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15 May 2025

Development of a Built Environment–Self-Efficacy–Activity Engagement–Self-Rated Health Model for Older Adults in Urban Residential Areas

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Department of Construction Management and Real Estate, School of Economics and Management, Nanjing Tech University, No. 30 Puzhu South Road, Nanjing 211816, China
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College of Health Engineering, Nanjing City Vocational College, No. 1 Gao Shan Road, Nanjing 210000, China
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Author to whom correspondence should be addressed.
Buildings2025, 15(10), 1660;https://doi.org/10.3390/buildings15101660
This article belongs to the Special Issue Advances of Healthy Environment Design in Urban Development—2nd Edition
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Abstract

The aging population has posed significant challenges to the built environment (BE) in urban residential areas, particularly in addressing older adults’ activity and health needs. Understanding how the BE influences older adults’ activity and health is crucial for promoting active and healthy aging. This study explored the interactions among the BE, self-efficacy (SE), activity engagement (AE), and self-rated health (SH) for older adults in urban residential areas. A random sampling technique selected 372 older adults residing in urban residential areas to participate in the questionnaire survey. Spearman correlation and hierarchical regression analysis were used to develop the BE-SE-AE-SH model for older people based on social cognitive theory. Accessibility, land use mix, and street connectivity affect activity engagement by influencing older persons’ walking and self-care abilities. Land use mix discourages walking ability and activity engagement, while esthetics encourages activity engagement. Land use mix, street connectivity, transportation, walking ability, self-care ability, and activity engagement enhance older adults’ self-rated health. Practical recommendations for age-friendly urban residential areas include the following: (1) optimize elevators and footpaths; (2) decentralize small businesses and create multi-use parking; (3) shorten crossings and enhance pavements; (4) add natural and humanistic elements; (5) limit car speed and install traffic signals.

1. Introduction

Population aging has become a global challenge, with the number of people aged 60 and over expected to reach 2.1 billion by 2050 [1]. This situation has sparked discussions on population aging in various research fields, including public health, social science, and management science [2,3,4,5]. Older population in urban areas is growing at nearly twice the rate of the total population, a trend likely to be more serious for the foreseeable future [6]. Rapid population growth puts enormous pressure on society to safeguard older people’s physical and mental health due to their mobility issues, social isolation, inadequate care services, and so on [7]. In addition, more than 180 million older persons in China suffer from chronic diseases, and the proportion of those suffering from one or more chronic diseases is as high as 75% [8]. Regular exercise is a healthy habit. Activity engagement in older adults maintains functional capacity and reduces morbidity, promoting positive health evaluations [9]. To improve engagement and task completion, older people need to believe in their ability to perform specific tasks, which is self-efficacy [10]. A supportive BE can enhance older adults’ self-efficacy, fostering greater confidence and capability in managing daily activities [11]. This, in turn, may promote higher levels of activity engagement and contribute to improved health outcomes.
The ‘age-friendly cities’ concept has gained global attention [12]. Issues like land overdevelopment, inadequate accessibility, and poor road design make the current urban built environment (BE) unsuitable for older people. The critical aging situation and rapid urbanization demand a change in the urban BE to adapt to demographic changes [13]. BE improvement is necessary to address older adults’ needs, thus improving their health status and reducing the healthcare burden of the government [14]. The urban residential area is the basic unit for meeting the needs of urban dwellers, providing necessary facilities and services for older persons’ activities [15]. Based on social cognitive theory, individuals, behaviors, and environments are interconnected [10]. Older people may have greater self-efficacy and intentions to participate in outdoor activities when satisfied with their neighborhood’s environment [16]. A well-designed BE in urban residential areas may improve their activity engagement and promote their health status [17]. Therefore, this study investigates the influence of the BE in urban residential areas on older adults’ self-efficacy, activity engagement, and self-rated health. The research’s insights and recommendations will enhance urban renewal and environmental improvements, ultimately facilitating the creation of a BE conducive to the well-being and convenience of older adults in urban settings.

2. Literature Review

2.1. Built Environment for Active and Healthy Aging

The BE comprises manufactured structures and infrastructure that support human activities and offer the necessities for living [18]. As an important component of residential areas, the BE is closely linked to residents’ daily lives [19]. Older people’s health and mobility decline with age, leading to their higher requirement of a BE that meets their various needs [20]. The World Health Organization has proposed national programs for age-friendly cities and communities, with a focus on age-friendly built environment retrofitting, such as accessibility, walking facilities, and transportation [21]. Handy et al. emphasized that a comprehensive assessment of the built environment should cover core elements such as land use mix, street connectivity, and esthetics [17]. Brownson et al. assessed the impact of the built environment on physical activities focusing on land use mix, esthetics, and transportation [15]. In an experimental study investigating the walkability of neighborhood environments, BE indicators—including street connectivity, walking facilities, esthetics, and transportation—were utilized [22]. Given the importance of these built environment features in supporting older adults’ needs related to activity and health, six key factors were identified. Therefore, this study focuses on environmental factors such as accessibility, land use mix, street connectivity, walking facilities, esthetics, and transportation.
Accessibility refers to the ease of movement and access for individuals with mobility limitations [23]. Previous studies have highlighted that poor accessibility is prevalent in urban areas and fails to meet the activity and living needs of vulnerable populations, especially older persons [24]. Insufficient accessibility limits daily outdoor mobility for older people. Research has found that good accessibility can alleviate symptoms of depression and anxiety [25]. Residential areas equipped with elevators improve access to homes, thus promoting independence [26]. Wheelchair-accessible pathways and handrails remove mobility barriers for older adults [27]. Furthermore, disability-friendly toilets in public areas may boost the participation of older adults in residential area activities. Land use mix, which reflects the diversity of the BE, probably influences older adults’ weekly travel time and distance [28]. Research suggests that poor land use mix may lead to cognitive decline [29]. Proximity to local stores helps older adults shop without long travel, making them more likely to walk. However, parking challenges may reduce the frequency of older adults’ participation in various social activities, especially for those with long-distance mobility issues [30]. In addition, geographic features such as ravines and hills can restrict routes, thus impeding the mobility of older persons and their contact with the outdoor environment [31].
Street connectivity reflects the density and directness of neighborhood street network connections [32]. Scholars have found that well-connected streets encourage older adults to reach out to places that provide life and recreational services [33]. A well-connected network of neighborhood streets simplifies travel for older adults and facilitates smooth movement from origin to destination. Neighborhoods with fewer cul-de-sacs and shorter distances between intersections encourage walking, which improves mental health and alleviates feelings of isolation [34]. High connectivity, such as four-way intersections, expands route options, promotes safety, and may thus reduce depressive symptoms [35]. Walking facilities are designated areas or structures designed explicitly for pedestrian activity [36]. Older adults often require appropriate physical activity to maintain their health, with walking or cycling being particularly beneficial [37]. Well-maintained sidewalks may enhance safety, supporting independent travel and daily life. Resting places help older adults to rest and recuperate, boosting physical activity and enhancing walking experiences [15]. Moreover, accessible walking and bicycle trails meet the needs of older adults, encouraging their active residential area participation.
Esthetics, including attractive landscapes, architecture, and recreational spaces, contribute to creating a comfortable and engaging environment [38]. The esthetics of the BE may help reduce sedentary time in older adults. Attractive natural sights, such as landscaped areas and scenic views, can enhance visual appeal and promote well-being and life satisfaction [39,40]. Similarly, esthetical buildings foster residential pride, strengthen social interactions, and promote mobility among residents [41]. Nearby open recreation areas like parks and beaches can encourage active travel and increase residential area involvement among older adults [42]. Smooth transportation makes it easy for older adults to participate in various activities, effectively avoiding the risk of loneliness and depression [43]. Low traffic speeds make residential area streets more accessible and walkable, often creating a safer pedestrian environment. Furthermore, crosswalks and pedestrian signals enhance older pedestrians’ awareness, allowing them to safely cross busy areas [44]. In addition, the duration of pedestrian signals and crosswalk availability influences older adults’ decisions when crossing busy streets [45]. Public transit stops within walking distance can improve mobility and independence by offering convenient transportation options [46].

2.2. Older People’s Self-Efficacy for Daily Activity

Self-efficacy is an individual’s belief in their ability to manage functioning and control life events [10]. It is sensitive to environmental changes, as individuals’ self-efficacy levels vary in response to changes in their surroundings [47]. As a key concept in social cognitive theory, self-efficacy fosters active performance in various residential area activities, leading to older people developing consistent exercise habits [10]. Self-efficacy boosts daily activity participation in older adults, especially those with cognitive impairments and chronic conditions [48]. Individuals with high self-efficacy are more likely to implement and adhere to health behaviors [49]. This study identified two dimensions of self-efficacy in older adults—walking ability and self-care ability—using the self-efficacy scale and the functional independence measure scale [50].
Walking can help older people access a wide range of services in the residential area and promote functional capacity in older people [51]. Walking ability maintains older adults’ independence and predicts future cognitive decline [52]. The confidence in and completion of activities such as walking, descending stairs, and entering or exiting vehicles indicate an individual’s walking ability [53]. Older adults may find it challenging to take a day-long trip alone due to physical strength and fitness. Additionally, walking may lower the risk of mobility impairment in sedentary older adults [54]. Self-care ability denotes an individual’s capacity to carry out everyday tasks and participate in life activities [55]. Older adults’ self-care abilities are assessed by their independence in daily tasks like housework, dressing, and bathing [56]. The maintenance of self-care ability builds self-confidence and motivates older adults to tackle health challenges [57]. Compared to younger adults, older adults frequently face additional difficulties in addressing their care needs, highlighting their need for a supportive BE that enhances self-efficacy in self-care.

2.3. Features of Older People’s Activity Engagement

Activity engagement involves individuals in activities that require mental, physical, or social effort, reflecting their active participation in daily life [58]. Older adults often have low levels of activity engagement, remaining inactive for much of the day and demonstrating limited participation in social and leisure activities [59]. Activity engagement has been related to improved functional independence, increased social participation, and reduced isolation and vulnerability [60]. Older adults are among the most physically inactive age groups, worsening many chronic diseases [61]. In addition, some scholars concluded that a more significant proportion of moderate-to-high-intensity physical activity during the day is associated with better cognitive functioning [62]. Moderate engagement in sedentary activities, like tea ceremonies, knitting, chatting, and playing cards, may help maintain functions and reduce premature morbidity [59]. Participating in recreational activities such as singing, dancing, and playing instruments can improve mobility, strength, and balance, which can support older persons in maintaining independence [63]. Outdoor standing activities, such as walking and hiking, further amplify these benefits by providing exposure to nature [64]. To promote healthy aging, practical support should be tailored to the needs of older adults in the BE, emphasizing activity engagement.

2.4. Older People’s Self-Rated Health

Health is a state of soundness that includes physical, mental, social, and other dimensions [65]. Supportive environments may enhance older people’s participation in outdoor activities. Esthetically pleasing environments may alleviate anxiety and benefit sensory and mental health [66]. Regular participation in physical, social, and leisure activities can enhance physical functioning, reduce isolation, and improve health outcomes [67]. Subjective health perception strongly predicts health status and mortality risk, correlating with physical conditions [68]. Health in older adults is a multidimensional concept involving subjective perceptions of physical, sensory, emotional, and functional well-being [69,70]. The habit and ability to keep personal belongings neat and clean reflects an older individual’s functional independence [71]. Autonomous decision-making fosters a sense of control and satisfaction in personal matters, likely benefiting older adults’ mental and emotional well-being [72]. Compared to younger years, self-rated happiness may indicate life quality and physical health. Sensory impairments, like hearing and vision loss, are associated with accelerated cognitive decline, an increased risk of falls, and diminished health status.

3. Conceptual Model

Despite extensive research on the relationship between BE and older adults’ activity engagement and health status, several critical gaps remain. Existing studies have primarily focused on the direct associations between the BE and either activity engagement or health, neglecting to explore interactions among all these variables. Additionally, prior work has largely analyzed individual BE components independently, while the role of self-efficacy has received insufficient attention. To address these gaps, this study aims to explain the interactions among the BE, self-efficacy, activity engagement, and self-rated health for older adults in urban residential areas. Therefore, this paper develops its conceptual model based on the literature review and the theory proposed by Bandura (see Figure 1) [10]. The social cognitive theory suggests that an individual’s behavior is influenced by the external environment and regulated by internal cognitive processes [73]. According to the literature review and this theory, environmental factors (i.e., the BE), behavioral factors (i.e., activity behaviors), and subjective factors of the individual (i.e., self-efficacy and health status) are viewed as relatively independent. This study hypothesized that the BE (i.e., accessibility, land use mix, street connectivity, walking facilities, esthetics, and transportation) influences the self-efficacy (i.e., walking and self-care ability), activity engagement, and self-rated health of older persons. Self-efficacy affects activity engagement and self-rated health. Older adults’ active activity engagement is also hypothesized to positively contribute to their self-rated health.
Figure 1. Conceptual BE-SE-AE-SH model for older adults in urban residential areas.

4. Research Methodology

4.1. Questionnaire Survey

Based on the literature review and theoretical model, a questionnaire was developed to gather data from older adults to investigate the relationships among the BE, self-efficacy, activity engagement, and self-rated health. The questionnaire comprised three sections: (1) the respondents’ basic characteristics; (2) the BE scale for urban residential areas adapted from the neighborhood environment walkability scale (NEWS) [22]; and (3) self-efficacy, activity engagement, and self-rated health scales [11,74,75,76]. The self-efficacy scale was adapted from the Multiple Sclerosis Self-efficacy Scale (MSSE) [11], and the items in the activity engagement scale and self-rated health scale were derived from previous studies [74,75,76]. Respondents used a Likert scale from 1 (strongly disagree) to 5 (strongly agree) to rate their perceptions. Before data collection, the university’s research ethics committee viewed the overall study process to ensure compliance with institutional ethical standards. Prior to the formal distribution of the questionnaire, a pilot study involving 20 participants was carried out to identify and refine ambiguous or problematic items. The questionnaire was conducted through face-to-face interviews. The study purpose and data use were clearly explained to the participants who were informed that all data would be kept confidential. Participants had the right to abstain from answering sensitive questions without consequence. To ensure linguistic accuracy, the questionnaire was translated into Chinese and reviewed for clarity to accommodate older adults’ cognitive and comprehension capacities. Upon completion of the questionnaire, all participants received a gift to convey the researchers’ appreciation.

4.2. Sample

This study utilized a random sampling technique, carefully selecting participants according to predefined criteria. The criteria stipulated that participants must be 60 years or older and be senior citizens residing in urban residential areas. Before administering the formal questionnaire, individuals with significant cognitive impairments or disabilities were excluded. A total of 450 questionnaires were distributed to older people living in urban areas of Nanjing, China. The offline survey targeted older residents from 33 selected neighborhoods involving different construction years and renewal degrees, including older unrenewed neighborhoods, older renewed neighborhoods, and newer neighborhoods. At last, 372 questionnaires were deemed valid following an offline distribution and collection process, yielding a commendable response rate of 82.7%. Among the 372 respondents, 177 (47.6%) were male and 195 (52.4%) were female. Around 30.9% of respondents were aged 60 to 64, 20.4% were aged 65 to 69, 21.8% were aged 70 to 74, 13.2% were aged 75 to 79, and 13.7% were aged 80 or older. Concerning family size, 24.2% of respondents reported having only one child, 41.7% reported having two children, and 34.1% reported having three or more children.

4.3. Statistical Analysis Methods

A variety of statistical analysis techniques were employed in this study to develop the BE-SE-AE-SH model. Factor analysis was used to condense numerous items into factors. Reliability analysis was performed to gauge the internal coherence of these factors. Correlation coefficients denote the magnitude and direction of the association between two factors. Hierarchical regression analyses were conducted to build the relationships among four dimensions [77]. Only the results confirmed by all these statistical analyses can be used to develop the formulation of the BE-SE-AE-SH model for older adults in urban residential areas.

5. Results

5.1. Factor Analysis and Reliability Test

Principal component analysis was utilized to discern the underlying structure among the items and categorize them into representative factors [77]. In this investigation, varimax rotation was applied, and factors were extracted based on their eigenvalues (i.e., eigenvalues exceeding 1), utilizing a sample size of 372 items. The results from the factor analysis and reliability analysis about the BE, self-efficacy, activity engagement, and self-rated health are reported in Table 1 and Table 2. The analysis excluded items with factor loadings below 0.50, and all items exceeded this threshold [78]. A reliability test was conducted to ensure the reliability of the factors. Cronbach’s alpha assessed reliability, and all factors achieved acceptable values above 0.55 [79]. The reliability analysis results for the BE are shown in Table 2. The item-to-sample ratios were greater than 20:1, fitting requirements for factor analysis [80]. The Kaiser–Meyer–Olkin (KMO) values (i.e., 0.875 and 0.870) exceeded the required threshold of 0.60.
Table 1. Factor analysis for self-efficacy, activity engagement, and self-rated health.
Table 2. Reliability analysis for BE.

5.2. Spearman Correlation Analysis

The factors present a non-parametric distribution, so the Spearman correlation analysis was adopted in this study. The correlation analysis shown in Table 3 examined the relationships between the BE, self-efficacy, activity engagement, and self-rated health among older adults. This analysis was conducted at significance levels with p-values of 0.01 and 0.05. The findings indicated that walking ability (SE1) and self-care ability (SE2) were positively linked with each other (0.703). Both of them were positively correlated with all of the following factors, except for accessibility (BE1): activity engagement (AE: 0.677 and 0.742, respectively), self-rated health (SH: 0.625 and 0.722), land use mix (BE2: 0.132 and 0.329), street connectivity (BE3: 0.351 and 0.456), walking facilities (BE4: 0.268 and 0.291), esthetics (BE5: 0.270 and 0.246), and transportation (BE6: 0.300 and 0.337). Activity engagement and self-rated health were positively correlated with all BE factors except accessibility.
Table 3. Correlations between BE, self-efficacy, activity engagement, and self-rated health.

5.3. Hierarchical Regression Analysis

Hierarchical regression analysis was employed to realistically test the hypotheses concerning direct and indirect relationships among the BE, self-efficacy, activity engagement, and self-rated health for older adults in urban residential areas. The independent variables were sequentially introduced into the regression equation following their hypothesized order, enabling an assessment of how subsequent variables influenced the dependent variable while controlling for previously entered variables [77]. Additionally, stratified regression facilitated the determination of the individual impact of each independent variable on the dependent variable within a set of independent variables. Based on the significance of p-values and R² values obtained from the analysis, four distinct models were developed: two models concerning self-efficacy (SE1 and SE2), one related to activity engagement, and one associated with self-rated health (see Table 4).
Table 4. Multiple regression model for BE, self-efficacy, activity engagement, and self-rated health.
The conceptual model served as the foundation for determining the order of entry into the regression model based on the proximity of the independent variables to the dependent variable. In the two self-efficacy models, one group was entered as the dependent variable, another self-efficacy group constituted the first set of independent variables, and the BE formed the second set of independent variables. Model 1b indicates that walking ability is positively predicted by self-care ability and negatively predicted by accessibility and land use mix, with an explained variance of 49.5%. Model 2b reveals that self-care ability can be positively predicted by walking ability, land use mix, and street connectivity, with an explained variance of 55.4%. The activity engagement model (Model 3) used overall activity engagement as the dependent variable, with self-efficacy and BE entered as the first and second blocks of independent variables. Similarly, the self-rated health model (Model 4) used self-rated health as the dependent variable, with activity engagement, self-efficacy, and BE entered sequentially as independent variable blocks. In total, 31.5% of variance explained in the activity engagement model (Model 3) demonstrated that walking ability, self-care ability, and esthetics positively influence activity engagement, while land use mix detracts from activity engagement. Model 4c indicated that activity engagement, walking ability, self-care ability, land use mix, street connectivity, and transportation positively impact older adults’ self-care health with a 63.6% variance.

5.4. Model Establishment

To ensure the reliability of the findings, the integrated model was constructed based on the established conceptual model (see Figure 1), considering only the relationships confirmed by factor analysis, reliability tests, correlation analysis, and regression analysis (within-method research triangulation) [81]. The integrated model corroborates the intricate interrelationships between the BE in urban residential areas, self-efficacy, activity engagement, and self-rated health of older adults (see Figure 2). The model highlights the significant impact of the BE on older people’s self-efficacy and directly affects activity engagement and self-rated health. Accessibility (BE1), land use mix (BE2), and street connectivity (BE3) influence walking ability (SE1) and self-care ability (SE2), which are mutually reinforcing activity engagement and enhancing self-rated health. While land use mix (BE2) affected all other factors, walking facilities (BE4) had no significant influence. Furthermore, esthetics (BE5) positively predicted activity engagement, and transportation (BE6) positively predicted self-rated health.
Figure 2. BE-SE-AE-SH model for older adults in urban residential areas. Note: —significant positive relationship confirmed by regression analysis; —significant negative relationship confirmed by regression analysis.

6. Discussion

6.1. Effect of BE on Self-Efficacy

A decrease in accessibility features—such as elevators, wheelchair access, handrails, and accessible toilets—may compel older adults to exert more effort while walking, potentially increasing their physical activity levels [14] (see Figure 3a,b). Older persons with limited mobility who rely on assistive devices may struggle to walk without wheelchair ramps or handrails [82] (see Figure 3c). If rest areas are not nearby, older adults may have to walk long distances to access various facilities [83]. The absence of accessible design may inadvertently enhance older adults’ stamina and endurance within their residential areas [84,85]. The regression analysis revealed that land use mix in residential areas is negatively associated with walking ability in older adults, while this relationship was positive in the Spearman correlation analysis. This variation may stem from the influence of other independent variables or a potential non-linear relationship between land use mix and walking ability. Centralized commercial facilities with a high land use mix enable older adults to meet their shopping needs without extensive walking. To accommodate older adults’ daily travel needs, many local shopping areas provide parking spaces for non-motorized vehicles (see Figure 3d). However, this may exacerbate the reduction in walking distance. Unfortunately, overcrowded motor vehicle parking in neighborhoods severely deteriorates walking conditions, requiring older people to remain vigilant of oncoming pedestrians and vehicles (see Figure 3e). Excessively mixed land use complicates the walking environment, posing significant challenges for older adults with impaired walking abilities [51].
Figure 3. Actual BE of urban residential areas: (a) a toilet for people with disabilities; (b) residential buildings with elevators, wheelchair access, and handrails; (c) entrance to residential buildings without wheelchair ramps and handrails; (d) non-motorized parking lots in local shopping areas; (e) crowded motor vehicle parking and various stores; (f) congested, centralized commercial areas; (g) children’s playground and basketball court; (h) attractive natural sights and ancient Chinese-style buildings; (i) speed limit signs; (j) transit bus stops within walking distance.
Diverse and accessible mixed land use allows older people easy access to basic services from various stores, such as pharmacies, courier stations, and groceries [4] (see Figure 3e). Streets with cul-de-sacs are meandering and disconnected. They make it difficult for older people to navigate neighborhoods safely and efficiently. Previous studies have shown that urban residential areas with shorter intersection distances promote self-care among older adults by facilitating walking and improving access to basic services [86]. More four-way intersections in urban environments can improve their self-care ability by improving convenience and safety to reach various destinations [87]. Better walking ability enables older adults to independently access services and engage in residential area activities, enhancing self-care ability. Additionally, older individuals can promote walkability by increasing self-care practices [55].

6.2. Effect of BE and Self-Efficacy on Activity Engagement

The regression analysis indicated a negative link between land use mix in residential areas and activity involvement among older persons, which is different from the Spearman correlation analysis results. This variation may result from a suppression effect or a possible non-linear relationship between land use mix and activity engagement. Specifically, moderate land use mix enhances activity demand through improved service proximity. Unreasonable mixed land use manifested by congestion and over-centralization may limit the older adults’ willingness to engage in active travel and participate in outdoor activities, especially in small or densely populated residential areas [28,42] (see Figure 3f). The optimal land use mix requires comprehensively considering the activity needs of older adults while accounting for the broader effects of other built environment factors. A previous study found that open recreation areas significantly enhance the desire to participate in activities [88]. Spaces like children’s playgrounds and basketball courts promote intergenerational activities although not specifically designed for older adults. These spaces encourage older adults’ mobility and activities alongside younger generations [38] (see Figure 3g). Older people are also more likely to enjoy or visit attractive natural and architectural sites [89] (see Figure 3h). At the same time, enhanced walking ability encourages their engagement in more social and physical activities, promoting older persons’ sustained activity engagement [54]. Additionally, self-care ability empowers older adults to maintain autonomy, stay active, and build social networks [90].

6.3. Effect of BE, Self-Efficacy, and Activity Engagement on Self-Rated Health

Areas with a higher mix of land uses can lead to traffic congestion, air pollution, and safety hazards, thus reducing the frequency of walking and activities for older persons [28]. However, these areas also offer more medical services, shopping centers, and cultural facilities, which can meet older adults’ needs while promoting social interactions and mental health, and thus their self-rated health. Meanwhile, well-connected streets enhance older adults’ access to residential area facilities, fostering independence and health status [91]. However, high traffic speeds pose safety and health challenges for older adults, which is likely to cause traffic accidents and injuries, though some residential areas implement 30 km/h speed limits (see Figure 3i). Conversely, accessible crosswalks and transit stops reduce road accidents and create a safe and convenient travel environment, supporting physical and mental health [92] (see Figure 3j). Walking releases endorphins, which may reduce stress and improve mood, thereby contributing to the maintenance of better mental health [93]. Regular walking prevents and helps prevent chronic diseases [94]. Strong self-care abilities enhance daily task management and cognitive function. Engaging in sedentary, indoor, and outdoor standing activities, such as playing cards, dancing, gardening, or traveling, boosts physical fitness and psychological resilience [95]. These findings emphasize the importance of urban design in meeting older adults’ needs to improve their health.

7. Recommendations

7.1. Practical Recommendations

Urban managers should refurbish the accessibility of the BE in residential areas. It is recommended that elevators be selectively installed in residential buildings to meet the daily travel and activity needs of older individuals with mobility challenges [96]. For example, elevators in taller residential buildings should have usage restrictions for specific floors or periods. It is recommended that signboards be posted next to elevators to encourage older people without mobility issues or those on lower floors to use the stairs. Walkways should feature gentle slopes and varying textures to subtly challenge older people’s balance and coordination, promoting physical benefits with minimal risk. Continuous handrails along these walkways should be limited and provided only where necessary, such as at rough paths or resting points. This encourages older residents to move around without unnecessary support. In addition, public areas should include barrier-free toilets and rest areas to support outdoor activities for older individuals [97].
Land use mix should be prioritized as a factor of the BE for optimization and focused planning. Attempts could be made to decentralize small commercial areas and to locate within walking distance a variety of stores that cater to the daily needs of older persons. The centralized design of a single large commercial center should be avoided to increase older people’s short walking trips. At the same time, parking difficulties or terrain constraints may make older adults’ walking and self-care behaviors unpleasant or even unsafe. Multi-functional parking facilities, such as multi-story car parks or intelligent parking management systems, are recommended to facilitate safe travel for shoppers [98]. The pavement design should minimize canyons and steep slopes, incorporate gradual inclines, and avoid sudden terrain changes to encourage easy movement for older people. The layout of commercial facilities should ensure older persons’ easy access to basic services.
Dead-end streets should be avoided as much as possible to reduce walking detours for older people. It is recommended to increase the number of short street crossings. These crossings should be evenly distributed throughout the residential area to prevent poor accessible areas. Flat pavements and clear signages should be added to facilitate access to shops and hospitals [99]. Safe, well-lit, accessible intersections should be ensured to connect local facilities such as stores, medical centers, and parks that older individuals frequently access. Residential area events focused on health in neighborhoods with strong social ties encourage older adults to engage in various activities. In addition, rest areas with shade or residential area mini-health stations near intersections can assist older adults in completing daily walks while accessing health monitoring and support services.
The esthetic design of the BE, e.g., attractive natural landscapes, historical buildings, and open recreational areas, fosters a comfortable living space for older adults. It is suggested to increase the visual attractiveness of the residential area through planting evergreen plants [100]. Natural elements, such as flower beds or water features, are suggested to be incorporated into the walkways for paving boulevards or creating small wetland areas. Planting shade plants in activity areas can encourage outdoor engagement [101]. Attention should be paid to the tidiness and color design of building facades, adopting soft tones and exterior designs preferred by older people [102]. The environment must be clean and well maintained. Older residents should be encouraged to engage in residential area beautification, such as co-designing wall art or graffiti walls. Multi-functional open spaces should be created to promote social interactions among older adults. At the same time, recreational spaces that encourage intergenerational interactions between older adults and children should be added, as they enhance physical and social activities and reduce older people’s feelings of loneliness [58]. The developing history and cultural consensus of urban residential areas should be thoroughly examined to create urban landscapes that evoke older persons’ nostalgia [103].
It is recommended that traffic speed limits be more strictly enforced in residential areas, particularly in those with a high population of older individuals, to ensure the safety of older people while walking. In addition, traffic speed bumps, traffic signs, and electronic speed measuring devices can be installed to remind and help drivers to slow down. Overpasses or underpasses should be added on busy streets to ensure older adults’ safety. Additional pedestrian-only traffic signals should also be installed, and their duration should be sufficiently long, especially in areas with a large elderly population [104]. It is suggested that bus, metro, and light rail stations are within daily walking distance of older adults, preferably located near residential areas, healthcare centers, and parks. Sheltered waiting areas and seats are provided at these stops to increase the comfort of traveling for older people [105].

7.2. Research Limitations and Future Study

This study explores the interactions among the BE in urban residential areas, older adults’ self-efficacy, activity engagement, and self-rated health. However, the findings may be subject to common bias due to the reliance on self-report measures in the questionnaire [106]. Nevertheless, measures were implemented to mitigate the potential impact of methodological bias: (1) a preliminary trial was conducted before the formal distribution of the questionnaire, during which any problematic items were revised and adjusted to ensure the validity and reliability of the data; (2) factor analysis was employed to categorize the items of self-efficacy, activity engagement, and self-rated health into factors based on their underlying structure [79]; (3) all factors exhibited relatively high Cronbach’s alpha values, indicating that they were largely free of random measurement error [79]; and (4) the results obtained from factor analyses, reliability tests, correlation coefficients, and regression models were utilized to construct the final BE-SE-AE-SH model through method triangulation [107]. Therefore, it can be concluded that the results of this study are generally reliable. Despite these measures, data collection may still be challenging because the primary respondents are older adults. Some individuals may struggle to understand or answer the questions randomly, leading to inaccurate data. We employed a one-on-one questioning approach to collect questionnaire data to address this issue. We also provided training for the data collectors to ensure that the questions were phrased in clear and accessible language, allowing older adults to understand them and ensuring a consistent scoring standard.
This study produced several noteworthy findings. Although correlation and regression analyses established direct relationships among variables, future studies can further explore the mediating effect of self-efficacy and activity engagement. Offline data collection methods may lead to potential selection bias. Future studies could collect data according to seasonal variations with web-based questionnaires and increase the representativeness of the sample by expanding the study to the whole country or the world. Seasonal variations could be investigated as a moderating effect, which may potentially affect older people’s activity engagement. Given that only the effect of BE support was investigated, it is recommended that future studies consider social support as a moderating variable or a mediating variable. Moreover, more detailed and systematic research needs to be carried out on the land use mix. Future studies should consider its non-linear relationship with walking ability and activity engagement and examine whether it is affected by other potential variables. Given the geriatric health focus of this study, future studies should systematically incorporate health status as a covariate to better isolate the independent effects of environmental factors on self-efficacy, activity engagement, and self-rated health. This study used quantitative research to investigate the relationship between the BE, self-efficacy, activity engagement, and self-rated health among older adults in urban residential areas. Future research should incorporate qualitative methods, such as collecting objective environmental data, to provide a cross-validation of quantitative findings [108]. Older adults could be equipped with GPS devices to gather data on their activity trajectories, frequency, and duration, enabling a deeper understanding of their activity engagement [109]. Meanwhile, researchers could employ virtual reality to simulate the BE of urban residential areas and evaluate older adults’ behavioral performance in various simulated settings [110]. This will facilitate a deeper understanding of the interrelationship among the BE, self-efficacy, activity engagement, and self-rated health.

8. Conclusions

The built environment (BE) of urban areas has faced considerable issues due to the fast expansion of the older population, especially when meeting older persons’ activity and health demands. This study examines the interactions between the BE in urban residential areas, self-efficacy, activity engagement, and self-rated health. This study set out to test the research hypotheses through the BE-SE-AE-SH model for older adults in urban residential areas. To develop this model, this study adopted a scientific research methodology using questionnaires and a series of statistical tests. Based on the consistency of the results from factor analysis, reliability tests, correlation coefficients, and multiple regressions, a BE-SE-AE-SH model was developed. In light of the study’s findings, practical recommendations were proposed to make the built environment of urban residential areas more responsive to the needs of the aging population, including the following: (1) properly install elevators and continuous handrails and design footpaths for older people’s exercise; (2) decentralize small commercial areas and adopt multi-purpose parking facilities; (3) create more pedestrian-friendly infrastructure and shorter street crossings; (4) create esthetics that are more in line with humanistic ideas and natural elements; (5) limit the speed of vehicles and provide traffic signals for older pedestrians. The contributions of this study are as follows: (1) These practical insights can be used to improve urban planning and residential area design aimed at promoting healthy aging. (2) Based on empirical evidence, this study offers recommendations for policymakers, architects, and construction professionals, contributing to the development of age-friendly built environments.

Author Contributions

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

Funding

This research was funded by the National Natural Science Foundation of China (grant number 72401129); the Social Science Foundation of Jiangsu Province (grant number 24EYC008); the Social Science Foundation in Jiangsu Higher Education Institutions (grant number 2023SJYB0208); and the Jiangsu Province Social Science Applied Research Excellent Project (grant number 24SYB-061).

Institutional Review Board Statement

The study was conducted in accordance with the Declaration of Helsinki, and the protocol was approved by the Ethics Committee of Science and Technology at Nanjing Tech University (NJTECH-19) on 3 July 2024.

Data Availability Statement

The data presented in this study are available on request from the corresponding author due to privacy.

Acknowledgments

We sincerely thank all those who contributed to this paper.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Grinin, L.; Grinin, A.; Korotayev, A. Global aging: An integral problem of the future. How to turn a problem into a development driver? In Reconsidering the Limits to Growth: A Report to the Russian Association of the Club of Rome; Springer: Berlin/Heidelberg, Germany, 2023; pp. 117–135. [Google Scholar]
  2. Elnagar, E.; Düvier, C.; Batra, Z.; Christoffersen, J.; Mandin, C.; Schweiker, M.; Wargocki, P.J. Creating a comprehensive framework for design, construction and management of healthy buildings. Energy Build. 2024, 324, 114883. [Google Scholar] [CrossRef]
  3. Gu, T.; Li, D.; Li, L. The Elderly’s Demand for Community-Based Care Services and Its Determinants: A Comparison of the Elderly in the Affordable Housing Community and Commercial Housing Community of China. J. Healthc. Eng. 2020, 2020, 1840543. [Google Scholar] [CrossRef] [PubMed]
  4. Hung, H.-C.; Lin, Y.-C.; Hung, C.-H.; Chen, B. Enhancing resilience in ageing: The role of age-friendly community and individual health in reducing seismic risk. Int. J. Disaster Risk Reduct. 2024, 111, 104733. [Google Scholar] [CrossRef]
  5. Ren, Z.; Nikolopoulou, M.; Mills, G.; Pilla, F. Evaluating the influence of urban trees and microclimate on residential energy consumption in Dublin neighbourhoods. Build. Environ. 2025, 269, 112441. [Google Scholar] [CrossRef]
  6. OECD. Ageing in Cities. Available online: https://doi.org/10.1787/9789264231160-en (accessed on 30 April 2015).
  7. Qi, X.; Li, Y.; Hu, J.; Meng, L.; Zeng, P.; Shi, J.; Jia, N.; Zeng, X.; Li, H.; Zhang, Q.; et al. Prevalence of social frailty and its associated factors in the older Chinese population: A national cross-sectional study. BMC Geriatr. 2023, 23, 532. [Google Scholar] [CrossRef]
  8. Xu, T.; Huang, Z.; Li, B.; Jin, H.; Zhang, J.; Yang, H.; Huang, Y.; Zhang, X.; Chen, C. Association between home and community-based services utilization and self-rated health among Chinese older adults with chronic diseases: Evidence from the 2018 China Health and Retirement Longitudinal Study. BMC Public. Health 2024, 24, 117. [Google Scholar] [CrossRef]
  9. Fernández-Jiménez, C.; Dumitrache, C.G.; Rubio, L.; Ruiz-Montero, P. Self-perceptions of ageing and perceived health status: The mediating role of cognitive functioning and physical activity. Ageing Soc. 2024, 44, 622–641. [Google Scholar] [CrossRef]
  10. Bandura, A. Social cognitive theory: An agentic perspective. Annu. Rev. Psychol. 2001, 52, 1–26. [Google Scholar] [CrossRef]
  11. Schwartz, C.E.; Coulthard-Morris, L.; Zeng, Q.; Retzlaff, P. Measuring self-efficacy in people with multiple scleoris: A validation study. Arch. Phys. Med. Rehabil. 1996, 77, 394–398. [Google Scholar] [CrossRef]
  12. World Health Organization. Global Age-Friendly Cities: A Guide. 2007. Available online: https://www.who.int/publications/i/item/9789241547307 (accessed on 5 October 2007).
  13. Shi, J.; Liu, X.; Feng, Z. Age-friendly cities and communities and cognitive health among Chinese older adults: Evidence from the China Health and Retirement Longitudinal Studies. Cites 2023, 132, 104072. [Google Scholar] [CrossRef]
  14. Van Hoof, J.; Marston, H.R.; Kazak, J.K.; Buffel, T. Ten questions concerning age-friendly cities and communities and the built environment. Build. Environ. 2021, 199, 107922. [Google Scholar] [CrossRef]
  15. Brownson, R.C.; Hoehner, C.M.; Day, K.; Forsyth, A.; Sallis, J.F. Measuring the built environment for physical activity: State of the science. Am. J. Prev. Med. 2009, 36, S99–S123.e112. [Google Scholar] [CrossRef] [PubMed]
  16. Farmer, H.; Xu, H.; Dupre, M.E. Self-efficacy. In Encyclopedia of Gerontology and Population Aging; Springer: Berlin/Heidelberg, Germany, 2022; pp. 4410–4413. [Google Scholar] [CrossRef]
  17. Handy, S.L.; Boarnet, M.G.; Ewing, R.; Killingsworth, R.E. How the built environment affects physical activity: Views from urban planning. Am. J. Prev. Med. 2002, 23, 64–73. [Google Scholar] [CrossRef] [PubMed]
  18. Seyedrezaei, M.; Becerik-Gerber, B.; Awada, M.; Contreras, S.; Boeing, G. Equity in the built environment: A systematic review. Build. Environ. 2023, 245, 110827. [Google Scholar] [CrossRef]
  19. Zheng, Z.; Liu, W.; Lu, Y.; Sun, N.; Chu, Y.; Chen, H. The influence mechanism of community-built environment on the health of older adults: From the perspective of low-income groups. BMC Geriatr. 2022, 22, 590. [Google Scholar] [CrossRef]
  20. Chen, Y.; Zhang, J. A Study on the Influence of an Outdoor Built Environment on the Activity Behavior of the Elderly in Small Cities in Cold Regions—A Case Study of Bei’an City. Sustainability 2025, 17, 2260. [Google Scholar] [CrossRef]
  21. World Health Organization. National Programmes for Age-Friendly Cities and Communities: A Guide. 2023. Available online: https://www.who.int/publications/i/item/9789240068698 (accessed on 19 April 2023).
  22. Cerin, E.; Saelens, B.E.; Sallis, J.F.; Frank, L.D. Neighborhood Environment Walkability Scale: Validity and development of a short form. Med. Sci. Sports Exerc. 2006, 38, 1682. [Google Scholar] [CrossRef]
  23. Carlsson, G.; Slaug, B.; Schmidt, S.; Norin, L.; Ronchi, E.; Gefenaite, G. A scoping review of public building accessibility. Disabil. Health J. 2022, 15, 101227. [Google Scholar] [CrossRef]
  24. Mitropoulos, L.; Karolemeas, C.; Tsigdinos, S.; Vassi, A.; Bakogiannis, E. A composite index for assessing accessibility in urban areas: A case study in Central Athens, Greece. J. Transp. Geogr. 2023, 108, 103566. [Google Scholar] [CrossRef]
  25. P Pouso, S.; Borja, Á.; Fleming, L.E.; Gómez-Baggethun, E.; White, M.P.; Uyarra, M.C. Contact with blue-green spaces during the COVID-19 pandemic lockdown beneficial for mental health. Sci. Total Environ. 2021, 756, 143984. [Google Scholar] [CrossRef]
  26. Guo, B.; Zhang, L.; Li, Y. Research on the path of residents’ willingness to upgrade by installing elevators in old residential quarters based on safety precautions. Saf. Sci. 2019, 118, 389–396. [Google Scholar] [CrossRef]
  27. Hartblay, C. Good ramps, bad ramps: Centralized design standards and disability access in urban Russian infrastructure. Am. Ethnol. 2017, 44, 9–22. [Google Scholar] [CrossRef]
  28. Noordzij, J.M.; Beenackers, M.; Groeniger, J.O.; Timmermans, E.; Motoc, I.; Huisman, M.; Van Lenthe, F.; Activity, P. Land use mix and physical activity in middle-aged and older adults: A longitudinal study examining changes in land use mix in two Dutch cohorts. Int. J. Behav. Nutr. Phys. Act. 2021, 18, 1–10. [Google Scholar] [CrossRef] [PubMed]
  29. Cerin, E.; Barnett, A.; Shaw, J.E.; Martino, E.; Knibbs, L.D.; Tham, R.; Wheeler, A.J.; Anstey, K.J. Urban neighbourhood environments, cardiometabolic health and cognitive function: A national cross-sectional study of middle-aged and older adults in Australia. Toxics 2022, 10, 23. [Google Scholar] [CrossRef] [PubMed]
  30. Maresova, P.; Krejcar, O.; Maskuriy, R.; Bakar, N.A.A.; Selamat, A.; Truhlarova, Z.; Horak, J.; Joukl, M.; Vítkova, L. Challenges and opportunity in mobility among older adults–key determinant identification. BMC Geriatr. 2023, 23, 447. [Google Scholar] [CrossRef]
  31. Liu, J.; Wang, B.; Xiao, L. Non-linear associations between built environment and active travel for working and shopping: An extreme gradient boosting approach. J. Transp. Geogr. 2021, 92, 103034. [Google Scholar] [CrossRef]
  32. Jia, P.; Zou, Y.; Wu, Z.; Zhang, D.; Wu, T.; Smith, M.; Xiao, Q. Street connectivity, physical activity, and childhood obesity: A systematic review and meta-analysis. Obes. Rev. 2021, 22, e12943. [Google Scholar] [CrossRef]
  33. Song, S.; Yap, W.; Hou, Y.; Yuen, B. Neighbourhood built Environment, physical activity, and physical health among older adults in Singapore: A simultaneous equations approach. J. Transp. Health 2020, 18, 100881. [Google Scholar] [CrossRef]
  34. Xu, L.; Han, H.; Yang, C.; Liu, Q.J.S. The influence mechanism of the community subjectively built environment on the physical and mental health of older adults. Sustainability 2023, 15, 13211. [Google Scholar] [CrossRef]
  35. Chen, Y.-R.; Hanazato, M.; Koga, C.; Ide, K.; Kondo, K. The association between street connectivity and depression among older Japanese adults: The JAGES longitudinal study. Sci. Rep. 2022, 12, 13533. [Google Scholar] [CrossRef]
  36. Boakye, K.; Bovbjerg, M.; Schuna Jr, J.; Branscum, A.; Mat-Nasir, N.; Bahonar, A.; Barbarash, O.; Yusuf, R.; Lopez-Jaramillo, P.; Seron, P. Perceived built environment characteristics associated with walking and cycling across 355 communities in 21 countries. Cities 2023, 132, 104102. [Google Scholar] [CrossRef]
  37. Dogra, S.; Dunstan, D.W.; Sugiyama, T.; Stathi, A.; Gardiner, P.A.; Owen, N. Active aging and public health: Evidence, implications, and opportunities. Annu. Rev. Public. Health 2022, 43, 439–459. [Google Scholar] [CrossRef] [PubMed]
  38. Brady, E.; Prior, J. Environmental aesthetics: A synthetic review. People Nat. 2020, 2, 254–266. [Google Scholar] [CrossRef]
  39. Shen, J.; Fan, J.; Wu, S.; Xu, X.; Fei, Y.; Liu, Z.; Xiong, S. A Study on the Impact of a Community Green Space Built Environment on Physical Activity in Older People from a Health Perspective: A Case Study of Qingshan District, Wuhan. Sustainability 2025, 17, 263. [Google Scholar] [CrossRef]
  40. Inoue, T.; Manabe, R.; Murayama, A.; Koizumi, H. People–Place Relationships in Regenerative Urban Assemblages: Streetscape Composition and Subjective Well-Being of Older Adults. Land 2025, 14, 680. [Google Scholar] [CrossRef]
  41. Mercader-Moyano, P.; Flores-García, M.; Serrano-Jiménez, A. Housing and neighbourhood diagnosis for ageing in place: Multidimensional Assessment System of the Built Environment (MASBE). Sustain. Cities Soc. 2020, 62, 102422. [Google Scholar] [CrossRef]
  42. Yang, Y.; Sasaki, K.; Cheng, L.; Tao, S. Does the built environment matter for active travel among older adults: Insights from Chiba City, Japan. J. Transp. Geogr. 2022, 101, 103338. [Google Scholar] [CrossRef]
  43. Dabelko-Schoeny, H.; Maleku, A.; Cao, Q.; White, K.; Ozbilen, B. “We want to go, but there are no options”: Exploring barriers and facilitators of transportation among diverse older adults. J. Transp. Health 2021, 20, 100994. [Google Scholar] [CrossRef]
  44. Aghabayk, K.; Esmailpour, J.; Jafari, A.; Shiwakoti, N. Observational-based study to explore pedestrian crossing behaviors at signalized and unsignalized crosswalks. Accid. Anal. Prev. 2021, 151, 105990. [Google Scholar] [CrossRef]
  45. Bendak, S.; Alnaqbi, A.M.; Alzarooni, M.Y.; Aljanaahi, S.M.; Alsuwaidi, S.J. Factors affecting pedestrian behaviors at signalized crosswalks: An empirical study. J. Safety Res. 2021, 76, 269–275. [Google Scholar] [CrossRef]
  46. Duncan, M.; Torres, Y.V.; Gladwin, K.; Horner, M.; Wood, B. Transit-oriented development for older adults. J. Transp. Land. Use 2021, 14, 255–276. [Google Scholar] [CrossRef]
  47. Cervone, D.; Shadel, W.G.; Jencius, S. Social-cognitive theory of personality assessment. Pers. Soc. Psychol. Rev. 2001, 5, 33–51. [Google Scholar] [CrossRef]
  48. Leung, M.-Y.; Wang, C.; Famakin, I.O. Integrated model for indoor built environment and cognitive functional ability of older residents with dementia in care and attention homes. Build. Environ. 2021, 195, 107734. [Google Scholar] [CrossRef]
  49. Schwarzer, R.; Hallum, S. Perceived teacher self-efficacy as a predictor of job stress and burnout: Mediation analyses. Appl. Psychol. 2008, 57, 152–171. [Google Scholar] [CrossRef]
  50. Tadaka, E.; Kono, A.; Ito, E.; Kanaya, Y.; Dai, Y.; Imamatsu, Y.; Itoi, W. Development of a community’s self-efficacy scale for preventing social isolation among community-dwelling older people (Mimamori Scale). BMC Public. Health 2016, 16, 1–9. [Google Scholar] [CrossRef] [PubMed]
  51. Cheng, L.; De Vos, J.; Zhao, P.; Yang, M.; Witlox, F. Examining non-linear built environment effects on elderly’s walking: A random forest approach. Transp. Res. D Transp. Environ. 2020, 88, 102552. [Google Scholar] [CrossRef]
  52. Kikkert, L.H.; Vuillerme, N.; van Campen, J.P.; Hortobágyi, T.; Lamoth, C.J. Walking ability to predict future cognitive decline in old adults: A scoping review. Ageing Res. Rev. 2016, 27, 1–14. [Google Scholar] [CrossRef]
  53. Prescott, M.; Labbé, D.; Miller, W.C.; Borisoff, J.; Feick, R.; Mortenson, W.B. Factors that affect the ability of people with disabilities to walk or wheel to destinations in their community: A scoping review. Transp. Rev. 2020, 40, 646–669. [Google Scholar] [CrossRef]
  54. Lisko, I.; Kulmala, J.; Annetorp, M.; Ngandu, T.; Mangialasche, F.; Kivipelto, M. How can dementia and disability be prevented in older adults: Where are we today and where are we going? J. Int. Med. 2021, 289, 807–830. [Google Scholar] [CrossRef]
  55. Guo, Y.; Wang, T.; Ge, T.; Jiang, Q. Prevalence of self-care disability among older adults in China. BMC Geriatr. 2022, 22, 775. [Google Scholar] [CrossRef]
  56. Chica-Pérez, A.; Dobarrio-Sanz, I.; Correa-Casado, M.; Fernández-Sola, C.; Ruiz-Fernández, M.D.; Hernández-Padilla, J.M. Spanish version of the self-care self-efficacy scale: A validation study in community-dwelling older adults with chronic multimorbidity. Geriatr. Nurs. 2023, 53, 181–190. [Google Scholar] [CrossRef] [PubMed]
  57. Yap, X.Y.; Wai San Wilson, T.; Tan, Y.Q.; Yanhong, D.; Loh, L.X.; Tan, P.C.; Peiying, G.; Zhang, D.; Wu, X.V. Path analysis of self-care amongst community-dwelling pre-ageing and older adults with chronic diseases: A salutogenic model. Geriatr. Nurs. 2024, 59, 516–525. [Google Scholar] [CrossRef] [PubMed]
  58. Zhong, S.; Lee, C.; Foster, M.J.; Bian, J. Intergenerational communities: A systematic literature review of intergenerational interactions and older adults’ health-related outcomes. Soc. Sci. Med. 2020, 264, 113374. [Google Scholar] [CrossRef] [PubMed]
  59. Eckstrom, E.; Neukam, S.; Kalin, L.; Wright, J. Physical activity and healthy aging. Clin. Geriatr. Med. 2020, 36, 671–683. [Google Scholar] [CrossRef]
  60. Benedyk, A.; Reichert, M.; Giurgiu, M.; Timm, I.; Reinhard, I.; Nigg, C.; Berhe, O.; Moldavski, A.; von der Goltz, C.; Braun, U. Real-life behavioral and neural circuit markers of physical activity as a compensatory mechanism for social isolation. Nat. Mental Health 2024, 2, 337–342. [Google Scholar] [CrossRef]
  61. Cunningham, C.; O’Sullivan, R.; Caserotti, P.; Tully, M.A. Consequences of physical inactivity in older adults: A systematic review of reviews and meta-analyses. Scand. J. Med. Sci. Sports 2020, 30, 816–827. [Google Scholar] [CrossRef]
  62. Mellow, M.L.; Crozier, A.J.; Dumuid, D.; Wade, A.T.; Goldsworthy, M.R.; Dorrian, J.; Smith, A.E. How are combinations of physical activity, sedentary behaviour and sleep related to cognitive function in older adults? A systematic review. Exp. Gerontol. 2022, 159, 111698. [Google Scholar] [CrossRef]
  63. Rodrigues, I.B.; Ching, P.; Kalra, M.; Zheng, R.; Rabinovich, A.; Papaioannou, A.; Leckie, C.; Kobsar, D.; Fang, Q.; Bray, S. Exploring the perspectives of older adults who are pre-frail and frail to identify interventions to reduce sedentary behaviour and improve mobility: A thematic content analysis. BMC Public. Health 2024, 24, 1582. [Google Scholar] [CrossRef]
  64. Nguyen, P.-Y.; Astell-Burt, T.; Rahimi-Ardabili, H.; Feng, X. Effect of nature prescriptions on cardiometabolic and mental health, and physical activity: A systematic review. Lancet Planet. Health 2023, 7, e313–e328. [Google Scholar] [CrossRef]
  65. World Health Organization. Available online: https://www.who.int/about/governance/constitution (accessed on 7 April 1948).
  66. Wilson, R.L.; Hutton, A.; Foureur, M. Promoting mental health recovery by design: Physical, procedural, and relational security in the context of the mental health built environment. Int. J. Ment. Health Nurs. 2023, 32, 147–161. [Google Scholar] [CrossRef]
  67. Santini, Z.I.; Jose, P.E.; Koyanagi, A.; Meilstrup, C.; Nielsen, L.; Madsen, K.R.; Koushede, V. Formal social participation protects physical health through enhanced mental health: A longitudinal mediation analysis using three consecutive waves of the Survey of Health, Ageing and Retirement in Europe (SHARE). Soc. Sci. Med. 2020, 251, 112906. [Google Scholar] [CrossRef] [PubMed]
  68. Jylhä, M. What is self-rated health and why does it predict mortality? Towards a unified conceptual model. Soc. Sci. Med. 2009, 69, 307–316. [Google Scholar] [CrossRef] [PubMed]
  69. Christoforou, R.; Lange, S.; Schweiker, M. Individual differences in the definitions of health and well-being and the underlying promotional effect of the built environment. J. Build. Eng. 2024, 84, 108560. [Google Scholar] [CrossRef]
  70. Fırat Kılıç, H.; Arifoğlu, B.; Kızılkaya, N. The relationship between successful aging and health literacy in older adults. Perspect. Psychiatr. Care 2023, 2023, 1462914. [Google Scholar] [CrossRef]
  71. Sibarani, R.; Simanjuntak, P.; Sibarani, E.J. The role of women in preserving local wisdom Poda Na Lima ‘Five Advices of Cleanliness’ for the community health in Toba Batak at Lake Toba area. Gac. Sanit. 2021, 35, S533–S536. [Google Scholar] [CrossRef]
  72. Peterson, A.; Karlawish, J.; Largent, E. Supported decision making with people at the margins of autonomy. Am. J. Bioeth. 2021, 21, 4–18. [Google Scholar] [CrossRef]
  73. Bandura, A. Social cognitive theory of self-regulation. Organ. Behav. Hum. Decis. Process. 1991, 50, 248–287. [Google Scholar] [CrossRef]
  74. Chou, K.-L. Correlates of everyday competence in Chinese older adults. Aging Ment. Health 2003, 7, 308–315. [Google Scholar] [CrossRef]
  75. Wu, Z.; Schimmele, C.M. Psychological disposition and self-reported health among the ‘oldest-old’in China. Ageing Soc. 2006, 26, 135–151. [Google Scholar] [CrossRef]
  76. Yamada, Y.; Noriyasu, R.; Yokoyama, K.; Osaki, T.; Adachi, T.; Itoi, A.; Morimoto, T.; Oda, S.; Kimura, M. Association between lifestyle and physical activity level in the elderly: A study using doubly labeled water and simplified physical activity record. Eur. J. Appl. Physiol. 2013, 113, 2461–2471. [Google Scholar] [CrossRef]
  77. Pallant, J. PSS Survival Manual: A Step by Step Guide to Data Analysis Using IBM SPSS, 7th ed.; Open University Press: London, UK, 2020. [Google Scholar] [CrossRef]
  78. Comrey, A.L.; Lee, H.B. A First Course in Factor Analysis; Psychology Press: New York, NY, USA, 2013. [Google Scholar] [CrossRef]
  79. Yates, A. Multivariate Exploratory Data Analysis: A Perspective on Exploratory Factor Analysis; State University of New York Press: New York, NY, USA, 1987. [Google Scholar]
  80. Nunnally, J.C. An Overview of Psychological Measurement. Clinical Diagnosis of Mental Disorders; Springer: Boston, MA, USA, 1978; pp. 97–146. [Google Scholar] [CrossRef]
  81. Cooper, D.R.; Schindler, P. Business Research Methods; Mcgraw-Hill: New York, NY, USA, 2014. [Google Scholar] [CrossRef]
  82. Mjøsund, H.L.; Uhrenfeldt, L.; Burton, E.; Moe, C.F. Promotion of physical activity in older adults: Facilitators and barriers experienced by healthcare personnel in the context of reablement. BMC Health Serv. Res. 2022, 22, 956. [Google Scholar] [CrossRef] [PubMed]
  83. Tang, B.-S.; Wong, K.K.; Tang, K.S.; Wai Wong, S.; Science, C. Walking accessibility to neighbourhood open space in a multi-level urban environment of Hong Kong. Environ. Plan. B Urban. Anal. City Sci. 2021, 48, 1340–1356. [Google Scholar] [CrossRef]
  84. Hjorthol, R.J.; Levin, L.; Sirén, A. Mobility in different generations of older persons: The development of daily travel in different cohorts in Denmark, Norway and Sweden. J. Transp. Geogr. 2010, 18, 624–633. [Google Scholar] [CrossRef]
  85. Wang, Y.; Chau, C.K.; Ng, W.; Leung, T. A review on the effects of physical built environment attributes on enhancing walking and cycling activity levels within residential neighborhoods. Cities 2016, 50, 1–15. [Google Scholar] [CrossRef]
  86. Rosa, E. Why self-care matters for Roma people and beyond. Vulnerability and the (un) making of water and sanitation infrastructure at the margins of the city. Geoforum 2019, 101, 192–201. [Google Scholar] [CrossRef]
  87. Wang, Z.; Lee, C. Site and neighborhood environments for walking among older adults. Health Place 2010, 16, 1268–1279. [Google Scholar] [CrossRef]
  88. Ramakreshnan, L.; Fong, C.S.; Sulaiman, N.M.; Aghamohammadi, N. Motivations and built environment factors associated with campus walkability in the tropical settings. Sci. Total Environ. 2020, 749, 141457. [Google Scholar] [CrossRef]
  89. Gong, K.; Wang, C.; Yin, J. Effects of the Natural Environment on the Subjective and Psychological Well-Being of Older People in the Community in China. Buildings 2024, 14, 2854. [Google Scholar] [CrossRef]
  90. Lommi, M.; Matarese, M.; Alvaro, R.; Piredda, M.; De Marinis, M.G. The experiences of self-care in community-dwelling older people: A meta-synthesis. Int. J. Nurs. Stud. 2015, 52, 1854–1867. [Google Scholar] [CrossRef]
  91. Xiaohelaiti, X.; Liu, X.; Li, C.; Wang, T.; Wu, J.; Peng, B.; Zhang, Y. Neighborhood environment and body mass index in community-dwelling older adults in China: The mediating role of transport-related physical activity. Geriatr. Nurs. 2024, 55, 304–310. [Google Scholar] [CrossRef]
  92. Southworth, M. development. Designing the walkable city. J. Urban. Plan. Dev. 2005, 131, 246–257. [Google Scholar] [CrossRef]
  93. Holland, C.J.; Cole, M.; Owens, J. Exercise and mental health: A vital connection. Br. J. Sports Med. 2024, 58, 691–692. [Google Scholar] [CrossRef]
  94. Master, H.; Annis, J.; Huang, S.; Beckman, J.A.; Ratsimbazafy, F.; Marginean, K.; Carroll, R.; Natarajan, K.; Harrell, F.E.; Roden, D.M. Association of step counts over time with the risk of chronic disease in the All of Us Research Program. Nat. Med. 2022, 28, 2301–2308. [Google Scholar] [CrossRef]
  95. Leung, M.-Y.; Wang, C.; Wei, X. Structural model for the relationships between indoor built environment and behaviors of residents with dementia in care and attention homes. Build. Environ. 2020, 169, 106532. [Google Scholar] [CrossRef]
  96. Ruza, J.; Kim, J.I.; Leung, I.; Kam, C.; Ng, S.Y.M. Sustainable, age-friendly cities: An evaluation framework and case study application on Palo Alto, California. Sustain. Cities Soc. 2015, 14, 390–396. [Google Scholar] [CrossRef]
  97. Sholanke, A.; Eleagu, J. Appraisal of Universal Design Strategies for Enhancing Social Inclusion in The Development of Sporting Facilities. IOP Conf. Ser. Earth Environ. Sci. 2024, 1342, 012024. [Google Scholar] [CrossRef]
  98. Scheiner, J.; Faust, N.; Helmer, J.; Straub, M.; Holz-Rau, C. What’s that garage for? Private parking and on-street parking in a high-density urban residential neighbourhood. J. Transp. Geogr. 2020, 85, 102714. [Google Scholar] [CrossRef]
  99. Flores, N.V.; Salvador, L.C.C.; dos Santos, A.C.P.; Madero, Y.S. A proposal to compare urban infrastructure using multi-criteria analysis. Land Use Policy 2021, 101, 105173. [Google Scholar] [CrossRef]
  100. Wang, R.; Zhao, J. Effects of evergreen trees on landscape preference and perceived restorativeness across seasons. Landsc. Res. 2020, 45, 649–661. [Google Scholar] [CrossRef]
  101. Tomasi, M.; Nikolopoulou, M.; Giridharan, R.; Löve, M. A design workflow for effective solar shading of pedestrian paths. Build. Environ. 2024, 261, 111718. [Google Scholar] [CrossRef]
  102. Jin, J. From Architectural Image to Design Intention: A Study of Architectural Art Design. Mediterr. Archaeol. Archaeom. 2024, 24, 26–38. [Google Scholar]
  103. Leung, M.; Wang, C.; Li, L. Impact of indoor visual environment on emotional and behavioral symptoms of older people with dementia. Build. Environ. 2024, 265, 111980. [Google Scholar] [CrossRef]
  104. Wong, R.; Szeto, W.; Yang, L.; Li, Y.; Wong, S. Public transport policy measures for improving elderly mobility. Transp. Policy 2018, 63, 73–79. [Google Scholar] [CrossRef]
  105. Van Hagen, M. Waiting Experience at Train Stations; Eburon Academic Publishers: Delft, The Netherlands, 2011. [Google Scholar]
  106. Spector, P.E. Method variance as an artifact in self-reported affect and perceptions at work: Myth or significant problem? J. Appl. Psychol. 1987, 72, 438. [Google Scholar] [CrossRef]
  107. Denzin, N.K. The Research Act: A Theoretical Introduction to Sociological Methods; Routledge: New York, NY, USA, 2017. [Google Scholar] [CrossRef]
  108. Alam, M.K. A systematic qualitative case study: Questions, data collection, NVivo analysis and saturation. Qual. Res. Organ. Manag. 2021, 16, 1–31. [Google Scholar] [CrossRef]
  109. Bayat, S.; Naglie, G.; Rapoport, M.J.; Stasiulis, E.; Widener, M.J.; Mihailidis, A. A GPS-based framework for understanding outdoor mobility patterns of older adults with dementia: An exploratory study. Gerontology 2022, 68, 106–120. [Google Scholar] [CrossRef]
  110. Zhang, Y.; Liu, H.; Kang, S.-C.; Al-Hussein, M. Virtual reality applications for the built environment: Research trends and opportunities. Autom. Constr. 2020, 118, 103311. [Google Scholar] [CrossRef]
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