Next Article in Journal
The Impact of Green Technological Innovation on Industrial Structural Optimization Under Dual-Carbon Targets: The Role of the Moderating Effect of Carbon Emission Efficiency
Previous Article in Journal
Mapping and Potential Risk Assessment of Marine Debris in Mangrove Wetlands in the Northern South China Sea
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Sustainability
Volume 17
Issue 14
10.3390/su17146308
sustainability-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

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

Association of Community Walk Score with Chinese Seniors’ Physical Activity and Health Outcomes

by
Weiwei Liang
1,*,
Hongzhi Guan
2,
Hai Yan
1,* and
Mingyang Hao
1
1
Beijing Key Laboratory of Transportation Engineering, Beijing University of Technology, 100 Pingleyuan, Chaoyang District, Beijing 100124, China
2
State Key Laboratory of Bridge Engineering Safety and Resilience, Beijing University of Technology, 100 Pingleyuan, Chaoyang District, Beijing 100124, China
*
Authors to whom correspondence should be addressed.
Sustainability 2025, 17(14), 6308; https://doi.org/10.3390/su17146308
Submission received: 24 April 2025 / Revised: 12 June 2025 / Accepted: 26 June 2025 / Published: 9 July 2025
(This article belongs to the Topic Biophilic Cities and Communities: Human-Environment Interaction and Sustainable Governance)
Browse Figures
Versions Notes

Abstract

Improving community walkability can encourage older adults to walk, which is beneficial for enhancing their physical activity level (PAL) and keeping healthy. The first purpose of this study was to formulate an optimized community Walk Score measurement system from the perspective of Chinese seniors. It will be optimized from the aspects such as community service facility selection, weight determination, and distance decay function calculation. The second purpose was to verify its validity by exploring the correlation between Walk Score and subjective/objective community environment variables based on Spearman correlation analysis and the ANOVA method. The third purpose was to examine the relationship between Walk Score and Chinese seniors’ PAL and health outcomes by means of ordered/binary logistic regression. The results show the following: (1) Walk Scores are significantly correlated with partial objective environmental variables. (2) Walk Score was related to older adults’ physical activity level. (3) There was no significant relationship between Walk Score and two health outcomes. Walk Score can provide a supporting basis for urban renewal, older-community renovation, age-friendly community planning and design, and public health practitioners or policymakers.

1. Introduction

Positive aging refers to older people integrating physical activities into their daily lives and participating in society according to their own needs, desires, and abilities. A key determinant of healthy aging is active participation in daily community activities [1,2,3]. Outdoor activities have a positive impact on older adults’ mental health [4]. Walking is an easy physical activity [5] and the most common way for older adults to participate in daily activities [6], and it can improve the physical and mental health of older adults and ease the neighborhood atmosphere [6]. As a green, low-carbon, and pollution-free form of transport, walking has obvious ecological, economic, social, and health benefits [7], and, therefore, it has the potential to contribute to the three pillars of sustainability (economy, society, and the environment) [8].
The community is the main activity and living space for older adults [9]. Various service facilities within the community are crucial for older adults’ daily life. However, inadequate service facilities, a lack of accessible facilities [10], or community service facilities (markets, public toilets, etc.) being beyond the walking range of older people make it difficult for them to meet their travel demands [11], which is common in old residential areas in Chinese cities. The concepts of being “walking friendly” and “aging friendly” show that the Chinese Government attaches importance to the construction of “walking cities” and cares for seniors [7], and it has also put forward requirements for the walking environment and supported facilities in residential areas [10]. Walkability is a representation of the degree to which facilities meet the demand for pedestrian travel. The walkability of CSFs affects the convenience, health, and harmony of older adults’ lives in a community. It is necessary to build a more walkable community environment for older people in order to encourage them to engage in physical activities. Consequently, older adults’ health will be improved or enhanced. Therefore, it is of great research significance to develop a community walkability measurement tool for elderly individuals, which is crucial for designing a more walkable, aging, and sustainable community, and it will have a far-reaching impact on promoting the development of intensive and smart cities in China [12].
Liu Lianlian and Wei Wen compared the characteristics of and differences between various walkability evaluation methods and tools promoted internationally [13]. As a mainstream method for quantitatively measuring walkability internationally, Walk Scores have been widely used not only in urban renewal, community construction, and other areas, in countries such as the United States, the United Kingdom, and Australia, to help build a new form of urban development—pedestrian cities [14]—but also as important variables in studying the impact of such development on active transportation [8,15], transportation/leisure travel (e.g., duration [16,17,18,19], travel mode choice [20], and number of steps [21]), physical activity [22,23], and health status. Previous studies mainly focused on the relationship between Walk Scores and walking behavior and less on total physical activity. There is a need to confirm whether a more walkable community is associated with higher total physical activity, because total physical activity should be most closely related to health benefits [22]. In addition, a few studies on the influence of Walk Scores on physical activity and individual health have mainly been carried out in Western countries or regions [24], and there have been only a few studies in developing countries. It is not clear whether community Walk Score is related to Chinese senior citizens’ physical activity and health outcomes.
In 2013, a Walk Score was introduced to China by Lu Yintao and Wang De [14]. Chinese scholars optimized the Walk Score and mainly applied it to the evaluation of walkability in parks and green spaces [25], streets [12,26], communities [27], metro stations [28], and campuses [29]. In recent years, scholars in urban planning, geography, sociology, environmental science, and other fields have paid attention to walkability [6], and most of the research has focused on the entire population or specific groups (such as children, adults, and seniors) [30]. Walkability is mainly influenced by the type and spatial distribution of the destination, walking distance, and walking environment [31]. From the perspective of travel range (space) or walking distance, the 15 min life circle (such as 1000 m [27] or 1200 m [12]) constructed by Chinese urban planning and the half-hour walking distance (2400 m [6,32,33]) considered in some studies have difficulty in meeting the travel needs of older adults wishing to access community service facilities on foot. In fact, according to the travel habits and mobility of older adults, the 10 min age-friendly community life unit is mainly for older adults who are able to walk for 5~10 min to meet their daily needs, with a service radius of 500 m [34,35]. With the increasing aging of the population, the community needs to consider usability for seniors as the main demographic body [31]. It is of great significance to measure community walkability for Chinese urban seniors for the rational layout of service facilities and for planning/construction guidance for age-friendly communities [34]. Research on the measurement of community walkability based on the 10 min walking distance for Chinese seniors is relatively lacking.
It is necessary to systematically optimize and validate the Walk Score measurement method for certain populations or regions because different countries and regions have distinct cultural backgrounds and lifestyles. Two limitations should be further addressed. Firstly, residential living arrangements in China differ significantly from those in the West [24,30]. Furthermore, older adults are more reliant on community service facilities [34], and their needs for these facilities differ noticeably from those of the entire population or other groups [30]. Therefore, it is necessary to ascertain the types and weights of community service facilities that are frequently utilized by older citizens in China. Secondly, the walking characteristics of seniors are taken into consideration because the decay effect of amenity varies greatly among population groups [30]. Furthermore, not many studies have examined the relationship between Walk Score and subjective/objective community environmental variables to verify its applicability. Particularly for China, which has entered an aging society, a tool for the large-scale objective quantitative measurement of community walkability must be developed.
In response to the limitations of previous research, this study aims to propose a revised community Walk Score measurement method from the viewpoint of Chinese seniors, verify its validity, and further investigate whether the community Walk Score is associated with physical activity levels and health outcomes of older adults in China.
The rest of this paper is organized as follows. First, we introduce a revised method of the community Walk Score. The results of our analyses are then explained. Finally, we discuss our results and conclude our study.

2. Materials and Methods

2.1. Methodological Framework

Figure 1 shows the methodological framework, which comprises three main steps:
(1)
Measuring 10 min walkable communities for a seniors group through modifying the Walk Score measurement system.
(2)
Validation of Walk Score: exploring the relationship between Walk Score and objective/subjective community environmental variables.
Because the community walkability data had a non-normal distribution, we calculated non-parametric Spearman production correlations between Walk Score values and the objective community environment indicators measured by ArcGIS, as well as subjective community walking environment variables.
Walk Score was separated into two categories for convenience of analysis, “Car-dependent” and “somewhat walkable” communities, with a Walk Score of less than 40 deemed to have “poor” walkability, meaning that most daily trips rely on a car. “moderately walkable”, “very walkable” and “walker’s paradise” communities with a Walk Score more than 40 are regarded as having “good” walkability, meaning that most daily trips rely on walking. Additionally, we used one-way analysis of variance (ANOVA) to investigate the relationship between the community Walk Score categories and subjective perception of community walking environment elements.
(3)
Application of Walk Score: investigating the relationship between community Walk Score and Chinese seniors’ physical activity level and health outcomes.
After adjusting for sociodemographic covariates (e.g., gender, age, living conditions, scope and duration of physical activity, and others), we used an ordered logistic regression model to examine the association between Walk Score categories and older adults’ physical activity level, and we used binary logistic regression model to explore the association between Walk Score categories and two health outcomes. The odds ratio and 95% confidence interval (CI) were calculated for each variable. IBM SPSS 19.0 was used for inferential statistics, with a significance level of p < 0.05.

2.2. Measurement of 10 min Community Using the Modified Walk Score Measurement Method

The Walk Score mainly reflects the rationality of daily facility configuration within a certain walking range [36]. According to the spatial scale of the evaluation object, it can be divided into two evaluation levels: a single-point Walk Score and an area Walk Score. Walk Score measurement method calculation takes four steps: (1) assigning raw weights for selected service facilities; (2) calculating distances from each community location to the selected service facilities; (3) computing the total score based on the distances and modifying the scores according to decay factors (e.g., street intersections and block length); and (4) normalizing scores to 0–100 [30].
However, several obvious limitations have to be addressed for revising the Walk Score under a Chinese context [24,30]: (1) Amenities and weight: It targets the overall population, and the walking demands of special groups (e.g., seniors) have not been included in the assessment. (2) Decay function: the decay effect of amenity varies greatly among population groups. (3) Actual road network has not been considered when calculating Euclidean distance. We propose a modified Walk Score measurement method, taking into account seniors’ walking characteristics and amenity attributes (scale and category) (Figure 2). Details for each step are provided as follows:

2.2.1. Service Facility Selection and Weight Determination

Community service facilities that are closely related to the Chinese seniors’ daily life serve as the main research objects. Due to differences in urban construction both domestically and internationally, this study made appropriate adjustments based on national conditions, and the community service facilities selected should strive to meet the needs of the daily trips and physical activity for the older adults. Referring to the exiting research [34,37,38] and the Chinese Standard for Urban Community Design, the community service facilities were divided into nine categories in this study: shopping, fitness, leisure, education, culture, public transport site, medical care, provide for the aged, and public service.
The determination of facility weights is the foundation and key step in measuring Walk Score. The weight of community service facilities can be calculated in the following ways: (1) directly referring to the classification and weight determination of facilities by American scholars [39]; (2) using Analytic Hierarchy Process to determine the weights of facilities [31,40]; (3) developing a facility weight table (entire population [36,41,42], seniors) mainly includes three steps, creating a facility diversity table, calculating the satisfaction of classification requirements, and finishing the allocation of diversity requirements; (4) assigning values based on residents’ frequency of use of various facilities [6,32,33].
The types and weights of service facilities need to be determined due to the lifestyle and cultural differences between China and Western countries. The weight under Analytic Hierarchy Process (AHP) requires multi-round consultation with experts and relies on experts’ experience and subjective judgment. Although the facility weight table quantifies the diversity of facilities, seniors tend to choose nearby facilities and their usage is generally stable. We used questionnaires to obtain older adults’ needs for community service facilities. Respondents reported how frequently they utilized community service facilities. In this study, high, moderate and low usage frequency are assigned as 3, 2, and 1 [24]. The types and weights of service facilities finally determined in this study are shown in Table 1.

2.2.2. Distance Decay Function

The minimum cost path from multiple starting points to multiple destinations in the network is determined using the OD cost analysis in ArcGIS 10.7. The starting point is community location, the destination is various facilities, and the U-turn of the intersection is permitted when calculating the distance between community and service facilities using the road network. In this study, the service facilities that each community may access within 500 m are examined using the OD cost analysis.
According to the facility classification table, the initial weight of the facility will decay regularly as its distance from the starting point increases, i.e., the distance decay rate [43,44]. There are many patterns of distance attenuation, including cubic curves [6,32,33,45] and piecewise functions [12,36,41,46]. Distance decay is shown in Figure 3 referring to the average walking speed of older adults (50 m/min) [31,43]. When the facility is within 250 m from the starting point, this study assumes that no decay will occur. The decay rate is 10% when the distance is within 250~500 m; the decay rate is 40% when the distance is between 500 and 750 m; the decay rate is 75% when the distance is between 750 and 1000 m; the decay rate is 99% when the distance is between ~1000 and 1500 m; the measurement scope does not encompass distance more than 1500 m.

2.2.3. Score Calculation

The geographic center of the community will be considered as a point object since it is impossible to obtain the dwelling location of each survey respondent. The basic Walk Score is revised based on two factors considering that walking behavior is influenced by the surrounding environment, including the length of the block and the density of road intersections inside the research area [12,34,41]. Better walkability is taken into consideration when there are more intersections, a shorter block length, and a more diverse impact of walking route selection. The modified Walk Score impacted by the walking environment is further calculated to improve the accuracy of the measurement result based on the associated attenuation rate. As indicated in Table 2, we divide the density of road intersections and block length into five decay levels, with decay rates ranging from 0% to 5%.
The final Walk Score is obtained by normalizing the revised Walk Score to 0–100 (see Table 3) [36]. The specific calculation formula is as follows [12]:
W = ∑ i = 1 n ( W i × D i ) × ( 1 − L ) × ( 1 − μ ) / 100
W s c o r e = ( W − W m i n ) / ( W m a x − W m i n ) × 100
Among them, W is the calculation result of single-point Walk Score; Wi is the weight value of facilities i, i representing different facilities; Dl is the decay coefficient corresponding to a distance of l; L is the decay rate of street length; μ is the decay rate of intersection density; Wscore is the final single-point Walk Score; Wmin is the minimum value of the revised single-point Walk Score; Wmax is the maximum value of the revised single-point Walk Score.

2.3. Study Setting

2.3.1. Participants

A formal survey was conducted from March 2023 to March 2024. The subjects in this study need to meet the following criteria: (1) dwelling at urban communities for more than six months; (2) individuals aged 60 years or older, as well as retirees between the age of 50 and 59, who are referred to as “older adults” in this study; (3) not residing in a nursing institution.
The interviewees were from three different kinds of Chinese large cities: Beijing, Qingdao, and Yantai (see Table A1). Written informed consent was obtained from all participants.

2.3.2. Community Environment Variables

Residential communities’ geographic information was gathered from housing portals like Anjuke and Beke. These online platforms for housing information services offer customers a wide range of residential services, such as new and used houses, leasing, and decorating. These platforms provide some information, like the communities’ name, address, district, built time, and geographic coordinates [point longitude, point latitude]. The POI data of service facilities, including basic details such as place names, addresses, and spatial coordinates, were collected from publicly available government data. The road network was obtained on the OSM data platform, and the boundaries of urban districts were obtained by vector processing based on administrative zoning diagrams.
We also used ArcGIS 10.7 to objectively calculate community environmental variables, such as population density, intersection density, green coverage, the number of various service facilities within a 500 m buffer, and the closest distance between residential areas and various service facilities.
To gauge how older adults felt about the community walking environment, we used the Neighborhood Environment Walkability Scale (NEWS) (see Table A2).

2.3.3. Measuring the Physical Activity Level

The physical activity scale for the elderly (PASE), which was used as an instrument in this study, was designed to evaluate the activities that older adults participated in [47]. After being introduced to China, the PASE underwent local modifications [48] and was examined by both domestic and international researchers. The PASE comprised self-reported occupational, household and leisure items over a one-week period. Total PASE scores were computed by multiplying the time spent on each activity by its corresponding weights and adding up all activities [48]. The weight of each physical activity was determined in relation to these studies [48,49,50]. The range of the total PASE score was 0 to 360 [47,49,50]. Greater physical activity was indicated with higher scores [47]. Older adults’ PA scores were ranked from low to high, and their PA scores were separated into three groups, representing one-third of the sample: low PAL, medium PAL and high PAL [50].

2.3.4. Health Outcomes

Health outcomes comprised self-reported body mass index (BMI), the number of diseases, and self-rated physical health (poor or good). Based on self-reported height and weight, BMI was calculated and categorized as “normal weight, <24.0 kg/ m2” and “overweight/obesity, ≥24.0 kg/ m2” in accordance with Asian cutoff point guidelines [51]. The number of chronic diseases, such as hypertension, diabetes and hyperlipidemia, is determined by answering “yes” or “no” to a question.

2.3.5. Covariates

Additionally, this study investigated the socioeconomic characteristics of older adults, such as gender, age, income, occupation, and education; their living conditions (e.g., dwelling status, elevators, community types); and their activity–travel characteristics (e.g., walking time per trip, maximum walking time).

3. Results and Discussion

3.1. Participants’ Demographic Characteristics

A total of 529 valid questionnaires were collected for this investigation. Women participants accounted for 54%. The oldest respondent was 90 years old, and the mean age was 66. Approximately three-quarters of the respondents were under 75 years of age. Almost 70% of the participants had completed junior/high school. About 40% of those surveyed resided in older communities that were constructed before the year 2000. About 20% of the respondents claimed to make more than CNY 5000 per month, while nearly 40% claimed to make less than CNY 3000 per month. Of the respondents, 70% were overweight or obese, and 80% had one or more chronic diseases.

3.2. The Descriptive Statistics of Community Walk Score Distributions

Table 4, for the three case cities, shows that the majority of the communities outside the central city had poor walkability (walk score < 40), while the areas with good walkability (walk score ≥ 40) were mostly located in the core area. Overall, there was an uneven distribution of space across the community service facilities. According to the community walk score, 7.3% of the communities where the respondents live have good walkability.

3.3. The Association Between Community Walk Score and Bulit Environment Variables

Table 5 shows the association between community Walk Score and objective environment factors using Spearman correlation analysis and significance value. Walk Score and residential density were identified to be significantly positively correlated by Carr et al. (2010) [52], whereas Walk Scores and intersection density were found to be significantly correlated by Duncan et al. (2011) [53]. We found that Walk Scores were not significantly correlated with population density (p = 0.074) or intersection density (p = 0.099), which differs from findings in developed countries. This could be because of our country’ low road network density and high urban population concentration [46], as well as the different buffer scales that were developed. Furthermore, we found that the community Walk Score had a positive correlation with the number of various facilities in the buffer zone and a negative correlation with the closest distance to various service facilities. According to these findings, the Walk Score can be used in place of community environment density and accessibility to nearby destinations [8,52].
Table 6 displays the correlation between the subjective community environment characteristics and community Walk Score/category. We were surprised to find that there was no correlation between the community Walk Score (or categories) and any of the subjective community environmental variables. In line with our findings, no correlations were found between the Walk Score and the summed score of participants’ perceived access to nearby facilities in this study [52]. Additionally, we discovered no relationship between the Walk Score and participants’ perception of surrounding facilities. This study also found that the Walk Score was positively correlated with crime [52]. In contrast, we found that there was no correlation between the Walk Score and the perception of security. This might occur as a result of the calm, peaceful and harmonious communal environment in China, where there is no obvious difference across communities.
Many studies demonstrate the validity of the Walk Score as a tool for assessing neighborhood walkability [8,52,53]; it was only used as a proxy for neighborhood density and amenities’ accessibility [8,52]. Compared to single-component measures, composite walkability measurements were more consistent in predicting walking behavior [54]. Future research should make use of additional PA environment metrics, including crime, aesthetics, topography and weather, which Walk Score does not include [8].

3.4. Association Between Walk Score and Physical Activity Level and Health Outcomes

Studies have shown that walkable built environment features are associated with increased levels of PA [55], and the overall Walk Score is the main predictor of physical activity outcomes [56]. This study took the physical activity level and two health outcomes as dependent variables, with socioeconomic attributes and activity characteristics as control variables. To investigate the relationship between the Walk Score and physical activity level, this study employed ordered logistic regression. The association between Walk Score and two health outcomes (normal or overweight/obese, with or without chronic diseases) was examined using a binary logistic regression model. The proportionate odds assumption was satisfied by the ordered logistic regression model (p = 0.347). Two binary logistic regression models had a good goodness of fit (p > 0.05). Table 7 presents the results of the logistic regression analysis.

3.4.1. Association with Total Physical Activity

Just as we expected, the community Walk Score as good (vs. poor: OR = 2.77, 95% CI = 1.10~6.95; p < 0.05) was found to have a positive association with older adults’ physical activity level (PAL). Compared to older adults living in car-dependent communities, the likelihood of their PAL increasing by one unit was 2.77-times higher for those living in a walkable community (see Table 7).
Living in a walkable community was related to more physical activities, which is in line with our findings [19]. Residing in walkable neighborhoods (i.e., no-car-dependent neighborhoods) was a strong predictor of walking at or more than the recommended 150 min per week for walking [57]. Our research supports the view that higher Walk Scores were related to higher physical activity. Our finding can provide reference for public health policymakers and urban planning designers. The Walk Score was easily utilized to identify and intervene in areas with limited community resources [53]. In order to encourage travel and physical activity, the Walk Score is a tool used to tell inactive individuals about service facilities that are available to them within walking distance [52,53].
To the best of our knowledge, the few exiting studies provided inconsistent findings regarding the association between Walk Score and older adults’ physical activity [58]. The physical activity of older adults (65 years and older) in Taiwan Province of China was unrelated to any of the Walk Score categories [58]. Given that highly walkable neighborhood environments in Taiwan are usually crowded, it is possible that older adults may have a tendency to spend more time indoors watching TV and indulging in sedentary behavior rather than going outside [58]. There seems to be an inverse association, where decreased walkability is associated with an increase in mean total PA [59]. One possible explanation for this could be that the built environment may not have an impact on total PA because it includes housework and occupational activities, which are more likely to take place indoors [59]. There was no discernible difference in Walk Scores or walkability for meeting the PA guidelines in the cross-sectional analyses at baseline [60], because only moderately vigorous PA was evaluated in this study [60].
Conflicting findings (i.e., positive, negative or no correlation) have been found in the literature on the association between the Walk Score and total physical activity. This could be due to the fact that the studies were conducted in different countries; the respondents came from a variety of cultural, economic and environmental backgrounds [58]; and the physical activity measurement indicators used varied.

3.4.2. Association with Health Outcomes

The relationship between the Walk Score category and two health outcomes was demonstrated via the adjusted binary logistic regression models. The two health outcomes in this study—being normal (p = 0.194) and not having any chronic diseases (p = 0.430)—did not significantly correlate with the Walk Score category (see Table 7).
Previous studies associated the Walk Score with several health outcomes, including body mass index (BMI) [16] and overweight and obesity [61,62]. Older adults who lived in more walkable neighborhoods had lower BMIs than those who lived in less walkable neighborhoods, indicating a significant walkability main effect [63]. Overweight/obesity was lower in high-walkability neighborhoods [22]. Body mass index (BMI) generally decreased as the neighborhood became more walkable [16]. Compared with residents of “Walker’s Paradise” areas, those in very car-dependent areas had significantly higher odds of being overweight or obese [61]. Our study demonstrated no significant relationship between the Walk Score and overweight/obesity, which was consistent with other studies that found no associations between the Walk Score and overweight/obesity [58,62,64]. This suggests that the health outcomes of Chinese urban older adults may not be directly impacted by community walkability in a 500 m buffer, as determined by the Walk Score method [58]. More research in various buffers should be conducted to confirm these findings in the future.

4. Conclusions

In order to achieve the research goal of developing, verifying, and applying a community Walk Score measurement system from the perspective of Chinese older adults, we chose three different types of cities as research cases. In particular, we chose the 10-minute walking distance (500 m) as our spatial scale, because most Chinese older people consider this to be a comfortable and acceptable walking distance.
The community Walk Score measurement system was first optimized in this study based on the following aspects: (1) The types of community service facilities (CSFs) that Chinese seniors used were selected based on their demand for CSF, and the facility weights were determined based on their frequency of use, acknowledging the critical role that destination accessibility (service facilities) plays in assessing community walkability. (2) Calculating the distance between the community and various service facilities based on the road network. (3) Determining the distance attenuation function based on senior Chinese groups’ travel characteristics.
Second, Spearman correlation analysis and ANOVA were used to explore the correlation between the Walk Score and subjective/objective community environmental variables to evaluate its validity. The Walk Score serves as a substitute for the density of community environment and accessibility to nearby destinations. Future studies are recommended to make use of supplementary PA environment measures that the Walk Score does not include.
Lastly, this study investigated the relationship between the community Walk Score and Chinese seniors’ physical activity level (PAL) and health outcomes. The Walk Score was related to older adults’ physical activity level, but it was not significantly associated with two health outcomes.
Senior groups’ travel and health in China are being focused on more with the acceleration of population aging. Assessing community walkability is crucial in light of the trend of the people-oriented urban renewal movement. The evaluation system can also be used by researchers to determine whether the spatial distribution of service facilities resources is sensible and to identify and intervene in areas where community resources are scarce. The Walk Score can provide a supporting basis for urban renewal, old community reconstruction and age-friendly community planning and design [34]. The Walk Score can also be used by public health practitioners or policymakers to educate inactive physical activity participants about the facilities they can use within walking distance [52] and to help older adults boost their physical activity in order to preserve and enhance their health.
There are some limitations of this study as well. This study validated the measurement system with an overall sample size. The survey’s scope can be broadened in the future to obtain more information about the variations in older adults’ usage demands for community service facilities by gender and age groups. Another limitation is the use of self-reported data to calculate BMI and the physical activity level. Furthermore, differences in the associations between community walkability and health outcomes can be further explored within various buffer distances.

Author Contributions

Conceptualization, W.L. and H.G.; methodology, W.L., H.G., H.Y. and M.H.; software, W.L.; investigation, W.L.; data curation, W.L.; writing—original draft preparation, W.L.; writing—review and editing, W.L., H.G., H.Y. and M.H.; visualization, W.L.; supervision, W.L. and H.Y.; funding acquisition, H.G. 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 (No. 71971005; No. U24A20198) and the Beijing Municipal Natural Science Foundation (No. 8202003).

Institutional Review Board Statement

This study was conducted in accordance with the Declaration of Helsinki. Ethical approval is not required for this type of study base on the national legislation (Chinese Government, 2023).

Informed Consent Statement

Informed consent was obtained from all participants.

Data Availability Statement

The data used to support the findings of this study are available from the authors upon request.

Acknowledgments

The authors are very grateful to all the participants. We would also like to thank the anonymous reviewers for their helpful comments and suggestions, which greatly improved the quality of this paper.

Conflicts of Interest

The authors declare no conflicts of interest.

Appendix A

Table A1. Study area.
Table A1. Study area.
Case City The Locations of Participants
BeijingChaoyang district
QingdaoShibei district, Licang district, Laixi district
YantaiZhifu district, Fushan district, Laishan district, Muping district, Laiyang district, Haiyang district
Table A2. Community environmental variables and their basic descriptions.
Table A2. Community environmental variables and their basic descriptions.
VariableDescription
Population densityPopulation per square kilometer
Intersection densityNumber of the intersections per square kilometer
Shopping facilities densityNumber of commercial facilities within the buffer
Fitness facilities densityNumber of fitness facilities within the buffer
Leisure facilities densityNumber of leisure facilities within the buffer
Public traffic facilities densityNumber of bus stations within the buffer
Medical care facilities densityNumber of medical facilities (e.g., hospitals) within the buffer
Public service facilities densityNumber of public facilities (e.g., public toilets) within the buffer
Distance to Shopping service facilitiesDistance to the nearest shopping facilities (km)
Distance to Fitness service facilitiesDistance to the nearest fitness facilities (km)
Distance to Leisure service facilitiesDistance to the nearest leisure facilities (km)
Distance to Bus stopsDistance to the nearest public traffic facilities (km)
Distance to HospitalDistance to the nearest medical facilities (km)
Distance to Public toiletsDistance to the nearest public service facilities (km)
Perception of Service FacilitiesPerception of supporting living facilities such as shops, bus stops, parks, etc. within walking distance
Perception of Roads ConditionPerception of the greening, cleanliness, lighting, and flat road conditions around the community
Perception of Traffic ConditionPerception of traffic obstacles, street traffic flow, pedestrian routes, traffic accidents, average speed of vehicles, and other situations around the community
Perception of SecurityPerception of daytime and nighttime security around the community

References

  1. Adams, K.B.; Leibbrandt, S.; Moon, H. A critical review of the literature on social and leisure activity and wellbeing in later life. Ageing Soc. 2011, 31, 683–712. [Google Scholar]
  2. Levasseur, M.; Desrosiers, J.; Whiteneck, G. Accomplishment level and satisfaction with social participation of older adults: Association with quality of life and best correlates. Qual. Life Res. 2010, 19, 665–675. [Google Scholar]
  3. Sowa, A.; Tobiasz-Adamczyk, B.; Topór-Mądry, R.; Poscia, A.; la Milia, D.I. Predictors of healthy ageing: Public health policy targets. BMC Health Serv. Res. 2016, 16 (Suppl. S5), 289. [Google Scholar]
  4. Maarten, K.; Jonas, D.V. Does active travel make people healthier, or are healthy people more inclined to travel actively? J. Transp. Health 2020, 16, 100844. [Google Scholar]
  5. Tudor-Locke, C.E.; Myers, A.M. Challenges and opportunities for measuring physical activity in sedentary adults. Sports Med. 2001, 31, 91–100. [Google Scholar] [PubMed]
  6. He, C.; Shan, Z.; Yuan, M. A Study on walkability Evaluation of Daily Service Facilities in Residential Communities: A Case Study of Jiang’an District, Jianghan District, Wuhan City. In Proceedings of the 2020/2021 China Urban Planning Annual Conference and 2021 China Urban Planning Academic Season, Chengdu, China, 25–30 September 2021. [Google Scholar]
  7. Wang, K.; Yue, L.; Wang, J. Walkability evaluation of university campus in cold region: A case study of three universities in Harbin. Sci. Technol. Eng. 2020, 20, 3238–3245. [Google Scholar]
  8. Hall, C.M.; Ram, Y. Walk score® and its potential contribution to the study of active transport and walkability: A critical and systematic review. Transp. Res. Part D Transp. Environ. 2018, 61, 310–324. [Google Scholar]
  9. Jing, J.T.; Yu, F.F.; Cong, L.P. Discussion on the Construction of Community Elderly Service Facility System under the Background of Aging Population. In Proceedings of the 2012 China Urban Planning Annual Conference, Kunming, China, 17–19 October 2012; pp. 1–11. [Google Scholar]
  10. Yao, Z.; Qin, L. Research progress and review of aging suitability renewal of old neighborhood. Urban Probl. 2021, 95–102. [Google Scholar] [CrossRef]
  11. Huang, J.; Wu, M. An Investigation and Analysis of Travel Characteristics and Related Factors of the Elderly Population in Megacities—The Case of the Central Area in Shanghai. Urban Plan. Forum 2015, 93–101. [Google Scholar] [CrossRef]
  12. Ge, B.; Zhu, F.; Wang, Y. Walkability evaluation of living service facilities in Heping District, Tianjin. Shanxi Archit. 2023, 49, 29–33. [Google Scholar]
  13. Liu, L.; Wei, W. International Experiences of Tools and Approaches for Assessment of Walkability. Urban Plan. Int. 2018, 33, 103–110. [Google Scholar]
  14. Lu, Y.; Wang, D. Walkability Measuring in America and Its Enlightenment. Urban Plan. Int. 2012, 27, 235–244. [Google Scholar]
  15. Vale, D.S.; Saraiva, M.; Pereira, M. Active accessibility: A review of operational measures of walking and cycling accessibility. J. Transp. Land Use 2016, 9, 209–235. [Google Scholar]
  16. Hirsch, J.A.; Moore, K.A.; Evenson, K.R.; Rodriguez, D.A.; Roux, A.V.D. Walk Score® and Transit Score® and Walking in the Multi-Ethnic Study of Atherosclerosis. Am. J. Prev. Med. 2013, 45, 158–166. [Google Scholar]
  17. Brown, S.C.; Pantin, H.; Lombard, J.; Toro, M.; Huang, S.; Plater-Zyberk, E.; Perrino, T.; Perez-Gomez, G.; Barrera-Allen, L.; Szapocznik, J. Walk Score®: Associations with Purposive Walking in Recent Cuban Immigrants. Am. J. Prev. Med. 2013, 45, 202–206. [Google Scholar]
  18. Tuckel, P.; Milczarski, W. Walk Score (TM), Perceived Neighborhood Walkability, and walking in the US. Am. J. Health Behav. 2015, 39, 242. [Google Scholar] [PubMed]
  19. Cole, R.; Dunn, P.; Hunter, I.; Owen, N.; Sugiyama, T. Walk Score and Australian adults’ home-based walking for transport. Health Place 2015, 35, 60–65. [Google Scholar]
  20. Duncan, D.T.; Méline, J.; Kestens, Y.; Day, K.; Elbel, B.; Trasande, L.; Chaix, B. Walk Score, Transportation Mode Choice, and Walking Among French Adults: A GPS, Accelerometer, and Mobility Survey Study. Int. J. Environ. Res. Public Health 2016, 13, 611. [Google Scholar] [PubMed]
  21. Hajna, S.; Ross, N.A.; Joseph, L.; Harper, S.; Dasgupta, K. Neighbourhood walkability, daily steps and utilitarian walking in Canadian adults. BMJ Open 2015, 5, e8964. [Google Scholar]
  22. Sallis, J.F.; Saelens, B.E.; Frank, L.D.; Conway, T.L.; Slymen, D.J.; Cain, K.L.; Chapman, J.E.; Kerr, J. Neighborhood built environment and income: Examining multiple health outcomes. Soc. Sci. Med. 2009, 68, 1285–1293. [Google Scholar]
  23. Su, M.; Tan, Y.-Y.; Liu, Q.-M.; Ren, Y.-J.; Kawachi, I.; Li, L.-M.; Lv, J. Association between perceived urban built environment attributes and leisure-time physical activity among adults in Hangzhou, China. Prev. Med. 2014, 66, 60–64. [Google Scholar]
  24. Su, S.; Pi, J.; Xie, H.; Cai, Z.; Weng, M. Community deprivation, walkability, and public health: Highlighting the social inequalities in land use planning for health promotion. Land Use Policy 2017, 67, 315–326. [Google Scholar]
  25. Lwin, K.K.; Murayama, Y. Modelling of urban green space walkability: Eco-friendly walk score calculator. Comput. Environ. Urban Syst. 2011, 35, 408–420. [Google Scholar]
  26. Long, Y.; Zhao, J.; Li, S.; Zhou, Y.; Xu, L. The Large-Scale Calculation of “Walk Score” of Main Cities in China. New Arch. 2018, 4–8. [Google Scholar] [CrossRef]
  27. Lu, Y. Walkability Evaluation based on People’s Use of Facilities by walking. Urban Plan. Forum 2013, 113–118. [Google Scholar]
  28. Sun, G.; Webster, C.; Chiaradia, A. Objective assessment of station approach routes: Development and reliability of an audit for walking environments around metro stations in China. J. Transp. Health 2017, 4, 191–207. [Google Scholar]
  29. Zhang, Z.; Wang, H.; Fisher, T. The development, validation, and application of the campus walk score measurement system. Transp. Policy 2024, 152, 40–54. [Google Scholar]
  30. Weng, M.; Ding, N.; Li, J.; Jin, X.F.; Xiao, H.; He, Z.M.; Su, S.L. The 15-minute walkable neighborhoods: Measurement, social inequalities and implications for building healthy communities in urban China. J. Transp. Health 2019, 13, 259–273. [Google Scholar]
  31. Song, S.; You, Z. Research on the evaluation system of living circle based on the walking characteristics of the elderly from the perspective of urban renewal. In Proceedings of the Annual National Planning Conference 2022/2023, Wuhan, China, 1–4 April 2023. [Google Scholar]
  32. Li, S.; Zhao, D.; Chen, Z. A Study on the Suitability of Urban Spatial Structure Based on the Measurability of Community Life Circle: A Case Study of Wuling District, Changde City. Theory Res. Urban Constr. 2023, 161–163. [Google Scholar] [CrossRef]
  33. Du, M.; Huang, L.; Wang, H.; Pan, Q. Research on the Walkability Measurement of the Yangzhou Old Urban Area Based on Big Data. Mod. Inf. Technol. 2022, 6, 154–157. [Google Scholar]
  34. Hu, J. Daily service facilities in community life circles from the perspective of the elderly: Measurement and research on walkability. In Proceedings of the Annual National Planning Conference 2022/2023, Wuhan, China, 1–4 April 2023. [Google Scholar]
  35. Planning Guidelines for Age-Friendly Community Life Unit; Chinese Society for Urban Studies: Beijing, China, 2022.
  36. Wu, J.; Qin, W.; Peng, J.; Li, W. The Evaluation of Walkability and Daily Facility Distribution Reasonability of Futian District, Shenzhen Based on Walk Score. Urban Dev. Stud. 2014, 21, 49–56. [Google Scholar]
  37. Huang, J.; Hu, G.; Li, M. The Allocative Suitability of Community Facilities from the Perspective of the Elderly-Based on Walk Score Method. Urban Plan. Forum 2016, 6, 45–53. [Google Scholar]
  38. Liu, R. Research on the Optimization of Walking Space in the Central Urban Area of Beijing from the Perspective of Elderly Health. Master’s Thesis, North China University of Technology, Beijing, China, 2021. [Google Scholar]
  39. Available online: https://www.walkscore.com (accessed on 23 April 2025).
  40. Liu, Y. The Walkability Measurement and the Validation Study of the Taoyuan District in Shenzhen. Doctoral Thesis, Harbin Institute of Technology, Shenzhen, China, 2013. [Google Scholar]
  41. Yuan, S.; Wu, J. Walkability Evaluation of Daily Service Facilities in Siming District of Xiamen City based on Walking Score. Chin. Overseas Archit. 2022, 77–82. [Google Scholar] [CrossRef]
  42. Zhang, L.; Sun, Y.; Zheng, Z.; Zhang, C. Urban Neighborhood Walkability Assessment of Daily Service Facilities: A Case Study of Kaifeng. Urban Insight 2018, 133–144. [Google Scholar] [CrossRef]
  43. Liu, R.; Yang, X. Research on Community Walkability from the Perspective of Elderly Health. In Proceedings of the 2020 Industrial Architecture Academic Exchange Conference, Beijing, China, 21 November 2020; Volume 6. [Google Scholar] [CrossRef]
  44. Zhou, Y.; Long, Y. Large-scale Evaluation for Street Walkability: Methodological Improvements and the Empirical Application in Chengdu. Shanghai Urban Plan. Rev. 2017, 1, 88–93. [Google Scholar]
  45. Xiao, Y.; Wang, H.; Zhan, Q. Evaluation of Walking Accessibility to Community Health Care from Life Circle Perspective—Taking the Main Urban Area of Wuhan. J. Geomat. 2023, 48, 121–126. [Google Scholar]
  46. Jin, L.; Lu, W.; Sun, P. Walkability Measurement of the Built Environment and Its Validity in Dalian. New Archit. 2022, 100–105. [Google Scholar]
  47. Washburn, R.A.; Smith, K.W.; Jette, A.M.; Janney, C.A. The Physical Activity Scale for the Elderly (PASE): Development and evaluation. J. Clin. Epidemiol. 1993, 46, 153. [Google Scholar]
  48. Yu, H.; Qiu, J. Physical Activity Scale for Elderly (PASE): A Reliability-Validity Study for Physical Activity of Chinese Elderly. J. Shanghai Univ. Sport 2014, 38, 45–49, 60. [Google Scholar]
  49. Wang, L. The Correlation Between Physical Activity, Muscle Function and Quality of Life of Elderly Women in Changchun. Doctoral Thesis, Northeast Normal University, Changchun, China, 2018. [Google Scholar]
  50. Gao, X. Research on the Relationship Between Physical Activity of the Elderly and Medical Utilization as Well as Quality of Life—Case Study of a Small Town in Center Taiwan. Doctoral Thesis, Shanghai University of Sport, Shanghai, China, 2012. [Google Scholar]
  51. Weight Management Guidelines, 2024th ed.; National Health Commission of the People’s Republic of China: Beijing, China, 2024; p. 30.
  52. Carr, L.J.; Dunsiger, S.I.; Marcus, B.H. Walk Score™ as a Global Estimate of Neighborhood Walkability. Am. J. Prev. Med. 2010, 39, 460–463. [Google Scholar]
  53. Duncan, D.T.; Aldstadt, J.; Whalen, J.; Melly, S.J.; Gortmaker, S.L. Validation of walk score for estimating neighborhood walkability: An analysis of four US metropolitan areas. Int. J. Environ. Res. Public Health 2011, 8, 4160–4179. [Google Scholar] [PubMed]
  54. Vargo, J.; Stone, B.; Glanz, K. Google walkability: A new tool for local planning and public health research? J. Phys. Act. Health 2012, 9, 689–697. [Google Scholar] [PubMed]
  55. Cradock, A.L.C.; Duncan, D.T. The Role of the Built Environment in Supporting Health Behavior Change. In The Handbook of Health Behavior Change, 4th ed.; Riekert, K.A., Ockene, J.K., Pbert, L., Eds.; Springer Publishing Company: New York, NY, USA, 2014; pp. 437–462. [Google Scholar]
  56. Duncan, D.T. What’s Your Walk Score®?: Web-based neighborhood walkability assessment for health promotion and disease prevention. Am. J. Prev. Med. 2013, 45, 244–245. [Google Scholar]
  57. Towne, S.D., Jr.; Won, J.; Lee, S.; Ory, M.G.; Forjuoh, S.N.; Wang, S.; Lee, C. Using Walk Score™ and Neighborhood Perceptions to Assess Walking Among Middle-Aged and Older Adults. J. Community Health 2016, 41, 977–988. [Google Scholar] [PubMed]
  58. Liao, Y.; Lin, C.-Y.; Lai, T.-F.; Chen, Y.-J.; Kim, B.; Park, J.-H. Walk Score® and Its Associations with Older Adults’ Health Behaviors and Outcomes. Int. J. Environ. Res. Public Health 2019, 16, 622. [Google Scholar]
  59. Twardzik, E.; Judd, S.; Bennett, A.; Hooker, S.; Howard, V.; Hutto, B.; Clarke, P.; Colabianchi, N. Walk Score and objectively measured physical activity within a national cohort. J. Epidemiol. Community Health 2019, 73, 549–556. [Google Scholar]
  60. Riley, D.L.; Mark, A.E.; Kristjansson, E.; Sawada, M.C.; Reid, R.D. Neighbourhood walkability and physical activity among family members of people with heart disease who participated in a randomized controlled trial of a behavioural risk reduction intervention. Health Place 2013, 21, 148–155. [Google Scholar]
  61. Chiu, M.; Shah, B.R.; Maclagan, L.C.; Rezai, M.-R.; Austin, P.C.; Tu, J.V. Walk Score and the prevalence of utilitarian walking and obesity among Ontario adults: A cross-sectional study. Health Rep. 2015, 26, 3–10. [Google Scholar]
  62. Xu, Y.; Wen, M.; Wang, F. Multilevel built environment features and individual odds of overweight and obesity in Utah. Appl. Geogr. 2015, 60, 197–203. [Google Scholar]
  63. King, A.C.; Sallis, J.F.; Frank, L.D.; Saelens, B.E.; Cain, K.; Conway, T.L.; Chapman, J.E.; Ahn, D.K.; Kerr, J. Aging in neighborhoods differing in walkability and income: Associations with physical activity and obesity in older adults. Soc. Sci. Med. 2011, 73, 1525–1533. [Google Scholar]
  64. Sriram, U.; LaCroix, A.Z.; Barrington, W.E.; Corbie-Smith, G.; Garcia, L.; Going, S.B.; LaMonte, M.J.; Manson, J.E.; Sealy-Jefferson, S.; Stefanick, M.L.; et al. Neighborhood Walkability and Adiposity in the Women’s Health Initiative Cohort. Am. J. Prev. Med. 2016, 51, 722–730. [Google Scholar] [PubMed]
Sustainability 17 06308 g001
Figure 1. Methodological framework.
Figure 1. Methodological framework.
Sustainability 17 06308 g001
Sustainability 17 06308 g002
Figure 2. Modifying the Walk Score measurement method.
Figure 2. Modifying the Walk Score measurement method.
Sustainability 17 06308 g002
Sustainability 17 06308 g003
Figure 3. Distance decay.
Figure 3. Distance decay.
Sustainability 17 06308 g003
Table 1. Types and weights of community service facilities.
Table 1. Types and weights of community service facilities.
No.Major CategoriesSubclassWeight
1shoppingmall, shopping center2
Supermarket, convenience store, vegetable market3
2fitnesssports venue, fitness center2
3leisurepark and square3
4educationkindergarten, primary school, middle school1
5culturecommunity cultural activity center/station2
6public transport bus stop3
7medical carehospital, community healthcare service, pharmacy1
8elderly careelderly care institution/service station1
9public servicepublic toilet3
Table 2. Comparison table of intersection density and block length decay rate.
Table 2. Comparison table of intersection density and block length decay rate.
Intersection Density (/km2)Decay Rate
(%)		Block Length
(100 m)		Decay Rate
(%)
≥770≤1200
[58~77)1(120,150]1
[47~58)2(150,165]2
[35,47)3(165,180]3
[23,35)4(180,195]4
<235>1955
Table 3. Evaluation table of Walk Score.
Table 3. Evaluation table of Walk Score.
ScoreDescription
90~100 Walker’s Paradise Daily trips do not rely on a car
70~89Very walkableMost daily trips rely on walking
40~69Moderately walkableSome daily trips rely on walking
20~39Somewhat walkableMost daily trips rely on a car
0~19Car-dependentAlmost all daily trips rely on a car
Table 4. Number distribution of walkable communities (walk score ≥ 40) in case cities.
Table 4. Number distribution of walkable communities (walk score ≥ 40) in case cities.
CityProportion of in the CityProportion of in the Core Area
Beijing2.8%2.0%
Qingdao2.6%1.7%
Yantai3.9%2.7%
Table 5. Correlation between Walk Score and objective environmental variables.
Table 5. Correlation between Walk Score and objective environmental variables.
Variables Spearmanp Value
population densityPop D0.1460.074
count of service facilities within the bufferShopping 0.505 ***0.000
Fitness0.408 ***0.000
Leisure0.322 ***0.000
Public traffic0.393 ***0.000
Medical care0.459 ***0.000
Public service0.382 ***0.000
the shortest distance to service facilities Shopping −0.517 ***0.000
Fitness−0.431 ***0.000
Leisure−0.472 ***0.000
Public transport−0.427 ***0.000
Medical care−0.429 ***0.000
Public service−0.527 ***0.000
*** significant at alpha 0.01 using 99% confidence intervals.
Table 6. Correlation between Walk Score and subjective community environmental variables.
Table 6. Correlation between Walk Score and subjective community environmental variables.
VariablesSpearmanp ValueFp Value
Perception of Service Facilities−0.0060.9400.4120.706
Perception of Roads Condition−0.0140.8661.0960.296
Perception of Traffic Condition0.0530.5210.6350.426
Perception of Security−0.0400.6280.0390.844
Table 7. The association of Walk Score category with physical activity level and health outcomes.
Table 7. The association of Walk Score category with physical activity level and health outcomes.
BPExp (B)95% CIH–L Test (P)
PAL (Ref: low)1.0180.0302.771.10~6.95/
Normal (Ref: overweight/obesity)0.6730.1941.960/0.434
No Chronic Diseases (Ref: have)0.4770.4301.611/0.318
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Liang, W.; Guan, H.; Yan, H.; Hao, M. Association of Community Walk Score with Chinese Seniors’ Physical Activity and Health Outcomes. Sustainability 2025, 17, 6308. https://doi.org/10.3390/su17146308

AMA Style

Liang W, Guan H, Yan H, Hao M. Association of Community Walk Score with Chinese Seniors’ Physical Activity and Health Outcomes. Sustainability. 2025; 17(14):6308. https://doi.org/10.3390/su17146308

Chicago/Turabian Style

Liang, Weiwei, Hongzhi Guan, Hai Yan, and Mingyang Hao. 2025. "Association of Community Walk Score with Chinese Seniors’ Physical Activity and Health Outcomes" Sustainability 17, no. 14: 6308. https://doi.org/10.3390/su17146308

APA Style

Liang, W., Guan, H., Yan, H., & Hao, M. (2025). Association of Community Walk Score with Chinese Seniors’ Physical Activity and Health Outcomes. Sustainability, 17(14), 6308. https://doi.org/10.3390/su17146308

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

Article Metrics

Back to TopTop