The association between physical activity level in older adults and community park characteristics: a configural path analysis

Abstract

Community parks serve as crucial venues for older adultly daily activities, yet the pathways through which their spatial characteristics influence physical activity level remain insufficiently explored. This study investigates 31 community parks in Changchun City, employing a multi-source data integration approach (field observation, streetscape semantic recognition, and GIS spatial analysis) combined with fuzzy-set Qualitative Comparative Analysis (fsQCA) to examine the configurational effects of environmental factors on older adultly physical activity. The findings reveal: (1) site scale (r = 0.207), spatial vitality (r = 0.207), and transportation accessibility (r = 0.141) are key positive factors promoting physical activity, whereas number of facilities for physical activity (r=-0.104), surrounding substitute facilities (r=-0.057), and green space shape (r=-0.086) exhibit significant negative impacts; (2) Environmental elements function through nonlinear combinations, with site scale and transportation accessibility forming the core conditions across 6 effective enhancement pathways, confirming a tri-dimensional synergistic mechanism of “space-function-accessibility”; (3) Based on the characteristics of the elements, three types of promoting older adult´ physical activity construction paths are extracted: natural quality type, balanced service type and neighborhood adaptation type. The research shows that the age-appropriate transformation of community parks should follow the principle of “linkage of elements, type adaptation and system collaboration” to provide theoretical support for the construction of age-friendly urban park system.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12877-026-07051-3.

Introduction

The rapid aging of society has brought numerous physical and mental health challenges to older adults [1]. Against this backdrop, urban parks stand out as non-pharmaceutical means to promote health and well-being among older adults [2]. The 2024 Urban Capacity Index Report for Actively Responding to Population Aging in China (available at: https://www.ndrc.gov.cn/wsdwhfz/202411/t20241111_1394405. html) identifies park green spaces as a key indicator in assessing age-friendly cities. Among various types of urban green spaces, community parks are among the most frequently utilized green spaces by older adults due to their convenient locations and safe environments [3, 4].

According to the Urban Green Space Classification Standard (CJJ/T 85–2017) issued by China’s Ministry of Housing and Urban-Rural Development, community parks are defined as independent green spaces equipped with basic recreational and service facilities. They primarily serve the daily leisure needs of nearby residents within designated community areas and typically cover an area of more than 1 square kilometer [5]. An increasing number of studies have emphasized the positive correlation between community parks and physical activity among older adults [6]. These parks provide important spaces for physical exercise and social interaction, offering a wide range of health benefits and playing a vital role in achieving the World Health Organization’s (WHO) goal of healthy aging [7–12]. Empirical studies indicate that approximately 80.00% of older adults engage in physical activities within three blocks of their residence, a proximity that encourages more frequent and sustained participation [13, 14]. Research has demonstrated that appropriate physical exercise such as walking and Tai Chi helps maintain physical health in older adults, effectively slows age related physical decline, preserves cognitive function, and may even reduce the risk of dementia and other chronic diseases [15–21].

While existing studies have established a strong relationship between the spatial characteristics of community parks and the physical activity of older adults, most have focused on macro-level community environmental indicators or single-dimensional aspects, with few studies analyzing how these factors interact under different conditions to shape behavioral outcomes. From the perspectives of person–environment fit and environmental press theory, the environment is not a passive backdrop but rather influences older adults’ behavioral choices by interacting with their abilities and needs, providing support or exerting pressure in the process [22, 23]. As community parks serve as primary venues for physical activities among older adults, their physical environment has a noticeable impact on older adults’ behavior. According to the social-ecological model of physical activity proposed by Sallis, interpersonal, social, and physical environmental factors jointly influence physical activity [24]. Unlike interpersonal and social factors, the physical environment is a tangible and modifiable aspect that can be optimized or strategically planned to better accommodate the needs of older adult populations. Previous studies have identified several physical environmental factors that significantly influence physical activity among older adults, including accessibility [25, 26], park green space area [27, 28], and the level of greenery [29, 30]. Additionally, green space scale [31], vegetation coverage [32], and activity offerings [33] have also been repeatedly validated as significant contributing factors. Furthermore, in studies focused on constructing age-friendly park environments, the spatial distribution of facilities has been shown to play an essential role [34, 35]. However, the interactions among these factors under different conditions and the pathways through which they affect older adults’ physical activity remain insufficiently explored.

To address these gaps, we conducted a pathway study on factors influencing older adults’ physical activity in 31 community parks in Changchun, a city with a highly aging population in China. We utilized a case-based configurational approach—the fuzzy-set qualitative comparative analysis (fsQCA) method, we integrated quantitative statistical and qualitative analytical frameworks to investigate the interactions between community park features and older adults’ physical activity. fsQCA is a case-oriented method based on set theory and configurational thinking that does not rely on assumptions of normal distribution or large sample sizes [36]. It aims to reveal the sufficiency relationships between combinations of causal conditions and outcomes, overcoming the traditional regression model’s dependence on variable independence and linear relationships [37, 38]. This makes it particularly suitable for exploring the complexity of interdependent factors in social phenomena such as physical activity among older adults. The study identified pathways that promote higher-level physical activity and proposed corresponding optimization strategies. The research aims to provide actionable insights for improving older adults’ health and to offer a high-quality spatial foundation for creating age-friendly cities.

Our research questions are as follows:

1. What community park features influence the level of older adults’ physical activity?

2. How do these community park features affect the level of older adults’ physical activity?

3. How can the effect transmission mechanisms through which community park characteristics influence older adults’ physical activity be analyzed based on the configurational pathways identified by fsQCA?

Materials and methods

Study sites

Changchun, located in northeastern China, ranks fourth in the country in terms of population aging, according to the 2020 Seventh National Population Census data (available at: https://www.gov.cn/guoqing/2021-05/13/content_5606149.htm). According to the latest statistics from the Changchun Municipal Bureau of Statistics, by the end of 2023, the number of permanent residents aged 60 and above in Changchun reached 2,214,600, accounting for 23.20% of the total population. Of this total, among the urban population, the number of older adults aged 60 or above stood at 773,100, while those aged 65 or above reached 520,100—accounting for 18.94% and 12.74% of the total urban population, respectively. As a representative city with a rapidly aging population, Changchun faces significant challenges in developing an age-friendly urban environment, particularly in its densely populated central districts.

Given this context, the most frequently used community parks were selected as the focus of this study. The research samples were determined through a three-level screening process, building on established research methods [39]. First, park point-of-interest (POI) data for Changchun were obtained from the Baidu Map Open Platform. The POI attributes of the parks were then identified by cross-referencing the park directory published by the Changchun Forestry and Landscape Bureau and the Urban Green Space Classification Standards (CJJ/T85-2017). A total of 41 community parks were ultimately selected in the main urban area of Changchun.

Next, the POI and road network data (sourced from www.openstreetmap.org) for these 41 parks were imported into QGIS 3.24. The road network distance from residential community points to each park was used to measure the coverage of the destination service area, making the selection results more closely aligned with the actual travel behavior of the older adult population. Based on the average single-trip distance of older adults being approximately 1.01 km [40], a 1000-meter service area was created around each park’s latitude and longitude coordinates. This service area was used solely for sample screening to identify potential activity coverage. Subsequently, the parks were ranked in descending order according to the number of residential communities within their service areas. In addition, principles of spatial representativeness, geographic distribution, and variability in park size and land-use characteristics were considered to ensure adequate coverage of different types of community parks. Based on these criteria, all candidate parks underwent systematic field investigations. Field observations followed a standardized protocol and were conducted continuously for three days at each sample park, including one weekend day. During two peak periods each day—the morning (7:00–9:00) and evening (17:00–19:00)—the behavior mapping method was used to record types of physical activities, activity frequency, average duration, participant density, and the use of major facilities. In parallel, semi-structured interviews were conducted with older adult park users to capture their perceptions of the park environment, including barrier-free facilities, greening quality, amenities, safety, accessibility, and overall suitability for physical activity. These interviews were intended to provide contextual understanding of environmental factors and were not directly incorporated into the quantitative models. Based on a comprehensive assessment of use intensity, functional diversity, spatial representativeness, and physical accessibility, 10 parks were excluded due to persistently low activity levels, limited functional attributes, or insufficient representativeness of typical community park environments. Ultimately, 31 representative community parks were selected as the final study sample, as illustrated in Fig. 1.

Fig. 1.

Distribution map of 31 community parks

Screening of park features

This study integrates landscape elements and the service attributes of community parks, classifying community park features into three dimensions: natural environment, artificial facilities, and site planning.

1. Natural Environment Features:

   • a
     Green rate of greenery
   • b
     Greening implantation rate
   • c
     Green space shapes
     These features were quantified primarily based on field survey data and online data.
2. Artificial Facilities Features:

   • a
     Number of facilities for physical activity
   • b
     Number of rest facilities
   • c
     Number of surrounding substitute facilities
     These features were quantified mainly through field survey data.
3. Site Planning Features:

   • a
     Transportation accessibility
   • b
     Site scale
   • c
     Spatial vitality

These features were quantified based on Changchun’s road network data obtained from OpenStreetMap (https://www.openstreetmap.org). A detailed breakdown of these features and their quantification methods is provided in Table 1.

Table 1.

Community park characterization data acquisition

Feature Type	Variable		Variable Definition	Quantification Technique	References
Natural environmental features		Green rate of greenery (%)	The average ratio of the area occupied by plants in the image (GA) to the total area of the image (IA). The green view index for the community park research unit is represented by the average proportion of green pixels in the images from four directions.	Photographs were taken using a camera with a 24-mm focal length at a fixed height of 1.6 m above ground level, and semantic segmentation was performed using SemanticSegmentation_v1.0.	[41–44]
		Greening implantation rate (%)	The ratio of the vertical projection area of vegetation to the built-up land area within the community park.	Government websites, phone and email communications, park layout board information records, Bigemap, ArcGIS, and Office measurement tools.	[45–47]
		Green space shapes	The Landscape Shape Index (LSI) represents the shape of a green space. The specific formula for calculating the Landscape Shape Index is: where LSIi, denotes the shape index of green space patch i, P i is the perimeter of patch i, and Ai is the area of patch i.	The UNI-T land measurement instrument is used for calculating land area.	[48, 49]
Artificial facility features		Number of facilities for physical activity (count)	During the data collection phase, we focused on collecting the specific number of facilities in four types of areas: sports equipment zones, exercise pathway areas, activity square areas, and specialized sports areas.	On-the-spot investigation.	[8, 13, 50, 51]
		Number of rest facilities (count)	This includes the number of seating areas, as well as the number of artificial shading and shelter structures.	On-the-spot investigation.	[52–54]
		Number of surrounding substitute facilities (count)	This refers to fitness facilities located around the surveyed parks. Starting from the center of the older adults’ activity area within the community park, we used the Amap (Gaode Map) to search for nonmotorized public sports and recreational facilities within a 1 km radius. The data for surrounding alternative facilities near the community park samples were recorded.	Gaode map area search	[55, 56]
Site planning features		Transportation accessibility	Based on the digitized road network of Changchun City (https://www.openstreetmap.org), we conducted an accessibility assessment of open spaces. Using the entrances of each community park as starting points, 10-minute walking buffers for older adults were generated using the Mapbox isochrone API. Park accessibility was then compared by analyzing the area of these buffers.	ArcGIS network analysis technology, Mapbox API.	[57–60]
		Site scale (ha)	Data on the construction area and water area of the target parks were obtained through the website of the Changchun Forestry and Landscape Bureau (http://ccylj.changchun.gov.cn/zjyl/index_7.html), as well as through phone and email inquiries.	Government websites, phone and email data collection, and park layout board information recording.	[61–63]
		Spatial vitality	We selected the Baidu heatmaps for the period from September 10 to 16, 2023, including Mondays, Wednesdays, Fridays, and both Saturday and Sunday. From 06:00 to 22:00, we captured data every 2 h using a scale of 1:100,000, resulting in a total of 45 snapshots, which were used as the foundational data for regional vitality analysis.	ArcGIS georeferencing, ImageJ pixel statistics.	[64, 65]

Study participants

According to international standards, individuals aged 60 and above are classified as older adults. Article 2 of the Law of the People’s Republic of China on the Protection of the Rights and Interests of the Older Adults (available at: https://www.gov.cn/guoqing/2021-10/29/content_5647622.htm) stipulates that the age threshold for older adults is 60 years, meaning that all Chinese citizens aged 60 or older are considered older adults.

The target group of this study consists of park users over the age of 60, specifically ranging from 60 to 92 years, who are capable of independent physical activities and visit the park at least once a week. Participants who are nonlocal residents (living in the area for less than one year) or under the age of 60 were excluded from the study. The study protocol, consent forms, agreements, and data collection procedures were approved by the Ethics Review Committee of the College of Forestry and Grassland at Jilin Agricultural University. All participants included in the study provided informed consent. Before the survey began, participants received an oral explanation of the study’s objectives and procedures. After obtaining their consent, they were invited to assist in completing the questionnaire. For participants who had difficulty reading written text, the survey staff assisted by reading aloud the options until the survey was completed.

Calculation of physical activity value

In international physical activity standards, exercise intensity is quantified using the Metabolic Equivalent of Task (MET) [66]. MET represents the ratio of energy expenditure during physical activity to energy expenditure at rest. It is based on the energy expenditure during rest or sedentary behavior, which is generally defined as 3.5 milliliters of oxygen consumed per kilogram of body weight per minute, or 1 MET = 3.5 ml O₂/ kg· min.

1. Low-intensity physical activities have a MET value of less than 3.0 and include activities such as walking, singing, and Tai Chi.

2. Moderate-intensity physical activities range between 3 to 6 METs and include activities such as jogging, badminton, and table tennis.

3. High-intensity physical activities exceed 6 METs and include activities such as running and ballroom dancing.

The physical activity value of the older adult participants per week reflects their actual level of physical activity in the community park areas. Therefore, the formula for calculating the older adult participants’ physical activity value is as follows:

The physical activity data in this study were primarily measured using a localized adaptation of the International Physical Activity Questionnaire (IPAQ), which has been validated in multiple studies as an effective tool for assessing physical activity among older adults in China [67, 68]. To more accurately assess the physical activity patterns of older adults in urban community parks, structured field observations were conducted prior to the formal survey. Systematic records identified the main forms of exercise among local older adults, such as Tai Chi, square dancing, and shuttlecock kicking. Based on these findings, the IPAQ was locally adapted through culturally appropriate modifications to activity descriptions. Specifically, the questionnaire was first translated into Chinese following a standardized translation procedure, and several activity descriptions in the original version were adjusted to expressions more familiar to older adults in China to improve comprehension and clarity. Importantly, the fundamental structure, measurement framework, and core content of the IPAQ were not modified.

Subsequently, reliability and validity tests were conducted on the modified questionnaire using SPSS 26.0 to ensure its robustness. The Cronbach’s α coefficient was 0.854, indicating high internal consistency. The Kaiser-Meyer-Olkin (KMO) value was 0.792, and Bartlett’s test of sphericity yielded a significance level of 0.000 (Sig. < 0.1), confirming that the questionnaire possesses good structural validity and reliability. Furthermore, cognitive interviews and pilot testing were conducted with older adults in community parks to further verify the comprehensibility and practical applicability of the questionnaire items.

Procedure and measurement

In the 31 community parks’ older adult activity areas within the main urban district of Changchun, a non-predefined encounter sampling method was employed to invite residents aged 60 and above to participate in this survey. The data collection period spanned from June 1, 2023, to July 30, 2023, during the hours of 6:00–9:00 AM and 5:00–8:00 PM. These two times windows were selected because they coincide with peak outdoor exercise periods for older adults, particularly during clear weather, when participation in outdoor activities is highest.

Before collecting survey data, all participants were fully informed about the study and provided their consent. Considering the behavioral characteristics and physical conditions of older adults, the survey was conducted through individual interviews, with researchers recording the responses. The survey questionnaires were distributed by research staff at different locations simultaneously. The survey utilized a question-guided format to collect data on demographic characteristics, physical activity patterns, and environmental perceptions from the respondents.

To ensure sample validity, a target of 60 questionnaires per park was set. A total of 1,780 questionnaires were collected. After evaluating the completeness and quality of the responses, 21 invalid questionnaires were excluded, resulting in 1,759 valid questionnaires and an effective response rate of 98.82%. These valid questionnaires provided the necessary data to calculate the physical activity values of the older adult participants. It should be clarified that the fsQCA analysis in this study was conducted at the park level (n = 31), based on aggregated data from the individual level.

Research process

This study began by defining the research sites and their boundaries using urban road network data, point-of-interest (POI) data, and population demographic statistics. A modified version of the International Physical Activity Questionnaire (IPAQ) was employed, combined with survey methods and on-site observational techniques. A random sampling approach was used to conduct semi-structured interviews with older adult individuals in the selected community parks, gathering detailed information on their physical activity patterns.

To quantify the environmental features of the parks, various technical methods were utilized, including field surveys, semantic segmentation of street view images, and GIS mapping. Nine environmental features were assessed:

1. Green rate of greenery

2. Greening implantation rate

3. Green space shape

4. Number of rest facilities

5. Number of facilities for physical activity

6. Number of surrounding substitute facilities

7. Transportation accessibility

8. Spatial vitality

9. Site scale

SPSS software was used to perform Pearson correlation and multiple regression analyses on the collected data. Based on significance levels and standardized coefficients, two indicators—green space shapes and the number of rest facilities—were excluded from further analysis. The remaining seven environmental features—green rate of greenery, greening implantation rate, number of facilities for physical activity, number of surrounding substitute facilities, transportation accessibility, spatial vitality, and site scale—were analyzed using fuzzy-set qualitative comparative analysis (fsQCA). All fsQCA analyses were conducted using the Windows version of fsQCA 3.0 software (www.fsqca.com), strictly following the standard procedures outlined in the software’s user manual.

A statistical analysis of the physical activity levels of 1,759 older adult participants over the course of a week was conducted, with these activity levels serving as the outcome variable and the seven environmental features as condition variables. By constructing configurations and identifying the most influential patterns, we derived pathways demonstrating how community park features influence older adults’ physical activity. The complete experimental process is illustrated in Fig. 2.

Fig. 2.

Experimental flowchart. (a) data processing processes; (b) fsQCA modelling process. Note: Greenery means Green rate of greenery; Implantation means Greening implantation rate; Shape means Green space shapes; Facilities means Number of facilities for physical activity; Rest means Number of rest facilities; Surrounding means Number of surrounding substitute facilities; Accessibility means Transportation accessibility; Vitality means Spatial vitality; Scale means Site scale; Outcome means Physical activity value outcome

Statistical analysis

The study employs a systematic analytical approach to ensure the reliability and validity of the findings. First, SPSS 26.0 software (IBM Corporation, Armonk, NY, USA) is used to analyze the reliability of the questionnaire by calculating Cronbach’s α coefficient. Additionally, factor analysis is conducted to assess the structural validity of the questionnaire. Next, Pearson correlation analysis is performed to examine the relationships between variables. This is followed by multiple regression analysis to compare the effects of potential factors and identify key variables influencing the outcome. Subsequently, selected variables are calibrated by converting qualitative or quantitative data into fuzzy sets. Membership functions are established to describe the degree of membership of elements within the set. Based on this calibration, necessity tests are conducted, and a one-factor necessity analysis is performed on the condition variables to determine whether a variable is an independent necessary condition for a specific outcome. A truth table is then generated to organize and analyze the data. Finally, conjunctive (AND) and disjunctive (OR) algorithms from fuzzy logic are applied to identify the configurations of conditions that may lead to the desired outcome. This is followed by configuration analysis and robustness testing to ensure the stability and reliability of the identified pathways.

Results

Descriptive analysis of respondent sample

We analyzed the demographic characteristics of the respondents. The proportion of male participants (52.07%) was slightly higher than that of females (47.92%). According to the new age classification standards reported by the WHO [69], older adults aged 60–74 are considered young-old, those aged 75–89 are classified as middle-old, and those aged 90 and above are referred to as older adults in extreme old age. Based on this classification, we categorized the respondents by age group in this study, with older adults aged 60–74 years constituted the largest group of respondents, accounting for 77.20% of the total sample. Those aged 75–89 years represented 22.68%, while only a small fraction (0.12%) of respondents were aged 90 years or older. This distribution suggests that adults aged 60–74 are the primary demographic engaging in activities in community parks (Table 2).

Table 2.

Sample statistics of respondents

Item	Information Attribute	Number	Proportion
Gender	Male	916	52.07%
	Female	843	47.92%
Age	60–74 years	1358	77.20%
	75–89 years	399	22.68%
	90 years and above	2	0.11%
Body Mass Index (BMI)	Normal	801	45.54%
	Abnormal	958	54.46%
	Total	1759	100%

Descriptive statistics

Through an in-depth analysis of the current status of 31 community parks in the central urban area of Changchun, we employed a combination of methods, including questionnaire surveys, geographic information mapping technology, and publicly available online resources, to collect data on nine park features and physical activity conditions. The findings are organized into three categories: site planning features, natural environmental features, and artificial facility features.

1. Site Planning Features: The park with the highest accessibility is "Xinghuacun Park," with a value of 1.07, while the lowest is "Ruyi Park," with a value of 0.15. The site area ranges from 1.11 ha to 27.20 ha, with 18 parks, or approximately 58.06% of the total, having an area of 10.00 ha or less. The number of community parks with a spatial vitality index greater than or equal to 0.50 is 17, accounting for approximately 54.83% of the total (Fig. 3 (a), (b), (c)).

2. Natural Environmental Features: The park with the highest green rate of greenery is "Xinghuacun Park" (0.67), while the lowest is "Wetland Park" (0.22). There are 20 community parks with a green rate of greenery of 60.00% or higher, accounting for approximately 64.51% of the total. The park with the highest green space shape index is "Riverside Park" (0.97), and the lowest is "He Yuan Park" (0.49) (Fig. 3 (d), (e), (f)).

3. Artificial Facility Features: The park with the highest number of physical activity facilities is "Kuancheng District Govermment Central Park," with a total of 64, while "Kuan Cheng Pick Up Light Park" and "Baishu Garden" have the fewest, each with only 2. The park with the most rest facilities is "Sanjia Lake Park," with 544, while "Jichai Cultural Square Park" has the fewest, with 32. There are 4 community parks with 5 or more substitute physical activity facilities in the surrounding area, accounting for approximately 12.90% of the total. Among these, "Ting Tao Park" has the highest number of such facilities, with a total of 6, making it the community park with the most substitute physical activity facilities in the surrounding area (Fig. 3 (g), (h), (i)).

4. Based on the physical activity data of older adults, it was found that 24 community park samples had a mean physical activity value exceeding 2000.00 kcal/week, accounting for 77.42% of the total number of community parks. Among them, "Sanjia Lake Park" had the highest mean physical activity value of 2713.20 kcal/week, while "Wetland Park" ranked second with a mean value of 2645.40 kcal/week. "Xinghuacun Park" had the lowest mean physical activity value at 1580.51 kcal/week (Fig. 4).

Fig. 3.

Landscape features of 31 community parks. (a) Accessibility; (b) Scale; (c) Vitality; (d) Greenery; (e) Implantation; (f) Shape; (g) Facilities; (h) Rest; (i)Surrounding. Note: P1,Yuhua Park; P2,Xinghuacun Park;P3,Pearl River Park;P4,Fuk Yuen Park;P5,Changchun Deyuan Park;P6,Junzilan Park; P7,Kuancheng District Government Center Park; P8,Green Sandal-wood Lake Park;P9,Vanke Picking Up Light Park; P10,Shanshuiwan Park; P11,Liwang Wetland Park; P12,Laoqian Park; P13,Yingjun Park; P14,Forest Garden; P15,Jinjiang Park; P16,Sanjia Lake Park; P17,Qianjin Park; P18,Gongqingyuan Garden; P19,He Yuan Park; P20,Wanxing Community Park; P21,Wetland Park; P22,Jichai Culture Square Park; P23,Riverside Park; P24Ting Tao Pavilion; P25,Kuan Cheng Pick Up Light Park; P26,Ruyi Park; P27,Mudan Park; P28,Culture Square Park; P29,Xianghe Square Park; P30,Baishu Park; P31,Beihai Wetland Park

Fig. 4.

Mean physical activity levels of older adults in 31 community parks. Note: Outcome means physical activity value outcome

Relationship between community park features and physical activity of older adults

Correlation analysis

In the research process, it is essential to strike a balance between an appropriate sample size and a parsimonious model structure. To prevent an excessive number of condition variables from generating configurations that outnumber the available cases, which could lead to overlapping and overly diverse models, it is necessary to limit the number of conditions included in the analysis. As an initial step, correlation analysis was employed to identify potential features associated with a progressive increase in older adults’ physical activity levels.

The analysis results show that all 9 community park features are significantly correlated with the physical activity value outcome (P < 0.01) (Fig. 5). The positive correlations include: site scale (0.207), greening implantation rate (0.067), green rate of greenery (0.121), number of rest facilities (0.197), spatial vitality (0.207), and transportation accessibility (0.141). The negative correlations include: number of physical activity facilities (-0.104), number of surrounding substitute facilities (-0.057), and green space shape (-0.086). Upon examining the absolute values of the correlation coefficients, it is evident that the high correlation between site scale and the number of rest facilities could lead to multicollinearity issues in subsequent analyses. Since site scale demonstrates a stronger correlation with the physical activity value outcome compared to the number of rest facilities, the latter was excluded from the regression analysis to ensure the robustness of the results (Table 3).

Fig. 5.

Correlation coefficients between older adults’ physical activity levels and community park features. Note a：** Correlation is significant at the 0.01 level (two-tailed); * Correlation is significant at the 0.05 level (two-tailed). Note b：Scale means Site scale; Vitality means Spatial vitality; Accessibility means Transportation accessibility; Rest means Number of rest facilities; Facilities means Number of facilities for physical activity; Surrounding means Number of surrounding substitute facilities; Implantation means Greening implantation rate; Greenery means Green rate of greenery; Shape means Green space shapes; Outcome means Physical activity value outcome

Table 3.

Association coefficients between factor features and physical activity values in older adults

Model 1 (Dependent Variable: Physical Activity Value Outcome)	Unstandardized Coefficient		Standardized Coefficient	t	Significance	Collinearity Statistics
	B	Standard Error	Beta			Tolerance	VIF
(Constant)	3416.668	2319.802		1.473	0.141
Site scale	221.487	38.486	0.178	5.755	0.000	0.543	1.840
Greening implantation rate	4123.158	1540.624	0.069	2.676	0.008	0.796	1.256
Green rate of greenery	5686.317	2337.443	0.062	2.433	0.015	0.810	1.235
Number of facilities for physical activity	-39.506	17.431	-0.067	-2.266	0.024	0.589	1.699
Number of surrounding substitute facilities	-494.210	169.495	-0.071	-2.916	0.004	0.877	1.140
Spatial vitality	4057.605	1760.687	0.068	2.305	0.021	0.594	1.685
Transportation accessibility	3418.110	1136.947	0.077	3.006	0.003	0.789	1.268
Green space shape	-1820.526	2036.499	-0.024	-0.894	0.371	0.701	1.427

Multiple regression analysis

This study employed the physical activity value outcome of older adults as the dependent variable. Independent variables included site scale, greening implantation rate, green coverage rate, spatial vitality, accessibility, number of physical activity facilities, number of surrounding substitute facilities, and green space shape. These variables were incorporated into a multiple regression analysis to preliminarily screen them and clarify the relative influence of various features on physical activity levels. From the perspective of promoting physical activity among older adults, condition variables that demonstrated high collinearity and low contribution were excluded from the fsQCA. This restriction helped avoid the inclusion of redundant or noisy variables, thereby enhancing the reliability of the study. The results demonstrated that the model exhibited good explanatory power, with an R² value of 0.439 and an adjusted R² value of 0.437. Among the variables, only green space shape showed a significance level exceeding 0.05. The variance inflation factors (VIF) for all variables were below 5.0, indicating no multicollinearity issues.

Given that this study primarily emphasizes the effect size between variables rather than predicting specific outcomes, correlation coefficients and standardized Beta coefficients were utilized as the primary evaluation criteria, while other indicators were assessed with a slightly more lenient threshold. Based on the Beta coefficients, site scale was found to exert the greatest influence on the physical activity value outcome, followed by greening implantation rate, green coverage rate, spatial vitality, accessibility, physical activity facilities, and surrounding substitute facilities. Although green space shape had the smallest effect, all variables contributed to some extent in influencing the physical activity value outcome (Table 3).

Construction of the community park fsQCA model

Through correlation analysis, potential features contributing to the gradual improvement of physical activity value outcomes among older adults were identified. Subsequently, multiple regression analysis was employed to clarify the impact ranking of each factor on the physical activity value outcome. This process facilitated the selection of condition variables for inclusion in the fuzzy-set Qualitative Comparative Analysis (fsQCA). The final condition variables selected for fsQCA were site scale, greening implantation rate, green coverage rate, number of physical activity facilities, number of surrounding substitute facilities, spatial vitality, and transportation accessibility. These variables, along with the physical activity value outcome of older adults as the outcome variable, collectively formed the comparative analysis model. Prior to conducting the fsQCA, the variables underwent calibration, necessity tests, and truth table generation, culminating in the development of a path model illustrating the impact of community park features on physical activity among older adults in Changchun (Fig. 6).

1. Variable Calibration: Calibration is essentially the process of assigning set membership scores to specific conditions within a given context, which requires setting three qualitatively meaningful thresholds. Before proceeding to subsequent steps, the original case information must be transformed into set membership scores ranging from 0 to 1. In this study, the direct calibration method was applied. All seven condition variables were calibrated using a three-value set approach. The selection of thresholds was informed by prior studies utilizing fsQCA and was constructed based on both theoretical considerations and practical relevance to ensure the robustness of the results [36]. Three qualitative anchors were defined: the 0.95 quantile as the threshold for full membership, 0.5 as the crossover point (with 0.5 inputted as 0.501 to prevent data loss due to the software excluding cases with exact 0.5 membership scores), and the 0.05 quantile as the threshold for full non-membership. Utilizing the Calibrate function in fsQCA 3.0, the seven condition variables—site scale, greening implantation rate, green coverage rate, number of physical activity facilities, number of surrounding substitute facilities, spatial vitality, and transportation accessibility—as well as the outcome variable, were subjected to structured fuzzy set calibration (Table 4).

2. Necessity Test: An examination of the necessary condition analysis (Table A1) shows that none of the seven condition variables reached a consistency threshold of 0.90. This finding indicates that none of the condition variables were indispensable for determining the outcome variable. In other words, no single feature is indispensable for achieving high physical activity values.

3. Truth Table Generation: Following data calibration, this study established a case number threshold of 1, a consistency threshold of 0.8, and a PRI (Proportional Reductidon in Inconsistency) threshold of 0.7. These thresholds were set to eliminate condition variable combinations that were not observed in any cases. When the consistency of a condition combination was greater than or equal to 0.8, the result was assigned a value of 1; otherwise, it was assigned a value of 0. This approach effectively filtered out condition combinations inconsistent with actual observations, ensuring more accurate and reliable results. Since no condition combinations with identical variables yielded contradictory results (both 0 and 1), no conflicting configurations emerged, allowing the fsQCA to proceed without issues (Table A2).

Fig. 6.

fsQCA model of park features and their associations with older adults’ physical activity. Note: Greenery means Green rate of greenery; Implantation means Greening implantation rate; Shape means Green space shapes; Facilities means Number of facilities for physical activity; Rest means Number of rest facilities; Surrounding means Number of surrounding substitute facilities; Accessibility means Transportation accessibility; Vitality means Spatial vitality; Scale means Site scale

Table 4.

fsQCA variable calibration

Variable Type	Gather	Calibrate the anchor point
		Fully Subordinate (0.95)	Intersection (0.501)	Not affiliated at all (0.05)
Outcome variables	Physical activity value outcome	High activity value threshold 2565.042	Median activity value threshold 2095.194	Low activity value threshold 1734.411
Conditional variables	Site scale	High site thresholds 24.000	Medium order of magnitude site thresholds 6.740	Low magnitude site thresholds 1.365
	Greening implantation rate	High coverage thresholds 0.822	Median coverage threshold 0.642	Low coverage thresholds 0.342
	Green rate of greenery	High view threshold 0.574	Median view threshold 0.420	Low view threshold 0.272
	Number of facilities for physical activity	High facility thresholds 53.000	Median facility thresholds 12.001	Low facility thresholds 2.500
	Number of surrounding substitute facilities	High facility thresholds 5.000	Median facility thresholds 3.001	Low facility thresholds 1.000
	Spatial vitality	Higher viability threshold 0.730	Median viability threshold 0.510	Low viability threshold 0.260
	Transportation accessibility	Thresholds are easily reached 1.037	The median reaches the threshold 0.632	It is difficult to reach the threshold 0.329

Configuration analysis

After conducting the necessity test on the condition variables and confirming that none of them were necessary conditions for the outcome variable, this study further combined multiple condition variables to perform configuration analysis. The objective was to identify sufficient conditions for high physical activity value outcomes. During this process, the consistency threshold was set to 0.8, the case threshold to 1, and the PRI (Proportional Reduction in Inconsistency) consistency threshold to 0.7. The data were standardized using fsQCA 3.0 software, yielding three solutions: the complex solution (Table A3), the simple solution (provided in Supplementary Materials Table A4), and the intermediate solution (provided in Supplementary Materials Table A5).

The analysis revealed that high physical activity value outcomes in community parks in Changchun are generated through seven distinct pathways. The overall consistency of these pathways reached 0.938, and the overall coverage was 0.567. Among the solutions, the intermediate solution provided the most robust explanation, indicating that these seven pathways collectively promote the generation of high physical activity value outcomes. Consequently, the intermediate solution was selected as the final solution for explaining high physical activity value outcomes (Table 5). The study results further indicate that the 7 pathways have a 93.77% likelihood of ensuring that older adults in community parks achieve high levels of physical activity. Overall, 56.72% of the community park feature characteristics influence the physical activity of older adults. Therefore, the combinations of feature characteristics corresponding to these pathways can be considered sufficient conditions for high physical activity levels.

Table 5.

Formation of high physical activity values

Condition Variables	Pathway 1	Pathway 2	Pathway 3	Pathway4	Pathway 5	Pathway 6	Pathway 7
Site scale	●	●	X	●	●	X	●
Greening implantation rate	⊙	V	V	⊙	V	V	V
Green rate of greenery	V	V	V	⊙	⊙	V	⊙
Number of facilities for physical activity		⊙	V	V	V	⊙	⊙
Number of surrounding substitute facilities	X		⊙	X	X	⊙	⊙
Spatial vitality	V	⊙	V	V	⊙	⊙	V
Transportation accessibility	●	●	●	X	X	X	●
Solution Coverage			0.567173
Solution Consistency			0.937688

●indicates the presence of a core condition, ⊙indicates the presence of a peripheral condition, V indicates the absence of a core condition, X indicates the absence of a peripheral condition, Blank indicates that the condition can either be present or absent

To elucidate the differences and similarities in the synthesized condition features across different pathways, this study categorized the pathways by extracting their key commonalities. Pathways with similar combinations of condition variables were grouped under the same category. Ultimately, the overall pathway outcomes were categorized into three distinct types: natural quality type, balanced service type, and neighborhood adaptation type. This classification not only deepens the understanding of the pathways but also enhances the rigor and accuracy of the study (Table 6). Specifically, the size of the peripheral color blocks represents the frequency of specific features. The connecting lines indicate shared elements and potential associations among different configurations (Fig. 7). Furthermore, the overall distribution and combination patterns reflect the primary type to which each specific configuration belongs.

Table 6.

Formation of high physical activity values

Path Type	Pathway	Condition Variables
Natural Quality Type	Pathway 1	Site scale*Greening implantation rate*~Green rate of greenery*~Number of surrounding substitute facilities*~Spatial vitality*Transportation accessibility
	Pathway 4	Site scale*Greening implantation rate*Green rate of greenery*~Number of facilities for physical activity*~Number of surrounding substitute facilities*~Spatial vitality*~Transportation accessibility
	Pathway 5	Site scale*~Greening implantation rate*Green rate of greenery*~Number of facilities for physical activity*~Number of surrounding substitute facilities*Spatial vitality*~Transportation accessibility
Balanced Service Type	Pathway 2	Site scale*~Greening implantation rate*~Green rate of greenery*Number of facilities for physical activity*Spatial vitality*Transportation accessibility
	Pathway 7	Site scale*~Greening implantation rate*Green rate of greenery*Number of facilities for physical activity*Number of surrounding substitute facilities*~Spatial vitality*Transportation accessibility
Neighborhood Adaptation Type	Pathway 3	~Site scale*~Greening implantation rate*~Green rate of greenery*~Number of facilities for physical activity*Number of surrounding substitute facilities*~Spatial vitality*Transportation accessibility
	Pathway 6	~Site scale*~Greening implantation rate*~Green rate of greenery*Number of facilities for physical activity*Number of surrounding substitute facilities*Spatial vitality*~Transportation accessibility

* indicates “AND” (conjunction), ~ indicates “NOT” (negation)

Fig. 7.

Configure the pathway. Note : Scale means Site scale; Implantation means Greening implantation rate; Greenery means Green rate of greenery; Facilities means Number of facilities for physical activity; Surrounding means Number of surrounding substitute facilities; Vitality means Spatial vitality; Accessibility means Transportation accessibility

Discussion

Landscape elements of community parks influencing physical activity among older adults

The results indicate that site scale has the most significant impact on the physical activity value outcome for older adults. Specifically, a strong correlation exists between the size of green spaces in community parks and the physical activity levels of older adults. This finding can be partially explained by the following reasons: larger green spaces increase vegetation availability and per capita green area, providing fresh air and an undisturbed environment that encourages physical activity among older adults [70]. Additionally, extensive paved areas are among the most critical spatial factors for physical activity, as large-scale community parks are more likely to offer ample paved surfaces [71]. This study further confirms that the accessibility of community parks also significantly influences the physical activity levels of older adults. Due to their limited mobility, older adults’ activities are often restricted to areas near their residences. Compared to other types of green spaces, community parks provide better accessibility, which increases the frequency of their activities in these areas and consequently contributes to the accumulation of physical activity value outcomes [72, 73].

Moreover, factors such as greening implantation rate, green coverage rate, spatial vitality, physical activity facilities, and surrounding substitute facilities are also identified as age-friendly elements in community parks. However, their influence on physical activity levels is relatively similar [74–76]. For instance, studies have shown that the presence of well-maintained greenery and accessible facilities significantly enhances the attractiveness of parks for older adults, thereby promoting regular physical activity [77, 78]. Furthermore, the integration of age-friendly design principles, such as shaded seating areas and smooth walking paths, has been found to further encourage park usage among older adults [79]. Unlike previous studies, this study finds that the green space shape index of community parks does not significantly impact the physical activity levels of older adults, contrary to expectations. This may be because older adults are more influenced by “eye-level greenery,” which fosters their engagement in physical activity, rather than the actual spatial contours of the site [44]. This finding aligns with recent research emphasizing the importance of perceived greenness and micro-level environmental features in promoting physical activity among older adults.

Pathways of community park features influencing physical activity in older adults

The complexity of older adults’ activities in green spaces lies in the interdependence of various contributing factors. A synergistic effect of multiple elements promotes physical activity behaviors among older adults [80]. Through fsQCA analysis, this study identified seven distinct pathways, each with unique characteristics. These pathways were constructed by combining two varying levels—higher and moderate—of site scale, greening implantation rate, green rate of greenery, facilities for physical activity, surrounding substitute facilities, spatial vitality, and accessibility. These combinations explain the mechanisms behind the high physical activity value outcome observed among older adults in community parks. The overall results were further grouped into three subtypes based on the shared characteristics of these condition combinations: natural quality type, balanced service type, and neighborhood adaptation type. The natural quality type emphasizes the influence of a park’s ecological environment and natural landscape on promoting physical activity among older adults. As previous research has indicated, while the natural environment of community parks may not directly lead to increased physical activity, it can create comfortable and attractive green spaces that encourage more frequent movement [13]. Additionally, the maintenance of these areas affects older adults’ experience of greenery and their subjective well-being, which in turn may influence their choice to walk near green spaces [81]. This aligns with recent studies highlighting the role of biophilic design in enhancing mental and physical health outcomes for older populations [82].

The balanced service type highlights the importance of well-developed park facilities and diverse service offerings in motivating physical activity. Furthermore, providing walking-friendly infrastructure, such as benches for resting along routes to destination parks, plays a crucial role in supporting physical activity among older adults [83]. This finding is consistent with research by Zhang, Y [84], which underscores the significance of accessible and well-maintained facilities in fostering active aging. Furthermore, the integration of age-friendly exercise equipment and shaded rest areas has been shown to significantly increase park usage among older adults.

Lastly, the neighborhood adaptation type represents a pathway that integrates parks with the surrounding community environment, effectively supporting and promoting daily physical activity among older adults. This approach aligns with the concept of “age-friendly communities,” which emphasizes the importance of creating environments that are inclusive and supportive of older adults’ needs [85]. By ensuring seamless connectivity between parks and residential areas, as well as providing accessible transportation options, communities can better facilitate regular physical activity among older adults.

Implications for urban community park planning to enhance physical activity

This study reveals pathways linking landscape elements of 31 urban community parks to physical activity among older adults, offering valuable insights for updating, designing, and reevaluating existing urban parks. These findings can assist policymakers in reconsidering urban green space development from a health and well-being perspective, particularly in reallocating space and resources during green space renewal processes [58]. The research adopts a configurational perspective, identifying key influencing factors and conducting case comparison studies of conditional variables, reporting three categories of community parks: natural quality type, balanced service type, and neighborhood adaptation type, and explores the pathways through which these categories impact the level of older adults’ activities in parks. The study also identifies key aspects of environmental development that support high-quality green space activities for older adult. Furthermore, these findings can assist urban planners in making rough predictions of physical activity within the context of green space equity, emphasizing that not only the objective environment of parks but also their perceived environment is equally important [86].

Therefore, future landscape planning and design should optimize community park features based on their distinct types. For the natural quality type, efforts should focus on enhancing environmental quality and adjusting planning layouts, leveraging visual landscape effects to stimulate older adults’ willingness to engage in physical activity. Recent studies have demonstrated that visually appealing green spaces with diverse vegetation and natural aesthetics significantly increase park usage and physical activity levels among older adults. For the balanced service type, the focus should be on group-oriented services by updating facilities, pathways, and paving components to reduce the risk of injury and encourage older adults to participate in outdoor activities. Research underscores the importance of age-friendly amenities, such as shaded seating areas, smooth walking paths, and accessible restrooms, in promoting regular physical activity among older adults [78].

For the neighborhood adaptation type, it is essential to strengthen accessibility to surrounding areas and increase convenience for older adult’ activities, thereby improving the frequency of physical activity. This includes enhancing the connectivity of public facilities outside the park and streamlining internal pathways. Studies have shown that proximity to green spaces and the presence of safe, well-connected walking routes are critical factors in encouraging regular physical activity among older adults. By implementing these strategies, community parks can maximize their value as sustainable resources for social welfare and public health.

Limitations and future prospects

In this study, we employed fuzzy-set Qualitative Comparative Analysis (fsQCA) to investigate the complex relationships between community park environ-mental characteristics and physical activity among older adults. This study contributes to the existing research framework in several important ways. First, by adopting a configurational analytical approach, it moves beyond the dominant single-factor or net-effect paradigm in physical activity research and highlights how multiple park features jointly shape older adults’ physical activity outcomes through different combinations. This perspective better reflects the complexity and nonlinearity dependency inherent in behavior–environment relationships. Second, we analyzed the landscape elements of community parks that influence older adult’ physical activity and identified the pathways through which these characteristics exert their effects. This approach enhances our understanding of the mechanisms by which community parks promote physical activity in older adults. By systematically examining the interplay between community park features, our research provides empirical evidence to support the design of age-friendly cities and sustainable urban parks.

However, this study is not without limitations. First, the research was conducted in Changchun, a city characterized by a cold climate [87], and data collection was limited to the summer season. The absence of longitudinal data comparing seasonal variations in community park characteristics and older adults’ subjective perceptions of physical activity restricts our ability to fully assess the long-term impacts of built environment changes on older adult’ physical activity levels. In addition, the study is based on 31 parks in Changchun, a city with a high degree of population aging and a distinct cold climate, and its external generalizability and applicability may be limited by the city’s specific sociocultural context, climate conditions, and the preferences of older adults. Nevertheless, the findings offer valuable insights into promoting physical activity among older adults, particularly in cold-climate cities, and address critical gaps related to the scale and accessibility of community green spaces. These in-sights can assist policymakers in understanding the broader implications of community park design on older adults’ physical activity.

Second, the reliance on manual observation and self-reported surveys to collect physical activity data introduces potential uncertainties. This method depends on respondents’ subjective recall, which may be influenced by factors such as memory decay and biases in estimating activity level and duration. In addition, the measurement of physical activity was confined to activities occurring within park boundaries, which may have led to an underestimation of the respondents’ overall activity levels. These limitations could affect the precision of the physical activity data. We focused on analyzing the mechanisms through which park environments influence populations with established usage habits, but inevitably introduced sample selection bias. However, given the sufficient sample size and the rigorous exclusion of invalid responses during data collection, this approach remains a practical and efficient means of capturing older adults’ physical activity levels and needs in community parks. Importantly, it also provides a reliable empirical foundation for exploring the configurational mechanisms linking park environments and physical activity.

Third, due to limited data availability and concerns regarding participant privacy, this study included only gender and BMI as descriptive variables rather than as explanatory variables in the fsQCA analysis. More detailed individual-level socioeconomic variables, such as income, education, and health status, were not incorporated into the analytical models, which may limit our understanding of population heterogeneity. In addition, this study did not include specific design indicators such as trail length and density as independent variables in the model. While this approach avoided multicollinearity with park area, it may have overlooked some subtle effects. Future research could place greater emphasis on the configuration characteristics of trail networks themselves to provide more refined design guidance. However, since the focus of this study is on spatial environmental characteristics at the community scale, we intentionally reduced the depth of individual analysis to provide a broader contextual overview. Despite these constraints, the study offers valuable community-level evidence on the health-related implications of park environments, complementing more micro-scale design studies in the existing literature.

Despite these limitations, our study establishes a robust and transferable analytical framework for understanding the pathways through which community parks promote physical activity among older adults. This framework offers methodological insights and theoretical transferability, laying the groundwork for future research to explore the interactions between park variables in greater depth. Future studies could incorporate longitudinal data to capture seasonal variations, integrating park environments with other built environment factors like community and household settings, employ objective measurement tools (e.g., accelerometers or GPS tracking) to enhance data accuracy, and expand the geographic scope to include diverse climatic, cultural contexts, and population sizes. Additionally, integrating a broader range of individual characteristics would allow exploration of the nuanced interactions between personal socioeconomic factors and determinants of physical activity environments. Such efforts would deepen our understanding of the relationship between urban green spaces and the health and well-being of aging populations, ultimately contributing to the development of more inclusive and sustainable urban environments.

Conclusions

This study employed fsQCA (fuzzy-set Qualitative Comparative Analysis) to investigate the causal relationships between community park features and physical activity among older adults. Specifically, it explored the environmental characteristics of community parks and their combinatorial pathways that influence the level of physical activity in this demographic. The configurational analysis yielded the following conclusions:

Firstly, the features most significantly associated with promoting physical activity among older adults include site scale, greening implantation rate, green coverage rate, the number of physical activity facilities, the availability of surrounding substitute facilities, spatial vitality, and transportation accessibility. These factors significantly impact the physical activity of older adults. However, none of the individual features was identified as a necessary condition for promoting physical activity. Therefore, the seven environmental elements must act in combination to effectively influence the physical activity levels of older adults. Secondly, fsQCA analysis identified seven configurational pathways, each possessing distinct characteristics. Among these, site scale and transportation accessibility emerged as core elements critical to fostering physical activity among older adults. Finally, based on the similarities and differences in the synthesized condition features across the pathways, the high- level physical activity pathways were categorized into three subtypes: (1) natural quality type, emphasizing green and natural elements; (2) balanced service type, integrating diverse amenities and facilities; and (3) neighborhood adaptation type, focusing on accessibility and integration with the surrounding community.

The findings provide valuable insights for policymakers and urban planners, high-lighting the pivotal role of community green spaces in promoting physical activity among older adults. This study underscores the need for targeted park renewal initiatives to create environments that support active aging. By offering empirical evidence, it contributes to the development of age-friendly urban spaces and provides a foundation for designing green spaces that align with the goals of building inclusive, health-promoting cities.

Abbreviations

fsQCA

Fuzzy-set Qualitative Comparative Analysis

WHO

World Health Organization

POI

Point-of-interest

MET

Metabolic Equivalent of Task

IPAQ

International Physical Activity Questionnaire

Authors’ contributions

Conceptualization, Y.C. and W.N.; methodology, C.Q., Y.C. and W.N.; software, C.Q., Q.W. and X.Z.; validation, Y.C. and W.N.; formal analysis, C.Q.; investigation, Y.C., W.N. and C.Q.; resources, Y.C. and W.N.; data curation, C.Q.; writing—original draft preparation, X.Z.,Q.W. and W.N.; writing—review and editing, Y.C. and W.N.; visualization, Q.W. and X.Z.; supervision, Y.C. , R.Y. and W.N.; project administration, Y.C. and W.N.; funding ac-quisition, Y.C., and W.N. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Data availability

The datasets used and analysed during the current study are available from the corresponding author on reasonable request.

Declarations

Ethics approval and consent to participate

This study was conducted in accordance with the ethical principles outlined in the Declaration of Helsinki. The research protocol was approved by the Ethics Committee of the College of Forestry and Grassland Science, Jilin Agricultural University (Approval No. 2024-CFGSJLAU-1-1). All experimental procedures were performed in compliance with the relevant institutional guidelines and regulations. Informed consent was obtained from all participants prior to their inclusion in the study. Clinical trial registration: not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.