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Article

Correlation Between Neighborhood Environment and Mental Well-Being of Older Adults: A Perspective Based on the Old Urban Residential Communities †

by
Jianjian Zhang
*,
Ziyi Tan
and
Yingqi Chen
College of Art and Design, Nanjing Tech University, Nanjing 210037, China
*
Author to whom correspondence should be addressed.
†
In this study, “neighborhood environment” refers specifically to respondents’ subjective perceptions of the environment.
Buildings 2026, 16(11), 2227; https://doi.org/10.3390/buildings16112227
Submission received: 17 April 2026 / Revised: 26 May 2026 / Accepted: 27 May 2026 / Published: 1 June 2026
(This article belongs to the Section Architectural Design, Urban Science, and Real Estate)
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Abstract

In China, communities are the primary living environments for older adults, where the neighborhood environment is closely linked to their mental well-being (MW). Old urban residential communities commonly encounter problems including poor housing quality, inadequate public resources, and substandard living conditions. The association between such neighborhood environments and the MW of older adults is particularly worthy of examination. Therefore, based on empirical survey data from Nanjing, China, and from a subjective perception perspective, this study explores how the perceived neighborhood environment in old urban residential communities correlates with older adults’ MW. The findings indicate that both the perceived built environment (BE) and social environment (SE) are correlated with the MW of older adults. The BE has a stronger correlation with MW than the SE, which mediates the correlation between the BE and MW. The correlation between the neighborhood environment and MW is moderated by factors including age, residence type, and average monthly income. Among the component factors of neighborhood environment in old urban residential communities, housing quality, shopping convenience, neighborhood interaction, and community services show significant positive correlations with the MW of older adults. These findings provide valuable implications for the age-friendly renewal of old urban residential communities, the development of age-friendly communities, and the improvement in the subjective well-being of older adults in such communities.

1. Introduction

By 2050, the population aged 60 and older is projected to reach approximately 2.1 billion [1]. With an increase in the older adult population, mental well-being (MW) issues among these individuals have attracted widespread attention. Globally, in 2021, 28.4% of the older adults experienced depression [2]. China currently has the largest older adult population in the world [3]. By 2050, the population of China aged 60 and older is projected to approach 500 million [4]. According to the China Health and Retirement Report (2019), 33.1% of Chinese adults aged 60 and older are at high risk of depression [5]. Limited pension and healthcare services make older adults in China particularly vulnerable to psychological issues associated with aging.
The community is the primary setting where older adults reside and engage in daily activities. In China, over 90% of older adults are expected to live at home with family care, spending most of their time in their communities—a practice known as “aging in place” [6]. However, the decline in family size has reduced the number of family members available to provide care, limiting direct family support for older adults [7] and increasing their reliance on community support. With the rapid aging of the Chinese population, the number of older adults living alone continues to rise, and this group is particularly dependent on community resources and services [8]. These trends underscore the critical role of the neighborhood environment in supporting the quality of life and MW of older adults. Older adults appear more susceptible than younger people to changes in their neighborhood environment. Retirement and physical decline often shrink their social circles and networks. Consequently, they spend more time interacting with their neighbors, and their daily activities are largely confined to the geographical boundaries of the community [9]. As their dependence on the community grows, neighborhood convenience and comfort become increasingly associated with their mental state [10,11].
Preliminary statistics estimate that Chinese cities have approximately 160,000 residential communities built before 2000. These communities accommodate over 42 million households across approximately 4 billion square meters of construction area [12] and are typically defined as old urban residential communities. Since the implementation of the 1998 urban housing system reform, housing in China has been fully commercialized, markedly improving housing quality in the 21st century. In contrast, because poor housing quality and inadequate facilities failed to meet the basic living needs of residents, urban communities built before 2000 were designated as old urban residential communities requiring renovation by the Chinese government [13]. In these communities, young residents typically move out for reasons such as education, employment, marriage, or raising children, resulting in a greater proportion of aging population [14]. Studies further report that the proportion of older adults residing in these communities is 9.9% higher than that of younger groups [15]. In contrast, older adults remain in their original communities due to financial constraints and reliance on local amenities and social ties [16,17]. Meanwhile, the inflow of migrants into old urban residential communities through home purchases or rentals has disrupted existing social networks, increasing the vulnerability of older residents to the adverse effects of substandard living conditions [18]. Therefore, examining the correlation between the neighborhood environment in old urban residential communities and the MW of older adults is particularly important.
Therefore, this study aimed to examine neighborhood environmental factors correlated with the MW of older adults by analyzing survey data from 15 old urban residential communities in Nanjing, China, focusing on built environment (BE) and social environment (SE).

2. Literature Review and Conceptual Framework

2.1. Neighborhood Environment and Individual Health

The neighborhood environment is a micro-territorial unit in urban space, shaped by the daily activities of residents, encompassing the BE and SE [19]. The BE refers to the man-made environment that supports residents’ daily life, work and leisure activities, encompassing residential buildings, commercial facilities, transportation infrastructure, green spaces, and represents a combination of elements related to land use, transportation systems and urban design [20]. The SE refers to the nonphysical aspect of the neighborhood environment, including interpersonal interactions, social structures, cultural norms, and institutions [21], reflecting the interpersonal connections, social atmosphere, and social governance conditions within the community.
Existing research indicates that both the BE and SE are associated with residents’ health. Regarding the BE, pedestrian-friendly and esthetically appealing environments help relieve stress, foster social inclusion, and promote well-being [22]. Conversely, issues such as inadequate pedestrian infrastructure, noise pollution, and limited green space can reduce residents’ life satisfaction [23]. In terms of the social environment, respondents generally identified respect, cooperative relationship building, and interpersonal connection as core dimensions associated with health [24]. These positive interpersonal relationships can potentially buffer against perceived neighborhood stressors and are closely associated with better mental health status [25].

2.2. Neighborhood Environment and Mental Well-Being of Older Adults

Relevant studies indicate that the BE shapes quality of life and MW by meeting individual needs through public facilities and green spaces [26]. High-quality and accessible facilities enhance satisfaction with the BE and contribute to promote MW [27]. Conversely, noise and pollution in the BE act as chronic stressors, undermining the mental health of older adults by fostering distrust and perceived environmental vulnerability [28]. A study shows that access to medical, commercial, and transportation facilities also mitigates the feelings of helplessness in older adults by providing essential services, thereby supporting MW [29].
Within the SE, positive community atmospheres, strong neighborhood cohesion, and friendships provide older adults with emotional support, reducing loneliness, strengthening their sense of belonging, and improving their MW [30]. Participation in community activities, neighborhood interaction, and social networks enables them to realize their value and gain a sense of involvement and satisfaction, which alleviates anxiety and stress related to retirement and aging [31]. Furthermore, cultivating positive relationships with neighbors helps mitigate the harmful effects of social isolation on one’s psychological state [32,33]. Community services, including senior activity centers, day care, and health talks, reduce loneliness and psychological stress among older adults by providing them with opportunities for social engagement [34].
Research shows that the BE and SE are associated with MW, but the BE may exert a stronger correlation with MW [35]. This may be because it directly provides resources and facilities that fulfill the basic survival needs of residents, including food, living space, and medical facilities [36]. The SE, however, has been identified as a crucial moderator. It can shape individuals’ perceptions of the BE, thereby amplifying or weakening the actual influences of the BE on MW [37]. Accordingly, the SE can moderate the associations of the BE with MW, indirectly shaping outcomes—a crucial consideration for understanding how neighborhood conditions relate to the MW of residents in old urban communities, where BE often have inherent deficiencies.
Differences in demographic and socioeconomic characteristics moderate the relationship between neighborhood environment and MW, highlighting its instability [38]. Studies report that these factors significantly correlated with MW outcomes [39,40]. For instance, older adults with lower economic status often have poorer MW due to limited financial resources that restrict their access to health-promoting resources [41,42]. Higher educational attainment is associated with better MW, potentially reflecting that older adults with higher education own greater health knowledge and engagement in health-promoting behaviors [43,44]. Furthermore, women are prone to MW challenges than men [45]. Aging increases the risk of physical illnesses, which in turn elevates MW risks [46]. Residence type also correlate with MW, with older adults living alone facing elevated MW risks compared with those living with family. This phenomenon is closely associated with social isolation and economic vulnerability that results from living alone [47,48].

2.3. Conceptual Framework

Current research on the relationship between neighborhood environment and MW in older adults rarely combines BE and SE for comprehensive examination [49,50]. Consensus is lacking on which environmental factors substantially associated with MW. For example, studies report that greater access to recreational and sports facilities may contribute to MW [51]. Other studies report that well-designed environments reduce mental health issues [52,53]. Frequent visits to retail stores are linked to better MW [54], and proximity to medical facilities negatively correlates with mental health [55]. Additionally, studies rarely focused on old urban residential communities. Compared with new urban residential communities, older ones have larger older adult populations and face issues such as building deterioration, diminished living comfort, limited public activity spaces, and the gradual breakdown of existing social networks [56]. Meanwhile, research shows that older adults in communities with poor living conditions are more likely to experience depressive symptoms [57]. Consequently, the neighborhood environment of old urban residential communities may have unique correlation with the MW of older adults.
This study aims to analyze and compare the relationship between BE and SE and the MW of older adults in old urban residential communities. A review of existing literature and theories guided the development of the research concept and proposed conceptual framework (Figure 1). Based on this review, key components of the BE and SE in old urban residential communities were identified, forming four hypotheses:
Hypothesis 1 (H1).
The BE and SE of old urban residential communities are positively correlated with the MW of older adults.
Hypothesis 2 (H2).
The BE of old urban residential communities has a stronger correlation with the MW of older adults than the SE.
Hypothesis 3 (H3).
The SE of old urban residential communities mediates the relationship between the BE and the MW of older adults.
Hypothesis 4 (H4).
The correlations of the BE and SE with the MW of older adults are moderated by demographic and socioeconomic factors.

3. Materials and Methods

3.1. Study Area and Data Collection

Following the Law of the People’s Republic of China on the Protection of the Rights and Interests of the Elderly, this study defines older adults as those aged 60 and above. The sample communities were selected from urban residential communities built before 2000. This study focused on Nanjing, a city located in the highly developed eastern region of China that is experiencing pronounced aging. According to the 2023 Nanjing City Report on the Information of the Elderly Population and the Development of the Elderly Care Industry, by the end of 2023, 2.09 million residents—21.97% of the total population—were aged 60 and above in Nanjing. Additionally, 1.53 million residents—16.01% of the total population—were aged 65 and above, slightly exceeding the national average [58]. Simultaneously, many old urban residential communities are concentrated in the inner-city districts of Nanjing, facilitating onsite research. Therefore, Nanjing is an ideal setting to examine how neighborhood environments of old urban residential communities correlate with the MW of older adults.
According to the number of old urban residential communities scheduled for renovation from 2021 to 2025, Gulou, Qinhuai, and Xuanwu districts are the three districts with the largest number of old residential community renovations in Nanjing. This indicates that these three districts contain many representative old urban residential communities. Therefore, this study selected these three districts and randomly chose five old urban residential communities from each district as research sites, totaling 15 communities (Figure 2 and Figure 3). Before the formal survey, we conducted a pretest of the questionnaire to assess its feasibility, reliability, and validity, making adjustments as needed. Between March and April 2025, we conducted a formal questionnaire survey in the selected communities. Trained investigators randomly recruited 60 adults aged 60 and above at roadsides, public spaces, and residential building entrances within each community, distributing questionnaires on-site and providing guidance to ensure complete responses. All participants were fully informed about the study and confirmed their consent by signing an informed consent form before participating. After excluding incomplete questionnaires, 836 valid responses were obtained. The questionnaire included demographic information and socioeconomic attributes, as well as MW assessments and neighborhood environmental conditions. This study obtained ethics approval from the Scientific Research Special Committee of Nanjing Tech University (approval number: NTJTECH-1-11).

3.2. Variable Measurement

The dependent variable was the MW status of older adults, assessed over the past 2 weeks using the WHO-5 Well-Being Index. The index comprises five questions rated on a six-point Likert scale from 0 (At no time) to 5 (All of the time), with higher scores indicating better MW. WHO-5 features concise and easy-to-understand items that are readily comprehensible to older adults. Additionally, it has demonstrated adequate reliability and validity in mental health surveys targeting older adults in China [59,60].
The neighborhood environment can be measured objectively and subjectively. Objective measurement quantifies the neighborhood environment using indicators that focus on its physical attributes. Subjective measurement obtains perceptions and assessments of neighborhood environmental characteristics from residents through surveys. It acknowledges that individuals may perceive or respond to the same environment differently. Although objective measurements reflect the actual environmental conditions, they may overlook the experiences of residents, causing inconsistencies between their assessment results and actual perceptions. For instance, Prins et al. report that perceived accessibility influenced physical activity more than the objective quantity of nearby parks and sports facilities [61]. Gebel et al. report that residents may perceive their environments as unsuitable for walking, even in areas with high objective walkability [62]. This discrepancy between perception and objective reality is particularly evident in older adults, who tend to perceive environmental details more acutely [63]. In contrast, subjective assessments align more closely with self-rated health because both rely on self-reported data [64]. Studies also show that subjective characteristics of neighborhood environment are closely related to MW and life satisfaction [65,66]. Therefore, to better link neighborhood environment measurements with self-assessed MW, in this study, we assess the neighborhood environment through subjective perception. Hereinafter, the terms BE and SE both refer to the respondents’ subjectively perceived BE and SE.
From the perspective of the BE, older adults experiencing physical decline may be particularly concerned about their housing environment and the availability of supporting facilities in their community, such as medical facilities, transportation facilities, and shopping facilities, which meet their daily living needs. Neighborhood green spaces and fitness facilities, serving as mediating pathways linked to physical activity engagement, also show indirect associations with older adults’ mental well-being [67]. Noise and sanitation problems may act as chronic stressors, causing persistent damage to the MW of older adults. Therefore, this study selected indicators including housing quality, transportation accessibility, medical facilities, shopping convenience, sanitary conditions, fitness facilities, neighborhood greening, and outdoor noise to measure the BE through satisfaction assessment.
From the perspective of the SE, the psychological characteristics can be mainly summarized into two aspects: social connections and sense of security [68]. Social connections of older adults within the community can provide continuous emotional support, effectively alleviate loneliness, and play a vital role in maintaining their MW [69]. Meanwhile, a safe community environment is associated with lower anxiety among older adults, and also correlates with better psychological well-being by facilitating their participation in outdoor activities [70]. Therefore, indicators including neighborhood friends and relatives, neighborhood interaction, neighborhood trust, community activities, community services, neighborhood safety, and neighborhood relations are adopted to measure the SE. Table 1 presents the specific indicators and their explanations.

3.3. Data Analysis

We adopted a multi-stage statistical analysis strategy. Structural Equation Modeling (SEM), PROCESS v4.0 (Model 1) and multiple linear regression were applied sequentially to explore the associations and underlying mechanisms between the BE, SE and the MW of older adults. SEM is a statistical method that simultaneously analyzes multiple interrelated dependent variables and examines complex mediating effects [71]. Therefore, this study employed SEM to examine the following: (1) The direct correlation between neighborhood environment and MW. (2) Whether the correlation of the BE with MW is stronger than that of the SE with MW. (3) The mediating effect of the SE in the relationship between the BE and MW. Subsequently, PROCESS v4.0 (Model 1) was employed to test the moderating effects of demographic and socioeconomic factors on the correlation between neighborhood environment and MW. This tool enables direct and robust estimation of interaction terms, simple slopes and the significance of conditional effects, yielding intuitive results that facilitate the interpretation of moderation patterns. Next, to further verify the correlations of component factors of BE and SE with MW, multiple linear regression was adopted for supplementary analysis. This approach can intuitively present the respective associations of each factor with MW, facilitating comparison with existing literature. Data analysis and model construction were conducted using SPSS 26.0 and Amos 24.0.
Before implementing SEM, Harman’s single-factor test was conducted to assess common method bias (CMB) in the sample. The result showed that the CMB value was 35.72% (<40 %), within the acceptable range [72].
Subsequently, the overall reliability of the scale was tested, yielding Cronbach’s alpha values of 0.908, 0.889, and 0.861 for the BE, SE, and MW (WHO-5), respectively, indicating good internal consistency of the scale content. In addition, the data passed Bartlett’s test of sphericity at the significance level of 0.05 (Sig. < 0.001) with a Kaiser–Meyer–Olkin value of 0.929, suggesting high data adequacy for factor analysis.
In the next phase, exploratory factor analysis (EFA) was conducted to evaluate the internal structure of the scale. Three common factors with eigenvalues > 1 were extracted, explaining 61.94% of the cumulative variance after rotation, indicating that these dimensions adequately represented the original data. To confirm that each question corresponded to the appropriate factor, the maximum variance rotation method was employed. The results showed that all factor loadings were >0.50, indicating adequate internal structural validity of the scale (Supplementary Table S1).
After performing an EFA, we assessed the convergent and discriminant validity of the factors. Standardized factor loading coefficients were used to calculate the average variance extracted (AVE) and construct reliability (CR) values for each dimension. Generally, CR values > 0.6 indicate acceptable reliability [73], and AVE values > 0.50 indicate adequate convergent validity, showing that latent variables explain over 50% of the variance [74]. The results showed that all three dimensions meet the AVE and CR criteria. Additionally, the loading coefficient of each item with the corresponding factor is >0.6. This indicates a strong correspondence between items and factors, with the model exhibiting adequate reliability and convergent validity (Supplementary Table S2).
After assessing convergent validity, we analyzed discriminant validity, typically assessed using the square root of AVE. Discriminant validity is considered adequate when the square root of a latent variable’s AVE exceeds the absolute value of its correlation coefficients with other latent variables [75]. In this study, all latent variables met this criterion, confirming that they retained sufficient discriminant validity despite their correlations (Supplementary Table S3). The fit of the SEM was evaluated using multiple goodness-of-fit indices, including CMIN/df, adjusted goodness-of-fit index, root mean square error of approximation, normed fit index, incremental fit index, comparative fit index, relative fit index, and parsimony goodness-of-fit index [76]. CMIN/df values < 3 indicate a good fit, while values between 3 and 5 are considered acceptable [77,78]. Other fit indices also met the recommended thresholds [79,80], indicating satisfactory overall model fit (Table 2).
In PROCESS v4.0 (Model 1), we used the mean score of MW factors as the dependent variable, the factor scores of BE and SE as independent variables, and demographic and socioeconomic factors as moderators, to examine whether demographic and socioeconomic factors moderate the associations between neighborhood environment and MW.
During the multiple linear regression analysis, we took the mean score of MW factors as the dependent variable, and simultaneously incorporated the scores of all component factors of the BE and SE into the regression model one by one to examine their independent correlations with MW. Prior to regression analysis, skewness and kurtosis were calculated to assess data normality. The results showed that the absolute skewness values of all indicators ranged from 0.283 to 0.917, and the absolute kurtosis values ranged from 0.017 to 1.088, satisfying the basic criteria for normal distribution [81] (Supplementary Table S4). Multicollinearity diagnosis was also performed, and all variance inflation factor (VIF) values were lower than 2.5, indicating no serious multicollinearity in the regression model. Meanwhile, we further explored the potential associations among variables and performed correlation analysis. The results revealed significant positive correlations among the BE, SE and MW (Supplementary Table S5).

4. Results

4.1. Descriptive Statistics Results

Table 3 shows that the proportion of males and females among the respondents was similar, accounting for 46.77% and 53.23%, respectively. Regarding age, most respondents were aged 70–79 years, representing 41.75% of the total sample. Subsequently, 32.66% of the respondents were aged 60–69 years, and 25.60% were over 80 years. Regarding educational attainment, most respondents had a low level of education, with 66.17% completing junior high school or below, and 13.76% attaining college or higher. Regarding residence, 44.98% of respondents lived with their spouses, 23.56% lived alone, 17.58% lived with their children, and 12.44% lived with their spouses and children. Regarding health status, 48.92% of respondents reported being in good health, and 13.64% reported very good health. Conversely, 7.18% reported being in poor or very poor health. Regarding income, most respondents did not have a high monthly average income, with 40.43% earning <CNY 3000 and 11.48% earning >CNY 8000.

4.2. Analysis of the Correlation of the Neighborhood Environment with the MW of Older Adults

We used SEM to examine the intricate relationship among multiple variables, including their path coefficients and significance levels. Meanwhile, demographic and socioeconomic factors are incorporated into the model as control variables to reduce confounding caused by individual heterogeneity. Path analysis revealed that the BE (path coefficient = 0.402, CR = 10.921, p < 0.001) and SE (path coefficient = 0.304, CR = 8.533, p < 0.001) have significant positive correlation with MW. This finding indicates that the BE and SE are positively correlated with the MW of older adults in old urban residential communities. Among these, the BE shows a stronger correlation with MW than the SE does (Table 4), confirming Hypotheses 1 and 2.
Additionally, since the BE positively correlates with the SE, this study examined its potential mediating effect. The results showed that the BE was positively correlated with MW through the SE (Table 5). This finding highlights the mediating role of the SE in the relationship between the BE and MW, validating Hypothesis 3. This suggests that the BE directly correlates with the MW of older adults, while also being associated with it partially through the mediating variable of the SE.

4.3. Moderating Effects of Demographic and Socioeconomic Factors

Demographic and socioeconomic factors, including sex, age, educational level, residence type, health status, and monthly average income, were examined as moderating variables to assess their roles in the relationships between BE, SE, and MW. Ordinal variables were assigned equidistant numerical values and treated as continuous. Multiclass nominal variables were dummy-coded. To test moderating effects, interaction terms between the focal predictors (BE/SE) and each moderator were constructed. For continuous and ordinal moderators, this was performed automatically using mean-centered variables via Model 1 in PROCESS v4.0. For dummy-coded nominal moderators, product terms were computed manually in SPSS beforehand, and the moderating effects were subsequently examined by including these terms in the regression analyses. The significance of each interaction term was used to determine the presence of a moderating effect (Table 6).
The results showed significant interaction effects of age, the “living with spouse and children” category of residence type and monthly average income with the BE and SE. This indicates that these variables moderate the correlation between the neighborhood environment and the MW of older adults (Table 7). Age had a negative moderating effect, indicating a stronger positive correlation between neighborhood environment and the MW of younger older adults. This may be because younger older adults generally have greater physical ability and more opportunities for outdoor activities, making them more sensitive to neighborhood environment [82].
Among the dummy variables for residence type, only “living with spouse and children” exhibited a positive moderating effect. This indicates that the positive correlation between neighborhood environment and MW is stronger among older adults residing with their spouses and children. This may be because such older adults tend to have a stronger sense of community belonging, which makes their MW more closely linked to the neighborhood environment. In contrast, older adults living alone tend to have a weaker sense of community belonging, and thus the association between the neighborhood environment and their MW is relatively limited [83].
Monthly average income showed a negative moderating effect, indicating a stronger significant correlation between neighborhood environment and the MW of low-income older adults. For low-income older adults, daily expenses are mainly basic consumables such as food, making them highly dependent on neighborhood facilities [84]. Meanwhile, these individuals also depend heavily on community support to ease the stress of living on a limited income [85]. This may render them more sensitive to and reliant on their neighborhood environment than older adults with higher incomes.

4.4. Analysis of the Correlation of Neighborhood Environment Factors with the MW of Older Adults

To further explore the relationship between neighborhood environment factors and MW, this study regarded each factor as the independent variable, MW as the dependent variable, and demographic and socioeconomic factors as control variables, and conducted multiple linear regression analysis. The results are shown in Table 8. For the BE, housing quality and shopping convenience were significantly positively correlated with MW. For the SE, neighborhood interaction and community services showed significant positive correlations with MW. Among the control variables, educational level was significantly positively associated with MW, while monthly average income had a significant negative correlation with MW.
Housing quality and MW are closely linked. Poor housing quality can increase the risk of physical illness, such as chronic and respiratory diseases, which in turn are correlated with the MW of older adults. In contrast, housing quality is also directly associated with the emotions of older adults. Long-term exposure to poor-quality housing may cause feelings of loss of control and disappointment, leading to acute and chronic stress [86].
Older adults in China generally shop frequently, particularly for daily items such as fruits, vegetables, and food. Despite the rise in online shopping, most older adults still prefer physical stores [87]. This frequent shopping behavior has fostered a close connection between older adults and local commercial facilities, making daily shopping a routine. Neighborhood shopping facilities not only provide necessities for older adults but also offer social opportunities, which is beneficial to reducing loneliness [88].
Neighborhood interaction is positively associated with the MW of older adults. According to the socioemotional selectivity theory, as individuals age, they perceive limited future time and hope to obtain greater emotional fulfillment and life satisfaction through social interaction, which helps relieve stress and negative emotions caused by aging [89]. At the same time, the physical functions of older adults decline, and their peer social networks (friends, classmates, and colleagues) naturally shrink, leading to their social interaction activities increasingly concentrating within the family and neighborhood. Studies show that older adults usually interact most with their children, followed by neighbors [90]. If their children live far away, neighbors often become the primary source of daily interactions and emotional support.
High-quality community services are a protective factor for the physical health and MW of housebound older adults. As physiological functions decline, older adults face a higher risk of chronic diseases. Convenient community services (food delivery, health education, and counseling, etc.) facilitate daily living and provide social support, thereby potentially contributing to enhancing MW [91].
Among demographic and socioeconomic factors, educational level is significantly positively associated with MW. As mentioned above, older adults with higher education tend to possess greater health knowledge and engage more in health-promoting behaviors. Monthly average income has a significant negative correlation with MW, a result that diverges from the findings of most existing studies. A possible explanation is that older adults with relatively higher incomes tend to hold higher psychological expectations for their living environment. However, constrained by practical factors such as the need to provide financial support for their children, they find it difficult to escape the suboptimal housing conditions of old urban residential communities. This discrepancy between expectations and reality may ultimately undermine their MW.

5. Discussion

This study reveals that the perceived neighborhood environment of old urban residential communities is positively correlated with the MW of older residents. Older adults currently face three main MW challenges: (1) restricted mobility from physiological decline [92], (2) loneliness from shrinking social circles [93], and (3) psychological strain from chronic illnesses [94]. Based on the correlation between neighborhood environment and MW, optimizing the BE and SE may be related to better responses to such challenges to a certain extent. At the level of environmental factors, this study shows that housing quality and shopping convenience in the BE are significantly positively correlated with the MW of older adults in old urban residential communities. Although studies identified medical facilities, fitness facilities, and neighborhood greening as notable psychological factors [95], this study reports no significant correlation among older adults in old urban residential communities.
This difference likely stems from the environmental characteristics of old urban residential communities in China and the lifestyles of older adults. These communities were mostly built in the 1980s and 1990s, and suffer from poor housing quality, outdated facilities, and a lack of elevators. Compared with residential communities built after 2000, housing quality is a key factor significantly correlated with the well-being of older adults. Furthermore, most older adults handle their daily shopping and require accessible facilities, such as supermarkets and grocery stores. An insufficient number of such facilities to provide adequate support to older adults may increase their psychological distress [96]. The correlations of medical facilities, fitness facilities, and neighborhood greening with older adults’ MW are not statistically significant, likely due to several factors: (1) The health status of respondents was generally good (only 7.18% rated their health as poor), making them less dependent on medical facilities. (2) Old urban residential communities suffer from limited green space and low-quality landscape and fitness facilities. As argued by J. Gehl, the environmental quality of public spaces influences residents’ spontaneous outdoor activities such as walking, as well as social interactions with others [97]. Similarly, C. Alexander pointed out that the utilization of outdoor facilities is closely related to the quality of their surrounding environment [98]. Relevant studies also show that the use of green spaces by older adults depends on environmental quality and the adequacy of fitness facilities [99]. In old urban residential communities, green spaces are usually small in size with poor landscape quality, while fitness facilities are monotonous and outdated. Such resources are difficult to integrate into daily life, thus showing no significant correlation with the MW of older adults.
In the SE, neighborhood interaction and community services are factors significantly correlated with older adults’ MW. Similarly to housing quality and shopping convenience in the BE, they directly cater to the daily living needs of older adults in old urban residential communities. Neighborhood interaction satisfies immediate demands such as emotional expression, while community services meet professional needs including housekeeping and medical consultation. Together, they cover the daily requirements for aging in place for older adults living in old urban residential communities. Therefore, this study reveals that the environmental factors in old urban residential communities that are actually associated with the MV of older adults are those most closely linked to their daily living demands. It should be noted that all constructs in this study were measured via subjective self-perception, so the observed associations may partly reflect shared subjective evaluation tendencies among respondents, rather than fully representing independent objective environmental effects.
Furthermore, The BE has a stronger association with the MW of older adults than the SE. This may be because the BE supports facilities that meet the basic living needs and provide the material foundation for the sense of security of older adults, thereby being directly correlated with their mental state [100,101]. Although the BE is more strongly correlated with the MW of older adults, the SE should not be overlooked. It is directly correlated with MW and also mediates the relationship between the BE and MW, a role particularly critical in old urban residential communities. As radical improvements to the BE are difficult, positive perceptions of the SE can help older adults rebuild social support networks and strengthen their sense of belonging to the community [102], thereby moderating the associations between suboptimal BE conditions and MW.
At the individual level, the neighborhood environment is differently correlated with the MW of older adults depending on their demographic and socioeconomic characteristics. The MW of younger older adults, those living with family, and those with lower incomes is more strongly correlated with the neighborhood environment. Our survey indicates that older adults in old urban residential communities generally have lower incomes. Living with their children may indicate that the entire family belongs to a lower socioeconomic class. Since adult children are often busy with work, and childcare services remain inadequate, older adults living with them typically bear the heavy responsibility of caring for their grandchildren and managing the household [103]. They prepare meals for their grandchildren and transport them to school and accompany them to outdoor activities. Although these caregiving responsibilities may support the sense of self-worth in older adults [104], they also consume their personal time, limit social interactions, and increase mental stress [105]. Because they shop and take their children out daily, this group of older adults interacts with the neighborhood environment most often and is more sensitive to its perception. If the neighborhood environment supports them, it will strengthen their sense of belonging; otherwise, it may add to their mental burden.
Acknowledging some limitations of this study is essential. First, as a cross-sectional study, this research confirms the association between the neighborhood environment of old urban residential communities and the MW of older adults, yet it cannot verify the causal relationship between them. Second, this study focused on a single city and may not represent all old urban residential communities. Third, the variables were measured using single-source subjective perception data collected through self-reported questionnaires, making them susceptible to biases such as perception bias, mood effects, or common method variance, which may lead to overestimation of the associations between variables. Harman’s single-factor test alone is insufficient to fully address the issue of common method bias. Future research should incorporate objective environmental measurements or longitudinal follow-up designs to more rigorously assess the causal relationship between the neighborhood environment and mental health. Fourthly, the WHO-5 focuses on the measurement of positive psychological well-being dimensions and cannot provide a comprehensive assessment of respondents’ mental health. Future research should adopt more holistic mental health assessment tools to examine the relationship between the neighborhood environment of old urban residential communities and the mental health of older adults.

6. Conclusions

From the perspective of subjective perception, this study investigates how the perceived neighborhood environment in old urban residential communities is associated with the MW of older adults. The results show that both perceived BE and SE are significantly positively associated with the MW of older adults, and that SE also significantly mediates the relationship between BE and MW. Age, residence type, and monthly income moderate the associations between neighborhood environment and MW. Key neighborhood environmental factors correlated with the MW of older adults include housing quality, shopping convenience, neighborhood interaction, and community services. These findings provide valuable implications for the age-friendly renewal of old urban residential communities, the development of age-friendly communities, and the improvement in the subjective well-being of older adults. Based on the observed associations, environmental renewal in old urban residential communities may benefit from addressing both the BE and SE. Given the limited space and the difficulty of adding large-scale green spaces and activity areas, priority could be given to improving housing quality for older adults, as well as accessibility to places such as supermarkets and markets, as these factors were correlated with higher life satisfaction among older adults. From the perspective of the SE, community authorities should create more opportunities for neighborhood interaction and provide older adults with more convenient living services. Such efforts may help older adults maintain their social networks, strengthen their sense of social belonging, and further foster their community identity and attachment. Meanwhile, it should be noted that the decline in the traditional multi-generational household model implies the loss of part of older adults’ social support resources. Neighborhood ties and community services alone are insufficient to fully compensate for this gap. The SE variables focused on in this study still belong to informal social support. The social support system at the societal level may need to further expand the effective supply of community-based elderly care services. Especially for old urban residential communities, improving the systems of daily care, health security, and leisure services could help compensate for potential deficiencies in the BE.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/buildings16112227/s1, File S1. Supplementary data; File S2. Original data.

Author Contributions

Conceptualization, J.Z.; methodology, J.Z.; software, Z.T.; validation, Z.T.; formal analysis, Y.C.; investigation, Z.T. and Y.C.; resources, Y.C.; data curation, Z.T.; writing—original draft preparation, J.Z. and Z.T.; writing—review and editing, J.Z.; visualization, Y.C.; supervision, J.Z.; project administration, J.Z.; funding acquisition, J.Z. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by Major Project of Philosophy and Social Science Research in Colleges and Universities of Jiangsu Province, grant number 2021SJZDA108 and Social Science Foundation of Jiangsu Province, grant number 22YSB018.

Institutional Review Board Statement

The study was conducted according to the guidelines of the Declaration of Helsinki, and approved by the Scientific Research Special Committee of Nanjing Tech University (protocol code NTJTECH-1-11 and date of approval 28 March 2024).

Informed Consent Statement

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

Data Availability Statement

The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.

Acknowledgments

The authors thank all participants for their engagement in this study.

Conflicts of Interest

The authors declare no conflicts of interest.

Abbreviations

The following abbreviations are used in this manuscript:
AGFI, Adjusted Goodness-of-Fit Index; AVE, average variance extracted; BE, built environment; CFI, Comparative Fit Index; CMB, common method bias; CMIN/df, Minimum Discrepancy divided by Degrees of Freedom; CR, construct reliability; EFA, Exploratory factor analysis; IFI, Incremental Fit Index; GFI, Goodness-of-Fit Index; MW, mental well-being; NFI, Normed Fit Index; PGFI, Parsimony Goodness-of-Fit Index; RMSEA, Root Mean Square Error of Approximation; SE, social environment; SEM, Structural equation modeling.

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Buildings 16 02227 g001
Figure 1. Conceptual framework.
Figure 1. Conceptual framework.
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Figure 2. Study sites.
Figure 2. Study sites.
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Figure 3. Sample community map and on-site environment.
Figure 3. Sample community map and on-site environment.
Buildings 16 02227 g003
Table 1. Variable selection and description.
Table 1. Variable selection and description.
Latent VariableObserved VariableCodeVariable Types and AssignmentExplanation
BEHousing qualityA1Ordered variables, 1 (very dissatisfied) to 5 (very satisfied)Evaluation of satisfaction with factors of BE
Transportation accessibilityA2Ordered variables, 1 (very dissatisfied) to 5 (very satisfied)
Medical facilitiesA3Ordered variables, 1 (very dissatisfied) to 5 (very satisfied)
Shopping convenienceA4Ordered variables, 1 (very dissatisfied) to 5 (very satisfied)
Sanitary
conditions		A5Ordered variables, 1 (very dissatisfied) to 5 (very satisfied)
Fitness facilitiesA6Ordered variables, 1 (very dissatisfied) to 5 (very satisfied)
Neighborhood greeningA7Ordered variables, 1 (very dissatisfied) to 5 (very satisfied)
Outdoor noiseA8Ordered variables, 1 (very dissatisfied) to 5 (very satisfied)
SENeighborhood friends and
relatives		B1Category variables, 0 = 1, 1–3 = 2, 4–6 = 3, 6 and above = 4Number of relatives and friends in the neighborhood
Neighborhood interactionB2Ordered variables, 1 (never exchanged) to 5 (exchanged weekly)Frequency of interaction with neighbors
Neighborhood trustB3Ordered variables, 1 (cannot be trusted at all) to 5 (can be trusted at all)Level of trust in surrounding neighbors
Community activitiesB4Ordered variables, 1 (almost none) to 4 (every week)Frequency of participation in community activities
Community servicesB5Ordered variables, 1 (very dissatisfied) to 5 (very satisfied)Satisfaction assessment with community service efforts
Neighborhood safetyB6Ordered variables, 1 (very dissatisfied) to 5 (very satisfied)Satisfaction assessment with community policing
Neighborhood relationsB7Ordered variables, 1 (very dissatisfied) to 5 (very satisfied)Quality of relationships with community residents
MWI have felt cheerful and in good spiritsC1Ordered variables, 0 (At no time) to 5 (All of the time)MW in the past two weeks
I have felt calm and relaxedC2Ordered variables, 0 (At no time) to 5 (All of the time)
I have felt active and
vigorous		C3Ordered variables, 0 (At no time) to 5 (All of the time)
I woke up feeling fresh and restedC4Ordered variables, 0 (At no time) to 5 (All of the time)
My daily life has been
filled with things that interest me		C5Ordered variables, 0 (At no time) to 5 (All of the time)
Abbreviations: BE, built environment; SE, social environment; MW, mental well-being.
Table 2. Structural equation modeling fit.
Table 2. Structural equation modeling fit.
Category of IndicatorName of IndicatorAdaptation CriteriaTest ResultsAcceptability
Absolute fit indexGFI>0.80.919Acceptance
AGFI>0.80.904Acceptance
RMSEA<0.080.050Acceptance
Incremental fit indexNFI>0.90.901Acceptance
IFI>0.90.930Acceptance
CFI>0.90.930Acceptance
Parsimonious fit indexCMIN/df<53.113Acceptance
PGFI>0.50.775Acceptance
Abbreviations: GFI, goodness-of-fit indices; AGFI, adjusted goodness-of-fit index; RMSEA, root mean square error of approximation; NFI, normed fit index; IFI, incremental fit index; CFI, comparative fit index; CMIN/df, Minimum Discrepancy divided by Degrees of Freedom; PGFI, parsimony goodness-of-fit index.
Table 3. Basic information of the interviewees (N = 836).
Table 3. Basic information of the interviewees (N = 836).
CategoryOptionsFrequencyPercentage
SexMale39146.77%
Female44553.23%
Age60–6927332.66%
70–7934941.75%
80 and above21425.60%
Educational levelNone 15418.42%
Elementary school17520.93%
Junior high school24128.83%
Senior high school (including technical secondary school)15118.06%
College and above11513.76%
Residence typeLiving alone 19723.56%
Living with spouse only 37244.50%
Living with children only 14717.58%
Living with spouse and children 10412.44%
Other 182.15%
Health statusVery poor 50.60%
Poor 556.58%
Fair 25330.26%
Good 40948.92%
Very good 11413.64%
Monthly average income (RMB)<3000 33840.43%
3000–5000 26531.70%
5000–8000 13716.39%
>8000 9611.48%
Total836100.00%
Table 4. SEM path analysis results.
Table 4. SEM path analysis results.
PathwayEstimateSE.CR.p
BE →MW 0.4020.05210.9210.000
SE →MW 0.3040.0598.5330.000
BE →SE 0.2910.0357.2790.000
Abbreviations: SEM, Structural equation modeling; BE, Built environment; SE, social environment; MW, mental well-being; All path coefficients listed in this table are standardized path coefficients.
Table 5. Mediation analysis results.
Table 5. Mediation analysis results.
PathwayParameterEstimateLowerUpperp
BE → SE → MWDirect correlation 0.4020.3330.4720.000
Indirect correlation 0.0890.0640.1190.000
Total correlation0.4900.4280.5510.000
Abbreviations: BE, Built environment; SE, social environment; MW, mental well-being.
Table 6. Summary of values assigned to variables of demographic and socioeconomic factors.
Table 6. Summary of values assigned to variables of demographic and socioeconomic factors.
VariableAssignment
SexMale = (1, 0); Female = (0, 1)
Age60–69 = 1; 70–79 = 2; 80 and above = 3
Educational levelNone = 1; Elementary school = 2; Junior high school = 3; Senior high school (including technical secondary school) = 4; College and above = 5
Residence typeLiving alone = (1, 0, 0, 0, 0); Living with spouse only = (0, 1, 0, 0, 0); Living with children only = (0, 0, 1, 0, 0); Living with spouse and children = (0, 0, 0, 1, 0); Other = (0, 0, 0, 0, 1)
Health statusVery poor = 1; Poor = 2; Fair = 3; Good = 4; Very good = 5
Monthly average income (RMB)Below 3000 = 1; 3000–5000 = 2; 5000–8000 = 3; above 8000 = 4
Table 7. Moderating effects of demographic and socioeconomic factors.
Table 7. Moderating effects of demographic and socioeconomic factors.
PathwayBStandard ErrortpR2F
SexBE → MW0.0850.0711.2100.2270.20872.732
SE → MW0.1170.0671.7450.0810.14848.225
AgeBE → MW−0.1190.046−2.5820.010 **0.21374.843
SE → MW−0.1380.044−3.1140.002 **0.15550.778
Educational levelBE → MW0.0130.0260.4980.6190.20772.319
SE → MW0.030.0271.1250.2610.14647.472
Residence type
(Living with spouse and children)		BE → MW0.3160.1152.7430.006 **0.22326.269
SE → MW0.3750.1173.2040.001 **0.16017.479
Health statusBE → MW−0.0160.039−0.4120.6810.20772.361
SE → MW–0.0520.04−1.3140.1890.14747.645
Monthly average income BE → MW−0.0710.033−2.1750.030 *0.23886.852
SE → MW−0.1160.034−3.4130.001 **0.19567.231
Abbreviations: BE, built environment; SE, social environment; MW, mental well-being; * p < 0.05, ** p < 0.01.
Table 8. Regression analysis of BE factors on MW.
Table 8. Regression analysis of BE factors on MW.
Unstandardized CoefficientsStandardized CoefficientsTSig.Collinearity Statistics
BStd. ErrorBetaToleranceVIF
(Constant)1.0820.216 5.0040.000
Housing quality 0.1700.0330.2175.1160.0000.4462.244
Transportation accessibility 0.0760.0390.0801.9490.0520.4742.108
Medical facilities 0.0160.0380.0180.4190.6750.4522.212
Shopping convenience 0.1320.0360.1503.6330.0000.4682.136
Sanitary conditions0.0180.0390.0190.4540.6500.4492.229
Fitness facilities 0.0030.0370.0030.0740.9410.4702.126
Neighborhood greening0.0010.0370.0010.0200.9840.4782.094
Outdoor noise−0.0150.034−0.017−0.4500.6530.5411.848
Neighborhood friends and
Relatives		0.0750.0390.0751.9530.0510.5481.823
Neighborhood interaction0.0620.0310.0902.0080.0450.4022.489
Neighborhood trust−0.0140.036−0.016−0.3860.7000.4532.210
Community activities0.0360.0290.0481.2400.2150.5431.841
Community services0.0850.0320.1042.6090.0090.5071.974
Neighborhood safety0.0390.0330.0471.1620.2460.4922.031
Neighborhood relations0.0210.0340.0260.6220.5340.4512.219
Sex (Female)−0.0450.054−0.025−0.8290.4070.9121.097
Age0.0550.0350.0461.5670.1180.9271.078
Educational level0.0850.0230.1213.7210.0000.7591.318
Residence type (Living with spouse only)−0.0990.068−0.055−1.4710.1420.5801.724
Residence type (Living with children only)−0.0720.083−0.030−0.8660.3870.6611.513
Residence type (Living with spouse and children)−0.0020.092−0.001−0.0260.9790.7021.424
Residence type (Other)0.1100.1840.0180.5990.5500.9171.090
Health status−0.0110.032−0.010−0.3500.7260.9651.036
Monthly aver-age income−0.2230.029−0.250−7.5630.0000.7321.365
R square0.351
Adjusted R square0.331
F18.251 (p = 0.000)
Dependent variable: MW
Abbreviations: BE, Built environment; MW, mental well-being.
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Zhang, J.; Tan, Z.; Chen, Y. Correlation Between Neighborhood Environment and Mental Well-Being of Older Adults: A Perspective Based on the Old Urban Residential Communities. Buildings 2026, 16, 2227. https://doi.org/10.3390/buildings16112227

AMA Style

Zhang J, Tan Z, Chen Y. Correlation Between Neighborhood Environment and Mental Well-Being of Older Adults: A Perspective Based on the Old Urban Residential Communities. Buildings. 2026; 16(11):2227. https://doi.org/10.3390/buildings16112227

Chicago/Turabian Style

Zhang, Jianjian, Ziyi Tan, and Yingqi Chen. 2026. "Correlation Between Neighborhood Environment and Mental Well-Being of Older Adults: A Perspective Based on the Old Urban Residential Communities" Buildings 16, no. 11: 2227. https://doi.org/10.3390/buildings16112227

APA Style

Zhang, J., Tan, Z., & Chen, Y. (2026). Correlation Between Neighborhood Environment and Mental Well-Being of Older Adults: A Perspective Based on the Old Urban Residential Communities. Buildings, 16(11), 2227. https://doi.org/10.3390/buildings16112227

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