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Article

Exploring the Patterns of Recreational Activity among Urban Green Spaces in Poland

by
Sandra Wajchman-Świtalska
1,*,
Olga Grabowska-Chenczke
2 and
Marcin Woźniak
3
1
Department of Forestry Management, Faculty of Forestry and Wood Technology, University of Life Sciences in Poznań, Wojska Polskiego St. 71C, 60-625 Poznań, Poland
2
Department of Law and Enterprise Management in Agribusiness, Faculty of Economics, University of Life Sciences in Poznań, Wojska Polskiego St. 28, 60-637 Poznań, Poland
3
Faculty of Human Geography and Planning, Adam Mickiewicz University in Poznań, Bogumił Krygowski St. 10, 61-680 Poznań, Poland
*
Author to whom correspondence should be addressed.
Sustainability 2023, 15(6), 5425; https://doi.org/10.3390/su15065425
Submission received: 7 February 2023 / Revised: 8 March 2023 / Accepted: 17 March 2023 / Published: 19 March 2023
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Abstract

:
The aim of this study was to investigate the relationship between the socio-demographic background, patterns of recreational activity, and their impact on mood regulation strategies used by urban green spaces (UGS) visitors in Poland. In our research approach, we collected data from 376 participants through an online survey. In the next step, we developed structural equation models: one general model and two additional models for men and women. We discovered that both socio-demographic characteristics, as well as the variety of visited green spaces impact people’s mood regulation strategies. In our research approach, latent variable places that consists of different types of green spaces is the key concept that positively affect mood regulation strategies; visiting more places reduces the tendency to decrease mood and increases the tendency to increase mood. Moreover, we identified some important gender similarities and differences. There is causation between the types of leisure activity and the frequency of a leisure activity among men and women. However, in the case of women, the frequency of a leisure activity is positively associated with the tendency to increase mood; in the case of men, the association is negative. The research results provide a deeper insight into of the patterns of green leisure that shape the subjective well-being of urban green space visitors in Poland.

1. Introduction

Urban green spaces (UGS) are pieces of land in an urban area covered by vegetation. Such spaces differ in size, plant type, facilities and services [1,2,3], such as the urban park, community garden, grassland, forest, street green, and green roof [4]. Research shows that they have a wide range of services such as social [5,6,7], environmental [8,9,10], economic [11], public health [12,13,14], psychological [15,16], and educational [6]. Additionally, UGS play a key role in urban sustainability. Sustainable management strategies in urban green areas can bring economic and environmental benefits at the urban scale [17]. The complexity of factors that influence the use of urban green space through the application of systems thinking was presented by Salvia et al. [18]. The authors engaged in an ethnographic approach to focus on the in-depth knowledge of the local community and its stakeholders. The case study presented in this paper shows the importance of informing organizational decision-makers about factors and strategic interconnections in the urban green space. A similar approach was presented by Monteiro et al. [19] in their integrative literature review on green infrastructure planning principles. The authors postulate the uniform process of green infrastructure development that is based on the principles of connectivity, multifunctionality, multiscale, integration, diversity, applicability, governance, and continuity, which, consequently, better addresses future sustainable green infrastructure planning needs.
Providing recreation opportunities to urban dwellers working in a fast-paced work environment is another form of sustainable approach to urban planning and management [20,21,22]. Thus, urban dwellers use UGS mainly for physical exercise, social interactions, relaxation, and mental restoration [23]. Recreational activity within UGS is diverse in terms of its type, demand, and cultural differences among individuals [24]. Meanwhile, some research alarms that within cities, UGS are not always equitably distributed. Access is often highly stratified based on income, ethno-racial characteristics, age, gender, (dis)ability, and other factors [25,26]. Pinto et al. [27] reported significant differences in the socio-demographic characteristics of the UGS’ users and their motivations. Lee et al. [28] also highlight significant differences in park and outdoor recreation behaviors that result from a variety of demographic or social characteristics, (e.g., age, gender, race/ethnicity, socioeconomic status, and residential location).
It has been proven that physical effort is conducive to achieving and maintaining emotional balance [29,30,31]. Among important factors influencing the well-being of individuals is the frequency of visits to green space [32,33], feelings of connectedness to nature [34,35], quantity of available green space [36,37], or the biodiversity of the green space [38]. Providing individuals with mental well-being has a positive effect on their functioning in society, attitudes, needs, commitment to the achievement of personal goals and aspirations, and self-esteem [39,40,41,42].
Several studies show that leisure activities support well-being and mental health [43,44]. Leisure is described as a health-promoting behavior and a mediating factor in improving work and family, and alleviating the negative effects of work-related stress on health [45,46]. Special attention is given to the synergistic benefit of adopting physical activities whilst being directly exposed to nature (‘green exercise’) [47].
In order to systematize the discussion on the positive effects of exposure to nature on the physical and mental health, we use the term urban green leisure (UGL) to define leisure activities that are undertaken within urban green areas. UGL has already been related to benefits on mental health and well-being.
In 2005, Bedimo-Rung et al. [14] postulated a conceptual model of how the relationships between park use correlate with park benefits and physical activity. The authors categorized the most important park environment characteristics: park features, conditions, access, aesthetics, safety, and policies. They found that design issues, such as the size of a park, its layout, sun and shade balance, topography, or visual appeal, may also be key motivators for undertaking physical activity in urban parks.
In our study, we also refer to Lachowycz and Jones’s [48] socio-ecological comprehensive framework that presents key moderating factors that influence the strength of association between green space access and health, including mental health. The framework became a starting point to investigate the characteristics of green leisure activities and to explain specific patterns of factors’ influence. Lachowycz and Jones [48] focused on the identification of moderating factors, such as gender, ethnicity and socio-economic status, living context, green space type, and climate. These factors, which change the strength or direction of associations, provide a better understanding of who, when, and how green space exposure can be a benefit (e.g., improving health and well-being). The socio-ecological models follow a core principle according to which changes within the physical environment lead to changes in health behaviors and psychological states [48,49].
Another model, the multi-level leisure mechanisms framework, was proposed by Fancourt et al. [50]. The authors aimed at explaining how leisure activities affect mental and physical health. In their model, leisure is described as voluntary free-time activities not related to employment responsibilities, which people engage in mostly for enjoyment. The review of mechanisms of interactions on psychological, biological, social, and behavioral levels highlights the non-linearity and complexity of the process. Even though the model shows 600 mechanisms of action by which leisure engagement can affect health and health behavior, it also shows that not every form of leisure is a panacea for mental and/or health disorders. According to the model, leisure can be both positive and maladaptive depending on the context. As far as psychological processes are involved, physical leisure activities usually have immediate and positive transformative effects on affective states and build psychological resources of an individual [51,52,53]. However, leisure activities do not affect all health indicators equally. Some health indicators are less stable than others, e.g., show random fluctuations over time and different types of physical leisure activities may affect mental well-being and physical health differently. For example, research conducted by Pagano et al. revealed that the health indicator “feeling hassled” showed more random patterns for very physically active participants, whereas the health indicator “drinking alcohol” showed the cyclic patterns for not very physically active participants. This led to the conclusion that a person with lower but random levels of some health indicators might be generally healthier than a person with consistently higher but cyclic levels of the health indicators [54].
Psychological well-being is often related to emotional intelligence [EI], the concept developed in 1990 by Salovey and Mayer [55]. They describe emotional intelligence as the ability to perceive, understand, regulate, and use emotions to promote emotional and intellectual growth [56]. The current findings provide evidence for the relationship between the use of different EI strategies, cognitive emotion regulation strategies, and subjective well-being [57]. Emotional self-regulation is also considered an important factor that influences the way people navigate stressful and challenging situations [58]. However, emotional regulation was also studied as a psychological intervention used to maximize the positive outcomes of leisure experiences, e.g., during travel experiences [59]. Gao and Kerstetter [59] found that tourists actively sought for and applied 11 different strategies to regulate their emotions in the unique travel context. Urban green spaces make both direct and indirect contributions to the subjective well-being of citizens [60]. Exposure to nature and daylight is recognized to be highly important for reducing stress levels, and improving mood and attention control. Viewing nature elicits a so-called ‘soft fascination’, which enables restoration from directed attention fatigue, as opposed to ‘hard fascination’ (e.g., while watching TV), which draws attention automatically in an all-consuming fashion [61].
Even though the stress-reducing potential of nature among city dwellers was reported in many studies [62,63,64], we should take into account that urban citizens present a wide range of individual preferences for coping strategies and mood regulation. It is claimed that mood (the way a person feels most of the time) is one of the key indicators of mental well-being [65,66].
Past research showed that green spaces can not only alleviate stress, but also improve overall mood, and help individuals experience their surroundings with all possible senses [67]. Recreational physical activity, common in green spaces, promotes positive moods and reduces stress levels [68]. Research by Kondo et al. [69] proved a positive association with attention, mood, and physical activity. The example of Sapporo city brings evidence for the growing number of green areas’ visitors walking or running because of the increasing health awareness rapidly rising in recent years [70].
In a major Dutch study, Van den Berg et.al. [71] showed that respondents with more green space near their homes were less affected by a stressful life event than those with low green space access, suggesting that green space may buffer stress and anxiety. Similarly, Barton and Pretty [68] in a study conducted in the United Kingdom, showed that there were significant impacts of green exercise on several measures of mood and self-esteem.
Even though research in the field of affective psychology highlights the problem of the interchangeable use of the terms ‘emotions’ and ‘moods’, it is not always easy to keep a clear separation between the two concepts. Therefore, it is necessary to provide measurement tools that are specific to moods and do not mistake them for emotions [72]. Emotions and moods have basically different functions. Emotions are usually aroused in specific situations that usually require adaptive activity, whereas moods are rather prolonged and generalized, and they rather change the way information is processed [73]. In our research, we focused mainly on individual’s mood-regulation tendencies (tendency to raise or lower one’s mood), which is a slightly distinct phenomenon from stress, affect, and emotion regulation.
Mood regulation can be both functional/adaptive and dysfunctional/maladaptive. Functional mood regulation helps to restore psychological well-being and promotes problem-solving and reappraisal. Dysfunctional mood regulation is related to procrastination, or avoidance, which serve as a kind of short-term mood repair but in the long term, they bring feelings of guilt and shame [74,75].
People differ in their tendencies for raising or lowering their moods. Wojciszke [76] found that the tendency to raise and lower one’s mood (increasing versus decreasing type) may constitute a permanent disposition for an individual. For example, individual tendencies toward mood regulation are related to differences in reactivity to affective stimuli, which partially explains why people representing the decreasing type have problems with ‘‘switching up’’ to mood improvement processes. As a consequence, we may assume that subjective well-being may be highly influenced by individual differences in stable tendencies toward mood regulation, and these tendencies may promote either positive or negative moods (or both of them), as preferred. Since therapeutic practices usually focus on promoting mood improvement, it is time that we also pay attention to the full range of possible mood regulation strategies (e.g., a hot versus a cold type of mood regulation), and match them to the individual’s abilities, needs, and reactivity [77].
Therefore, we formulated the following research questions:
  • What is the relationship between socio-demographic characteristics, types of visited UGS and mood regulation strategies?
  • What is the relationship between mood regulation strategies and the preference for types of recreation in UGS?
We used structural equation modeling and mediation analysis to determine the mediating factors for mood regulation tendencies. An analysis of socio-demographic patterns of recreational activity and their impact on mood regulation strategies used by urban green space visitors may help to shape urban green spaces and the offer of recreation therein in such a way as to be particularly beneficial to the mental health of city dwellers. Due to the globalization process and internationalization of urban recreation management, insights from a Polish perspective may be helpful also for UGS management practices in other culturally similar countries, e.g., in Central Europe.

2. Materials and Methods

2.1. Diagnostic Survey

During the study, we used the method of diagnostic survey. The survey was conducted between 22 March and 30 April 2022. The data were collected by an anonymous questionnaire available online via Google Forms. The survey was published online in various social media groups (e.g., on Facebook) and distributed among the respondents who were not compensated for the survey, and completed it voluntarily. In order to extend the scope of the research, we used the method of snowball sampling [78,79]. The respondents were asked to distribute the questionnaire among their social networks.
We considered several demographic variables, including age, gender, place of residence, level of education, and number of children. Participants also completed the mood regulation scales (MRS) to measure individual differences in tendencies toward mood improvement or deterioration [76].
The mood regulation scales consist of 30 items: 15 items for the mood improvement scale (MIS) and 15 items for the mood deterioration scale (MDS). The mood improvement scale includes items for measuring behavioral and cognitive strategies linked to a positive mood increase and items related to a negative mood decrease. The mood deterioration scale includes items that measure activities that people undertake to decrease their positive mood and items that measure negative mood increase practices. The respondents answered questions on how often they use different regulation strategies by using a five-point Likert scale, ranging from “never” to “always”. We developed a questionnaire regarding recreational activities within urban green areas and asked questions on the frequency, preferences for places, and types of green leisure activities.

2.2. Respondents

We obtained 376 correctly filled out questionnaires (262 from women—69.7%; 114 from men—30.3%). The share of the place of residence demonstrates that people living in the village (25%) are more than residents of cities, with >500 thou. inhabitants (31.1%). A relatively equal number of respondents (26.9%) represented smaller cities with ≤100 thou. inhabitants. Since urban green areas in Poland are usually a part of a dispersed wedge and ring system [80,81], the beneficiaries of such places are not only inhabitants of cities, but also of neighboring towns, including villages and small towns. It is consistent with the demographic trend in the big cities in Poland. The rapid development of neighboring municipalities is followed by the migration of people from the cities to the surrounding suburban areas or counties, even though the social life of these people continues to take place in urban areas, including urban green areas [82]. Therefore, it is also possible that respondents adopt a broader definition of urban green areas and refer to any kind of vegetated and unsealed land of higher biological value within urban boundaries. However, the questions in the survey referred directly to urban green spaces, so the respondents were informed about the scope of the research. The social characteristics of the respondents in the context of demographic data shows Table 1.

2.3. The Measurement Models

Structural equation models (SEM) are extensions of common regression techniques that allow for a parallel investigation of several inputs and outputs. The framework allows also for the analysis of so-called latent variables, which are not observed directly. Therefore, the model includes three main elements: observed (exogenous) variables, latent variables, and potential relations between them. These elements are combined within: (1) The structural part of the SEM model, which links latent variables to each other via systems of simultaneous equations; and (2) The measurement part, which links latent variables to explanatory variables [83].
The measurement part of the model is solved with confirmatory factor analysis, which provides an overview of the contribution of the observed variables to the hypothetical construct. On the basis of these loads, one can assess the importance of each of the exogenous variables [84]. In the study, we developed three latent constructs: demographics, places, and activities. The measured variables that contribute to these factors are listed in Table 2.
In turn, the structural part of the model relies on regression techniques that are applied to measure the casual relationship between measured or latent variables. This approach is, however, much more flexible and allows for the mixing of variables of different types [85].
In this study, we developed two structural equation models (SEM) to answer the research questions. The first model (M1) investigates the relationship between the demographic factor (respondents’ metric variables), places factor (types of places used for green recreation), and mood regulation strategies (the tendency to improve and deteriorate mood). Despite the potential gender differences, we were unable to estimate a separate model for men and women because of misspecification bias in the men’s model (114 observations were insufficient to estimate 24 model parameters).
The second model (M2) presents mood regulation strategies together with the frequency of recreational activities as an outcome of types of factors that comprise groups of leisure activities performed in urban green spaces. In this case, as the number of model parameters was reasonable, we estimated two models separately: one for women and one for men. The endogenous and exogenous variables included in both models are provided in Table 2.

3. Results

Below, in Figure 1, Figure 2 and Figure 3, we present the results of the analysis; path diagrams for developed SEM models supplemented with diagnostics. The numbers on paths are standardized coefficient values. Double arrows stand for correlation. The arrows leading to endogenous variables situated at the bottom of the figure are regression lines and provide an overview of the specific loads of latent factors to the regression outcome; the arrows leading to the exogenous variables situated at the top of the figure provide information about the contribution of each measured variable to the latent factor. Dashed lines are coefficients fixed by the software.
The demographics factor (DEM) is loaded with the four exogenous variables that have a positive impact on the overall value of the construct. Age (0.96) and the number of children (0.59) contribute the most. Educational level and place of residence are significant; however, their loads are smaller. We also identified a correlation between these two variables, i.e., with the size of the city, the education level also rises. Places factor (PLA) consists of three measured variables that positively load it. Parks (using parks for recreational activity) contribute the most.
The model identified the significant causal relationship between the demographic factor and the tendency to decrease mood (mod). It means that the higher overall values of the demographic factor (higher values of demographics factor (DEM) are associated with higher values of measured variables that contribute to the construct) are associated with lower values of the tendency to decrease the mood (this factor lowers the tendency to decrease the mood). Another significant connection was identified between the places factor and the tendency to increase the mood. The rising places factor (higher values of places factor (PLA) are associated with a larger number of places used for leisure activity) rises the tendency to improve the mood (mop)—the variable grows together with the number of places used for leisure activity. We also observed a negative relation between the places factor and the tendency to decrease mood—this tendency drops as the number of places used for green recreation rises. Therefore, the places factor has a twofold impact on our endogenous variables—as it rises, it induces the tendency to improve mood and deteriorates the tendency to decrease mood.
We also identified a significant and a negative correlation between latent variables; mainly, higher values of the demographic factor correlate with lower values of the places factor. However, both of these latent constructs reduce the mod variable. Among exogenous variables, a positive correlation was observed between the place of residence (liv), education level (edu) and visiting parks for leisure activities (par); with the size of the city, the education level rises together with the frequency of visiting parks. We also identified a significant negative correlation between visiting forests (foe) and parks (par); the people who visit forests tend not to choose parks for their recreational activities.
In turn, in the models presented in Figure 2 and Figure 3, we were interested whether the particular recreational activities are somehow associated with the frequency of leisure activity and mood regulation strategies. Latent variable recreational activities (ACT) presented in Figure 2 and Figure 3 consisted of several measurable actions as listed in Table 2. We can easily observe some differences in the contribution of observed variables to the latent factor across gender. There is also a clear division on variables that contribute positively and negatively to the variances of factors. It is worth noting that the former refers to the proactive forms of recreation (e.g., running, riding a bike, Nordic walking, and skiing) and the latter are associated with receptive (more passive) ways of green recreation (e.g., photographing nature, observing nature, and mushroom picking).
In the men’s model, riding a bike (bik) and running (run) explain more of the variance of the latent factor, while in the women’s model, the activities factor is positively associated with cycling at the first place. In turn, in both cases, photographing nature (pho), walking (wal), and observing nature (nat) contribute negatively to the activities factor. Thus, these recreational activities reduce the overall variety of the types of recreational activities. In the case of men, observing nature and photographing decrease the factor most significantly, while among women, these factors are photographing nature and walking. In other words, people (both men and women) engaged in proactive forms of recreation tend to join multiple activities, while people preferring receptive approaches to recreation focus on one selected activity.
Observed variables differ also in the significant correlation signs across genders. While in the case of men, the leisure frequency is positively correlated with the tendency to decrease mood, among women, the coincidence is negative. The same regards the connection between leisure frequency and the tendency to increase mood. Data for women reveal a positive correlation here, while in the case of men, the dependency is negative. Although these correlations are weak, they are still statistically significant.
In the case of women, we were unable to confirm a causal relationship between mood regulation strategies, and types of recreational activities. In the men’s model, we identified a significant relationship between the activities factor (ACT) and the tendency to decrease mood. Higher values of this variable cause a decline in the tendency to decrease mood; however, they seem insignificant regarding the tendency to increase mood.
We also identified a connection between the activities factors and the frequency of leisure activity (lef); rising recreational activities (higher values of recreational activities (ACT) are associated with a larger number of activities marked by the respondents) (ACT) may positively impact the frequency of leisure activities. This relation is weak in the case of women and strong in the case of men.
Finally, Table 3 presents model diagnostics in terms of goodness of fit.
Models were estimated with the sem function available in the R lavaan package, which provides routines for the estimation of various multivariate statistical models [86]. Model I was estimated with the diagonally weighted least square method and Model II, with the usual maximum likelihood approach. The estimators were chosen to minimize standardized root mean square residual (SRMR) and root mean square error of approximation (RMSEA). Higher values of RMSEA/SRMR indicate a lack of fit [87]. It is a common and robust measure for detecting model misspecification. RMSEA should be no more than 0.06. Model I has the highest value of this statistic but it is still in a reasonable range.
Chi-square tests the hypothesis that there is a discrepancy between the model-produced covariance matrix and the original matrix [88]. The test is highly sensitive to sample size. A higher sample size is usually associated with significant chi-square results [89], which is observed in case of Model I (p-value = 0.013). In this case, a sample of N = 376 is considered large; therefore, chi-square statistics should be supplemented with other fit indices (CFI, SRMR, RMSEA). These statistics do not confirm the chi-square result. In the case of Model II (both men and women), no discrepancies were detected.
Finally, CFI ranges from 0.0 to 1.0 and represents the amount of variance explained by the model. Hu and Bentler [90] argue that the CFI should be close to 0.95 or higher. Comparing the estimated models, they all keep the index in reasonable ranges. The exception is Model II (women), where CFI dropped to 0.85, but other fit indices do not indicate a misspecification problem.

4. Discussion

In our research, we proposed an in-depth analysis of the relationship between socio-demographic characteristics, places of recreation at UGS, and mood regulation strategies. We also investigated the relationship between mood regulation strategies and the preference for types of recreation in UGS. We studied gender differences in the patterns of green leisure.
We detected some important similarities and differences with regard to gender. There is a causation between types of leisure activity, frequency of leisure activity, and the tendency to decrease the mood among men (i.e., a higher number of types results in higher frequency and lowers the tendency to decrease mood). However, we have not observed such a connection among women. We may conclude that physical activity plays a more important role among male adults regarding their psychological well-being. The finding corresponds with the systematic review of psychological and behavioral correlates of recreational running. Pereira et al. [91] found that the most frequently reported positive outcomes of recreational running were improvements in mood and well-being. In the research conducted by Rendi et al. [92] among 76 male participants, men experienced psychological benefits that included positive engagement, revitalization, and tranquility. On the other hand, the results of event-related potential (ERP) studies examining gender differences to emotional stimuli suggest that women have greater reactivity in general than men [93,94]. This may partially explain the gender differences observed when exploring the impact of leisure activity on mood regulation tendencies.
Another reason why in the case of surveyed men leisure frequency is positively correlated with the tendency to decrease mood, and among women the coincidence is negative, is possibly the type of motivation for green recreation. This assumption needs further studies, as the tendency to increase mood may be motivated differently than the tendency to decrease mood. Similarly, many researchers postulated a more comprehensive approach to the benefits of physical exercises that takes into account not only the simple valance of positive versus negative affect (e.g., pleasure vs. displeasure) but also the degree of arousal associated with affect (e.g., enthusiastic vs. calm) [95,96].
Our study specifically focused on the tendency to increase/decrease mood, understood as the individual difference that explains the way a person feels most of the time [77]. The tendency serves as one of the most important indicators of mental well-being, and needs further analysis in the context of the benefits of green recreation, which may shape the quality of life among urban green spaces’ visitors. Even though subjective well-being is usually associated with the increase in positive affect and the decrease in negative affect [97], some researchers claim that negative emotions can also contribute to high subjective well-being (SWB). SWB is built on adaptive affective regulation strategies: the acceptance of one’s negative emotions (e.g., emotional fatigue associated with the feeling of physical exertion after exercising) and seeking out positive aspects (positive reappraisal) [77,98]. It is also important to verify the gender differences in the perceptions of urban green spaces characteristics that may be responsible for the level of satisfaction from green leisure. For example, Braçe et al. [99] found gender differences in the perceptions of several characteristics of UGS (i.e., safety, lighting, bike lanes, cleanliness, pleasant views, walking routes, shaded areas, recreational areas, off-leash dog areas, children’s playgrounds, and drinking fountains). As a result, we may observe gender differences in the patterns of green recreation that meet or do not meet the need of UGS’s users in the field of planning, design, and development of urban green areas.
We identified a significant causal relationship between the demographic factor and the tendency to decrease mood. It means that higher overall values of the demographic factor (higher values of demographics factor (DEM) are associated with higher values of measured variables that contribute the this construct) are associated with lower values of the tendency to decrease the mood (this factor lowers the tendency to decrease the mood). Demographic aspects (age and the number of children in particular) are associated with the kind of tendency people have to regulate their mood, which is a crucial aspect of well-being.
The findings complement the body of research on the socio-demographic predictors of psychological well-being and mental health [100,101]. For example, a literature review performed by Lee et al. [100] demonstrated that well-being across domains tends to increase with age. Calvo et al. [102,103] found that with the exception of physical health, respondents over 50 years of age reported higher levels of well-being in all studied domains.
A latent variable that refers to places of green recreation rises the tendency to increase the mood (i.e., higher values of places (i.e., larger number of places used for leisure activity) rises the tendency to increase the mood). These results suggest that the demographics (mainly age, education level, number of children, and place of residence) and the way we design public green areas are crucial aspects to be considered when planning initiatives that would encourage people to undertake green recreation in order to regulate their psychological well-being. Even though green spaces are purposefully designated for their recreational or esthetic merits, the role of accessibility of green spaces is also important for maintaining quality of life in residential community development [104]. Even though the notion that exposure to nature is psychologically healthful is very old [105], we still look for the reasons why contact with nature (and what kind of exposure to nature) results in a significant reduction in negative affect. Our research provides the result that latent variable places are the key concept that positively affects mood regulation strategies: visiting more places reduces the tendency to decrease mood and increases the tendency to increase mood. The result supports the observation that contact with nature in a variety of places within urban green spaces is highly beneficial.
We observed that with the size of a city, the education level rises and so does the frequency of visiting parks. We also identified a significant negative correlation between visiting forests and parks; the people who visit forests tend not to choose parks for their recreational activities, as the former seems to be the better alternative (if available). The results also suggest that there is no homogenous group of those who undertake physical activities in green urban areas. There are different preferences and different incentives for green leisure among various groups of UGS. We found that practicing some types of recreational activities significantly decreases the variety of other forms of green leisure. For example, men who observe and photograph nature have the tendency to limit other forms of green recreation, while among women, the recreational activities factor is significantly decreased by photographing nature and walking. We consider these findings crucial for green space planning to meet even the most hermetic needs of UGS visitors.
This piece of research is limited in scope because we studied only some possible types of green recreation correlated with only several socio-demographic characteristics, and therefore, investigation should be extended, taking into consideration several patterns of city dwellers’ behaviors, such as work patterns, health conditions, financial situation, etc. Furthermore, our sample included only Polish participants, and therefore, we cannot generalize these findings to other countries/cultures, where other variables may play a key role.
Since green recreation is a promising area for interdisciplinary study on both objective and subjective well-being, we propose to continue a wide range of research that would encompass the field of urban architecture, forestry, tourism and leisure, as well as sociology, psychology, and behavioral economics.

Author Contributions

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

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Data are contained within the article.

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. Fitted model of mood regulation strategies as an outcome of demographic and places factors. age—age; edu—education level; kid—the number of children; liv—place of residence; par—parks; foe—forests; gar—gardens; DEM—demographics latent variable; PLA—places latent variable; mop—the tendency to increase mood; mod—the tendency to decrease the mood. Squares at the bottom are endogenous variables; circles are latent variables or factors; squares at the top are observed (measured) variables. The wider the path, the more significant the parameter is. Source: own elaborations on a basis of data, N = 376.
Figure 1. Fitted model of mood regulation strategies as an outcome of demographic and places factors. age—age; edu—education level; kid—the number of children; liv—place of residence; par—parks; foe—forests; gar—gardens; DEM—demographics latent variable; PLA—places latent variable; mop—the tendency to increase mood; mod—the tendency to decrease the mood. Squares at the bottom are endogenous variables; circles are latent variables or factors; squares at the top are observed (measured) variables. The wider the path, the more significant the parameter is. Source: own elaborations on a basis of data, N = 376.
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Figure 2. Fitted model of leisure frequency and mood regulation strategies as outcomes of the types of recreational activities (women).
Figure 2. Fitted model of leisure frequency and mood regulation strategies as outcomes of the types of recreational activities (women).
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Figure 3. Fitted model of leisure frequency and mood regulation strategies as outcomes of the types of recreational activities (men). run—running; bik—riding a bike; nor—Nordic walking; ski—skiing; pho—photography; wal—walking; mus—mushroom picking; nat: observing nature; mop—the tendency to increase mood; lef—leisure frequency; mod—the tendency to decrease the mood; ACT—leisure activities latent variable. Squares at the bottom are endogenous variables; circles are latent variables or factors; squares at the top are observed (measured) variables. The wider the path, the more significant the parameter is. Source: own elaborations on a basis of data, N = 262 (women); N = 114 (men).
Figure 3. Fitted model of leisure frequency and mood regulation strategies as outcomes of the types of recreational activities (men). run—running; bik—riding a bike; nor—Nordic walking; ski—skiing; pho—photography; wal—walking; mus—mushroom picking; nat: observing nature; mop—the tendency to increase mood; lef—leisure frequency; mod—the tendency to decrease the mood; ACT—leisure activities latent variable. Squares at the bottom are endogenous variables; circles are latent variables or factors; squares at the top are observed (measured) variables. The wider the path, the more significant the parameter is. Source: own elaborations on a basis of data, N = 262 (women); N = 114 (men).
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Table
Table 1. Social characteristics of the respondents in the context of demographic data.
Table 1. Social characteristics of the respondents in the context of demographic data.
Demographic DataShare (%)
Genderfemale69.7
male30.3
Age16–206.2
21–3027.6
31–4022.8
41–5027.8
51–6011.2
>604.3
not answered0.8
Place of residencevillage25.0
city ≤ 20 thou. inhabitants14.4
city 21–100 thou. inhabitants12.5
city 101–500 thou. inhabitants17.0
city > 500 thou. inhabitants31.1
Level of educationlower secondary0.5
vocational3.2
secondary21.0
post-secondary3.7
higher71.5
Number of children050.0
116.2
226.3
35.3
40.8
50.3
90.3
not answered0.8
Table
Table 2. Endogenous and exogenous variables used in measurement models.
Table 2. Endogenous and exogenous variables used in measurement models.
Latent Var.Variables *DescriptionMin/MaxRemarks
endogenous
vars		lef
(M2)		the frequency of leisure activity1—never/7—everydayordinal variable with 7 levels
mop
(M1, M2)		the tendency to increase mood2/5numerical variable
mod
(M1, M2)		the tendency to decrease mood1/4.93numerical variable
demographicsliv (M1)place of residence1—village/5—city above 500,000ordinal variable with 5 levels
age (M1)age of respondent16/73numerical
kid (M1)number of children0/9numerical
edu (M1)education level1—primary to 5—higher educationordinal variable with 5 levels
placesfoe (M1)use forests for leisure activity0—no; 1—yesdummy
par (M1)use parks for leisure activity0—no; 1—yesdummy
gar (M1)use gardens for leisure activity0—no; 1—yesdummy
activitiesrun (M2)running0—no; 1—yesdummy
bik (M2)riding a bike0—no; 1—yesdummy
pho (M2)photographing nature0—no; 1—yesdummy
wal(M2)walking0—no; 1—yesdummy
nor (M2)Nordic walking0—no; 1—yesdummy
ski (M2)skiing 0—no; 1—yesdummy
mus (M2)mushroom picking0—no; 1—yesdummy
nat (M2)observing nature0—no; 1—yesdummy
* The first three variables are endogenous; M1 stands for Model I; M2 stands for Model II.
Table
Table 3. Fit measures for the Models I and II.
Table 3. Fit measures for the Models I and II.
Fit MeasureModel IModel II (Women)Model II (Men)
chi-square (p-value)0.0130.20.68
root mean square error of approximation (RMSEA)0.0470.0360.000
Std. root mean square residual (SRMR)0.0440.0410.043
comparative fit index (CFI)0.970.851
no of parameters251721
estimatordiagonally weighted least squaremaximum
likelihood		maximum
likelihood
Source: own computations.
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Wajchman-Świtalska, S.; Grabowska-Chenczke, O.; Woźniak, M. Exploring the Patterns of Recreational Activity among Urban Green Spaces in Poland. Sustainability 2023, 15, 5425. https://doi.org/10.3390/su15065425

AMA Style

Wajchman-Świtalska S, Grabowska-Chenczke O, Woźniak M. Exploring the Patterns of Recreational Activity among Urban Green Spaces in Poland. Sustainability. 2023; 15(6):5425. https://doi.org/10.3390/su15065425

Chicago/Turabian Style

Wajchman-Świtalska, Sandra, Olga Grabowska-Chenczke, and Marcin Woźniak. 2023. "Exploring the Patterns of Recreational Activity among Urban Green Spaces in Poland" Sustainability 15, no. 6: 5425. https://doi.org/10.3390/su15065425

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