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Article

The Impact of the Neighborhood Built Environment on the Commuting Patterns and Health of Patients with Chronic Diseases: A Case Study of Changshu, China

by
Hao Wu
1,
Hongbin Wang
1,*,
Duanyang Liu
1,
Yang Cao
2 and
Yawei Qu
3
1
Key Laboratory of Transportation Meteorology of China Meteorological Administration, Nanjing Joint Institute for Atmospheric Sciences, Nanjing 210041, China
2
Jiangsu Province City Layout Design Research Institute, Nanjing 210036, China
3
College of Intelligent Science and Control Engineering, Jinling Institute of Technology, Nanjing 211169, China
*
Author to whom correspondence should be addressed.
Sustainability 2022, 14(18), 11201; https://doi.org/10.3390/su141811201
Submission received: 15 August 2022 / Revised: 30 August 2022 / Accepted: 5 September 2022 / Published: 7 September 2022
(This article belongs to the Special Issue Urban and Social Geography and Sustainability)
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Abstract

:
The health of patients with chronic diseases is affected not only by factors such as eating and living habits, but also by the neighborhood built environment, and by travel conditions. Using 18 blocks in the main urban area of Changshu, China, we performed structural equation modelling, to explore the relationships between the neighborhood built environment, the commuting patterns of patients with chronic diseases, and patient health. We first divided the commuting patterns into four categories—walking/biking, public transportation, electric vehicles/motorcycles, and cars—and conducted a street-scale questionnaire survey. Secondly, we divided ‘health’ into three latent variables—physical health, mental health, and healthy behavior—and analyzed the factors influencing the street environment. Finally, we verified our theoretical framework through a mathematical statistical analysis model. We found that: (1) service facility, environmental quality, and community safety perception in the neighborhood built environment significantly impacted commuting patterns; (2) the patient’s physical health was significantly correlated with healthy behaviors and daily commuting patterns; and (3) socioeconomic attributes directly affected neighborhood environmental satisfaction, and indirectly affected the patient’s health. Neighborhood environmental satisfaction also directly affected the patient’s health, and there was a cross-influence relationship between these factors. We propose strengthening the walkability and connectivity of the neighborhood built environment, and improving the health awareness of patients, and their willingness to participate in healthy behaviors.

1. Introduction

According to survey data, nearly 60% of all patients with chronic diseases in China are working people between the ages of 20 and 60. Commuting is an important part of daily transportation for urban employees, accounting for more than 50% of total daily trips in China [1]. In the current context of rapid national economic development, urban space is expanding, and the proportion of patients with chronic diseases who choose to commute by car is on the rise. The increasing number of car-commuting trips has led to tremendous pressure on urban traffic, causing traffic congestion and environmental pollution. Simultaneously, workers’ over-reliance on car commuting also increases their sedentary time, leading to insufficient physical activity, greater mental stress, and negative emotions, which can cause various health problems [2,3,4].
At present, both patients and national governments are paying increasing attention to national health issues. In 2016, the State Council issued and implemented China’s first medium-and-long-term strategic plan in the field of health, the ‘Healthy China 2030’ Plan Outline. This reflected the importance that the Chinese government attaches to national health and living environment issues.
Several researchers have explored the relationship between the built environment and travel behavior, but these studies have mostly focused on the built environment at the macro-level, such as density [5,6,7,8], diversity [9,10,11,12], design [13,14,15,16], and destination accessibility [17,18]. Environmental satisfaction comprises people’s subjective satisfaction with, and psychological judgement of, changes in the surrounding environment, and it is the psychological basis of people’s behavior towards the environment [19]. It is subjective because even people living in areas with the same built environment may have different levels of satisfaction and behaviors relating to their environment, due to their personal experiences, attitudes, preferences, and individual socioeconomic attributes [20].
The International Health Organization defines ‘chronic disease’ as a general term for chronic non-communicable diseases, mainly referring to tumors and cardiovascular and cerebrovascular diseases caused by environmental pollution, dietary habits, lifestyle, and other factors [21,22]. This study focused on three types of chronic diseases: hypertension, hyperlipidemia, and diabetes. Chronic diseases are mostly concentrated in middle-aged and elderly people, and the duration of the disease is relatively long [23]. In general, chronic diseases arise without a specific source of infection, primarily occurring due to the combined influence of various factors, such as the patient’s daily life environment, behavior, eating habits, and family genetics. Therefore, patients with chronic diseases are more sensitive to their living environment than are patients with other diseases. A safe, convenient, open, and esthetically pleasing neighborhood built environment helps promote active physical activity among chronically ill patients, thereby improving their physical and mental health [24,25,26].
However, few studies have analyzed the relationship between commuting behavior and satisfaction with the built environment. The existing research also lacks a unified understanding of the measurement indicators for the built environment. The neighborhood environment identified in this paper refers to an area enclosed by natural boundaries or urban roads [27,28]. The land-use scale and population scale of the neighborhood environment are similar to the residential area. People in a neighborhood environment have the same sense of regional belonging and cultural identity. The built environment of the neighborhood refers to the artificially constructed physical environment within the living area of the residents. The content of the neighborhood built environment includes houses, schools, factories, parks, commercial areas, and roads. Among published studies, Humpel et al. propose that swift commute routes, subjectively identified by residents, are conducive to walking behavior [29]. Panter et al. found that an increased sense of danger when cycling or crossing the road is related to the proportion of car trips, and discourages walking behavior [30]. Others have indicated that different choices for commuting have different health effects [31,32,33,34,35]. In addition, some studies suggest that the choice of different commuting patterns among patients with chronic diseases will have different health effects [3,4,33,34,35]. The health status and health behavior of patients with chronic diseases may affect their choice of commuting pattern [36,37,38,39,40]. At present, scholars have focused on the impact of commuting behavior on residents’ health; relatively few scholars have focused on the relationship between commuting behavior and the health status of patients with chronic diseases.
There is no consistent research conclusion on the impact of the neighborhood built environment on the choice of commuting pattern among patients with chronic diseases at home and abroad, and there is a lack of research on the correlation between the neighborhood built environment and the health status of patients with chronic diseases. Therefore, this study explored this relationship, taking Changshu City as a case study, and selecting factors such as the built-up environment of the streets at the micro-level, and the choice of commuting pattern of patients with chronic diseases. The results help expand existing research on the relationships among perceptions of the built environment, patient health, and commuting patterns. This study will help urban management departments formulate targeted policies, and improve the health awareness of patients with chronic diseases, and their willingness to participate in healthy behaviors. It is hoped that the selection, by patients with chronic diseases, of healthy behaviors and commuting patterns, will help those patients to improve their own health.

2. Data and Methods

2.1. Data Collection

Changshu City is a county-level city in Jiangsu Province. Changshu City has 8 towns and 6 streets under its jurisdiction, with a total area of 1276.32 square kilometers. In 2020, the resident population of Changshu City was 1.677 million. With the increasing aging of the population, there were 339,200 chronic disease patients in Changshu in 2019, accounting for 20.23% of the permanent population. Changshu City has a relatively high economic level, and is at the forefront of China’s county-level cities. In the past 10 years, the urban construction level of Changshu has developed rapidly. The roads and infrastructure of the city are relatively complete, and the people’s living standards are generally high. The data used in this study come from the questionnaire survey on ‘Physical Activity and Health Status of Chronic Disease Patients in Changshu City’ conducted from December 2018 to January 2019. The survey randomly selected 18 typical communities in the main urban area of Changshu City, as case sites (Figure 1), and used random sampling to complete the questionnaire. The survey screened patients aged 18 and above, with chronic diseases, who were working full-time. The content mainly involved the patient’s socioeconomic background, choice of daily commuting pattern, neighborhood built environment, and self-assessed personal health status (see Appendix A). A total of 1754 questionnaires were distributed; 1632 valid questionnaires were recovered, for an effective recovery rate of 93.04%. This study included 1632 valid responses in a regression model; Table 1 shows the socioeconomic attributes of the participants.
In the process of designing the questionnaire, a number of medical ethics standards from relevant studies were used for reference [41]. These ethical standards put forward a series of requirements for the investigation of special populations. Therefore, we distributed 150 questionnaires for survey testing before the start of the survey phase. Specifically, a confidentiality option for patient privacy was added to the questionnaire content. The content of the questionnaire fully informed the respondents of the risks they faced, and of their rights. In addition, the research team conducted a full discussion before carrying out the questionnaire survey work. Possible private information included the name, age, disease type, and residential address of the patients with chronic diseases. For the content of the questionnaire, we solicited guidance and advice from medical ethics experts. In addition, the content of the questionnaire was also authorized and approved by the Changshu Health Ethics Review and Management Committee (Approval Code: 20190407001). We kept the above information strictly confidential, and promised to use it only for this research.
The participants consented to participate in a survey of their personal social and economic background and daily activity habits, and to assess their built environment. In particular, the questionnaire was anonymized, to protect the participants’ privacy. This study generally examined the impact of social and economic background, and the built environment, on the medical choices of the patients. We also obtained the basic attribute information of the residential areas of the respondents, using the internet. We used a geographic information system-mapping platform to gather the spatial location and attribute information. To further protect participant privacy, we actively deleted name attributes when we obtained data through the questionnaire. This study only explored the characteristics and mechanisms of group activity, and did not conduct a differential analysis of individual patients.
We set options in the questionnaire to distinguish patients with chronic diseases, and to obtain details such as their own attributes, subjective evaluations of the patients’ neighborhood built environment, and daily commuting patterns. We asked the respondents to try their best to complete the questionnaire. We analyzed the daily commuting behavior and living environment of the patients who had similar attributes, through regression models. The activities were those performed by groups of patients with similar socioeconomic backgrounds, living environments, and behavioral habits.

2.2. Research Methods

A structural equation model (SEM) is a kind of multivariate data analysis tool that examines the relationship between variables based on a covariance matrix. It is actually an extension of the general linear model, which can analyze and process multiple dependent variables at the same time. We focused on the impact of the neighborhood built environment on commuting patterns and on the health of patients with chronic diseases. The model used the patient’s health level as a dependent factor, and the built environment, as well as commuting patterns, as mediating factors. The commuting patterns could be refined into four categorical variables. As the traditional, multiple regression method had difficulty supporting the analysis in this study, the SEM used to analyze the impact of the neighborhood built environment on the choice of commuting pattern and on the health of patients with chronic diseases was through the effect size. Specifically, effects could be decomposed into direct, indirect, and total effects, each of which could explain different effects between variables [42,43].

3. Variable Selection and Theoretical Model

3.1. Variable Selection

3.1.1. Classification of Commuting Patterns

This study divided commuting patterns into four categories displayed in Table 2, based on the amount of physical activity undertaken by patients with chronic diseases. The modes of walking and bicycling produced the most physical activity, and are recognized herein as the unified commuting pattern category ‘walking/bicycling’. The mode ‘public transportation’ involved a combination of different commuting patterns that included walking, bicycling, and using public transit, all of which include some amount of physical activity. ‘Electric vehicles and motorcycles’ comprised a motorized transportation mode that is most commonly used for short-distance travel, while ‘automobiles’ (car commuting) included the use of private cars, taxis, and unit shuttles. People who commute via automobile mode typically sit quietly during their commutes, and this mode involves the least physical activity [44,45,46].

3.1.2. Exploratory Analysis of Neighborhood Built Environment Variables

The neighborhood built environment data came from a questionnaire survey, and several questions were set, for chronically ill patients’ feelings about the neighborhood environment. The questionnaire options were set using a 5-point Likert scale, with 1 representing ‘strongly disagree’ and 5 representing ‘strongly agree’. This study used SPSS 20.0 software to generate the data which reflected the built environment of the patients’ locations in Changshu City. The generation ensured that the data were credible. The overall Cronbach’s alpha value of the survey data on the neighborhood built environment for chronically ill patients in Changshu City was 0.676, compared with 0.695 for the standardized group.
The data showed that each item in the neighborhood built environment questionnaire was independent and acceptable. The results in Table 3 show that the Cronbach’s Alpha values of the neighborhood built environment in the questionnaire were mostly less than 0.695. Thus, the data validity of each item in the neighborhood built environment questionnaire was good. To determine the feasibility of each observation variable in the neighborhood built environment, the model conducted exploratory factor analysis on the 18 indicators of the neighborhood built environment in the questionnaire. To avoid the problem of collinearity among the selected influencing factors, this study conducted Kaiser–Meyer–Olkin (KMO) and Bartlett tests on 18 variables of the neighborhood built environment. In the test results, the KMO was 0.777, and Bartlett’s sphericity test value was significant at 0.000. These indicator values indicated that the differences between the options were large, and suitable for factor analysis (Table 4).
To improve the studied factors and make them more credible, we used the maximum variance method to rotate each factor orthogonally; the five common factors had a cumulative variance contribution rate of 58.22%. We further employed the rotation component matrix for data analysis (Table 5); the five common factors were respectively defined as service facility, environmental quality, road condition, traffic safety, and community safety satisfaction. They were combined together in the SEM as the latent variables of neighborhood environment satisfaction.
The model was used to analyze the perception effect, on patients with chronic dis-eases, of the neighborhood built environment. The mean deviation, standard deviation, and standard error of latent and observed variables are shown in Table 6. Among the five latent variables, the community safety satisfaction evaluation score was the highest, with an average value of 3.835, followed by the service facility and road condition satisfaction scores, with average values of 3.785 and 3.623, respectively. The average traffic safety satisfaction score was low, at only 2.927, while the environmental quality satisfaction score was the lowest, at just 2.683. The results showed that patients with chronic diseases in Changshu had a good perception of community safety, community service facilities, and road conditions around the community, but a poor perception of environmental quality and traffic safety.

3.1.3. Selection of Personal Health Variables

Related research results show that residents’ choice of daily travel transportation mode is related to mental health, physical health, and body mass index (BMI) [36]. Particularly, Hansson suggests that patients with chronic diseases may not be willing or may not continue to choose walking or bicycling to commute [37]. Existing research has concluded that there is no uniform standard for the selection of patients’ health variables: mental and physical health are frequently included, but studies generally lack attention to healthy behaviors [45]. Regarding the definition of the concept of health proposed by the World Health Organisation, we considered ‘being healthy’ primarily as being in good physical and mental condition, and having the ability to actively adapt to the social environment [47]. Therefore, we divided the patient’s health into three categories—mental health, physical health, and healthy behavior—which constituted three latent variables of a comprehensive evaluation of the patient’s health.
The concept of good mental health generally refers to a person’s mental, emotional, and conscious aspects; good mental health is characterized by being energetic, and having positive self-esteem and good interpersonal relationships [47]. Therefore, our survey examined the participants’ mental health from four aspects, to evaluate: (1) whether they were tired; (2) whether they were inattentive and slow to respond; (3) their self-evaluation of health; and (4) whether they had good neighborhood relationships.
The latent variables of physical health included three aspects: (1) the patient’s BMI; (2) whether the patient had a chronic disease; and (3) the patient’s current sleep quality. BMI was calculated, in order to determine whether the participating residents were obese or overweight. ‘Chronic diseases’ specifically referred to typical chronic conditions, such as diabetes, hyperlipidemia, hypertension, stroke, and coronary heart disease; ‘poor sleep quality’ was reflected by sleep factors, such as frequent insomnia and dreams.
We also measured four aspects of healthy living behaviors: sleep time; exercise and fitness; housework; and going for a walk. Research has revealed that length of sleep will directly affect the patient’s physical and mental health, and that insufficient sleep time will increase the patient’s BMI value [48], which also has an indirect impact on the patient’s choices of commuting methods. A patient’s daily physical activity level and health perception can be measured from three aspects: daily walking; fitness exercise; and housework activities. These factors may also influence the choice of daily transportation and commuting patterns. The descriptive statistical results of the patients’ personal health variables are shown in Table 7.

3.2. Theoretical Model

Due to the different life experiences, attitude preferences, and socioeconomic attributes of the patients, despite living in the same community, they chose different daily commuting patterns. Therefore, it was necessary to further explore the mechanism behind the patients’ daily commuting patterns. Specifically, it was necessary to study the influence of individual patients’ daily health behaviors and habits on their choice of transportation mode. Studies have found that social and economic attributes such as gender, age, income, education level, and car ownership have a significant impact on individuals’ commuting patterns [5,49]. Therefore, it was necessary to consider the common influencing factors, including the patient’s street environment satisfaction, the patient’s healthy daily habits satisfaction, and other factors in the relevant models that may have influenced the patient’s travel patterns.
In summary, the theoretical model framework proposed in this study is shown in Figure 2. The research assumed that socioeconomic attributes had a direct impact on transportation and commuting patterns. Socioeconomic attributes may have had a direct impact on the patient’s satisfaction with the neighborhood environment, and an indirect impact on the patient’s daily commuting pattern. At the same time, the patient’s daily commuting pattern may also have had a direct impact on the patient’s satisfaction with the neighborhood environment. In addition, the patient’s satisfaction with the neighborhood environment had a direct impact on the patient’s daily commuting pattern, and an indirect impact on the patient’s physical and mental health. A structural equation model was constructed, to verify the above assumptions.

4. Results and Discussion

4.1. SEM Fit Index

This study established the initial model through AMOS 20.0 software, used the maximum likelihood method to estimate the data, and obtained the final model after model evaluation and correction. The Chi-square value was 75.845, the degree of freedom was 55, and the p-value significance was 0.033, indicating that the final model was very close to the optimal model. When evaluating the SEM fitness index, we comprehensively considered three types of fitness indicators—absolute fitness, value-added fitness, and simplified fitness—and compared the model results with actual data. We also measured the consistency of data fitting [42]. Comparing the model fit index with the reference value (Table 8), we found that the fit effect of the model met the experimental expectations.

4.2. Impact of Neighborhood Environmental Satisfaction on Daily Commuting Pattern and Personal Health

Neighborhood environmental satisfaction directly affected the patient’s daily commuting pattern. It also indirectly affected the patient’s health, by influencing the patient’s daily commuting pattern (Table 9). We further analyzed the specific impact of neighborhood environment satisfaction on the patient’s commuting pattern, and found that service facilities satisfaction had a significant negative correlation effect on the patient’s commuting pattern. The results showed that when patients were satisfied with the service facilities in their living environment, they were more likely to choose walking/biking as their daily commuting pattern. The composition of good service facilities included commercial service facilities, leisure service facilities, street lighting, and other infrastructure in the neighborhood environment, as well as public transportation service facilities. Among them, commercial service facilities, leisure landscape service facilities, street lighting, and other infrastructure could help improve the travel environment for slow traffic, and could effectively improve the patient’s willingness to walk/ride, and the patient’s frequency of walking/riding. In addition to service facility satisfaction, the patient’s daily commute pattern was also directly affected by the patient’s level of environmental quality satisfaction. The model results show that when patients were satisfied with the level of their neighborhood environment, they were more likely to choose the walking/biking commute pattern. Community safety satisfaction had a significant positive direct effect on the patient’s choice of commuting pattern. The higher the patient’s recognition of the community as being safe, the more likely the patient was to choose the car for daily commuting. The level of the patient’s satisfaction with the roads around the patient’s residential area also had a significant positive and indirect effect on the patient’s commuting pattern. Specifically, the greater the patient’s satisfaction with road facilities, the more likely the patient was to choose commuting modes such as walking. However, traffic safety satisfaction did not significantly influence the patient’s commuting pattern.
Moreover, the results showed that the patient’s satisfaction with the neighborhood environment also significantly correlated with the patient’s health status. Specifically, the quality of the living environment had a significant positive direct association with the patient’s mental health and healthy behavior. The results showed that the higher the patient’s satisfaction with the community’s environmental quality, the higher the patient’s mental health level. At the same time, the patient was more likely to engage in healthy behaviors, such as exercise, fitness, and walking. The patient’s satisfaction with road conditions had a significant positive direct effect on the patient’s mental health and physical health. The results showed that the more satisfied the patient was with the road conditions, the better was the patient’s mental and physical health. In addition, the patient’s perception of traffic safety also had a significant positive effect on the patient’s mental health. That is, the better the patient’s perception of traffic safety, the better the patient’s mental health. Studies have shown that patients who have a good perception of the safety of the community environment are healthier [19], but the impact of the perception of community safety on the health of the patients in this study was not significant.

4.3. Impact of Commuting Pattern on Patients’ Health

The effects of daily commuting patterns on the health status of the patients with chronic disease is shown in Table 10, including a significant positive direct effect on their health behavior. The model analysis results show that the patients with chronic diseases who chose to commute by car were in better health. The possible reason was that the self-driving commute mode required the patient to have good sleep quality and stay energized. The patient could cope with the stress of traffic congestion and high-energy concentration during car commutes. Moreover, patients with chronic diseases who paid more attention to healthy behaviors were less likely to choose the car commute mode; a possible reason was that these patients could better integrate physical activity into their daily life by using walking and cycling to commute, without the need for a fixed time and place of exercise. In addition, the commuting patterns of chronically ill patients had no significant effect on their mental health.

4.4. Correlation Analysis of Patients’ Socioeconomic Attributes with Neighborhood Environmental Satisfaction, Commuting Choices, and Health

The standardized correlation coefficient of path internal/external relationship is shown in Table 11 and Table 12. From the model analysis results, it can be seen that factors such as patient gender, income level, and car ownership had a significant negative impact on neighborhood environmental satisfaction, while patient family income, education level, and family structure had a significant positive impact on neighborhood environmental satisfaction. Neighborhood environment satisfaction had a significant positive impact on walking/bicycle commuting patterns, and had a synergistic positive impact on the patient’s physical health and healthy habits. Neighborhood environment satisfaction had a significant negative impact on bus travel mode, indicating that patients in a good neighborhood built environment were more inclined to choose walking/bicycle travel mode.
Table 13 reflects the direct, indirect, and overall effects of the patients’ socioeconomic attributes on their commuting pattern, neighborhood environmental satisfaction, and personal health. Socioeconomic attributes directly affected the patients’ choice of commuting pattern. Socioeconomic background affected the patients’ satisfaction with their neighborhood environment, and indirectly affected their choice of daily commute mode, and their health. In addition, socioeconomic attributes, gender, car ownership, family population size, education level, and household registration all had significant direct effects on the choice of commuting pattern. Among these, gender had a significant negative effect on the commuting pattern, indicating that female patients were more willing to choose the walking/bicycle commuting pattern than male patients. Car ownership had a significant positive direct effect on the choice of commuting pattern. The patients who owned cars in their families were more inclined to choose the car as the mode of transportation for daily commute. The family population structure and the location of the household registration had a significant positive direct effect on the choice of commuting by car. It showed that the patients with larger families and local household registration were more willing to choose the car as their commuting pattern. The specific reason was that the larger the number of families, the greater the demand for car use. The patients with local household registration had better economic conditions, and could afford the cost of commuting by car. Those patients were therefore more likely to choose cars for commuting. In addition, the results showed that the patients with higher education were more likely to choose the car-commuting pattern. The interaction pattern is shown in Figure 3.
The patient’s social and economic background affected the patient’s satisfaction with the surrounding environment and choice of commuting pattern, thereby affecting personal health behaviors. Gender factors had a significant positive direct effect on the patient’s neighborhood environmental satisfaction. There was a significant negative direct effect on satisfaction with road conditions: that is, female residents’ satisfaction with environmental quality was better than that of male patients, and their satisfaction with road construction quality was worse than that of male patients. Age had a significant positive direct effect on the perception of environmental quality and traffic safety: the older the patient, the better the perception of environmental quality and traffic safety. The patient’s monthly income had a significant negative direct effect on the perception of road conditions: the higher the patient’s monthly income, the lower the patient’s satisfaction with the street environment. There was a significant negative direct effect between car ownership rate and service facility satisfaction. The patients who owned cars had a relatively wider range of daily travel, and the types and choices of service facilities that could be used were wider. The patient’s subjective evaluation of facilities near the community was correspondingly reduced.
In addition, socioeconomic attributes indirectly affected the health of the patients, by affecting their commuting patterns. This effect came from the patients’ level of environmental perception, and the choice of commuting pattern. Gender had a significant positive direct effect on road environmental perception, and a significant negative direct effect on road conditions perception: that is, female patients with chronic diseases had a better perception of environmental quality than male patients. Women’s perception of road conditions was worse than that of men with chronic diseases.
Age had a significant positive indirect effect on the patient’s mental health. However, there was a significant negative indirect effect on the patient’s daily healthy living habits, indicating that the older patients had better mental health levels than the younger patients, but less sleep and exercise; their frequency of healthy behaviors, such as fitness, was lower than that of the younger patients. In addition, the result showed that education level had a significant positive and indirect effect on the patient’s mental health: that is, the mental health of the patients with high education was generally better than the mental health of the patients with low education.
The patient’s monthly income had a significant negative direct effect on the patient’s perception of road conditions—that is, the higher the patient’s monthly income, the worse was the perception of road conditions, in patients with chronic diseases. Car ownership had a significant negative direct effect on the perception of service facilities: that is, the level of community service was perceived by patients with chronic diseases, who owned a car, to be worse. The possible reason was that chronically ill patients with cars had a wider range of daily travel space, could reach more high-level service facilities, and made a correspondingly lower evaluation of community-level infrastructure services. At the same time, there was a significant positive direct effect between car ownership and the patient’s perception of road status and community safety. A possible explanation was that the street environment where families with cars were located was generally better, and their perception level was also better. Family demographics and higher education had significant positive direct effects on the patient’s perception of traffic safety. The patients with chronic diseases, who had a larger family population and higher education, experienced better traffic safety. At the same time, household registration had a significant positive direct effect on the perception of environmental quality. The results show that the patients with chronic diseases who had local household registration had a better perception of environmental quality than those with non-local household registration.

5. Conclusions

5.1. Key Findings

In this study, we used the SEM to analyze the impact of neighborhood environment perception, personal health, and socioeconomic attributes, at the micro-level, on the daily commuting patterns and health of patients in the city of Changshu. The following research conclusions were obtained:
(1) The patient’s satisfaction with the neighborhood environment had a significant impact on the patient’s choice of commuting pattern. Specifically, service facility satisfaction and environmental quality satisfaction in the neighborhood environment satisfaction measurement index had a significant negative direct effect on the patient’s choice of commuting pattern. The patient’s level of community safety satisfaction had a significant positive direct effect on the patient’s commuting pattern. At the same time, the patient’s daily commuting pattern also had a direct impact on the patient’s satisfaction with the neighborhood environment.
(2) Among the health variables, the patient’s physical health was significantly correlated with healthy behaviors and daily commuting patterns.
(3) Among the socioeconomic attributes, gender, car ownership, family population size, education level, and household registration had a significant influence on the patient’s choice of commuting pattern.
(4) Socioeconomic attributes had a direct effect on neighborhood environmental satisfaction, and an indirect effect on the patient’s health. Neighborhood environmental satisfaction also had a direct effect on the patient’s health. In addition, the patient’s satisfaction with the neighborhood environment had a direct impact on the patient’s daily commuting pattern, and an indirect impact on the patient’s physical and mental health. There was a cross-influence relationship between these influencing factors.

5.2. Implications

The results and discussion of the findings of this study show that the patients’ commuting patterns had a significant impact on their health levels. Thus, we suggest that the relevant city management departments organize more health promotion activities and national fitness activities, to increase health awareness and patients’ willingness to participate in health behaviors, and that they implement policies to guide more patients to actively choose the walking/bicycle commuting pattern.
Based on these research findings, we believe that a ‘people-oriented’ concept should be embodied in the planning and construction of the urban built environment. We need to improve patients’ subjective perceptions of the built environment. Governments need to pay more attention to service facilities and environmental quality in the urban built environment. Patients’ physical and mental health can be further improved by improving community service facilities, effectively improving patients’ subjective feelings about the community environment, and attracting patients towards adopting a healthier and greener commuting pattern.

5.3. Limitations and Future Research Directions

Although the research yielded some meaningful findings, there were also some limitations. We measured three aspects of chronic disease patients’ health: their mental health, physical health, and healthy behavior. However, due to the complexity of personal health measurement standards, the health indicators considered in this article were still not comprehensive enough. We intend to establish a more systematic and comprehensive indicator system for future research, to explore the causal relationship between the patient’s own health and the patient’s commuting pattern, in future.
In addition, this paper took ‘residential quarters’ as the survey unit. The physical spatial extent of these residential communities may not be consistent with the extent of the patient’s living space. Therefore, this would lead to the occurrence of the Uncertain Geographical Environment Problem (UGCoP). Different spatial unit scales would lead to deviations in the correlation analysis results between the influencing factors. Therefore, we will conduct in-depth survey interviews, or use GPS devices, to record detailed trajectory data in the follow-up study. This kind of method can more accurately reflect the scope of daily life and the patient’s activity trajectory. This refined survey method can help us capture the extent of the patient’s actual living environment, and the different elements of the neighborhood built environment. The fine-grained activity data will help us to find the mechanism of the neighborhood built environment, based on the commuting patterns and patients’ health.
Based on past research, this paper looked deeply into the impact mechanism of the neighborhood built environment on daily traffic patterns and the health of patients with chronic disease. The research results of this paper enrich the related research in this field in China. On the one hand, when health geography studies the impact of the environment on people, it should pay more attention to the feelings of special groups of people, in regard to the environment, and consider the actual activities of people in time and space. On the other hand, this study also has a certain significance for community planning and management. Firstly, community-planning research needs to pay attention to the intervention effect of the environment on health, and incorporate public health into the decision-making factors of transportation and urban planning. Secondly, urban communities need to strengthen the construction of slow-moving transportation systems, provide more convenient transportation facilities for special groups of people who are sick, and promote their healthy behaviors: for example, by improving urban road design, improving the landscape comfort of slow-moving streets, and deploying more safety facilities on community streets. Improvements to the built environment of their neighborhoods can help encourage residents to travel in healthier ways. Thirdly, there is a need to promote the mutual communication of community residents, and to enhance the overall cohesion of the community. Relevant community party branches and neighborhood committees can hold voluntary services, cultural and sports activities, health education lectures, and other activities. These activities can promote the formation of a healthy and positive community spirit and culture among community members, in the long-term, and a frequent communication process.
In general, the health level of patients with chronic diseases is affected by a combination of factors. The patient’s daily commuting patterns are also related to various factors, such as socioeconomic attributes and neighborhood environmental satisfaction. Factors such as the research time, the case location, the sample size, and social and cultural customs also had a direct or indirect impact on the results of this study. The survey period and the space environment limited the results.

Author Contributions

Conceptualization, H.W. (Hongbin Wang) and D.L.; Data curation, H.W. (Hao Wu) and Y.C.; Formal analysis, Y.Q.; Methodology, H.W. (Hao Wu); Visualization, Y.C. and Y.Q.; Writing—original draft, H.W. (Hao Wu); Writing—review & editing, H.W. (Hongbin Wang). All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Basic Research Fund of CAMS (Grant No. 2022Y023), the 333 Project of Jiangsu Province (BRA2018420), the Beijige foundation of NJIAS (BJG202209), the Jiangsu Innovative and Enterpreneurial Talent Programme (JSSCBS20221645) and the Research Fundation of Jinling Institute of Technology (JIT-B-202108).

Conflicts of Interest

The authors declare no conflict of interest.

Appendix A. Questionnaire about Commuting Pattern for Chronic Disease

Dear Mr/Mrs,
Here is a questionnaire on daily commuting patterns and traffic behavior. We would like to thank you for your participation in this survey. Your active participation will help the investigation run smoothly. We assure you that the following survey results are only used for academic research and academic exchange activities. Our research results will not be used for commercial purposes. At the same time, the questionnaire data will not be disclosed to the public, and your personal information will be kept strictly confidential. Therefore, please be sure to fill in this questionnaire truthfully.
Thank you very much for your help with this investigation.
Section I Basic information
1. Please select your sex?
 □ Female □Male
2. Please select your age?
 □above 60 yr □45–60 yr □31–45 yr □Less than 31 yr
3. Please select your education level?
 □Bachelor degree and above □High school □Junior high school □Primary school and below
4. How many people living in your house?
 □Four and above □Three wo or below
5. How much is your personal monthly income (RMB)?
 □Below 2000 □2001–4000 □4001–6000 □6001–8000 □8001–10,000 □10,001–15,000 □15,000 and above
6. What type of transportation do you use for your daily commute?
 □Bus □Bicycle □Car □Walking □Others
7. Do you have a local account?
 □Yes □No
8. Would you willing to participate in our survey?
 □Yes □No
—————————————————————————————————————————
Section II Your satisfaction on neighborhood environment
9. Do you feel that there are sufficient supermarkets or shopping malls in the living area?
   □Yes □No
10. Do you feel convenient to walk to the nearest bus station?
   □Yes □No
11. Do you feel convenient to walk to the nearest street green space or park?
   □Yes □No
12. Are there many intersections around your community?
   □Yes □No
13. Are there many different roads around my community?
   □Yes □No
14. Are there many sanitation of roads around your community?
   □Yes □No
15. Do you satisfied with the lighting situation of roads around my community at night?
   □Yes □No
16. Are the streets around your community are flat?
   □Yes □No
17. Are there more walking roads around my community?
   □Yes □No
18. Are there more pedestrian crossing facilities around your community?
   □Yes □No
19. Are there attractive natural landscapes around your community?
   □Yes □No
20. Are there attractive cultural landscapes around your community?
   □Yes □No
21. Are there many fast-driving cars around your community?
   □Yes □No
22. Are there frequent traffic accidents around your community?
   □Yes □No
23. Do there have many road obstacles in your living environment that make you feel inconvenient to walk/riding?
   □Yes □No
24. Is the security around your community very good?
   □Yes □No
25. Do you think the law and order situation in the living area is good?
   □Yes □No
—————————————————————————————————————————
Section III Your satisfaction on individual health
26. Do you feel fatigued and tired easily for a long time?
   □Yes □No
27. Do you feel inattention and slow reaction?
   □Yes □No
28. What do you think of your individual health?
   □Not good □Normal □Good □Very good
29. How is your relationship with your neighbors?
   □Poor □Fine □Good □Very good(close)
30. How is your physical fitness index?
   □Lean or normal □Weight □Over weight □Obesity
31. Are you suffering from chronic diseases?
   □Yes □No
32. What types of chronic diseases do you have? (multiple options)
   □Hypertension □Hyperlipidemia □ Diabetes □Cardiovascular disease □Other
33. Are you suffering from poor sleep quality?
   □Yes □No
34. Do you have the habit of regular exercise and fitness?
   □Yes □No
35. Do you have the habit of doing housework frequently?
   □Yes □No
36. Do you have the habit of taking regular walks?
   □Yes □No

References

  1. Sugiyama, T.; Ding, D.; Owen, N. Commuting by car: Weight gain among physically active adults. Am. J. Prev. Med. 2013, 44, 169–173. [Google Scholar] [CrossRef]
  2. Liu, R.F.; Guan, C.-Q. Mode Biases of Urban Transportation Policies in China and Their Implications. J. Urban Plan. Dev. 2005, 131, 58–70. [Google Scholar] [CrossRef]
  3. Wener, R.E.; Evans, G.W. Comparing stress of car and train commuters. Transp. Res. Part F-Traffic Psychol. Behav. 2011, 14, 111–116. [Google Scholar] [CrossRef]
  4. Panter, J.; Desousa, C.; Ogilvie, D. Incorporating walking or cycling into car journeys to and from work: The role of individual, workplace and environmental characteristics. Prev. Med. 2013, 56, 211–217. [Google Scholar] [CrossRef]
  5. Sandow, E. Commuting behavior in sparsely populated areas: Evidence from northern Sweden. J. Transp. Geogr. 2008, 16, 14–27. [Google Scholar] [CrossRef]
  6. Munshi, T.; Zuidgeest, M.; Brussel, M.; van Maarseveen, M. Logistic regression and cellular automata-based modelling of retail, commercial and residential development in the city of Ahmedabad, India. Cities 2014, 39, 68–86. [Google Scholar] [CrossRef]
  7. Ding, C.; Wang, D.; Liu, C.; Zhang, Y.; Yang, J. Exploring the influence of built environment on travel mode choice considering the mediat-ing effects of car ownership and travel distance. Transp. Res. Part A-Policy Pract. 2017, 100, 65–80. [Google Scholar] [CrossRef]
  8. Sun, B.D.; Dan, B. Impact of urban built environment on residential choice of commuting pattern in Shanghai. Acta Geograph. Sin. 2015, 70, 1664–1674. [Google Scholar]
  9. Cervero, R.; Kockelman, K. Travel demand and the 3Ds: Density, diversity, and design. Transp. Res. Part D-Transp. Environ. 1997, 2, 199–219. [Google Scholar] [CrossRef]
  10. Munshi, T. Built environment and mode choice relationship for commute travel in the city of Rajkot, India. Transp. Res. Part D-Transp. Environ. 2016, 44, 239–253. [Google Scholar] [CrossRef]
  11. Chai, Y.W.; Xiao, Z.P.; Liu, Z.L. Low-carbon optimization strategies based on CO2 emission mechanism of household daily travels: A case study of Beijing. Geogr. Res. 2012, 31, 334–344. [Google Scholar]
  12. Yin, C.Y.; Shao, C.F.; Wu, P. Impacts of built environment on commuting behavior based on a multilevel modeling approach. J. Transp. Syst. Eng. Inf. Technol. 2018, 18, 122–127. [Google Scholar]
  13. Qing, S. The effect of population density, road network density, and congestion on household gasoline consumption in US. urban areas. Energy Econ. 2011, 33, 445–452. [Google Scholar]
  14. Bento, A.M.; Cropper, M.L.; Mobarak, A.M.; Vinha, K. The Effects of Urban Spatial Structure on Travel Demand in the United States. Rev. Econ. Stat. 2005, 87, 466–478. [Google Scholar] [CrossRef]
  15. Queralt, A.; Molina-García, J. Physical Activity and Active Commuting in Relation to Objectively Measured Built-Environment Attributes Among Adolescents. J. Phys. Act. Health 2019, 16, 371–374. [Google Scholar] [CrossRef]
  16. Wei, Y.P.; Pan, C.L. Urban Land-Use characteristics and commuters’ travel pattern: A case study of west Hangzhou. City Plan. Rev. 2012, 36, 76–84. [Google Scholar]
  17. Naese, P. Tempest in a teapot: The exaggerated problem of transport-related residential self-selection as a source of error in empirical studies. J. Transp. Land Use 2014, 7, 57–79. [Google Scholar] [CrossRef]
  18. Zhao, P. The Impact of the Built Environment on Bicycle Commuting: Evidence from Beijing. Urban Stud. 2013, 51, 1019–1037. [Google Scholar] [CrossRef]
  19. Zhang, S.Y.; Lin, S.N.; Li, Z.G.; Guo, Y. Influence of neighborhood environmental satisfaction on self-rated health of residents in cities of China; A case study of Wuhan. Hum. Geogr. 2019, 34, 32–40. [Google Scholar]
  20. Humpel, N.; Marshall, A.L.; Leslie, E.; Bauman, A.; Owen, N. Changes in neighborhood walking are related to changes in satisfactions of environmental attributes. Ann. Behav. Med. 2004, 27, 60–67. [Google Scholar] [CrossRef]
  21. Doyle, S.; Kelly-Schwartz, A.; Schlossberg, M.; Stockard, J. Active Community Environments and Health: The Relationship of Walkable and Safe Communities to Individual Health. J. Am. Plan. Assoc. 2006, 72, 19–31. [Google Scholar] [CrossRef]
  22. Hamine, S.; Gerth-Guyette, E.; Faulx, D.; Green, B.B.; Ginsburg, A.S. Impact of mHealth Chronic Disease Management on Treatment Adherence and Patient Outcomes: A Systematic Review. J. Med. Internet Res. 2015, 17, e52. [Google Scholar] [CrossRef] [PubMed]
  23. Hill, N.R.; Fatoba, S.T.; Oke, J.L.; Hirst, J.A.; O’Callaghan, C.A.; Lasserson, D.S.; Richard Hobbs, F.D. Global Prevalence of Chronic Kidney Disease—A Systematic Review and Meta-Analysis. PLoS ONE 2016, 11, e0158765. [Google Scholar] [CrossRef]
  24. Shen, Y.; Na, T.; Liu, Z. Job-housing distance, neighborhood environment, and mental health in suburban Shanghai: A gender difference perspective. Cities 2021, 115, 103214. [Google Scholar] [CrossRef]
  25. Song, J.; Zhou, S.; Peng, Y.; Xu, J.; Lin, R. Relationship between neighborhood land use structure and the spatiotemporal pattern of PM2.5at the microscale: Evidence from the central area of Guangzhou, China. Environ. Plan. B Urban Anal. City Sci. 2022, 49, 485–500. [Google Scholar] [CrossRef]
  26. Sun, P.; Lu, W. Environmental inequity in hilly neighborhood using multi-source data from a health promotion view. Environ. Res. 2022, 204 Pt A, 111983. [Google Scholar] [CrossRef]
  27. Frehlich, L.; Christie, C.; Ronksley, P.; Turin, T.C.; Doyle-Baker, P.; McCormack, G. The association between neighborhood built environment and health-related fitness: A systematic review protocol. JBI Evid. Synth. 2021. publish ahead of print. [Google Scholar] [CrossRef] [PubMed]
  28. Zhang, K.; Lovasi, G.S.; Odden, M.C.; Michael, Y.L.; Newman, A.B.; Arnold, A.M.; Kim, D.H.; Wu, C. Association of Retail Environment and Neighborhood Socioeconomic Status with Mortality Among Community-Dwelling Older Adults in the United States: Cardiovascular Health Study. J. Gerontol. Ser. A 2021, glab319. [Google Scholar] [CrossRef]
  29. Ding, C.; Cao, X.; Yu, B.; Ju, Y. Non-linear associations between zonal built environment attributes and transit commuting patterncommuting pattern accounting for spatial heterogeneity. Transp. Res. Part A Policy Pract. 2021, 148, 22–35. [Google Scholar] [CrossRef]
  30. Panter, J.; Griffin, S.; Ogilvie, D. Active commuting and satisfactions of the route environment: A longitudinal analysis. Prev. Med. 2014, 67, 134–140. [Google Scholar] [CrossRef] [PubMed]
  31. Booth, M.L.; Owen, N.; Bauman, A.; Clavisi, O.; Leslie, E. Social-cognitive and perceived environmental influences associated with physical activity in older Australians. Prev. Med. 2000, 31, 15–22. [Google Scholar] [CrossRef] [PubMed]
  32. Feng, J.X.; Huang, X.; Tang, S.S. Comparing the influences of objective and subjective built environments on physical activities participation among the elderly: A case study of Nanjing, China. Shanghai Urban Plan. Rev. 2017, 3, 17–23. [Google Scholar]
  33. Pielak, M.; Czarniecka-Skubina, E.; Trafiałek, J.; Głuchowski, A. Contemporary Trends and Habits in the Consumption of Sugar and Sweeteners—A Questionnaire Survey among Poles. Int. J. Environ. Res. Public Health 2019, 16, 1164. [Google Scholar] [CrossRef] [PubMed]
  34. García-Torres, F.; Castillo-Mayén, R. Differences in Eysenck’s Personality Dimensions between a Group of Breast Cancer Sur-vivors and the General Population. Int. J. Environ. Res. Public Health 2019, 16, 1240. [Google Scholar] [CrossRef]
  35. Fu, T.T.; Zhang, Y.; Chai, Y.W. Implications of commuting pattern for suburban residents’ health in large Chinese cities: Evi-dences from Tiantongyuan in Beijing. Prog. Geogr. 2018, 37, 547–555. [Google Scholar]
  36. Carse, A.; Goodman, A.; Mackett, R.; Panter, J.; Ogilvie, D. The factors influencing car use in a cycle-friendly city: The case of Cambridge. J. Transp. Geogr. 2012, 28, 67–74. [Google Scholar] [CrossRef] [PubMed]
  37. Hansson, E.; Mattisson, K.; Björk, J.; Ostergren, P.-O. Relationship between commuting and health outcomes in a cross-sectional popula-tion survey in Southern Sweden. BMC Public Health 2011, 11, 834. [Google Scholar] [CrossRef] [PubMed]
  38. Patterson, R.; Panter, J.; Vamos, E.P.; Cummins, S.; Millett, C.; Laverty, A.A. Associations between commute mode and cardiovascular disease, cancer, and all-cause mortality, and cancer incidence, using linked Census data over 25 years in England and Wales: A cohort study. Lancet Planet. Health 2020, 4, e186–e194. [Google Scholar] [CrossRef]
  39. Mattisson, K.; Idris, A.O.; Cromley, E.; Håkansson, C.; Östergren, P.-O.; Jakobsson, K. Modelling the association between health indicators and commute mode choice: A cross-sectional study in southern Sweden. J. Transp. Health 2018, 11, 110–121. [Google Scholar] [CrossRef]
  40. Cao, C.; Zhen, F.; Wang, X. A research on the choice of urban employees’ commuting pattern and its influencing factors based on health perspective: A case of Nanjing. Hum. Geogr. 2020, 35, 114–122. [Google Scholar]
  41. Bani, M.A.; Arazpour, M.; Farahmand, F.; Mousavi, M.E.; Hutchins, S.W. The efficiency of mechanical orthoses in affecting parameters associated with daily living in spinal cord injury patients: A literature review. Disabil. Rehabil. Assist. Technol. 2014, 10, 183–190. [Google Scholar] [CrossRef]
  42. Wu, M.L. Structural Equation Model: Operation and Application of Amos; Chongqing University Press: Chongqing, China, 2010. [Google Scholar]
  43. Cao, X.S.; Lin, Q. A SEM-based study on urban community resident’s travel behavior in guangzhou. Acta Geogr. Sin. 2011, 66, 167–177. [Google Scholar]
  44. Hamer, M.; Chida, Y. Active commuting and cardiovascular risk: A meta-analytic review. Prev. Med. 2008, 46, 9–13. [Google Scholar] [CrossRef]
  45. Oja, P.; Titze, S.; Bauman, A.; de Geus, B.; Krenn, P.; Reger-Nash, B.; Kohlberger, T. Health benefits of cycling: A systematic review. Scand. J. Med. Sci. Sports 2011, 21, 496–509. [Google Scholar] [CrossRef] [PubMed]
  46. Yang, L.; Panter, J.; Griffin, S.J.; Ogilvie, D. Associations between active commuting and physical activity in working adults: Cross-sectional results from the Commuting and Health in Cambridge study. Prev. Med. 2012, 55, 453–457. [Google Scholar] [CrossRef] [PubMed]
  47. Bayon, V.; Leger, D.; Gomez-Merino, D.; Vecchierini, M.-F.; Chennaoui, M. Sleep debt and obesity. Ann. Med. 2014, 46, 264–272. [Google Scholar] [CrossRef]
  48. Santos, G.; Maoh, H.; Potoglou, D.; von Brunn, T. Factors influencing modal split of commuting journeys in medium-size European cities. J. Transp. Geogr. 2013, 30, 127–137. [Google Scholar] [CrossRef]
  49. Ko, J.; Lee, S.; Byun, M. Exploring factors associated with commute mode choice: An application of city-level general social survey data. Transp. Policy 2018, 75, 36–46. [Google Scholar] [CrossRef]
Sustainability 14 11201 g001 550
Figure 1. Distribution of the surveyed communities.
Figure 1. Distribution of the surveyed communities.
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Figure 2. Model framework of the influence of neighborhood environment satisfaction on patients’ daily commuting patterns and health.
Figure 2. Model framework of the influence of neighborhood environment satisfaction on patients’ daily commuting patterns and health.
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Figure 3. Interaction pattern diagram of social and economic attributes on commuting patterns, neighborhood environmental satisfaction, and individual health.
Figure 3. Interaction pattern diagram of social and economic attributes on commuting patterns, neighborhood environmental satisfaction, and individual health.
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Table
Table 1. Socioeconomic attributes of participants.
Table 1. Socioeconomic attributes of participants.
VariableSymboln (%)DescribeVariableSymboln (%)Describe
Sexa1972 (100) 10,001–15,000 yuan 120 (12.38)6
Male 517 (53.22)0>15,000 yuan 119 (12.22)7
Female 455 (46.78)1Car Ownera4972 (100)
Agea2972 (100)RangeNo 345 (35.53)0
Less than 31 yr 52 (5.4)1Yes 627 (64.47)1
31–45 yr 230 (23.7)2Family population structurea5972 (100)
45–60 yr 371 (38.2)3Less than 3 713 (73.31)0
Above 60 yr 319 (32.7)4More than 4 259 (26.69)1
Personal monthly incomea3972 (100)RangeEducationa6972 (100)
<2000 yuan 48(4.82)1High school/technical secondary school and below 216 (22.19)0
2001–4000 yuan 180(18.49)2Bachelor/college and above 756 (77.81)1
4001–6000 yuan 209(21.54)3Domicilea7972 (100)
6001–8000 yuan 150 (15.43)4Outsider 280 (28.78)0
8001–10,000 yuan 146 (15.11)5Local 692 (71.22)1
Table
Table 2. Descriptive statistics of commuting pattern variable (t). N = 972.
Table 2. Descriptive statistics of commuting pattern variable (t). N = 972.
Commuting PatternSample Size (%)Classification
Walking/bicycling300 (30.87)1
Public transit305 (31.35)2
Electric car/motorcycle103 (10.61)3
Automobile264 (27.17)4
Total972 (100)
Table
Table 3. Validity test of built environment survey data on streets where patients with chronic diseases were living.
Table 3. Validity test of built environment survey data on streets where patients with chronic diseases were living.
Neighborhood Environment Satisfaction
Questionnaire (Yes/No)		SymbolCronbach’s Alpha
I can walk to the closest hypermarket or shopping mall easily and convenientlyD10.632
My community is surrounded by more walking roads around my communityD20.672
My community has more pedestrian pavementD30.631
The natural landscapes around my community are attractiveD40.619
The cultural landscapes around my community are attractiveD50.623
My community suffered lots of fast-driving carsD60.615
There are frequent traffic accidents around my communityD70.692
There are lots of obstructions around my community (such as motor vehicles occupying pavements, etc.)D80.701
I can walk to the closest bus stop easily and convenientlyD90.655
I can walk to the closest park or street green space easily and convenientlyD110.621
My community is surrounded by lots of intersections D120.634
My community is surrounded by lots of different roads D130.639
Sanitation of roads around my communityD140.612
Lighting situation of roads around my community at nightD150.635
The streets around my community are flatD160.624
My community is very safeD170.615
The security of my community is very good at nightD180.617
Table
Table 4. KMO and Bartlett’s test.
Table 4. KMO and Bartlett’s test.
Kaiser–Meyer–Olkin Value0.761
Bartlett’s sphere testApproximate chi-square test2782.433
df172
Sig.0.000
Table
Table 5. Rotation component matrix of neighborhood built environment factor.
Table 5. Rotation component matrix of neighborhood built environment factor.
Component Factor
12345
D10.543
D20.632
D30.625
D50.527
D60.653
D4 0.694
D12 0.707
D13 0.735
D7 0.523
D8 0.575
D9 0.618
D10 0.754
D11 0.719
D14 0.736
D15 0.638
D16 0.672
D17 0.854
D18 0.848
Notes: Quantitative Model: principal component; rotation method: orthogonal rotation method with Kaiser standardization.
Table
Table 6. Mean deviation, standard deviation, and standard error of latent variables and observed variables of neighborhood environmental satisfaction.
Table 6. Mean deviation, standard deviation, and standard error of latent variables and observed variables of neighborhood environmental satisfaction.
Neighborhood
Environment Satisfaction Latent Variables
(Variable Symbols)		Measured Indicators of Neighborhood
Environment
Satisfaction		nMeanSDSEM
Service facility satisfaction 9723.6230.6180.021
D19723.5721.0720.036
D29724.0890.6940.027
D39723.2721.0890.032
D59723.5880.7330.031
D69723.7620.7570.029
Environmental quality satisfaction 9722.4890.8360.035
D49722.9781.0820.049
D129722.3771.1370.047
D139722.1861.0890.028
Road situation satisfaction 9723.3890.4780.01
D79723.1760.8820.028
D89723.2790.8070.027
D99723.4080.8150.029
D109723.5321.0170.032
D119723.7260.6930.018
Traffic safety satisfaction 9722.8450.7520.027
D149723.2761.0720.035
D159722.1630.8360.031
D169722.9881.0720.033
Community safety satisfaction 9723.5720.6720.022
D179723.5810.7540.018
D189723.7720.7630.027
Table
Table 7. Descriptive statistics of the patients’ health variables.
Table 7. Descriptive statistics of the patients’ health variables.
Personal Health Latent VariablePersonal Health Observed VariablesSymbolSample Size (%)Variable Description
Mental healthFatigued and easily tiredb1972 (100)Logical
No 831 (85.53)0
Yes 141 (14.47)1
Inattention and slow reactionb2972 (100)Logical
No 867 (89.23)0
Yes 105 (10.77)1
Self-rated health satisfactionb3972 (100)Grade
not good 35 (3.70)1
normal 344 (35.37)2
good 463 (47.59)3
very good 130 (13.34)4
Neighborhood relationsb4972 (100)Grade
poor 18 (1.77)1
fine 608 (62.54)2
good 306 (31.51)3
very good (close) 40 (4.18)4
Physical healthBMI ab5972 (100)Grade
lean or normal weight 614 (63.18)1
overweight 289 (29.74)2
obesity 69 (7.07)3
Suffering from chronic diseasesb6972 (100)Logical
no 853 (87.78)0
yes 119 (12.22)1
Poor sleep qualityb7972 (100)Logical
no 791 (81.35)0
Yes 181 (18.65)1
Healthy daily habitsSleeping timeb8972 (100)Continuous
Work outb9972 (100)Logical
no 727 (74.76)0
yes 245 (25.24)1
Does houseworkb10972 (100)Logical
no 322 (33.12)0
yes 650 (66.88)1
Daily walkb11972 (100)Logical
no 589 (60.61)0
yes 383 (39.39)1
Notes: According to ‘Management Measures for Nutrition Improvement Work (Health and Disease Control [2010] No. 73)’, issued by the Ministry of Health in 2010, a BMI means body mass index, BMI = weight (kg)/height (m)2, BMI scores indicate nutrition status related to obesity as obesity, ≥28; overweight, 24–28; normal 18.5–23; underweight, <18.5.
Table
Table 8. Model fitness index.
Table 8. Model fitness index.
Fit IndexReference ValueModel Results
Absolute Fit Indexp
χ2<0.050.33
RMR<0.050.042
RMSEA<0.050.031
GFI>0.900.926
AGFI>0.900.907
Value-added fitness index
IFI>0.900.897
NFI>0.900.821
RFI>0.900.808
TLI (NNFI)>0.900.903
CFI>0.900.911
Parsimonious fit index
PGFI>0.50.672
PNFI>0.50.661
NC (χ2 freedom ratio degree, CMIN/DF1–31.512
Table
Table 9. Direct, indirect, and total effects of neighborhood environmental satisfaction on commuting patterns and individual health.
Table 9. Direct, indirect, and total effects of neighborhood environmental satisfaction on commuting patterns and individual health.
Variable SymbolConcrete EffectServiceEnvironmentRoadTrafficCommunity
tOverall effect−0.119 **−0.047 *−0.021−0.0160.042
Direct effect−0.121 **−0.053 *−0.042−0.0120.043 *
Indirect effect−0.003−0.0060.021 *−0.006−0.002
PhysicalOverall effect−0.030.0250.097 *0.0170.003
Direct effect−0.030.0250.097 *0.0170.003
Indirect effect–––––
MentalOverall effect−0.0690.113 *0.218 **0.113 *0.032
Direct effect−0.0720.113 *0.218 **0.113 *0.032
Indirect effect–––––
BehavioralOverall effect−0.0580.100 *0.011−0.143−0.032
Direct effect−0.0580.100 *0.011−0.143−0.032
Indirect effect–––––
Notes: The above values are all standardized values; * significant at the 10% confidence level; ** significant at the 5% confidence level; – indicates that there was no such relationship in the model.
Table
Table 10. Total, direct, and indirect effects of patients’ commuting patterns on health.
Table 10. Total, direct, and indirect effects of patients’ commuting patterns on health.
Variable SymbolConcrete EffectMentalPhysicalBehavioral
tOverall effect−0.0370.118 *0.042 **
Direct effect−0.0370.118 *0.042 **
Indirect effect………
* Significant at the 10% confidence level; ** significant at the 5% confidence level; … indicates that there was no such relationship in the model.
Table
Table 11. Standardized correlation coefficient of path internal relationship.
Table 11. Standardized correlation coefficient of path internal relationship.
Path Internal RelationshipCoefficientPath Internal RelationshipCoefficientPath Internal RelationshipCoefficient
Sex→Neighborhood environment satisfaction (e1)−0.960Neighborhood environment satisfaction→Automobile (e8)0.784Public transit→Mental health (e15)0.156
Car ownership→Neighborhood environment satisfaction (e2)−0.518Neighborhood environment satisfaction→Public transit (e9)−0.731Walking/Biking→Physical health (e16)0.834
Family structure→Neighborhood environment satisfaction (e3)0.044Walking/Biking→Neighborhood environment satisfaction (e10)0.791Automobile→Physical health (e17)0.672
Education level→Neighborhood environment satisfaction (e4)0.394Automobile→Neighborhood environment satisfaction (e11)0.843Public transit→Physical health (e18)0.472
Monthly Income→Neighborhood environment satisfaction (e5)−0.425Public transit→Neighborhood environment satisfaction (e12)0.672Walking/Biking→Healthy daily habits (e18)0.533
Household registration→Neighborhood environment satisfaction (e6)0.195Walking/Biking→Mental health (e13)0.101Automobile→Healthy daily habits (e19)0.726
Neighborhood environment satisfaction→Walking/Biking (e7)0.356Automobile→Mental health (e14)0.156Public transit→Healthy daily habits (e20)0.567
Table
Table 12. Standardized correlation coefficient of path external relationship.
Table 12. Standardized correlation coefficient of path external relationship.
Path External RelationshipCoefficientPath External RelationshipCoefficientPath External RelationshipCoefficient
e7←→e100.408e12←→e130.257e17←→e18−0.189
e8←→e110.367e13←→e140.413e18←→e19−0.117
e9←→e120.353e15←→e130.334e18←→e16−0.165
e10←→e130.272e16←→e140.285e16←→e20−0.136
Table
Table 13. Total, direct, and indirect effects of social and economic attributes on commuting patterns, neighborhood environmental satisfaction, and individual health.
Table 13. Total, direct, and indirect effects of social and economic attributes on commuting patterns, neighborhood environmental satisfaction, and individual health.
Variable SymbolConcrete Effecta1a2a3a4a5a6a7
tOverall effect−0.157 ***−0.0120.0530.201 ***0.085 **0.14 **0.099 **
Direct effect−0.158 ***−0.0150.0510.185 **0.094 **0.129 *0.091 **
Indirect effect0.0010.0040.0010.016−0.0060.0110.009
RoadOverall effect−0.117 **−0.006−0.105 **0.115 **−0.012−0.0890.002
Direct effect−0.117 **−0.006−0.105 **0.115 **−0.012−0.0890.002
Indirect effect…………………
TrafficOverall effect−0.0170.172 **0.037−0.0660.133 **0.106 *−0.036
Direct effect−0.0170.172 **0.037−0.0660.133 **0.106 *−0.036
Indirect effect…………………
ServiceOverall effect0.045−0.003−0.017−0.064 *0.014−0.044−0.032
Direct effect0.045−0.003−0.017−0.064 *0.014−0.044−0.032
Indirect effect…………………
EnvironmentOverall effect0.131 ***0.097 **−0.010.01−0.0610.0760.117 ***
Direct effect0.131 ***0.097 **−0.010.01−0.0610.0760.117 ***
Indirect effect…………………
CommunityOverall effect−0.0340.065−0.0650.175 ***00.052−0.044
Direct effect−0.0340.065−0.0650.175 ***00.052−0.044
Indirect effect…………………
MentalOverall effect0.034 *0.032 *0.025−0.0260.0080.045 **0.010
Direct effect…………………
Indirect effect0.034 *0.032 *0.025−0.0260.0080.045 **0.010
PhysicalOverall effect−0.0120.004−0.0070.011−0.001−0.0030.003
Direct effect…………………
Indirect effect−0.0120.004−0.0070.011−0.001−0.0030.003
BehavioralOverall effect−0.011−0.032 *−0.0010.005−0.01−0.02−0.004
Direct effect…………………
Indirect effect−0.011−0.032 *−0.0010.005−0.01−0.02−0.004
Notes: The above values are all standardized values; * significant at the 10% confidence level; ** significant at the 5% confidence level, *** significant at the 1% confidence level; … indicates that there was no such relationship in the model.
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Wu, H.; Wang, H.; Liu, D.; Cao, Y.; Qu, Y. The Impact of the Neighborhood Built Environment on the Commuting Patterns and Health of Patients with Chronic Diseases: A Case Study of Changshu, China. Sustainability 2022, 14, 11201. https://doi.org/10.3390/su141811201

AMA Style

Wu H, Wang H, Liu D, Cao Y, Qu Y. The Impact of the Neighborhood Built Environment on the Commuting Patterns and Health of Patients with Chronic Diseases: A Case Study of Changshu, China. Sustainability. 2022; 14(18):11201. https://doi.org/10.3390/su141811201

Chicago/Turabian Style

Wu, Hao, Hongbin Wang, Duanyang Liu, Yang Cao, and Yawei Qu. 2022. "The Impact of the Neighborhood Built Environment on the Commuting Patterns and Health of Patients with Chronic Diseases: A Case Study of Changshu, China" Sustainability 14, no. 18: 11201. https://doi.org/10.3390/su141811201

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