INTRODUCTION

Chronic health conditions like heart disease, diabetes, and depression significantly affect illness rates and mortality. Precursor risk factors such as physical inactivity, obesity, and high blood pressure (BP) significantly contribute to the development of these conditions [1]. As these risk factors are prevalent in many communities, addressing them is a critical public health priority. While poor physical and mental health have many consequences and contributing factors, a lack of physical activity (PA) may be a key antecedent. Regular PA is associated with several benefits, including reduced risk of cardiovascular disease, lower body mass index (BMI), and improved mental health status. Thus, promoting PA and time spent in natural spaces may provide a cost-effective treatment solution for the aforementioned health issues [2].

Public parks are a unique aspect of the built environment because they can improve PA, promote mental health, facilitate social interactions, and have considerable community economic benefits [3,4]. In addition to promoting PA, parks and other natural spaces can provide exposure to nature, which has been increasingly linked to improved mental health and well-being through the reduction of stress, anxiety, and depression [5,6]. Past studies have found that park usage and PA levels are related to proximity to parks, further highlighting the need for public park availability [7]. Previous research has also suggested that higher perceived park quality and ease of access may increase the number of visitors a park has and the quantity of PA they engage in at the park [8,9]. Unlike gyms or exercise classes, parks provide a free resource for PA opportunities. This is especially important from a public health perspective because health disparities exist among disadvantaged populations, and increased levels of PA can have a host of benefits, including lowering all-cause mortality, risk of hypertension, obesity, high cholesterol, and depression [10–12].

Although the role of parks in promoting PA and mental health is intuitively plausible, current evidence regarding the relationship between green spaces and health is mixed. Some studies have shown a relationship between access to green space and health at individual and population levels, with better or closer access to green spaces correlating with lower BMI values and obesity rates and better mental health [13–15]. Other studies have shown no significant relationship or mixed results between access to green spaces and health metrics [16,17]. For example, Xu et al. [18] found that the proximity of parks was inversely related to the likelihood of being overweight or obese. The quality of parks and green spaces in relation to health has been another growing area of research, with similarly mixed results [16,19]. One cross-sectional study in the United Kingdom found no relationship between park quality metrics and obesity [16]. In contrast, a systematic review by Nguyen et al. [19] reported a significant association between park cleanliness and lack of incivilities (e.g., vandalism, graffiti, litter) with depression (i.e., cleaner parks containing fewer incivilities were correlated with lower depression rates). However, facilities and overall maintenance of the parks did not show any significant relationship with mental or physical health metrics [19].

Given this mixed evidence and other gaps in the existing literature, more research is needed about how park availability and quality are related to health and well-being. Additionally, most previous research has focused on physical health metrics, like BMI, obesity rates, and/or diabetes rates [13,14,16,18] with less attention paid to mental health outcomes. To address these gaps, this study analyzed numerous metrics related to both mental and physical health, as well as diverse park access and park quality variables, to provide a more comprehensive overview of how parks may contribute to community health.

Specifically, the central questions of this study were: (1) What is the relationship between park access (park number and size) and the percentage of adults with obesity, no leisure time physical activity (no LTPA), high BP, coronary heart disease (CHD), high cholesterol, diabetes, depression, and poor mental health at the census tract level? (2) What is the relationship between diverse park quality metrics (transportation access, facility availability, facility quality, amenity availability, park aesthetics, park quality concerns, neighborhood quality concerns, and total park quality concerns) and the percentage of adults with obesity, no LTPA, high BP, CHD, high cholesterol, diabetes, depression, and poor mental health at the census tract level?

Context

South Carolina (SC) ranks poorly in many health metrics compared to United States averages and other states. For example, SC is currently positioned among the worst 25% of states in obesity rates, percentage of people with multiple chronic conditions, and poor mental and physical health rates, and it was ranked 41st in overall health in 2022 [20]. This study encompassed neighborhoods with diverse socio-demographic profiles, including a wide spectrum of income levels, racial compositions, and educational backgrounds. Understanding these varied community characteristics is crucial for examining how different groups interact with their local parks and experience distinct health outcomes. The inclusion of such diverse neighborhoods enabled a more comprehensive analysis of the relationships between community factors, park usage, and health impacts across various population segments.

METHODS

Study Setting

The setting for this study included the cities of Columbia, West Columbia, Cayce, and Forest Acres, all located in Richland and Lexington counties within SC. As of 2022, there were 178 368 people living within the study setting, which encompassed 167.31 square miles [21]. In total, there were 80 census tracts completely or partially within the study setting. Ten tracts were excluded due to missing health information and/or because they contained exclusively military land. There were 76 unique parks in the study area; however, 3 were excluded because they were located within the 10 excluded census tracts. We also did not include parks in the dataset that were only indoor community centers or did not contain green space. Thus, 70 census tracts that contained a total of 73 parks were analyzed for this study. Specifically, of the 70 census tracts, 38 contained 1 or more parks, while 32 had no parks. Figure 1 shows the census tracts and parks in the study setting.

Measures and Data Collection

Park variables

This study used data about parks collected within a previous study [22]. City boundaries, census tract boundaries, and park locations were visualized in ArcGIS Pro (Esri, Redlands, CA, USA). Park names and addresses were gathered on each park’s respective city or county Department of Parks and Recreation website. Google Maps was used to ensure that no parks were missed or counted twice. Park addresses were inputted into ArcGIS Pro (Esri) along with municipal city boundaries and census tract boundaries. We then used the aggregate points feature of ArcGIS (Esri) to determine which parks fell within each census tract. For tracts containing parks, 2 park availability scores were assigned to each census tract by summing the number of parks and the total number of park acres within the tract.

An online version of the Community Park Audit Tool (CPAT) based in Qualtrics was used to conduct objective audits of all 73 parks. CPAT has demonstrated strong interrater reliability in past research [23] and consists of 4 sections: park information, access and surrounding neighborhood, park activity areas, and park quality and safety. Based on the previously developed ParkIndex, CPAT data were used to calculate multiple metrics related to park quality [24]. The park quality metrics were composed of multiple indicators, which were each weighted equally (since there was no specific evidence to support the differential weighting of park elements). Park quality metrics were only calculated for tracts containing at least 1 park and the mean score for all parks within the census tract was used to calculate each park quality indicator described below.

Transportation access was assessed via 7 CPAT components: sidewalks, bike routes, traffic signals on adjacent roads, public transportation visible from the park, parking, external paths connected to the park, and visible signs clarifying park characteristics, such as hours and rules. To calculate the park-level transportation access score, the sum of these variables was divided by 7 and multiplied by 100 (max=100).

Seventeen recreational facilities for PA were captured to measure facility availability: playground, swing set, sports field, baseball field, swimming pool, splash pad, basketball court, tennis court, volleyball court, trail, fitness station, skate park, dog park, green space, lake, disc golf, and other designated PA areas (for any additional facilities noted by the auditor). Because not all parks could be expected to have all these facilities, the sum of these variables was divided by 7 and then multiplied by 100 to assign a park a facility score (max=100). Based on our past use of the CPAT tool in diverse parks, parks with at least 7 of the 17 potential facilities were considered to have high facility availability. This cut-off reflected a balance between large, well-equipped parks and smaller community parks, making sure that parks with a moderate number of facilities were recognized without penalizing neighborhood parks that did not have an extensive number of facilities.

Census tracts with higher facility quality scores contained parks with more PA facilities that were both usable (i.e., being present and not having any issues preventing use) and in good condition (i.e., looking clean and maintained; half a point for each criterion per facility). A facility quality score was calculated by dividing the sum of all facility quality scores by the number of facilities present in the park and multiplying by 100 (max=100).

The CPAT also assessed the park’s amenity availability, including restrooms, drinking fountains, lights, picnic tables, benches, and trash cans. To arrive at the park-level amenity availability score, the sum of these variables was divided by 6 and multiplied by 100 (max=100).

The parks were also evaluated for 7 park aesthetics features, including landscaping, artistic features, historical/educational features, wooded areas, trees throughout, water features, and meadows. Having at least 5 of these features gave a park a maximum score such that the park’s aesthetics score was calculated by dividing these variables by 5 and multiplying by 100 to reach a percentage (max=100). A threshold was established to recognize parks with a reasonable number of aesthetic features, acknowledging that not every park is expected to possess all 7 identified elements. This approach strikes a balance between acknowledging well-landscaped, larger parks and smaller parks that may have limited resources to incorporate a complete array of aesthetic elements.

Each park was appraised for the presence of park quality concerns: graffiti, vandalism, litter, animal waste, excessive noise, poor maintenance, evidence of threatening behavior, or dangerous spots. The total park quality concerns score was calculated by summing the 8 variables, dividing them by 8, multiplying them by 100, and subtracting from 100 (in order to be recoded in the same direction as the other park quality variables; max=100).

A number of neighborhood quality concerns were evaluated in the area around and visible from the park, including poor lighting, graffiti, vandalism, litter, heavy traffic, noise, vacant or unfavorable buildings, poorly maintained properties, lack of street surveillance, and evidence of threatening behavior and persons. As with the previous variable, these 10 concerns were summed, divided by 10, multiplied by 100, and then subtracted from 100 to produce a park’s neighborhood quality concerns score (recoded similarly to the other park quality variables; max=100).

Finally, the mean of all 7 aforementioned park quality metrics was calculated in order to arrive at a total park quality score for each park (out of 100).

Health metrics

Health data for each census tract were collected from the CDC PLACES dataset, which combines small area estimation, multilevel regression, and poststratification techniques to estimate the spatial distribution of disease burden and health-related behaviors among adults aged 18 years and above [25]. PLACES integrates geographically referenced health surveys with comprehensive data from the Centers for Disease Control and Prevention’s 2021 Behavioral Risk Factor Surveillance System (BRFSS), 5-year estimates from the American Community Survey (ACS), and random effects at the state and county levels.

The measures selected for this study were the percentage of adults in each tract with obesity, no LTPA, high BP, CHD, high cholesterol, diabetes, depression, and poor mental health. Obesity was a calculated value (from participants’ self-reported height and weight) with BMI values equal to or greater than 30.0 kg/m² indicating obesity. To assess the percentage of adults with high BP, CHD, diabetes, and depression, participants were asked, “Have you ever been told by a doctor, nurse, or other health professional that you have (high BP, CHD, diabetes, depression)?” Responses of “yes” were included in these metrics. For the no LTPA metric, participants were asked, “During the past month, other than your regular job, did you participate in any PAs or exercises such as running, calisthenics, golf, gardening, or walking for exercise?” Responses of “no” were included in this metric. For poor mental health, participants were asked, “Now thinking about your mental health, which includes stress, depression, and problems with emotions, for how many days during the past 30 days was your mental health not good?”, and responses equal to or greater than 14 days were included.

Socio-demographic characteristics

Demographic data for all tracts were collected from the United States Census Bureau [21]. Demographic variables included median age, percentage of the population with a bachelor’s degree or higher, median household income, and percentage of the population identifying as non-Hispanic White. These variables were included as covariates during analyses.

Statistical Analysis

Descriptive statistics (means, standard deviations [SDs], and ranges) were used to characterize the entire study area for demographic variables, park variables, and health metrics. Linear regression models were used to analyze the tract-level relationship between each park variable (separately) and each health metric, controlling for all tract-level demographic variables. The standard of statistical significance was a p-value less than 0.05. SPSS version 29.0 (IBM Corp., Armonk, NY, USA) was used for all analyses.

Ethics Statement

We should not need an ethics statement as all data is publicly available.

RESULTS

Table 1 contains descriptive statistics for the study setting for demographic, park, and health metrics. Across all tracts, the rates of obesity (38.33%) and high BP (34.57%) were greatest, while CHD (5.70%) and diabetes (13.19%) had the lowest prevalence. The mean number of parks per census tract was 1.04 (SD, 1.43) and the average park acreage was 22.47 (SD, 103.92). The mean±SD scores (out of 100) for the 7 park quality attributes were as follows: transportation access (52.28±19.57), facility availability (51.66±25.97), facility quality (87.98±20.22), amenity availability (70.55±24.75), park aesthetics (48.86±29.59), park quality concerns (88.05±12.94), neighborhood quality concerns (85.14±12.36), and total park quality score (68.97±11.43).

Table 2 contains results from linear regression analyses between all park variables and each health metric. The number of parks per tract was significantly and positively related to obesity (B=0.75; standard error [SE], 0.33) and no LTPA (B=0.83; SE, 0.41). Transportation access was significantly and positively related to obesity (B=0.10; SE. 0.04) and high BP (B=0.12; SE, 0.06). Finally, a better tract score for neighborhood quality concerns was significantly and negatively related to the prevalence of poor mental health (B=−0.16; SE, 0.05). For reference, this equates to a difference of 3.96% of adults with poor mental health between areas with low versus high neighborhood quality concerns (with an effect size of 0.89 based on a 2-SD difference in neighborhood quality concerns).

DISCUSSION

Key Findings

This study sought to investigate the relationships between 10 park access and quality variables and 8 health metrics at the census tract level. The previous literature contains mixed results about the relationship between parks and health, but few studies have taken into account as many different park access, park quality, and health variables as this research [13–19].

With 1 exception, the associations that were observed were in the unexpected direction and did not suggest beneficial relationships between park features and obesity, no LTPA, and high BP. However, a beneficial association was observed between lower concerns over park quality and better mental health. Specifically, this study yielded 5 significant results (i.e., positive relationships between park number and obesity, park number and no LTPA, transportation access and obesity, transportation access and high BP, and a negative relationship between neighborhood quality concerns and poor mental health). Several factors may explain these findings. For example, a higher number of parks alone may be insufficient to encourage usage, LTPA, and other health benefits. As well, other factors such as different levels of vulnerability in a neighborhood (e.g., the rate of crime, poverty, lack of awareness, socioeconomic barriers, unwalkable neighborhoods) may contribute to a reluctance to spend time in those parks [26,27]. Further, culture and people’s habits within a particular setting could be another explanation. For instance, it may be more popular to spend leisure time and be physically active in indoor venues instead of parks in some areas. Likewise, weather conditions in some areas, like the temperate Southeastern states, could be another reason parks have fewer health benefits there [28]. It is also possible that the positive association between transportation access and high BP is the result of other environmental stressors present in areas with higher accessibility. For example, traffic, noise, and pollution are all detrimental to cardiovascular health in these areas. In contrast, the negative relationship between neighborhood quality concerns and poor mental health may be due to the coping mechanisms that residents have faced due to chronic stresses they have experienced over time and thus have reported lower poor mental health [29].

Other studies have started to examine specific park access and quality variables in relation to outcomes such as PA and obesity, including in youth populations. For example, Jiang et al. [30] used the CPAT tool to investigate the association between park access and quality and childhood obesity. According to their results, children’s access to parks and the quality of those parks, particularly their aesthetic features, were found to have a significant relationship with reduced likelihood of obesity. Although these findings contrast with our results using adult-reported health data, both studies leveraging the CPAT tool highlight the importance of considering park access and park quality impacts on obesity prevalence in a community.

Our results found no other significant associations between the remaining variables, including park acreage, and other park quality variables. Several explanations could account for these findings. First, it is possible that some of the variables (both park access/quality and health-related) were too specific and focused to yield many significant findings; the results may have been significant more often if some variables had been combined into broader categories. A similar study conducted in the United Kingdom by Hobbs et al. [16] utilized a tool similar to the CPAT and had variables reasonably equivalent to our amenity availability and facility quality metrics; they, likewise, found no significant relationships between these metrics and obesity. Second, there were multiple confounding variables that were not included in our analyses. For example, the census tract food environment was not analyzed, but this likely has a large impact on a number of the health outcomes we examined [18,31].

Third, the mixed and inconclusive evidence about the relationship between park access, quality, and health from this and previous studies could be for a variety of reasons. For example, various studies define “green space” differently, with some including vegetation and parks, and others standardized metrics like the normalized difference vegetation index [13,17, 19]. Additionally, definitions for proximity to parks vary, with some studies using a “buffer” zone of varying distances, and some studies using precise calculated distances [13–15,17–19,32].

Limitations and Strengths

This study had a number of limitations. The study setting included 70 census tracts, but most of our analyses only included the 38 tracts that contained parks. This may not have been a large enough sample to gain a full picture of the relationships between the park variables and health metrics. Additionally, the presence of gyms and other PA-encouraging opportunities were not included (in part because they are not free and available to all). We also did not include a p-value adjustment in examining the multiple associations between park variables and health outcomes. Finally, this was not a longitudinal study, so causality cannot be extrapolated or inferred.

There are some important strengths of this study to consider as well. This study utilized geographic information systems (GIS) files and objective audits of parks to capture the many aspects of park access and quality. Additionally, this study capitalized on the new CDC PLACES dataset to examine 8 different physical and mental health metrics. Finally, we explored these issues in a relatively expansive and diverse setting in the Southeastern United States, with various socio-demographic profiles, where resource access and health disparities are substantial.

Recommendations for Research and Application

There are multiple suggestions for studies that could be conducted to further investigate relationships between park access and quality and health. First, a larger sample size would be ideal, as expanding the sample area to a broader region or an entire state could provide more reliable results. Additionally, incorporating more mental health metrics, such as anxiety, stress, or medications taken for mental health disorders, could enrich our understanding of the relationship between park access, quality, and mental well-being [33,34]. Further, including subjective data about residents’ attitudes and perceptions of park access and quality would yield important insights for both researchers and practitioners. Likewise, including qualitative data about what people like and do not like about their neighborhood parks would provide valuable mixed methods data to augment the quantitative analyses presented here. Additionally, it may be important to investigate whether park elements should be weighted differently in examining their impact on health outcomes. For example, the availability of PA facilities and safety features could potentially have a stronger impact on health behaviors than aesthetic qualities or amenities. Differential weights could be applied to park elements either using a data-driven approach, such as regression coefficients or factor analysis, or using expert input. Likewise, park attributes related to availability and quality could be combined or aggregated to provide a more holistic picture of the impact of parks on health [24]. Finally, evidence is growing that dose and exposure to nature are associated with improved mental health and well-being, so it would be valuable to examine these as potential predictors or covariates [35,36].

This study, despite its small sample size, provides a valuable demonstration project for how municipalities or other entities can examine and document the contributions of parks to diverse health issues. Such data and evidence are available from studies across the United States and globally, but local evidence may be more impactful for spurring action and investment related to new parks or modifications to park facilities, amenities, or quality. For example, Kaczynski et al. [24] described how their ParkIndex metric could be used as an intervention planning tool to forecast the associated gains in local park use (and its related health benefits) from adding a park or making renovations to existing parks. Efforts to connect parks with community health will necessitate a comprehensive understanding of park availability and quality through audits using a tool such as the CPAT. However, such data may be valuable for diverse purposes and can involve the cooperation of local park agencies, community organizations, and advocacy groups. For example, Greer et al. [37] described use of the CPAT by community members to assess their neighborhood parks, and Besenyi et al. [38] stated that advances in empowerment and advocacy resulted from youth conducting park audits using an electronic version of the CPAT (eCPAT). At a broader scale, national organizations (e.g., National Recreation and Park Association, Trust for Public Land) have prioritized detailed cataloging of local parks and their attributes, but such efforts are still in an early stage. As such data become increasingly available (and/or are collected locally by community partners), analyses such as the present study that provide important information about parks and health can become more commonplace.

Conclusions

Overall, this study examined relationships between multiple park variables and diverse health metrics. Public parks are an important fixture of the built environment and provide many benefits to individuals and the community in a variety of ways, including providing opportunities for PA and improved health. By using GIS and the CPAT tool as objective measures of different aspects of the public parks in the sample area, this study was able to characterize how park access and quality variables affect diverse aspects of community health. Overall, more research needs to be conducted in an expanded study setting to provide a more comprehensive view of the complex relationships between parks and health.

Notes

Data Availability

The datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request.

Conflict of Interest

The authors have no conflicts of interest associated with the material presented in this paper.

Funding

This study was supported by the University of South Carolina, Office of Research.

Acknowledgements

None.

Author Contributions

Conceptualization:Pellizzari J, Kaczynski AT. Data curation: Kaczynski AT. Formal analysis: Pellizzari J, Hesam Shariati F. Funding acquisition: Kaczynski AT. Methodology: Pellizzari J, Kaczynski AT. Project administration: Kaczynski AT. Visualization: Hesam Shariati F, Kaczynski AT. Writing – original draft: Pellizzari J. Writing – review & editing: Hesam Shariati F, Kaczynski AT.

Figure 1

Study census tracts and parks.

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