, IBM SPSS Statistics 25 was employed to analyze the reliability and validity, and then structural equation modeling (SEM) analysis was constructed using IBM SPSS Amos 26.

2.3.1. Reliability and Validity Analysis

Considering that the IEQ section of the questionnaire was self-made, exploratory factor analysis (EFA) was conducted to establish the factor structure. The EFA utilized the maximum likelihood (ML) method to extract factors from the questionnaire results and verify the dimensional structure [77]. Items with low factor loadings (defined as less than 0.5) were removed [78], including “daytime brightness”, “ nighttime brightness”, “ventilation”, “dryness”, and the bedroom’s “humidity” as well as the bathroom’s “temperature”. This process resulted in a final measurement model comprising 7 dimensions: anxiety symptoms (SAS), bedroom overcrowding (DSE), bedroom acoustic comfort (DAE), bedroom thermal comfort (DHE), bathroom overcrowding (BSE), bathroom lighting comfort (BLE), and indoor air quality (IAQ). The final factor structure explained was 77.045% of the total variance, indicating that the information in the research items could be effectively extracted. Before EFA, the Kaiser–Meyer–Olkin (KMO) measure of sampling adequacy and Bartlett’s test of Sphericity were calculated to ensure that the construct was highly suitable for factor analysis. The KMO model was 0.856, which was significant in Bartlett’s test of Sphericity (p < 0.001), indicating good data validity.
Confirmatory factor analysis (CFA) was utilized to determine the potential factor structure of the measurement model, verify the structural validity, assess the composite reliability, and evaluate the model fit, to assess whether the measurement model can be effectively used to test the hypotheses [79,80]. The results are presented in Table 2.
Cronbach’s α and composite reliability (CR) were used to measure the reliability of the measurement model [81]. Cronbach’s α with values above 0.7 is generally considered reliable, while values between 0.6 and 0.7 are also acceptable [82,83]. CR indicates the internal consistency of the construct indicator, and a generally acceptable value is above 0.7 [78], while Fornell and Larcker [84] suggest a CR value above 0.6. The Cronbach’sα and CR values of 7 dimensions in the measurement model were all acceptable, indicating that the model’s reliability meets the requirements.
The average variance extracted (AVE) measures the amount of variance captured by the latent variables through the measurement variables and characterizes the reliability and convergent validity of the dimensions. Ideally, an AVE value above 0.5 is desirable, while values between 0.36 and 0.5 are considered acceptable [84]. The AVE values of the seven dimensions all met the requirements, indicating that the convergent validity of the measurement model is effective. Discriminant validity shows the degree of difference between individual dimensions and others [78], which is considered reliable when the AVE of a specific construct is always greater than the average shared variance. The results indicate that the measurement model has discriminant validity, as shown in Table 3.
Model fit indices are empirical values that explain the quality of the model and can be used to determine the model’s acceptability, with indicators available for reference [85]. CMIN/DF (χ2/df), goodness of fit index (GFI) root mean square error of approximation (RMSEA), comparative fit index (CFI), normed fit index (NFI), and Tucker–Lewis Index (TFI) are widely used as reference factors for model fit [86]. χ2/df between 2 and 5 was considered acceptable [78]. Gefen and Straub [87] suggested that CFI, GFI, NFI, and TLI are greater than 0.9, while RMSEA is less than 0.08 indicating a good model fit. The fit indices for the measurement model are shown in Table 2, indicating good model fit and allowing for further testing of the SEM.

2.3.2. Structural Equation Modeling (SEM)

After confirming the reliability and validity of the model, SEM was utilized to compute the structural model. SEM is a statistical technique capable of modeling multiple independent and dependent variables [88]. It enables simultaneous factor analysis and path analysis, accommodating measurement errors and the potential structure between variables [89]. The resulting standardized path coefficients show the strength and direction of correlation between explicit and latent variables and between latent and potential variables [78].
In this study, a theoretical model and three hypotheses were developed based on existing theories linking IEQ to residents’ mental health. Data were collected through an online questionnaire for analysis. After EFA and CFA, a measurement model was constructed. SEM was then applied, and the model was subjected to maximum likelihood estimation (ML) to build a structural model to quantify how dormitory IEQ collectively affects university students’ anxiety symptoms.

3. Results

3.1. Sample Characteristics

A total of 518 questionnaire results were received from the regular survey, and after manual screening, 73 questionnaires from other dormitory areas or incomplete questionnaires were deleted, and finally, 445 valid questionnaires were included in the study. A descriptive analysis was conducted to reflect the satisfaction levels of respondents towards their basic information, anxiety levels, and IEQ. The means and standard deviations of respondents’ basic information and dormitory environment scores are shown in Table 4.
Among the 445 respondents, 69.7% were male and 30.3% were female, with an average age of 20.45 years. The SAS indicated that the majority (61.3%) of respondents did not exhibit anxiety symptoms, while a small portion (32.5%) had mild anxiety symptoms, and a very small portion (6.1%) experienced moderate or severe anxiety symptoms. Regarding specific symptoms, “Anxiety and panic” had a total score of 28, with an average score of 10.66; “Somatic control” had a total score of 20, with an average score of 8.92; and “Vestibular sensations” and “Gastrointestinal/muscular sensations” both scored 16, with average scores of 5.01 and 6.42, respectively.
Regarding satisfaction with the dormitory IEQ, higher satisfaction scores correlate with higher satisfaction levels. Students expressed lower satisfaction with the width (M = 2.99), size (M = 2.96), and odor (M = 2.83) of the bathroom, while they were more satisfied with the nighttime brightness (M = 4.47), room height (M = 4.17), window size (M = 4.14), and humidity (M = 4.24) of the bedroom.
Pearson correlation analysis was performed to investigate the correlation between perceived IEQ and anxiety levels, which are shown in Table 5. Overall comfort was significantly negatively correlated with SAS scores (r = −0.387, p < 0.01), indicating that the higher the students rated the comfort of the dormitory environment, the lower their anxiety levels. Among all the IEQ factors, acoustic comfort (r = −0.494, p < 0.01) and IAQ (r = −0.439, p < 0.01) of the bedroom showed a significant correlation to the result of SAS, while other factors were not correlated.

3.2. The Effect of Perceived IEQ on Anxiety Symptoms

The results of the SEM are shown in Figure 3 and Table 6, and the fit indices meet the model fit criteria [87]. The perceived IEQ explains 40% of the effect on anxiety symptoms. DAE (r = −0.55, p < 0.001) and IAQ (r = −0.15, p < 0.05) were significantly and negatively correlated with SAS, which indicated that the subjective satisfaction with the acoustic environment and IAQ of the dormitory mainly affected students’ anxiety symptoms, with higher satisfaction levels corresponding to lower anxiety levels. No significant correlation was found between the remaining factors (p ≥ 0.05). Some factors of the IEQ affected anxiety symptoms while others were not significant, consistent with the second hypothesis, and only some of the paths were significantly correlated with SAS (p < 0.05). Therefore, hypothesis (2) was accepted and hypotheses (1) and (3) were rejected.
In this study, the subjective perception of the acoustic environment had a greater impact on anxiety levels, with each 1 unit increase in satisfaction with the acoustic environment decreasing anxiety levels by 0.55 units. Moreover, the overall acoustic environment of the dormitory had a greater impact at night (r = 0.74, p < 0.001) than during the day (r = 0.64, p < 0.001). For every 1 unit increase in student satisfaction with IAQ in the dormitory, anxiety levels decreased by 0.15 units, with “Bathroom odor” (r = 0.80, p < 0.001) causing a greater degree of impact on IAQ. In terms of anxiety symptoms, the strongest somatic symptom was “Anxiety and panic” (r = 0.91, p < 0.001), which showed the strongest correlation, indicating feelings such as anxiety, fear, and panic, as well as symptoms such as flushing, palpitations, and nightmares. This was followed by “Gastrointestinal/muscular sensations” (r = 0.85, p < 0.001) and “Somatic control” (r = 0.81, p < 0.001), which manifested as gastrointestinal and muscular discomfort and dizziness, tremor, and numbness, respectively.

3.3. Analysis of Noise Source Locations

According to the SEM results, satisfaction with the acoustic environment had the greatest impact on self-reported anxiety symptoms among all the perceived IEQ factors, and noise had a greater influence at night. The location of dormitory noise in the questionnaire was analyzed, and the results are shown in Figure 4. The data indicate that more than half of the respondents (59.3%) perceived the primary source of daytime noise to be outside the windows, while 38.9% and 30.8% of respondents reported that the primary sources of nighttime noise were from outside the windows and within the dormitory. Compared to daytime, the disturbance caused by noise from adjacent rooms and within the dormitory was more significant for students at night.
Further analysis of the “supplement” content revealed that the daytime outdoor noise reported by students was mainly from campus activities and traffic. During the night, differences in students’ living habits can lead to noise disturbances that interfere with rest, and a small number of students also mentioned that the flushing sound from the bathroom plumbing can cause disturbances at night.

4. Discussion

4.1. Influence Mechanisms of the Perceived IEQ on Anxiety Symptoms

This study endeavored to explore the correlation between perceived IEQ and self-reported anxiety symptoms among university students. A survey on IEQ and SAS was conducted among students at a university in northern China. The results of the study showed that some perceived IEQ factors had an impact on anxiety symptoms, with perceived acoustic environment and indoor air quality being the main factors. In particular, nighttime noise annoyed students. However, this research also observed that other factors, including overcrowding and bathroom lighting environment, were weakly associated with students’ anxiety symptoms.
Previous studies have primarily focused on the impact of IEQ on mental health, including factors such as well-being, stress, depression, and anxiety. Overall, the results of this study are consistent with previous research, indicating that certain perceived IEQ factors (acoustic environment and IAQ) have a significant short-term impact on mental health [28,90]. Perceived thermal and lighting environments often have a greater long-term impact on mental health [23,28]. The relationship between IEQ and mental health is also related to the location, research time, and building function. In hot and humid summer regions, the impact of humidity and heat is more significant [28,51]. Lighting and thermal comfort in office and learning spaces have a greater impact on mental health, as these buildings are subject to regulations regarding ventilation systems and space dimensions [90]. Considering the differences in experimental time, climate, and dormitory type, more comprehensive research is needed for this type of building.
Numerous studies have confirmed the significant impact of noise on anxiety levels. Noise, as a source of environmental stress, can cause annoyance and sleep disturbances, ultimately affecting people’s moods and daily life [91]. Borsos et al. [92] found that the acoustic environment is a primary factor influencing happiness, with most participants considering noise as the main IEQ factor negatively affecting their health. The results of this study also support previous findings, indicating that acoustic comfort in dormitories is one of the main reasons why perceived IEQ affects university students’ anxiety psychology. The acoustic environment can influence anxiety psychology through various pathways [39,93], primarily by causing sleep disruptions, which impacts the physical and mental health of residents [94]. This study also found that nighttime acoustic environment satisfaction had a greater impact, with the main noise sources being from outside the windows and inside the room. This underscores the need for relevant researchers and designers to pay more attention to acoustic environment design and implement effective measures to reduce the disturbance caused by noise.
Regarding IAQ, particles, odors, and toxins have been shown to affect mental health [20]. Medical research also indicates that olfactory stimuli can affect stress levels, leading to feelings of tension and anxiety [95]. The results of this study also confirm previous findings. The dormitory buildings at the study site are corridor-style with rooms arranged on both sides, and ventilation effects are poor with windows only on one side, dormitory bathrooms without external windows can hardly eliminate bathroom odors that affect the dormitory environment by relying only on the ventilation system [96]. Unpleasant odors are closely related to mental disorders such as depression and anxiety [97], although there is no direct evidence indicating the relationship between bathroom odors and anxiety psychology. This could be explored in future research, along with studying the optimal ventilation system power to provide a basis for improving IAQ.
In previous studies, indoor thermal environments have often been recognized as a major factor influencing health [23,98]. However, this is inconsistent with the results of this study. The reason could be attributed to the research site’s location in Dalian, influenced by the ocean during summer, resulting in a relatively comfortable temperature averaging 22.6 °C [99], which is within the acceptable range for young people in existing studies [100]. In the “supplement” part of the questionnaire, some students also indicated that the current temperature was fine and could be regulated by clothing and blankets. Students generally expressed satisfaction with thermal environments in the dormitories, with cases of sleep disturbances due to overheating being rare, resulting in minimal impact on students’ anxiety levels. However, several students suggested installing fans or air conditioning. Studies have shown that extreme heat and cold impact anxiety symptoms [101]. Therefore, further research is necessary to explore various thermal environments.
The relationship between bedroom lighting environment comfort and anxiety symptoms was not represented in the SEM because of the low factor loadings. This may be because the dormitory design allows for the easy installation of curtains and desk lamps, enabling students to easily adjust the lighting sources to achieve a comfortable lighting level. Dormitory areas in Chinese universities are usually centrally managed, with lights out at set times. In addition, some students mentioned avoiding roommate interruptions by installing bed curtains in the “Supplement” section. As a result, student satisfaction with the light environment in dormitories is generally high. The impact of the lighting environment on mental health is often long-term, particularly the impact of nighttime light pollution on circadian rhythms, which can lead to mental health issues [102]. Natural light also has a clear regulatory effect on people’s emotions, and long-term lack of daylight can lead to vitamin D deficiency and anxiety symptoms [103]. It has also been shown that improving the lighting environment can improve sleep quality in the short term [104]. Therefore, future research can combine actual measurement data to study lighting environments with different illuminance and color temperatures or focus on satisfaction with lighting environments and natural light in longer-term questionnaire surveys.
Overcrowding was included in this study to determine its level among dormitory environmental factors causing anxiety; however, the results were still statistically insignificant. Previous research has shown that inadequate living space can make residents feel crowded and oppressed, leading to stress and anxiety [105]. High-density residential environments are known to impact mental health [25,106]. Chinese dormitories are typically small in size with high occupancy. Although students may often express dissatisfaction with the size of the dormitories, some students mentioned in the “supplement” section that they were “better than high school dormitory”. Based on the common situation in Chinese dormitories, students may have already adapted to living collectively in such spaces. Therefore, overcrowding is related to satisfaction with the dormitory environment but is not the main factor causing anxiety in university students. Furthermore, bedroom and bathroom spaces were categorized into two dimensions in EFA, indicating that students have different perceptions of these two types of spaces. However, existing discussions in the literature are limited. The study reveals that spatial design not only influences students’ satisfaction with dormitories but also has implications for other physical environments [48,107]. It is essential to conduct further research on the impact of space design on physical environments, comparing dormitory spaces of different sizes and capacities to explore dormitory designs that are more conducive to students’ mental well-being.

4.2. Anxiety Symptoms and Other Influences

As far as anxiety symptoms are concerned, the impact of the built environment as one of the stressors for anxiety is multifaceted. This study found that the impact of IEQ on anxiety mainly manifests in “anxiety and panic”. Physiologically, inappropriate indoor environments can affect individuals’ cardiopulmonary function and circadian rhythms [108]; psychologically, they can impact stress levels and attention [109]. This ultimately leads to anxiety, fear, and panic, as well as symptoms such as blushing, palpitations, and nightmares. Anxiety, in turn, affects indoor environmental satisfaction, sleep quality, and emotional levels, reducing students’ quality of learning and life [110]. Therefore, it is necessary to conduct further research on the causes of anxiety in university students and improvement measures.
Furthermore, in the SEM results, the IEQ explained 40% of the SAS, indicating that there are many other factors influencing students’ anxiety symptoms, such as physical activity, interpersonal relationships, and family issues [111]. During the research period, students may have faced the pressure of exams and end-of-term assignments, which affected the symptoms of anxiety. In addition, indoor design elements such as color and furniture can also influence anxiety symptoms [112]. Asim et al. [113] even found that indoor artwork, greenery, and sky views outside the windows can alleviate residents’ anxiety. In future research, it is important to consider incorporating these factors into the study to better understand the pathways through which indoor environments affect mental health. This will provide a basis for enhancing the living environment for students and can be extended to other types of residential buildings.

4.3. Limitation and Future Direction

First, this study utilized SEM to establish a correlation model between perceived IEQ and anxiety symptoms, but correlation does not imply causation. Therefore, future research should further explore the causal relationship. The study only reflects the results for typical northern dormitory buildings in the summer. There is still room for optimization in the questionnaire design, with a lack of detailed discussion on specific noise types and the air quality of bathrooms. Some questionnaire items were also excluded from the SEM due to reliability issues. Additionally, SAS only reflects short-term anxiety symptoms of the participants and does not indicate that the participants have an anxiety disorder. Future research should improve the IEQ survey questionnaire and conduct experiments on different types of typical buildings across multiple seasons to more comprehensively reflect the influence mechanism of dormitory IEQ on students’ anxiety symptoms.
Secondly, this study used the relationship between perceived IEQ and anxiety symptoms, which can only reflect the impact of improving environmental quality satisfaction, without the support of objective measurements to propose specific optimization measures. In future research, a combination of subjective and objective methods should be considered, focusing on the acoustic environment and IAQ, to enhance the scientific validity of the research and provide a basis for proposing targeted renovation measures. Finally, the dormitory environment is just one of many factors contributing to university students’ anxiety [111]. Future research should consider factors of university students such as lifestyles, academic stress, and well-being to reflect the pathways and mediating factors through which the indoor environment factor influences anxiety.

5. Conclusions

The dormitory plays a crucial role in the lives of university students, as a comfortable and conducive environment can greatly enhance their well-being and academic performance. This study focused on 445 students from the Xishan living area of Dalian University of Technology, investigating the subjective perception of students towards dormitory IEQ and its correlation with student anxiety symptoms through subjective questionnaires and SEM. The main conclusions are as follows:
1.
The perceived dormitory indoor environment of university students has an impact on their anxiety symptoms, explaining 40% of the SAS results, and only some factors have an influence.
2.
Under the comprehensive effect of indoor environmental factors, the acoustic comfort (r = −0.55, p < 0.001) and IAQ (r = −0.15, p < 0.05) of the dormitory has a significant impact on the anxiety symptoms of university students. The main anxiety symptoms determined by the SAS are “anxiety and panic (r = 0.91, p < 0.001)”, manifesting anxiety, fear, and panic, as well as physical symptoms such as flushing, palpitations, and nightmares.
This study contributes to the IEQ evaluation system from an architectural design perspective, focusing on anxiety—a common psychological issue among university students. It quantifies the impact of students’ subjective perceptions of IEQ factors in overall indoor environments on their anxiety levels, thereby expanding the knowledge base on the relationship between the built environment in higher education and student mental health. The results offer valuable guidance for dormitory design and renovation, crucial for creating a comprehensive and supportive learning environment, and can also serve as a reference for other dormitory buildings.
However, this study only investigated the subjective IEQ and has limitations such as a small sample size and short duration, and the research methodology needs to be improved. Future studies should adopt a combination of subjective and objective methods, investigate the effects of different seasons and dormitory types, and expand the sample size to more comprehensively reflect how the built environment impacts mental health.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/buildings14113530/s1, Table S1: The self-rating anxiety scale (SAS); Table S2: Description of the questionnaire.

Author Contributions

Conceptualization, F.G. and Z.Z.; methodology, F.G. and M.L.; software, M.L. and Z.D.; validation, M.L. and Z.D.; formal analysis, Z.D., H.Z. (Hongchi Zhang) and J.D.; investigation, F.G., M.L. and Z.D.; resources, F.G.; data curation, F.G., M.L. and Z.D.; writing—original draft preparation, F.G., M.L. and Z.D.; writing—review and editing, F.G., M.L., H.Z. (Hui Zhao), Z.Z., H.Z. (Hongchi Zhang), J.D. and D.Z.; visualization, H.Z. (Hui Zhao) and Z.Z.; supervision, H.Z. (Hui Zhao), Z.Z. and D.Z.; project administration, F.G.; funding acquisition, F.G. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by “the Fundamental Research Funds for the Central Universities” grant number No. DUT21RW204.

Institutional Review Board Statement

This study was reviewed and approved by the Ethics Committee of the Dalian University of Technology, with the approval number: DUTSAFA231226-01. All participants provided informed consent to participate in the study.

Informed Consent Statement

Informed consent for participation was obtained from all subjects involved in the study.

Data Availability Statement

According to the ethical agreement, the data related to this study cannot be disclosed. The data related to this study can be obtained from the corresponding author after approval by the Ethics Committee of the Dalian University of Technology and the signing of a data-sharing agreement letter.

Acknowledgments

We are grateful to the questionnaire participants for taking part in this study.

Conflicts of Interest

The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.

References

  1. WHO. World Mental Health Report: Transforming Mental Health for All: Executive Summary, 9789240050860 (electronic version), 9789240050877 (print version); World Health Organization: Geneva, Switzerland, 2022. [Google Scholar]
  2. WHO. WHO Global Strategy on Health, Environment and Climate Change: The Transformation Needed to Improve Lives and Well-Being Sustainably Through Healthy Environments; World Health Organization: Geneva, Switzerland, 2020. [Google Scholar]
  3. WHO. Comprehensive Mental Health Action Plan 2013–2030, 9789240050945 (online version), 9789240050952 (print version); World Health Organization: Geneva, Switzerland, 2022. [Google Scholar]
  4. Ministry of Education of the People’s Republic of China. Statistical Report on China’s Educational Achievements in 2022. Available online: http://en.moe.gov.cn/documents/reports/202304/t20230403_1054100.html (accessed on 23 March 2024).
  5. Zhu, W.; Li, J. Analysis and Exploration on the Integration of Mental Health Education into College Physical Education Practice. Comput. Intell. Neurosci. 2022, 2022, 5195909. [Google Scholar] [CrossRef] [PubMed]
  6. Chen, Y.; Zhang, Y.; Yu, G. Prevalence of mental health problems among college students in mainland China from 2010 to 2020: A meta-analysis. Adv. Psychol. Sci. 2022, 30, 991–1004. [Google Scholar] [CrossRef]
  7. American Psychiatric Association. Diagnostic and Statistical Manual of Mental Disorders: DSM-5™, 5th ed.; American Psychiatric Publishing, Inc.: Arlington, VA, USA, 2013; p. 947. [Google Scholar]
  8. Penninx, B.W.J.H.; Pine, D.S.; Holmes, E.A.; Reif, A. Anxiety disorders. Lancet 2021, 397, 914–927. [Google Scholar] [CrossRef] [PubMed]
  9. Kraft, J.D.; Grant, D.M.; White, E.J.; Taylor, D.L.; Frosio, K.E. Cognitive mechanisms influence the relationship between social anxiety and depression among college students. J. Am. Coll. Health 2021, 69, 245–251. [Google Scholar] [CrossRef]
  10. Gao, W.; Ping, S.; Liu, X. Gender differences in depression, anxiety, and stress among college students: A longitudinal study from China. J. Affect. Disord. 2020, 263, 292–300. [Google Scholar] [CrossRef]
  11. Gibb, S.J.; Fergusson, D.M.; Horwood, L.J. Burden of psychiatric disorder in young adulthood and life outcomes at age 30. Br. J. Psychiatry 2010, 197, 122–127. [Google Scholar] [CrossRef]
  12. Wang, Y.; Guang, Z.; Zhang, J.; Han, L.; Zhang, R.; Chen, Y.; Chen, Q.; Liu, Z.; Gao, Y.; Wu, R.; et al. Effect of Sleep Quality on Anxiety and Depression Symptoms among College Students in China’s Xizang Region: The Mediating Effect of Cognitive Emotion Regulation. Behav. Sci. 2023, 13, 861. [Google Scholar] [CrossRef]
  13. Allen, M.S.; Walter, E.E.; Swann, C. Sedentary behaviour and risk of anxiety: A systematic review and meta-analysis. J. Affect. Disord. 2019, 242, 5–13. [Google Scholar] [CrossRef]
  14. van den Bosch, M.; Meyer-Lindenberg, A. Environmental Exposures and Depression: Biological Mechanisms and Epidemiological Evidence. Annu. Rev. Public Health 2019, 40, 239–259. [Google Scholar] [CrossRef]
  15. Polemiti, E.; Hese, S.; Schepanski, K.; Yuan, J.; Schumann, G. How does the macroenvironment influence brain and behaviour—A review of current status and future perspectives. Mol. Psychiatry 2024, 29, 3268–3286. [Google Scholar] [CrossRef]
  16. Beemer, C.J.; Stearns-Yoder, K.A.; Schuldt, S.J.; Kinney, K.A.; Lowry, C.A.; Postolache, T.T.; Brenner, L.A.; Hoisington, A.J. A brief review on the mental health for select elements of the built environment. Indoor Built Environ. 2019, 30, 152–165. [Google Scholar] [CrossRef]
  17. ASHRAE. 1993 ASHRAE Handbook. Fundamentals, 0910110972 (print version); American Society of Heating, Refrigerating and Air-Conditioning Engineers: Atlanta, GA, USA, 1993. [Google Scholar]
  18. Evans, G.W. The built environment and mental health. J. Urban Health 2003, 80, 536–555. [Google Scholar] [CrossRef] [PubMed]
  19. Krieger, J.; Higgins, D.L. Housing and Health: Time Again for Public Health Action. Am. J. Public Health 2002, 92, 758–768. [Google Scholar] [CrossRef] [PubMed]
  20. Hoisington, A.J.; Stearns-Yoder, K.A.; Schuldt, S.J.; Beemer, C.J.; Maestre, J.P.; Kinney, K.A.; Postolache, T.T.; Lowry, C.A.; Brenner, L.A. Ten questions concerning the built environment and mental health. Build. Environ. 2019, 155, 58–69. [Google Scholar] [CrossRef]
  21. Dunn, J.R.; Hayes, M.V. Social inequality, population health, and housing: A study of two Vancouver neighborhoods. Soc. Sci. Med. 2000, 51, 563–587. [Google Scholar] [CrossRef]
  22. Choi, H.; Kim, H.; Hong, T.; An, J. Examining the indirect effects of indoor environmental quality on task performance: The mediating roles of physiological response and emotion. Build. Environ. 2023, 236, 110298. [Google Scholar] [CrossRef]
  23. Chen, Y.; Li, M.; Lu, J.; Chen, B. Influence of residential indoor environment on quality of life in China. Build. Environ. 2023, 232, 110068. [Google Scholar] [CrossRef]
  24. Thomson, H.; Thomas, S.; Sellstrom, E.; Petticrew, M. The Health Impacts of Housing Improvement: A Systematic Review of Intervention Studies From 1887 to 2007. Am. J. Public Health 2009, 99, S681–S692. [Google Scholar] [CrossRef]
  25. Tao, Z.; Wu, G.; Wang, Z. The relationship between high residential density in student dormitories and anxiety, binge eating and Internet addiction: A study of Chinese college students. Springerplus 2016, 5, 1579. [Google Scholar] [CrossRef]
  26. Zhang, D.; Mui, K.-W.; Wong, L.-T. Ten Questions Concerning Indoor Environmental Quality (IEQ) Models: The Development and Applications. Appl. Sci. 2023, 13, 3343. [Google Scholar] [CrossRef]
  27. Brink, H.W.; Loomans, M.; Mobach, M.P.; Kort, H.S.M. A systematic approach to quantify the influence of indoor environmental parameters on students’ perceptions, responses, and short-term academic performance. Indoor Air 2022, 32, e13116. [Google Scholar] [CrossRef] [PubMed]
  28. Dunleavy, G.; Bajpai, R.; Tonon, A.C.; Cheung, K.L.; Thach, T.-Q.; Rykov, Y.; Soh, C.-K.; de Vries, H.; Car, J.; Christopoulos, G. Prevalence of psychological distress and its association with perceived indoor environmental quality and workplace factors in under and aboveground workplaces. Build. Environ. 2020, 175, 106799. [Google Scholar] [CrossRef]
  29. Fang, W.; Liu, L.; Yin, B.; Ke, L.; Su, Y.; Liu, F.; Ma, X.; Di, Q. Heat exposure intervention, anxiety level, and multi-omic profiles: A randomized crossover study. Environ. Int. 2023, 181, 108247. [Google Scholar] [CrossRef] [PubMed]
  30. Kim, H.; Hong, T.; Kim, J.; Yeom, S. A psychophysiological effect of indoor thermal condition on college students’ learning performance through EEG measurement. Build. Environ. 2020, 184, 107223. [Google Scholar] [CrossRef]
  31. Janssen, H.; Ford, K.; Gascoyne, B.; Hill, R.; Roberts, M.; Bellis, M.A.; Azam, S. Cold indoor temperatures and their association with health and well-being: A systematic literature review. Public Health 2023, 224, 185–194. [Google Scholar] [CrossRef]
  32. Fisk, W.J.; Singer, B.C.; Chan, W.R. Association of residential energy efficiency retrofits with indoor environmental quality, comfort, and health: A review of empirical data. Build. Environ. 2020, 180, 107067. [Google Scholar] [CrossRef]
  33. Paul, S.; Brown, T.M. Direct effects of the light environment on daily neuroendocrine control. J. Endocrinol. 2019, 243, R1–R18. [Google Scholar] [CrossRef]
  34. Giménez, M.C.; Geerdinck, L.M.; Versteylen, M.; Leffers, P.; Meekes, G.; Herremans, H.; De Ruyter, B.; Bikker, J.W.; Kuijpers, P.; Schlangen, L.J.M. Patient room lighting influences on sleep, appraisal and mood in hospitalized people. J. Sleep Res. 2017, 26, 236–246. [Google Scholar] [CrossRef]
  35. Xiao, H.; Cai, H.; Li, X. Non-visual effects of indoor light environment on humans: A review. Physiol. Behav. 2021, 228, 113195. [Google Scholar] [CrossRef]
  36. Weber, S.; Mascherek, A.; Augustin, J.; Cheng, B.; Thomalla, G.; Hoven, H.; Harth, V.; Augustin, M.; Gallinat, J.; Kuhn, S. My home-my castle? Self-reported anxiety varies in relation to the subjective evaluation of home environment. Front. Psychol. 2023, 14, 1267900. [Google Scholar] [CrossRef]
  37. Yang, S.; Fu, Y.; Dong, S.; Yang, B.; Li, Z.; Feng, C.; Reinhardt, J.D.; Shi, W.; Jia, P.; Zeng, H.; et al. Association between perceived noise at work and mental health among employed adults in Southwest China. J. Affect. Disord. 2023, 343, 22–30. [Google Scholar] [CrossRef] [PubMed]
  38. Zaman, M.; Muslim, M.; Jehangir, A. Environmental noise-induced cardiovascular, metabolic and mental health disorders: A brief review. Environ. Sci. Pollut. Res. 2022, 29, 76485–76500. [Google Scholar] [CrossRef] [PubMed]
  39. Meng, Q.; Zhang, J.; Kang, J.; Wu, Y. Effects of sound environment on the sleep of college students in China. Sci. Total Environ. 2020, 705, 135794. [Google Scholar] [CrossRef] [PubMed]
  40. Xu, W.Y.; Wang, H.Q.; Su, H.; Sullivan, W.C.; Lin, G.S.; Pryor, M.; Jiang, B. Impacts of sights and sounds on anxiety relief in the high-density city. Landsc. Urban Plan. 2024, 241, 104927. [Google Scholar] [CrossRef]
  41. Zhang, D.; Kong, C.; Zhang, M.; Kang, J. Religious Belief-Related Factors Enhance the Impact of Soundscapes in Han Chinese Buddhist Temples on Mental Health. Front. Psychol. 2022, 12, 774689. [Google Scholar] [CrossRef]
  42. Ataei, Y.; Sun, Y.; Liu, W.; S. Ellie, A.; Dong, H.; Ahmad, U.M. Health Effects of Exposure to Indoor Semi-Volatile Organic Compounds in Chinese Building Environment: A Systematic Review. Int. J. Environ. Res. Public Health 2022, 20, 678. [Google Scholar] [CrossRef]
  43. Cho, J.; Sohn, J.; Noh, J.; Jang, H.; Kim, W.; Cho, S.K.; Seo, H.; Seo, G.; Lee, S.K.; Noh, Y.; et al. Association between exposure to polycyclic aromatic hydrocarbons and brain cortical thinning: The Environmental Pollution-Induced Neurological EFfects (EPINEF) study. Sci. Total Environ. 2020, 737, 140097. [Google Scholar] [CrossRef]
  44. Azhdari, S.S.; Yunesian, M.; Hassanvand, M.S.; Nodehi, R.N.; Darvishali, S.; Faridi, S.; Shamsipour, M. Associations of combined short-term exposures to ambient PM2.5 air pollution and noise annoyance on mental health disorders: A panel study of healthy college students in Tehran. Air Qual. Atmos. Health 2022, 15, 1497–1505. [Google Scholar] [CrossRef]
  45. Rickenbacker, H.J.; Vaden, J.M.; Bilec, M.M. Engaging Citizens in Air Pollution Research: Investigating the Built Environment and Indoor Air Quality and Its Impact on Quality of Life. J. Archit. Eng. 2020, 26, 04020041. [Google Scholar] [CrossRef]
  46. Lamport, D.J.; Breese, E.; Giao, M.S.; Chandra, S.; Orchard, F. Can air purification improve sleep quality? A 2-week randomised-controlled crossover pilot study in healthy adults. J. Sleep Res. 2023, 32, e13782. [Google Scholar] [CrossRef]
  47. Yang, F.; Sun, Y.; Wang, P.; Weschler, L.B.; Sundell, J. Spread of respiratory infections in student dormitories in China. Sci. Total Environ. 2021, 777, 145983. [Google Scholar] [CrossRef] [PubMed]
  48. Dong, Z.; Zhao, K.; Ren, M.; Ge, J.; Chan, I.Y.S. The impact of space design on occupants’ satisfaction with indoor environment in university dormitories. Build. Environ. 2022, 218, 109143. [Google Scholar] [CrossRef]
  49. Riva, A.; Rebecchi, A.; Capolongo, S.; Gola, M. Can Homes Affect Well-Being? A Scoping Review among Housing Conditions, Indoor Environmental Quality, and Mental Health Outcomes. Int. J. Environ. Res. Public Health 2022, 19, 15975. [Google Scholar] [CrossRef] [PubMed]
  50. Morganti, A.; Brambilla, A.; Aguglia, A.; Amerio, A.; Miletto, N.; Parodi, N.; Porcelli, C.; Odone, A.; Costanza, A.; Signorelli, C.; et al. Effect of Housing Quality on the Mental Health of University Students during the COVID-19 Lockdown. Int. J. Environ. Res. Public Health 2022, 19, 2918. [Google Scholar] [CrossRef]
  51. Mao, H.; Yu, H.; Tang, Y.; Zhang, K.; Luo, M.; Zhuang, L. Psychological State and Subjective Environmental Perception of College Students Residing in Dormitories during Quarantine: A Case Study. Buildings 2023, 13, 1065. [Google Scholar] [CrossRef]
  52. Fakhari, M.; Vahabi, V.; Fayaz, R. A study on the factors simultaneously affecting visual comfort in classrooms: A structural equation modeling approach. Energy Build. 2021, 249, 111232. [Google Scholar] [CrossRef]
  53. Awada, M.; Becerik-Gerber, B.; Liu, R.Y.; Seyedrezaei, M.; Lu, Z.; Xenakis, M.; Lucas, G.; Roll, S.C.; Narayanan, S. Ten questions concerning the impact of environmental stress on office workers. Build. Environ. 2023, 229, 109964. [Google Scholar] [CrossRef]
  54. Bergefurt, L.; Weijs-Perrée, M.; Appel-Meulenbroek, R.; Arentze, T. The physical office workplace as a resource for mental health-A systematic scoping review. Build. Environ. 2022, 207, 108505. [Google Scholar] [CrossRef]
  55. ASHRAE. Guideline 10-2016: Interactions Affecting the Achievement of Acceptable Indoor Environments, 1049-894X(online version); American Society of Heating, Refrigerating and Air-Conditioning Engineers: Atlanta, GA, USA, 2016. [Google Scholar]
  56. IWBI. WELL Building Standard v2; IWBI: New York, NY, USA, 2022. [Google Scholar]
  57. GB/T 50378-2019; Assessment Standard for Green Building. Ministry of Housing and Urban-Rural Development of the People’s Republic of China: Beijing, China, 2019. (In Chinese)
  58. Gifford, R. Environmental Psychology; Academic Press: San Diego, CA, USA, 2012. [Google Scholar]
  59. Kaplan, S. A Model of Person-Environment Compatibility. Environ. Behav. 1983, 15, 311–332. [Google Scholar] [CrossRef]
  60. Kuboshima, Y.; McIntosh, J. The impact of housing environments on social connection: An ethnographic investigation on quality of life for older adults with care needs. J. Hous. Built Environ. 2022, 38, 1353–1383. [Google Scholar] [CrossRef]
  61. Akbari, P.; Yazdanfar, S.A.; Hosseini, S.B.; Norouzian-Maleki, S. Housing and mental health during outbreak of COVID-19. J. Build. Eng. 2021, 43, 102919. [Google Scholar] [CrossRef]
  62. Zung, W.W. A rating instrument for anxiety disorders. Psychosomatics 1971, 12, 371–379. [Google Scholar] [CrossRef] [PubMed]
  63. Dunstan, D.A.; Scott, N. Norms for Zung’s Self-rating Anxiety Scale. BMC Psychiatry 2020, 20, 90. [Google Scholar] [CrossRef] [PubMed]
  64. Dunstan, D.A.; Scott, N.; Todd, A.K. Screening for anxiety and depression: Reassessing the utility of the Zung scales. BMC Psychiatry 2017, 17, 329. [Google Scholar] [CrossRef]
  65. Zhang, X.; Wen, K.; Ding, H.; Zhou, X. Experiencing art creation as a therapeutic intervention to relieve anxiety—A case study of a university’s ceramic art course. Front. Psychiatry 2024, 15, 1334240. [Google Scholar] [CrossRef]
  66. Lu, M.; Li, X.; Song, K.; Xiao, Y.; Zeng, W.; Shi, C.; Fan, X.; Li, G. Mental health disparities between physicians and nurses: Analyzing the impact of occupational stress and work environment fitness using random forest algorithm. J. Affect. Disord. 2024, 350, 350–358. [Google Scholar] [CrossRef]
  67. Tao, Y.; Liu, X.; Hou, W.; Niu, H.; Wang, S.; Ma, Z.; Bi, D.; Zhang, L. The Mediating Role of Emotion Regulation Strategies in the Relationship Between Big Five Personality Traits and Anxiety and Depression Among Chinese Firefighters. Front. Public Health 2022, 10, 901686. [Google Scholar] [CrossRef]
  68. Sun, W.; Wu, M.; Qu, P.; Lu, C.; Wang, L. Psychological well-being of people living with HIV/AIDS under the new epidemic characteristics in China and the risk factors: A population-based study. Int. J. Infect. Dis. 2014, 28, 147–152. [Google Scholar] [CrossRef]
  69. Cheng, C.; Liu, X.; Fan, W.; Bai, X.; Liu, Z. Comprehensive Rehabilitation Training Decreases Cognitive Impairment, Anxiety, and Depression in Poststroke Patients: A Randomized, Controlled Study. J. Stroke Cerebrovasc. Dis. 2018, 27, 2613–2622. [Google Scholar] [CrossRef]
  70. Olatunji, B.O.; Deacon, B.J.; Abramowitz, J.S.; Tolin, D.F. Dimensionality of somatic complaints: Factor structure and psychometric properties of the Self-Rating Anxiety Scale. J. Anxiety Disord. 2006, 20, 543–561. [Google Scholar] [CrossRef]
  71. ISO 28802: 2012; Ergonomics of the Physical Environment—Assessment of Environments by Means of an Environmental Survey Involving Physical Measurements of the Environment and Subjective Responses of People. ISO: Geneva, Switzerland, 2012.
  72. Hu, Y.; Xu, L.; Liang, W. A preliminary study on volatile organic compounds and odor in university dormitories: Situation, contribution, and correlation. Build. Simul. 2022, 16, 379–391. [Google Scholar] [CrossRef]
  73. Finell, E.; Tolvanen, A.; Ikonen, R.; Pekkanen, J.; Ståhl, T. Students’ school-level symptoms mediate the relationship between a school’s observed moisture problems and students’ subjective perceptions of indoor air quality. Indoor Air 2020, 31, 40–50. [Google Scholar] [CrossRef] [PubMed]
  74. Lee, S.; Wohn, K. Occupants’ Perceptions of Amenity and Efficiency for Verification of Spatial Design Adequacy. Int. J. Environ. Res. Public Health 2016, 13, 128. [Google Scholar] [CrossRef] [PubMed]
  75. Abd-Alhamid, F.; Kent, M.; Wu, Y. Assessment of Window Size and Layout Impact on a View Quality Perception in a Virtual Reality Environment. Leukos 2023, 20, 239–260. [Google Scholar] [CrossRef]
  76. Seyedrezaei, M.; Awada, M.; Becerik-Gerber, B.; Lucas, G.; Roll, S. Interaction effects of indoor environmental quality factors on cognitive performance and perceived comfort of young adults in open plan offices in North American Mediterranean climate. Build. Environ. 2023, 244, 110743. [Google Scholar] [CrossRef]
  77. Lloret, S.; Ferreres, A.; Hernández, A.; Tomás, I. The exploratory factor analysis of items: Guided analysis based on empirical data and software. An. Psicol. 2017, 33, 417–432. [Google Scholar] [CrossRef]
  78. Hair, J.; Black, W.; Babin, B.; Anderson, R. Multivariate Data Analysis: A Global Perspective, 7th ed.; Pearson: Upper Saddle River, NJ, USA, 2010. [Google Scholar]
  79. Halpin, P.F.; da-Silva, C.; De Boeck, P. A Confirmatory Factor Analysis Approach to Test Anxiety. Struct. Equ. Model. 2014, 21, 455–467. [Google Scholar] [CrossRef]
  80. Strauss, M.E.; Smith, G.T. Construct Validity: Advances in Theory and Methodology. Annu. Rev. Clin. Psychol. 2009, 5, 1–25. [Google Scholar] [CrossRef]
  81. Raykov, T. Estimation of Composite Reliability for Congeneric Measures. Appl. Psychol. Meas. 2016, 21, 173–184. [Google Scholar] [CrossRef]
  82. Eisinga, R.; Grotenhuis, M.; Pelzer, B. The reliability of a two-item scale: Pearson, Cronbach, or Spearman-Brown? Int. J. Public Health 2013, 58, 637–642. [Google Scholar] [CrossRef]
  83. Streiner, D.L. Starting at the beginning: An introduction to coefficient alpha and internal consistency. J. Pers. Assess. 2003, 80, 99–103. [Google Scholar] [CrossRef] [PubMed]
  84. Fornell, C.; Larcker, D.F. Evaluating Structural Equation Models with Unobservable Variables and Measurement Error. J. Mark. Res. 1981, 18, 39–50. [Google Scholar] [CrossRef]
  85. Hu, L.t.; Bentler, P.M. Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Struct. Equ. Model. 1999, 6, 1–55. [Google Scholar] [CrossRef]
  86. Lee, T.K.; Kim, J.T. Residents’ responses on indoor environment quality and energy use in apartments. Energy Build. 2015, 98, 34–38. [Google Scholar] [CrossRef]
  87. Gefen, D.; Straub, D.W. The Relative Importance of Perceived Ease of Use in IS Adoption: A Study of E-Commerce Adoption. J. Assoc. Inf. Syst. 2000, 1, 8. [Google Scholar] [CrossRef]
  88. Zyphur, M.J.; Bonner, C.V.; Tay, L. Structural Equation Modeling in Organizational Research: The State of Our Science and Some Proposals for Its Future. Annu. Rev. Organ. Psychol. Organ. Behav. 2023, 10, 495–517. [Google Scholar] [CrossRef]
  89. Xiong, B.; Skitmore, M.; Xia, B. A critical review of structural equation modeling applications in construction research. Autom. Constr. 2015, 49, 59–70. [Google Scholar] [CrossRef]
  90. Boegheim, B.; Appel-Meulenbroek, R.; Yang, D.; Loomans, M. Indoor environmental quality (IEQ) in the home workplace in relation to mental well-being. Facilities 2022, 40, 125–140. [Google Scholar] [CrossRef]
  91. Passchier-Vermeer, W.; Passchier, W.F. Noise exposure and public health. Environ. Health Perspect. 2000, 108, 123–131. [Google Scholar] [CrossRef]
  92. Borsos, Á.; Zoltán, E.; Pozsgai, É.; Cakó, B.; Medvegy, G.; Girán, J. The Comfort Map—A Possible Tool for Increasing Personal Comfort in Office Workplaces. Buildings 2021, 11, 233. [Google Scholar] [CrossRef]
  93. Golmohammadi, R.; Darvishi, E.; Faradmal, J.; Poorolajal, J.; Aliabadi, M. Attention and short-term memory during occupational noise exposure considering task difficulty. Appl. Acoust. 2020, 158, 107065. [Google Scholar] [CrossRef]
  94. Basner, M.; Babisch, W.; Davis, A.; Brink, M.; Clark, C.; Janssen, S.; Stansfeld, S. Auditory and non-auditory effects of noise on health. Lancet 2014, 383, 1325–1332. [Google Scholar] [CrossRef] [PubMed]
  95. Masuo, Y.; Satou, T.; Takemoto, H.; Koike, K. Smell and Stress Response in the Brain: Review of the Connection between Chemistry and Neuropharmacology. Molecules 2021, 26, 2571. [Google Scholar] [CrossRef] [PubMed]
  96. Li, Z.; Li, K.-Y.; Guo, X.; Jiang, F.-Y.; Liu, J.-Y. Field Study of Indoor Air Quality in a University Dormitory in Jinan City. Build. Energy Effic. 2019, 47, 13–19. [Google Scholar] [CrossRef]
  97. Rocha, K.; Perez, K.; Rodriguez-Sanz, M.; Obiols, J.E.; Borrell, C. Perception of environmental problems and common mental disorders (CMD). Soc. Psychiatry Psychiatr. Epidemiol. 2012, 47, 1675–1684. [Google Scholar] [CrossRef]
  98. Lima, F.; Ferreira, P.; Leal, V. A Review of the Relation between Household Indoor Temperature and Health Outcomes. Energies 2020, 13, 2881. [Google Scholar] [CrossRef]
  99. Zhang, H.; Guo, F.; Liu, K.; Wang, J.; Dong, J.; Zhu, P. Spatial differences in thermal comfort in summer in coastal areas: A study on Dalian, China. Front. Public Health 2022, 10, 1024757. [Google Scholar] [CrossRef]
  100. Guo, F. Comparative research on thermal adaptive model of elderly people and non-elderly people in naturally ventilated house. J. Dalian Univ. Technol. 2016, 56, 147–152. [Google Scholar] [CrossRef]
  101. Zhang, S.; Yang, Y.; Xie, X.; Li, H.; Han, R.; Hou, J.; Sun, J.; Qian, Z.; Wu, S.; Huang, C.; et al. The effect of temperature on cause-specific mental disorders in three subtropical cities: A case-crossover study in China. Environ. Int. 2020, 143, 105938. [Google Scholar] [CrossRef]
  102. Bedrosian, T.A.; Nelson, R.J. Timing of light exposure affects mood and brain circuits. Transl. Psychiatr. 2017, 7, e1017. [Google Scholar] [CrossRef]
  103. Borges-Vieira, J.G.; Cardoso, C.K.S. Efficacy of B-vitamins and vitamin D therapy in improving depressive and anxiety disorders: A systematic review of randomized controlled trials. Nutr. Neurosci. 2023, 26, 187–207. [Google Scholar] [CrossRef] [PubMed]
  104. Viola, A.U.; James, L.M.; Schlangen, L.J.M.; Dijk, D.-J. Blue-enriched white light in the workplace improves self-reported alertness, performance and sleep quality. Scand. J. Work Environ. Health 2008, 34, 297–306. [Google Scholar] [CrossRef] [PubMed]
  105. McCartney, S.; Rosenvasser, X. Privacy Territories in Student University Housing Design: Introduction of the Hierarchy of Isolation and Privacy in Architecture Tool (HIPAT). SAGE Open 2022, 12, 21582440221089953. [Google Scholar] [CrossRef]
  106. Evans, G.W.; Palsane, M.N.; Lepore, S.J.; Martin, J. Residential density and psychological health—The mediating effects of social support. J. Pers. Soc. Psychol. 1989, 57, 994–999. [Google Scholar] [CrossRef]
  107. Wang, Z.; Wu, Y.; Jia, Z.; Gao, Q.; Gu, Z. Research on Health and Thermal Comfort of Unit-Type Student Apartments in the Western China Science and Technology Innovation Harbor. Front. Public Health 2022, 10, 850107. [Google Scholar] [CrossRef]
  108. Meng, C.; Wang, W.; Hao, Z.; Liu, H. Investigation on the influence of isolated environment on human psychological and physiological health. Sci. Total Environ. 2020, 716, 136972. [Google Scholar] [CrossRef]
  109. Wu, J.; Yang, L.; Hu, Z.; Gao, F.; Hu, X. Perceptual response and cognitive performance during exposure to extremely cold environments. Energy Build. 2021, 251, 111358. [Google Scholar] [CrossRef]
  110. Kenardy, J.; Fried, L.; Kraemer, H.C.; Taylor, C.B. Psychological Precursors of Panic Attacks. Br. J. Psychiatry 1992, 160, 668–673. [Google Scholar] [CrossRef]
  111. Liu, X.Q.; Guo, Y.X.; Xu, Y. Risk factors and digital interventions for anxiety disorders in college students: Stakeholder perspectives. World J. Clin. Cases 2023, 11, 1442–1457. [Google Scholar] [CrossRef]
  112. Ojala, A.; Kostensalo, J.; Viik, J.; Matilainen, H.; Wik, I.; Virtanen, L.; Muilu-Mäkelä, R. Psychological and physiological effects of a wooden office room on human well-being: Results from a randomized controlled trial. J. Environ. Psychol. 2023, 89, 102059. [Google Scholar] [CrossRef]
  113. Asim, F.; Chani, P.S.; Shree, V. Impact of COVID-19 containment zone built-environments on students’ mental health and their coping mechanisms. Build. Environ. 2021, 203, 108107. [Google Scholar] [CrossRef] [PubMed]
Buildings 14 03530 g001
Figure 1. Summary of research framework.
Figure 1. Summary of research framework.
Buildings 14 03530 g001
Buildings 14 03530 g002
Figure 2. Overview of the dormitory environment at the study site. (a) Typical dormitory unit floor plan; (b) typical dormitory exterior view.
Figure 2. Overview of the dormitory environment at the study site. (a) Typical dormitory unit floor plan; (b) typical dormitory exterior view.
Buildings 14 03530 g002
Buildings 14 03530 g003
Figure 3. Result of structural equation model.
Figure 3. Result of structural equation model.
Buildings 14 03530 g003
Buildings 14 03530 g004
Figure 4. Results of daytime and nighttime noise source locations.
Figure 4. Results of daytime and nighttime noise source locations.
Buildings 14 03530 g004
Table 1. Summary of the questionnaire.
Table 1. Summary of the questionnaire.
Variable GroupQuestion
Basic informationAge, gender, grade
Living conditionsNumber of dormitory members, building number, floor number
Anxiety DisorderThe self-rating anxiety scale (SAS)
IEQBedroomThermal comfort, acoustic comfort, lighting comfort, indoor air quality, overcrowding
BathroomThermal comfort, lighting comfort, overcrowding, indoor air quality
TotalTotal dormitory indoor environment comfort
Table 2. Reliability, validity, and factor loadings of the measurement model.
Table 2. Reliability, validity, and factor loadings of the measurement model.
FactorsEstimateAVECRCronbach’s αModel Fit
SASAnxiety and panic0.9100.7090.9070.882χ2/df = 2.833
CFI = 0.947
GFI = 0.908
NFI = 0.921
TLI = 0.933
RMSEA = 0.064
Vestibular sensations0.788
Somatic control0.816
Gastrointestinal/muscular sensations0.850
DSERoom depth0.8520.6170.8850.885
Room width0.928
Room height0.537
Room Size0.930
Window size0.588
DAEDaytime0.6490.4790.6470.627
Nighttime0.732
DHEDaytime0.7550.5460.7820.776
Nighttime0.780
Overheat0.678
BSEBathroom depth0.8700.8660.9510.949
Bathroom width0.984
Bathroom area0.934
BLEDaytime0.7560.5340.6960.694
Nighttime0.705
IAQOdor0.6800.5520.7100.704
Bathroom odor0.801
Note: AVE: average variance extracted; CR: composite reliability; χ2/df: CMIN/DF; CFI: comparative fit index; GFI: goodness of fit index; NFI: normed fit index; TLI: Tucker–Lewis Index; RMSEA: root mean square error of approximation.
Table 3. Correlation matrix and root of AVE’s.
Table 3. Correlation matrix and root of AVE’s.
SASDSEDAEDHEBSEBLEIAQ
SAS0.842
DSE−0.2150.786
DAE−0.4940.2970.692
DHE−0.2880.3900.3640.739
BSE−0.1250.5840.1920.2730.931
BLE−0.1600.3770.1430.2320.3960.731
IAQ−0.3350.2980.3410.1770.2570.2300.743
Note: The diagonal in italics represents the square root of AVE from observed variance (items); off-diagonal represents the correlation between constructs.
Table 4. Statistical results of the questionnaire.
Table 4. Statistical results of the questionnaire.
Characteristic (n = 445)N (%)Mean (SD)
Personal CharacteristicAge 20.45 (2.37)
GenderMale310 (69.7%)
Female135 (30.3%)
AnxietyNormal (SAS < 50)273 (61.3%)
Mild anxiety (50 ≤ SAS < 60)145 (32.5%)
Moderate anxiety (60 ≤ SAS < 70)19 (4.2%)
Severe anxiety (SAS ≥ 70)8 (1.9%)
SymptomsAnxiety and panic (totaled 28)10.66 (3.70)
Somatic control (totaled 20)8.92 (3.49)
Vestibular sensations (totaled 16)5.01 (1.85)
Gastrointestinal/muscular sensations (totaled 16)6.42 (2.43)
BedroomThermal comfortDaytime temperature3.67 (1.48)
Nighttime temperature3.65 (1.46)
Overheat3.54 (1.55)
Acoustic comfortDaytime noise3.63 (0.99)
Nighttime noise3.90 (1.32)
Light comfortDaytime Brightness3.83 (1.33)
Nighttime Brightness4.47 (1.01)
OvercrowdingRoom depth3.49 (1.57)
Room width3.17 (1.54)
Room height4.17 (1.41)
Room Size3.12 (1.50)
Window size4.14 (1.42)
Room View3.14 (1.24)
Indoor air qualityOdor3.41 (0.93)
Humidity4.24 (1.28)
Dryness3.87 (1.39)
Ventilation3.28 (1.15)
BathroomThermal comfortTemperature3.66 (1.56)
Lighting comfortDaytime brightness3.58 (1.53)
Nighttime brightness3.98 (1.43)
OvercrowdingRoom depth3.14 (1.55)
Room width2.99 (1.49)
Room size2.96 (1.48)
Indoor air qualityOdor2.83 (1.01)
Table 5. Pearson’s correlation coefficient between factors of the perceived dormitory IEQ and anxiety.
Table 5. Pearson’s correlation coefficient between factors of the perceived dormitory IEQ and anxiety.
BedroomBathroomGeneral Comfort
Acoustic ComfortLight ComfortThermal ComfortIndoor Air QualityOvercrow-DingLighting ComfortThermal ComfortIndoor air QualityOvercrow-Ding
SAS −0.494 **−0.266 **−0.288 **−0.439 **−0.247 **−0.16 **−0.161 **−0.307 **−0.125 **−0.387 **
Note: ** Correlation is significant at 0.01 level (two-tailed). The darker the color, the stronger the correlation.
Table 6. Regression weights of the second-order confirmatory factor analysis.
Table 6. Regression weights of the second-order confirmatory factor analysis.
GroupUnstandardized EstimateStandardized EstimateS.E.C.R.p
SAS ← DSE0.0830.0320.1560.5300.596
SAS ← DAE−1.879−0.5460.330−5.701***
SAS ← DHE−0.141−0.0470.205−0.6890.491
SAS ← BLE−0.242−0.0830.219−1.1030.270
SAS ← BSE0.0940.0380.1500.6290.529
SAS ← IAQ−0.786−0.1460.398−1.973*
SASAnxiety and panic ← SAS10.908---
Somatic control ← SAS0.8460.8110.03723.140***
Vestibular sensations ← SAS0.4340.7840.02021.233***
Gastrointestinal/muscular sensations ← SAS0.6140.8470.02623.808***
DSELength ← DSE10.845---
Width ← DSE1.0760.9340.04027.217***
Height ← DSE0.6270.5830.04713.275***
Size ← DSE1.0270.9100.03926.653***
Window size ← DSE0.6430.5970.04514.144***
DAENight ← DAE10.737---
Day ← DAE0.6510.6360.0699.406***
DHEDay ← DHE10.761---
Night ← DHE1.0050.7770.07313.748***
Overheat ← DHE0.9120.6600.07811.743***
BSElength ← BSE10.869---
Width ← BSE1.0820.9840.03233.522***
Size ← BSE1.020.9340.03430.353***
BLEDay ← BLE10.750---
Night ← BLE0.8820.7060.1167.612***
IAQOdor ← IAQ10.668---
Toilet odor ←IAQ1.3020.8040.1578.273***
Model Fitχ 2/df = 2.94; CFI = 0.943; GFI = 0.903; NFI = 0.916; TLI = 0.929; RMSEA = 0.066
Note: *, *** Correlation is significant at 0.05 and 0.001 levels, respectively (two-tailed).
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Guo, F.; Luo, M.; Zhao, H.; Du, Z.; Zhang, Z.; Zhang, H.; Dong, J.; Zhang, D. Qualitative Mechanisms of Perceived Indoor Environmental Quality on Anxiety Symptoms in University. Buildings 2024, 14, 3530. https://doi.org/10.3390/buildings14113530

AMA Style

Guo F, Luo M, Zhao H, Du Z, Zhang Z, Zhang H, Dong J, Zhang D. Qualitative Mechanisms of Perceived Indoor Environmental Quality on Anxiety Symptoms in University. Buildings. 2024; 14(11):3530. https://doi.org/10.3390/buildings14113530

Chicago/Turabian Style

Guo, Fei, Mingxuan Luo, Hui Zhao, Zekun Du, Zhen Zhang, Hongchi Zhang, Jing Dong, and Dongxu Zhang. 2024. "Qualitative Mechanisms of Perceived Indoor Environmental Quality on Anxiety Symptoms in University" Buildings 14, no. 11: 3530. https://doi.org/10.3390/buildings14113530

APA Style

Guo, F., Luo, M., Zhao, H., Du, Z., Zhang, Z., Zhang, H., Dong, J., & Zhang, D. (2024). Qualitative Mechanisms of Perceived Indoor Environmental Quality on Anxiety Symptoms in University. Buildings, 14(11), 3530. https://doi.org/10.3390/buildings14113530

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop