Time-Dependent Effects of Exposure to Nature and Perceived Behavioral Control over Social Distancing in Shared Open Spaces on Psychological Distress of Residents during Pandemic of COVID-19

Background: Further research into the inuence of the COVID-19 epidemic on mental health is needed. Some research has highlighted the positive effects of nature exposure on mental health, as well as the importance of subjective assessments of green spaces. Methods: Considering both private and semi-public environments, the current longitudinal study examines the moderating effects of exposure to nature and perceived behavioral control over social distancing on the relationship between perceived interior crowding, social isolation, and psychological distress. Finally, it investigates whether these connections change through time. During the Iranian national lockdowns, data from 718 middle-aged women who completed an online questionnaire survey in two waves were used. Results and Conclusion: The ndings of structural equation modeling back up the idea that social isolation plays a key role in the association between perceived interior crowding and psychological distress. Multi-Group Analysis revealed that exposure to nature reduced the negative effects of perceived interior crowding on psychological distress, as predicted. Nevertheless, this relationship is time-dependent and nature exposure during the time did not necessarily assist in reducing negative impacts. The current research adds to the body of knowledge by identifying perceived behavioral control as a buffer against the adverse effects of social isolation on psychological distress over time. These ndings provide for a better understanding of psychological processes and could help in the promotion of design policies in the event of a pandemic.


Introduction
The global outbreak of coronavirus disease, which began in 2019, has a tremendous impact on the health of all people (Díaz de León-Martínez et al., 2020) and our quality of life. This pandemic has had a direct impact on recent sustainability notions, emphasizing the resilience of systems and their ability to organize and recover from changes and disruptions (Tahvonen & Airaksinen, 2018). Sustainability in urban planning began with a focus on development management, compact areas, and decentralization (Khavarian-Garmsira et al., 2021). The current pandemic, on the other hand, has raised doubts regarding the desirability of compact urban development and de ned planning aims to promote urban life and citizens' well-being (Hakovirta & Denuwara, 2020;Megahed & Ghoneim, 2020).
Well-being is linked not just to the environment through the dangers of pollution exposure, but also to mental health (Díaz de León-Martínez et al., 2020, p. 3). COVID-19 became a global pandemic and caused a variety of precautionary measures, including national quarantines and stay-at-home orders (Olszewska-Guizzo et al., 2021). Social isolation and loneliness have been linked to poorer mental health outcomes such as cognitive decline, anxiety, depression, and psychological distress in previous studies (Gorenko et al., 2021;Pakenham et al., 2020;Brooks et al., 2020). The effects of urban, socioeconomic, and environmental factors on people's health have been extensively researched and documented. Their relationship to COVID-19, on the other hand, is still a work in progress (Viezzer & Biondi, 2021). As a result, we must nd ways to mitigate the pandemic's adverse mental health effects and the resulting lockdowns in residential areas (Allam & Jones, 2020).
While lockdown restrictions compelled people to stay home with their families or cohabitants, there will be a substantial amount of perceived interior crowding. Chronic crowding in a high interior density residence is a substantial cause of stress and has a harmful impact on psychological health (Evans, 1979;Rollings & Evans, 2019, p. 592). At the same time, the physical and built environment has a direct impact on human health, both within the structures in which we live and through the open spaces that connect these buildings (Jens & Gregg, 2021). An increasing amount of data supports the importance of nature exposure and access to green spaces in promoting urban sustainability and improving human health ( It is necessary to have a good grasp of how people's mental health is affected by exposure to nature and experiences of physical distancing. There have been few longitudinal investigations that compare the psychological effects of the COVID-19 epidemic across time. By conducting empirical research in Mashhad, Iran, and studying the link between perceived interior crowding, social isolation, and psychological distress, this study intends to ll current gaps in the literature. The rst goal of this research was to see if social isolation and psychological distress increased over time. Second, social isolation is thought to have mediated the relationship between perceived interior crowding and psychological distress. Third, it is aimed to investigate if nature exposure can alleviate the in uence of perceived interior crowding on psychological distress, in keeping with prior studies demonstrating that exposure to nature may have a positive impact on mental health. Finally, based on previous research, it is hypothesized that perceived behavioral control to maintain social distance in shared open spaces will decrease the effect of social isolation on psychological distress. Finally, the last two hypotheses were tested over time utilizing two waves of data.

Perceived interior crowding and mental health
The density of interior spaces is referred to as crowding (Forsyth et al., 2007). Rather than square footage per person, the number of occupants per room is a measure of density more strongly linked to behavioral outcomes (Evans, 2006). Chronic crowding, according to research, is a major source of stress and has a negative impact on mental health. Reduced feelings of control, particularly over desired social interaction (Altman, 1975); overstimulation (Evans, 1979); interference with socially supportive relationships among home residents leading to social withdrawal (Rollings & Evans, 2019); and adverse health outcomes such as psychological distress and anxiety (Evans, 2003;Cavazza et al., 2021) are among the psychosocial processes that result from perceived crowding. Evans and Lepore (1993) proposed three mechanisms through which the negative effects of household crowding could lead to negative consequences: reduced access to valuable resources, increased social competition, and interfering with goal attainment; lack of perceived control over, and predictability of, the environment; and enforcing excessive stimulation, causing overload and unpleasant over-arousal. Furthermore, high social density is linked to a drop in perceived social support (Cavazza et al., 2021), while a lack of social support is linked to higher levels of depressed symptoms (especially for women) (Mair et al., 2010). As the above brief review suggests, perceived interior crowding not only has the potential to affect psychological distress negatively, but it also has the potential to be a perceptual factor linked to social withdrawal and social isolation, having an indirect negative impact on psychological distress.

Effects of exposure to the nature
The Attention Restoration Theory (ART), which claims that exposure to nature can restore depleted attention capacity (Kaplan, 2001), is one of the most well-known hypotheses explaining the salutogenic potential of natural environments. This theory contends that spending time in nature, even in urban open spaces with vegetation, replenishes weary attention, allowing individuals to function more effectively than when they are mentally exhausted (Kim & Miller, 2019).

The Theory of Planned Behavior (TPB)
Due to constraints on outside activities, it is critical in the COVID-19 context to reduce the crowding of shared open spaces where people spend their time (Kim & Kang, 2021). This emphasizes the need of attempting to increase control over social distancing and separation in these spaces. The issue's practical implication is that perceived behavioral control over social distancing can be altered by the built environment's characteristics.
The Theory of Reasoned Action (TRA) and its extension, the Theory of Planned Behavior (TPB), are the most well-known theories for predicting human behavioral intentions (Rhodes et al., 2006) and subsequent behaviors in a variety of disciplines, including social psychology and environmental studies (Armitage & Conner, 2001). The motive that leads to engagement in a particular behavior, behavioral intention, is essential to the TRA model (Ajzen, 2002). According to this concept, attitude and subjective norm are two important aspects determining a person's desire to behave, and behavioral intention then in uences actual behavior performance. The amount to which an individual senses public social pressure -from others such as friends and family members -towards the propriety of performing the conduct is referred to as subjective norm (Rossi & Armstrong, 1999).
The primary premise of TRA is that individuals have control over their actions (Armitage & Conner, 2001). Internal elements like knowledge and abilities, as well as external factors like convenience, can constrain or facilitate a behavior (Wan & Shen, 2015). TPB, as a condensed theory proposed by Ajzen (1991), incorporates the most critical aspects in understanding various behaviors. The TRA model now includes a new feature called Perceived Behavioral Control (PBC). The perceived ability and ease with which an individual can do a given activity is referred to as PBC (Wang et al., 2015, p. 86).
The e cacy of TPB in explaining behaviors has been well-proven (Armitage & Conner, 2001). TPB has also been applied in predicting behaviors to use urban green spaces by Wan

Study design
During the COVID-19 epidemic, fewer longitudinal studies are looking into the impacts of exposure to nature, perceived behavioral control over social distancing, and people's mental health. Over time, social isolation and psychological distress are expected to rise (H1). According to the presented model, perceived interior crowding not only has a direct negative impact on psychological distress (H2) but also serves as a potential perceptual factor associated with social isolation (H3), with the effect of perceived interior crowding on psychological distress being mediated through social isolation (H4). During the pandemic, the intensity of the link between social isolation and psychological distress changes (H5).
Another goal of this research was to see if nature exposure could help to mitigate the adverse effects of perceived interior crowding caused by stay-at-home orders on psychological distress, thereby acting as a buffer against potential environmental stressors. Exposure to nature, it is hypothesized, would reduce this link (H6). Given that residents in a pandemic situation must maintain social distance from others, it is expected that they will react more emotionally to environmental limits. TPB is a exible model that may be tailored to suit research needs. The fundamental pathways and variables can be modi ed and expanded to satisfy research needs using this user-friendly model. We investigate the role of perceived behavioral control in the relationship between social isolation and psychological distress using the TPB model. The consequences of social isolation on psychological distress would be moderated by perceived behavioral control over social distancing in shared open spaces (H7). To investigate the effect of time on the connections between variables, hypotheses 5 to 7 are examined using two waves of data at different time intervals. The experiment used a within-subject design, with data obtained at the start of the lockdown and one year later.

Study area and time
After China, Iran became the second focal location for spreading the COVID-19 worldwide in mid-February 2020. The head of the Ministry of Health and Medical Education's Public Relations and Information Center declared on April 23, 2020, that the third level of COVID-19 quarantine has begun. The rst order for a stay-at-home lasted 42 days (from 29 February until 10 April). Iran has had ve peaks of the Corona pandemic and its associated quarantine. Due to the signi cant spread of the Delta version of COVID-19, severe lockdown lasted over 10 days, from August 12 to August 21, 2021. People were forced to spend the entire day and night indoors with their family or cohabitants, leaving only for fundamental reasons.
Housing quality and living conditions were substantial predictors of the ward level COVID-19 mortality count, according to Hu et al., (2021). The study focused on six dense gated communities in Mashhad, Iran's secondlargest city, with comparable housing quality. The places were chosen based on middle-income status, housing rate, comparable residential density, the amount of green space in the community areas, and various housing layouts (see Table 5).

Participants and data collection
Participants were solicited via telegram groups to which inhabitants of the gated residential neighborhoods are members about two months after COVID-19's rst lockdown began. Respondents received a message with the survey URL and an explanation that participation in the survey was voluntary to complete an online survey questionnaire. Participants were educated on the research purpose and design before beginning the survey. They were demanded to take a 25-minute online survey about mental health outcomes and perceptual characteristics. To assure data quality, human veri cation and attention checks were used throughout the survey.
A Participant Information Sheet included information on gender, education background, known mortality cases in neighbors, colleagues, or family members involved, and pre-lockdown emotional-mental health at the start of the questionnaire. The latter was assessed with a single question: "In general, would you describe your emotional and mental health was... before the COVID-19 pandemic" was introduced. The responses were graded on a three-point scale (good, fair, and poor).
Demographic data on gender, age, education, household income, neighborhood disadvantage, employment status, and occupation were considered potential confounders of the relationship between perceptions and psychological well-being based on previous research on factors associated with psychological well-being (Cleary et al., 2019). The rst sample for the online survey consisted of 1384 women who lived in six gated communities, using the purposive sampling method. Participants who indicated the number of deaths on the Participant Information Sheet were eliminated from the sample due to known death cases, which may have in uenced psychological distress. Based on claimed pre-pandemic emotional-mental health, not living alone, and age, only one household member is included in the sample.
A total of 1037 competitors from the rst wave were accepted to participate in the second phase. Data was taken in April 2021, around a year after the state of emergency of COVID-19 was declared, at the peak of the pandemic's fourth wave, and during the lockdown. Due to a lack of accountability or missing data on the primary variables of interest, 329 participants were eliminated from the analyses. Finally, valid responses were found on all of the variables in a sample of 718 women (Mage = 49.63, SD = 12.39). Table 1

Statistical analyses
The level of social isolation and psychological discomfort between the two waves was compared using an independent samples t-test. Structural equation modeling (SEM) was used to investigate the relationship between variables in order to answer other research issues. To assess the research model, this study used the partial least square (PLS) technique for data analysis with Smart-PLS 3.0 software. A variance-based SEM can be used to estimate complex cause-effect models using latent variables. The key advantage of adopting the PLS approach is that it allows latent components to be modeled as re ective or formative constructs and has fewer sample size restrictions (Chin, 1998). The researchers employed a hierarchical component model using a two-stage method that included a measurement model and a structural model. Table 1 shows that the age range of 35-44 years was the most common (67.27%), followed by 45-54 years (32.73%). The majority of respondents (60.58%) held a university degree, followed by those with professional education (22.15%) and those who had just completed high school (13.09%).

Assessment of measurement model
The conceptual model was evaluated and causal linkages were investigated using SEM. The usage of PLS was deemed reasonable and most appropriate according to the research model. In the rst stage, the indicator reliability, construct reliability, convergent validity, and assessment of the measurement models were investigated, while the second stage established testing of the structural linkages proposed in the conceptual model. Table 3       Notes: The multi-group comparison is based on a non-parametric approach (MGA); *, **, *** indicate significance at the 5%, 1% and 0.1% levels.

Assessment of structural model
The associations among constructs were evaluated based on the study's aims. To determine the model's tvalues, 5000 bootstrapping samples were utilized, each with the same amount of observations as the original sample to create standard errors and t-values (Hair et al., 2011). With a P-value of less than 0.1, values of t equal to or greater than 1.96 suggest a signi cant level of the proposed link (Hair et al., 2017;Chin, 1998). The signi cance and magnitude of the path coe cients were used to estimate path links between the latent variables in the model. The results are summarized in Table 4.
Hair et al., (2017) suggested that the structural model could be evaluated using the coe cient of determination (R-square) and predictive relevance (Q-square). R 2 values of 0.67, 0.33, and 0.19, according to Chin (1998), must be considered for substantial, moderate, and weak estimations, respectively. The R 2 values in this study were above the acceptable level, with an acceptable (0.571) value for wave 1 and a moderate (0.363) value for wave 2, indicating that the corresponding construct has a good predictive potential. All of the path coe cients are also signi cant. The Q 2 calculation yielded 0.37(wave1) and 0.32(wave2) for psychological distress in the current investigation, showing that they are su ciently predictive.
Perceived interior crowding had a favorable and signi cant impact on psychological distress (ß = 0.56; p < 0.001 at wave 1 and ß = 0.38; p < 0.001 at wave 2), as demonstrated in Table 4, con rming H2. This is in line with the belief that people who have a negative perception of interior crowding are more likely to express psychological distress.
Regarding the direct impacts on social isolation, H3 was supported by the nding that perceived interior Both direct and indirect effects were examined to test Hypothesis 4, which predicts that social isolation will in uence the link between perceived interior crowding and psychological distress. There was a signi cant indirect effect of perceived interior crowding on psychological distress (β = 0.225, p < 0.000 at wave 1 and β = 0.325, p < 0.000 at wave 2).

Cross-sectional and longitudinal analyses
The model was calculated separately for the two waves to ensure that the structural relationships were signi cant. The results of the Multi-Group Analysis (MGA) technique, which examines the differences between the path coe cients among two waves were used to evaluate group differences in PLS-SEM ( Tables 6 and 7. Social isolation was signi cantly and positively linked with psychological distress at both time points (Table 6 and 7). Participants who felt isolated from others were more likely to experience psychological distress. For the fully corrected models, the effect sizes were comparable (β = 0.318, p0.001 at Wave 1 and =0.547, p0.001 at Wave 2). As can be observed, social isolation has an increasing in uence on psychological distress over time.
The MGA approach was used to assess the moderation effects of exposure to nature across time, supporting the change in moderation role of exposure to nature during the epidemic. Participants in Wave 1 who felt they had more exposure to nature in their areas were less likely to experience psychological distress. In Wave 2, exposure to nature was found to be a non-signi cant moderator, indicating that the effects of perceptions of green space on mental health were not stable over time; this nding contradicts Cleary et al., (2019) who found that the effects of perceptions of green space on mental health were stable over time.

Discussion
Subjective well-being has regularly been proved to be an independent predictor of health (Corley et al., 2021). In addition to its terrible physical implications, the COVID-19 pandemic has posed a mental health issue, prompting governments worldwide to impose lockdowns to prevent disease spread. When people's access to work, education, and public spaces is limited, their homes must play a special role in their daily lives and mental health (Meagher & Cheadle, 2020).
There is a well-established link between perceived interior crowding, social isolation, and poor mental health. It is also widely acknowledged that exposure to nature has a positive impact on mental health. Nonetheless, in the contemporary setting of COVID-19, little is known about the critical role of time in correlations between perceptual characteristics and the repercussions of social isolation (Melo & Soares, 2020). Our goal was to shed more light on this topic by examining changes in psychological distress in a cohort of healthy women during the pandemic-related stay-at-home order, using two waves of data. The current study adds to the body of knowledge by addressing the following issues: During a lockdown, did social isolation and psychological distress increase? Is there a link between nature exposure and psychological distress caused by perceived interior crowding? Is this moderating role temporally dependent? Is it possible that having a sense of behavioral control over social distancing from others in public areas could reduce the impact of social isolation on psychological distress?
The most important predictor variable in the model was perceived interior crowding, which had both direct and indirect effects on psychological distress. Perceived interior crowding was adversely associated with social isolation and psychological distress in both waves of this investigation, indicating that people's impressions of their homes can contribute to poor mental health outcomes. The model revealed that social isolation was the second strongest predictor and a signi cant mediator variable, con rming prior ndings (e.g., Wells & Harris, 2007). Our data indicated that social isolation is linked to psychological distress, con rming Durkheim's theory that lack of social integration has negative mental health repercussions (Berkman et al., 2000). Many areas of people's lives, including levels of physical activity, psychological and physical wellbeing, have been negatively impacted by key policies such as social distancing and self-isolation (Cellini et al., 2020; Corely et al., 2021).
Self-reported social isolation and psychological distress were considerably higher at wave 2 than at wave 1. identi cation is an essential driver of responses to the local environment and has a direct impact on mental health (Fong et al., 2019). The advantages of having an optimistic attitude in life were ampli ed by community togetherness. This conclusion emphasizes that a home's appraisal will vary depending on the amount to which particular sorts of psychological needs are addressed or not. Individuals' ability to directly organize the home environment to allow desirable behaviors can also be an important aspect of maintaining excellent mental health (Meagher et al., 2020). Effective self-regulation, for example, frequently entails engaging in future selfcontrol by establishing an atmosphere that encourages desired behaviors while discouraging undesired ones (Hyun-soo & Jung, 2021).
To promote mental health through the built environment, residential spaces should be planned and managed to accommodate differing preferences and perceptions, particularly among people from various socioeconomic backgrounds. Only individuals with a particular degree of technology skill and internet connection were eligible to participate; the study sample may be biased toward people from higher socioeconomic backgrounds. Because the relationship between perceived interior congestion and adverse outcomes varies by gender (Rollings & Evans, 2019), generalizing the ndings should be done with caution.
Given that this case study was limited to a small number of socio-demographic and health-related characteristics, as well as gated communities in a speci c context, future research should look at the outcomes in other cities and socio-cultural settings. It seems logical that the physical, social, and cultural contexts can foster or restrain social distancing; consequently, future research should compare the in uence of open space arrangements and home layouts. Our longitudinal data supports the core assumption that the effects of built environment features linked to mental health outcomes change with time. Future longitudinal studies could, however, concentrate on different waves of data collection in order to better address the issue of temporal ordering of the focal relationships. Finally, future research should con rm the ndings of this study using a larger, more representative sample in various settings and demographics.

Declarations
Ethics approval and consent to participate (Human Ethics, Animal Ethics or Plant Ethics) This research is not based on laboratory data and the satisfaction of all participants has been obtained.
-Consent for publication Not available -Availability of data and materials It will be available if needed.

-Competing interests
Not available -Funding This research did not receive any speci c grant from funding agencies in the public, commercial, or not-forpro t sectors.
-Authors' contributions S.F. Mousavinia conceived of the presented idea, developed the theory and performed the analytical methods. Conceptual model.