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Article

An Analysis of the Factors Associated with the Seasonal Variability of Physical Activity in Natural Environments in a Sample of Lithuanian Adults

by
Rasa Jankauskiene
and
Migle Baceviciene
*
Health Research and Innovation Science Center, Faculty of Health Sciences, Klaipeda University, 92294 Klaipeda, Lithuania
*
Author to whom correspondence should be addressed.
Behav. Sci. 2025, 15(6), 773; https://doi.org/10.3390/bs15060773
Submission received: 8 April 2025 / Revised: 25 May 2025 / Accepted: 30 May 2025 / Published: 3 June 2025
(This article belongs to the Special Issue Mental Health and the Natural Environment)
Review Reports Versions Notes

Abstract

Engagement in physical activity (PA) in natural environments is known to promote physical and psychological well-being, yet little is known about how such activity fluctuates across seasons and how it relates to individual characteristics and quality of life (QoL). This study aimed to assess the seasonal variability of PA in nature and examine its associations with sociodemographic factors, nature-related experiences, and overall QoL in a sample of Lithuanian adults. A total of 924 participants (680 women and 244 men) with a mean age of 40.0 ± 12.4 years completed an online survey. The study measures included sociodemographic characteristics, nature proximity, exposure, connectedness, perceived restoration in nature and QoL measured by the WHOQOL. Based on self-reported seasonal behaviour, participants were categorized into three groups: irregular or no PA in natural environments, seasonal variability, and regular year-round PA in natural environments. In the present study, we observed the lowest rates of PA in natural environments in winter and the highest in summer. Comparative analyses revealed that individuals with regular PA in natural environments reported significantly greater financial security, nature exposure and connectedness, proximity to green spaces, and overall QoL compared to less regular PA in nature groups. A multiple regression analysis identified financial security, nature proximity, nature exposure, connectedness, and perceived restoration in nature as significant and positive predictors of QoL. These findings point to the problem of inequity and suggest that inequitable access to PA in natural environments manifests as a higher seasonality of PA in nature. The practical implications of the study highlight the importance of addressing equity and promoting consistent PA in nature throughout seasons. During the dark, wet, and cold periods, it is important to implement targeted interventions that improve access to natural environments for individuals with lower financial security. This could help reduce inequity in physical activity. Promoting access to green spaces and fostering nature connectedness may be particularly valuable strategies in public health interventions aimed at enhancing QoL across diverse populations.

1. Introduction

1.1. Physical Activity in Natural Environments and Its Restoration Effect

A considerable body of research has yielded compelling evidence supporting the notion that exposure to natural environments exerts a positive effect on human health and well-being (Coventry et al., 2021; Hartig et al., 2014; Martin et al., 2020; Nejade et al., 2022; Twohig-Bennett & Jones, 2018). In an objective sense, natural environments refer to the physical characteristics and processes that originate from nonhuman sources and are typically observable by people. This includes the living nature of plants and animals, as well as bodies of water, air quality, weather conditions, and the landscapes shaped by geological activities (Hartig et al., 2014). The term “nature” significantly overlaps with the “natural environment”, which is an area with minimal or no visible signs of human presence or interference. The contemporary trend of reduced exposure to natural environments and augmented time spent in indoor settings, utilizing electronic devices, is associated with a diminished sense of connectedness with the natural world (Richardson et al., 2018). A recent international study of environmental programmes students in 41 countries found that students in wealthier countries tend to have weaker relationships with nature than those in less affluent countries (Kleespies & Dierkes, 2023). Other findings also suggest that when countries become wealthier, individuals may have less direct connectedness to nature (Dasgupta, 2013; Richardson et al., 2022), potentially due to urbanization, increased reliance on technology, and lifestyle changes.
In their novel review, Barragan–Jason and colleagues observed experimental studies in various geographical regions and concluded that physical connection with nature improved human cognition, social skills, physical and mental health, and positively affected psychological connection to nature. Correlational studies also identified positive relationships between psychological connection with nature and both mental and physical health (Barragan-Jason et al., 2023). The psychological connection with nature is defined as the extent to which individuals perceive themselves as being intertwined with the natural world (Tam, 2013). In contrast, the physical connection is characterized by the degree of interaction with natural environments (Barragan-Jason et al., 2023). Finally, evidence suggests that exposure to nature fosters a greater body appreciation, a sense of savouring life, and enhances the sense of living a meaningful life (Swami et al., 2016; Zhou & Geng, 2025).
Physical activity (PA) is one of the forms through which people can establish or maintain a connection with nature and experience the psychological and physical benefits that nature provides (Hartig et al., 2014). PA has been identified as a key mediator of the association between nature exposure and mental health (van den Berg et al., 2019) and between the quality of residential environment and restorative perceptions of the settlement’s environment (Wu et al., 2024). There are three general categories of activities that fall under the umbrella of PA in nature. The first domain, “work”, includes all activities people do at work in natural environments (i.e., gardening). The second domain is “active transport”, which refers to getting to and from work by walking or biking in nature. The third domain is “leisure recreation”, which includes any activities humans do for fun, like sports and other hobbies (i.e., recreational cycling) in natural environments (Hartig et al., 2014). A body of research has indicated that PA in natural environments (or green exercise) is associated with enhanced mental health benefits, including relaxation, positive affect, increased energy, enjoyment of nature, lowered stress and anxiety, and general well-being (Brito et al., 2022; Kajosaari & Pasanen, 2021; Lahart et al., 2019; Pasanen et al., 2018). These benefits appear to exceed those associated with PA in indoor settings (Lahart et al., 2019; Wicks et al., 2022). One of the recent explanations why green exercising is more restorative compared to indoor exercise is that nature provides high variability. Specifically, exercising in nature is related to higher attention and holistic cognitive and emotional involvement, meeting various challenging situations towards the environment than in indoor exercising (Araújo et al., 2019).
Two major theories have been utilized to explain the positive impact of nature on human health. Stress Recovery Theory (SRT), proposed by Roger Ulrich, is based on the idea that exposure to nature can have positive physiological and psychological effects, helping individuals recover from stress more effectively than in urban environments (Ulrich et al., 1991). Attention Restoration Theory (ART) suggests that exposure to natural environments can help restore human ability to focus and concentrate (S. Kaplan, 1995). Specifically, in urban settings, humans use effortful attention for tasks requiring concentration. On the contrary, natural environments contain elements that capture human attention effortlessly, allowing directed attention to rest and recover (R. Kaplan & Kaplan, 1989; Ohly et al., 2016). A systematic review concluded that working memory, cognitive flexibility, and attentional control may be improved after exposure to nature through the restoration of cognitive processes related to directing attention (Stevenson et al., 2018). For PA in nature, the variability of nature helps to be more mindful and to shift attention from stressful everyday situations that are usual in urban settings towards nature; therefore, exercise in nature might provide more psychological restoration compared indoor exercise (Araújo et al., 2019).

1.2. Seasonal Variability of PA in Natural Environments

In contrast to indoor exercise, the practice of PA in a natural environment is directly dependent upon meteorological factors and weather conditions such as extreme temperatures, wind, and precipitation level (Garriga et al., 2022; Świątek, 2014). Levels of PA fluctuate with the seasons, and the impact of poor or extreme weather has been identified as a significant barrier to participation in PA among various populations (Tucker & Gilliland, 2007). Temporal factors such as season and daylight-saving transitions influence the movement behaviours of humans, and in temperate climates, people’s health and quality of life are more at risk during the winter months than during the summer (Jia & Lubetkin, 2009; Reilly & Peiser, 2006). A recent meta-analysis explored 24 h movement behaviours and suggested evidence of seasonal variation in activity patterns, with sedentary behaviour highest in winter (+19 min), light PA higher during spring and in summer (+19 min), and moderate-to-vigorous PA higher during spring and highest in summer (+2 min) when compared to the yearly means (Ferguson et al., 2021). The study in China indicated that the thermal comfort of green spaces positively affects various aspects of PA, including longer outdoor PA (Niu et al., 2022).
Seasonal variations in PA have important implications for human health (Shephard & Aoyagi, 2009). In most developed societies, in parallel with PA, measurements of fitness indicators, including maximal oxygen intake and muscle strength, commonly increase during spring and summer, while body fat and changes in blood lipids, blood pressure, and blood coagulability increase during the winter (Shephard & Aoyagi, 2009; Tanaka et al., 2022). Further, all-cause and cardiac mortalities increase in the winter in economically developed countries (Shephard & Aoyagi, 2009). Finally, there is evidence that day length and temperature might have a significant moderating effect on the change in MVPA during a PA intervention. Specifically, the PA intervention was less effective on colder and on shorter days (Welch et al., 2018). Thus, decreasing PA seasonality and determining factors that influence it is an important public health task.
Reducing the seasonality of PA is an issue that previously received scientific attention. For example, the results of a study in Germany showed that participation in a sports club and a commercial sports facility were associated with reduced seasonal variation in PA and a significantly higher probability of participating in PA at a volume above the World Health Organization (WHO) threshold across all seasons (Schüttoff & Pawlowski, 2018). However, while substituting outdoor activities with indoor activities might be a solution for urbanized regions, this strategy might be complicated for people living in rural or remote places, since the availability of sports facilities might be limited. Further, replacing nature-based PA with indoor exercising might decrease the psychological restoration effect that is related to PA in natural environments. Finally, not all individuals who like to be physically active in nature would like to change their PA (i.e., gardening, working or bicycling in nature) to exercise in sports clubs and or commercial facilities because of possible differences in the motivational goals of these activities. This is especially true for populations of older ages. Therefore, it is important to understand factors that are related to the seasonal variability of outdoor PA. Exploring these factors might inform public health programmes aiming to decrease the seasonality of PA in natural environments and the quality of life of societies.
In most of the research, the seasonal variability of PA or movement behaviours was measured without distinction between organizational settings (Schüttoff & Pawlowski, 2018); therefore, it is difficult to understand the seasonal variations in PA in natural environments. However, there are some studies that report seasonal variability in different domains of outdoor PA. For instance, a study using a sample of Finnish golf players reported that all participants increased their total PA, demonstrated moderate walking activity (p < 0.001), and sat less (p < 0.001) in the summer season as compared with the winter season (Kettinen et al., 2023). Recent research also showed that time spent on low PA and outdoor gardening is decreasing in winter and increasing in spring and summer in older individuals living in mountainous agricultural areas of Japan (Nakashima et al., 2019). Research in Brazil concluded that more frequent use of public open spaces, including green spaces, was observed during the spring season, and moderate to vigorous PA (MVPA) was more frequent during the winter, at lower temperatures and thermal sensations (Rech et al., 2023). The seasonal variability of cycling was also observed in New Zealand (Tin Tin et al., 2012). Finally, a qualitative study reported that seasonal changes to the winter recreation environment led to a qualitatively different outdoor recreation experience. According to respondents, winter recreation settings can be thematically characterized as paradoxical, novel, wilder, emptier, non-normative, and providing additional outdoor recreation opportunities (Gatti et al., 2022). Summing it all up, the impact of seasonal variations and meteorological factors on PA conducted in outdoor spaces remains an understudied area (Roberts et al., 2017; Tucker & Gilliland, 2007).
Finally, previous studies reported that socioeconomic status is an important variable related to seasonal variability of PA in nature. It is well known that nature availability in living areas is associated with greater PA in natural environments (Calogiuri & Chroni, 2014; Pyky et al., 2019). PA in natural environments is free of charge and therefore is more available for socially deprived groups compared to organized sports and gyms. Nevertheless, socially deprived groups usually live in places that are distant from natural environments and are perceived as less safe (Schüle et al., 2019), more polluted, and noisy (European Environment Agency, 2018). The results of the previous study suggest that lower socioeconomic status is associated with a greater seasonal variability of exercise (Eisenberg & Okeke, 2009). However, more empirical data are necessary to better understand if the seasonal variability of PA in nature is related to financial security. These data might help to inform policies related to decreasing health inequalities.

1.3. The Present Study

Given the evidence that nature exposure has a positive effect on human health and that health effects depend on continuous participation in PA, it is important to identify factors associated with the seasonal variability of PA in natural environments. It is unclear how the seasonal variability of PA in natural environments is associated with various demographic and nature-related variables such as green-space proximity in the living area, nature exposure, nature connectedness, perceived restoration in nature and quality of life. Understanding the interrelationships between these factors might help to better understand how to effectively promote PA in nature and to tailor interventions during colder, wetter, and darker periods.
The aim of the study was to assess the seasonal variability of PA in natural environments and its relationships to demographic, nature-related variables, and quality of life in a sample of Lithuanian adult women and men. We hypothesized that PA in natural environments will be lowest in winter and highest in summer. Second, we expected that the higher seasonality of PA in nature will be related to lower socioeconomic status, green-space proximity, nature exposure, nature connectedness, perceived restoration in nature, and general quality of life.

2. Materials and Methods

2.1. Study Design and Procedure

This study was approved by the university Social Research Ethics Committee (Protocol No SMTEK-60) and implemented between January and April 2021. The present study is a part of a more extensive international study, “Body Image and Nature Survey (BINS)” (Swami et al., 2022). The study employed a cross-sectional survey design. The survey was conducted using an online questionnaire hosted on the Google Forms platform. Participants were recruited through a non-probabilistic sampling approach, and participation was entirely voluntary and uncompensated. The survey link was distributed through a sponsored Facebook advertisement targeting all major municipalities across the country. Eligibility criteria included being 18 years of age or older and fluency in the Lithuanian language. Before beginning the questionnaire, participants were presented with an overview of the study’s objectives, estimated time required to complete the survey (approximately 25–30 min), and assurances regarding anonymity. Participants provided digital informed consent before proceeding to the survey questions; those who did not consent were thanked and withdrawn from the survey automatically. Furthermore, respondents were free to withdraw from the survey at any time by closing their browser, and no responses would be recorded in such cases. The final sample included 924 Lithuanian adults (both women and men) with complete responses, as all questions in the survey were required to proceed.

2.2. Study Participants

A total of 924 Lithuanian adults participated, with an age range between 18 and 79 years (mean age 40.0 ± 12.4 years). The sample consisted predominantly of women (73.6%), and 26.4% were men. Regarding educational attainment, 11.3% had secondary education or less, 7.7% were current full-time students, 41.7% held an undergraduate degree, and 34.8% had postgraduate education; 69.9% resided in urban areas, while the rest resided in rural areas. Marital status distribution showed 18.4% were single, 17.2% were in a committed relationship, and 57.8% were married. Most participants (91.1%) identified themselves as part of the ethnic majority. Concerning financial security, 17.1% perceived themselves as financially less secure compared to others, 64.8% as equally secure, and 18.1% as more secure. Mean body mass index was 24.8 ± 4.6 kg/m2 and ranged from 16.4 to 44.9 kg/m2, and 3.8% were classified as underweight, 55.7% as normal weight, 28.3% as overweight, and 12.2% as obese.

2.3. Measures

Sociodemographic data measures included education assessed via the highest education level completed (secondary, in full-time studies, undergraduate, postgraduate); marital status classified as single, in a committed relationship, married, or other; ethnicity categorized as ethnic majority, ethnic minority, or not sure, with place of residence (urban or rural); and financial security assessed based on self-perceived financial security compared to others (less secure, like most, and more secure).
Body mass index (BMI) was calculated using self-reported weight and height, applying the standard formula: weight, kg/height, m2. Participants were then categorized into four groups according to the World Health Organization (WHO) classification (World Health Organization, 1997): underweight: BMI < 18.5 kg/m2, normal weight: BMI 18.5–24.9 kg/m2, overweight: BMI 25.0–29.9 kg/m2, and obese: BMI ≥ 30.0 kg/m2. This classification was used to describe the sample and explore group differences in BMI across genders.
Nature proximity was assessed using a single-item question asking participants to indicate the distance from their place of residence to nearby natural environments, such as parks or forests. Respondents selected one of the following options: more than 10 km, 5–10 km, 2–4 km, 1–2 km, and nearby (within 0–1 km). Responses were coded so that higher scores reflected greater proximity, indicating a shorter distance from the place of residence to natural environments.
The frequency of PA in nature was assessed by a single question: “How often do you engage in PAs such as exercising, walking, cycling, or performing physically demanding tasks in natural settings (e.g., forest, park)?” Participants responded using a 6-point Likert scale ranging from 1 (never or very rarely) to 6 (every day), with intermediate options indicating varying frequencies (2–3 times a month, once a week, 2–4 times a week, 5–6 times a week). This item was adapted from a previously used measure in a Lithuanian national survey, with modifications to focus specifically on activity in natural environments (Klumbiene et al., 2015). To account for seasonal variation, respondents were asked to rate their frequency of PA in nature separately for spring, summer, autumn, and winter. A mean score across the four seasons was then computed for use in analyses. The scale demonstrated excellent composite reliability, with McDonald’s ω = 0.947 (95% CI: 0.941–0.952). Next, based on the frequency of PA in nature across different seasons, all respondents were classified into three groups. Group 1 included non-exercisers or those engaging in irregular and infrequent PA in nature, defined as selecting response options 1 (never or very rarely) or 2 (2–3 times per month) consistently across all four seasons. Group 3 comprised regular all-year exercisers, who reported engaging in PA in nature at least once a week (response options 3 to 6) in every season. Group 2, labelled the seasonal variability group, consisted of participants whose responses did not meet the criteria for either group 1 or group 3, typically those reporting regular PA in nature during summer but avoiding it in winter. As there were minimal differences between groups 1 and 2, they were combined in the final step of the regression analysis.
Total PA was assessed based on the frequency of moderate-to-vigorous PA lasting at least 30 min (Klumbiene et al., 2015). Participants responded on a scale from 1 (never or very rarely) to 6 (every day), with separate assessments for winter and summer periods. The summer season was defined as the end of spring, summer, and the beginning of autumn, while the winter season was defined as the end of autumn, winter, and the beginning of spring. Additionally, the Godin Leisure-Time Exercise Questionnaire (LTEQ) was used to evaluate PA during the winter and summer seasons (Godin, 2011). Participants were asked to report the number of weekly sessions lasting at least 15 min for light, moderate, and vigorous exercise. According to the questionnaire guidelines, each session was weighted by a corresponding intensity coefficient—3 for light, 5 for moderate, and 9 for vigorous activity—and then summed to calculate a total score, reflecting the overall level of leisure-time PA per week in summer and winter seasons.
Nature exposure was assessed using the Nature Exposure Scale (NES) (Kamitsis & Francis, 2013). The NES has demonstrated unidimensional factor structure and good internal consistency in previous studies examining the psychological benefits of nature contact, and for Lithuanian translation (Matukynienė et al., 2021). The scale consists of four items designed to measure individuals’ subjective exposure to natural environments in their everyday and not everyday lives, and the extent to which natural surroundings are noticed. Participants rate how frequently they engage with nature using a 5-point Likert scale ranging from 1 (not at all) to 5 (very often). Higher average scores indicate greater self-reported exposure to natural environments. In the present study, the scale showed acceptable composite reliability (McDonald’s ω = 0.697, 95% CI = 0.665–0.728).
Connectedness to Nature Scale (CNS) was used to measure connectedness to nature (Mayer & Frantz, 2004). CNS is a 14-item self-report instrument designed to assess individuals’ affective, experiential sense of emotional connection with the natural world. Items reflect the extent to which people feel part of nature and identify with it. Responses are rated on a 5-point Likert scale ranging from 1 (strongly disagree) to 5 (strongly agree), with higher average scores indicating a stronger connectedness to nature. The CNS has demonstrated good internal consistency and unidimensional factor structure in our previous study (Matukynienė et al., 2021). In the present study, the scale showed excellent composite reliability (McDonald’s ω = 0.906, 95% CI = 0.897–0.915).
Restorative experiences in nature were assessed using the Restoration Outcome Scale (ROS) (Korpela et al., 2008). This scale includes nine items that capture various psychological outcomes associated with recent visits to natural environments. The items are rated on a 7-point Likert scale from 1 (not at all) to 7 (completely). The overall higher score calculated by averaging the response options indicates a greater restorative effect during the last visit to natural environments. The unidimensional factor structure and good psychometric properties were confirmed in our previous study (Matukynienė et al., 2021). For this study, the composite reliability was also high (McDonald’s ω = 0.977, 95% CI = 0.974–0.979).
The World Health Organization Quality of Life Questionnaire–BREF (WHOQOL-BREF) was used to assess participants’ perceived QoL (Skevington et al., 2004). The psychometric characteristics of the translated instrument were previously confirmed in the Lithuanian adult population (Ducinskiene et al., 2002). The instrument comprises 26 items, including two general items that assess overall QoL and general health, and 24 items that measure four specific domains: physical health, psychological well-being, social relationships, and environment. Responses are rated on a 5-point Likert scale, with higher scores indicating better perceived QoL. According to the recommendations of the WHOQOL-BREF group, the final score of the overall QoL was calculated in a range between 0 and 100, with the higher score indicating better QoL. Domain-specific scores were not included in the analysis. The WHOQOL-BREF has demonstrated good composite reliability in this study (McDonald’s ω = 0.924, 95% CI = 0.916–0.931).

2.4. Statistical Analysis

Power analysis using G*Power version 3.1.9.2 indicated that the sample size of 924 was sufficient to detect small-to-medium effects (f2 = 0.02–0.15) in multiple regression analyses with up to 15 predictors (α = 0.05, power = 0.80). The overall regression model explained 30% of the variance in overall QoL, corresponding to a large effect size (f2 = 0.43) according to Cohen’s criteria (Cohen, 1988). The sample size also provided adequate power for between-group comparisons using ANOVA.
Statistical analyses were conducted using JASP software version 0.19.3 obtained from the official website (JASP Team: Amsterdam, The Netherlands) and SPSS version 29 (IBM Corp., Armonk, NY, USA). Assumptions of normality of continuous variables were checked and met. The composite reliability of the scales was tested by McDonald’s ω (Hayes & Coutts, 2020). Group comparisons by gender and by seasonal patterns of PA in nature were performed using independent samples t-tests for continuous variables and chi-square (χ2) tests for categorical variables. Differences in PA between the summer and winter seasons were analyzed using a paired-sample t-test. To explore differences in study variables across the three identified seasonal PA in nature patterns (i.e., irregular/no PA, seasonal variability, regular PA), one-way analysis of variance (ANOVA) was employed. Post hoc comparisons with Bonferroni adjustments were conducted to identify specific group differences, with superscripts indicating statistically significant pairwise differences.
Finally, a multiple linear regression analysis was conducted to examine the predictors of overall QoL. Homoscedasticity was tested using the Breusch–Pagan test in SPSS. This test evaluates whether the variance of the residuals is dependent on the values of the independent variables, thereby indicating the presence or absence of heteroscedasticity (Breusch & Pagan, 1979). The test was not statistically significant, indicating that the assumption of homoscedasticity was met. Additionally, a visual inspection of residual plots did not reveal any systematic pattern. The model included sociodemographic variables (e.g., gender, age, financial security, education, marital status, residence), health-related variables (BMI), and nature-related measures (e.g., nature proximity, nature exposure, connectedness, perceived restoration), along with the seasonal PA in nature pattern. Continuous predictors were standardized to compute β coefficients, while categorical variables were entered as dummy-coded predictors with designated reference groups. The level of significance was set at p < 0.05.

3. Results

Sample characteristics with the comparison in gender groups are presented in Table 1. Women were more likely to have higher levels of education (postgraduate: 40.7% vs. 24.2%, p < 0.001). Urban residency was more common among women (71.8%) than men (64.8%; p = 0.041). No significant gender differences were found in marital status, ethnic group, or financial security. In terms of BMI, men were significantly more likely to be overweight, whereas women were more likely to be within the normal weight range (p < 0.001). The prevalence of PA in nature (at least once a week) during different seasons was highest in summer (n = 757; 81.9%), following spring (n = 712; 77.1%) and autumn (n = 698; 75.5%) and lowest in winter (n = 587; 63.5%). For total PA, similar tendencies in seasonality were observed. The frequency of respondents engaged in MVPA for at least 30 min once a week and more often was higher in the summer than in the winter season (n = 767; 83.0% and n = 672; 72.7%, respectively). Also, the total score of the LTEQ was significantly higher in the summer season as compared to the winter, accordingly (95.97 ± 54.74 vs. 77.57 ± 53.00, p < 0.001).
Seasonal patterns of PA in nature did not significantly differ by gender, education level, marital status, or place of residence (all p > 0.05, Table 2). However, financial security showed a strong association with PA patterns (χ2 = 22.33, df = 4, p < 0.001), with regular engagement in nature-based PA being more prevalent among those reporting higher financial security. For instance, 72.4% of participants in the “more secure” group engaged in regular PA in nature, compared to only 48.1% of the “less secure” group.
Next, a comparison of study measures in patterns of PA in nature seasonality is provided in Table 3. Specifically, participants in the group of more regular PA in nature demonstrated significantly greater nature exposure, green-space proximity, nature connectedness, and WHOQOL-BREF quality of life scores compared to those with irregular or seasonally variable activity (all p < 0.001). Age also differed significantly between groups (p = 0.011), with the regular PA group being older on average. No significant group differences were found for BMI or perceived restoration in nature.
The regression model significantly predicted overall quality of life, accounting for 30% of the variance (R2 = 0.30, Table 4). Higher financial security, greater nature exposure, perceived restoration, nature proximity, and nature connectedness emerged as significant positive predictors (all p < 0.05). Conversely, higher BMI, older age, and rural residence were associated with lower quality-of-life scores. Gender, marital status, education level, and regularity of PA in nature did not significantly predict overall quality of life when other factors were controlled.

4. Discussion

Considering the proven benefits of nature exposure on human health and the necessity of ongoing PA for these health effects, it is crucial to pinpoint factors that influence seasonal changes in PA within natural settings. The study aimed to evaluate how PA in natural environments varies with the seasons and explored its connections to demographic factors, nature-related variables, and quality of life among adult women and men. We hypothesized that PA in natural settings would be at its lowest during winter and peak in summer. Additionally, we anticipated that greater seasonal fluctuations in PA would be associated with lower socioeconomic status, proximity to green spaces, nature exposure, nature connectedness, perceived restorative effects of nature, and overall quality of life.
Generally, the findings of the present study confirmed both hypotheses. In our research, we observed the lowest rates of PA in nature in winter and the highest in summer. These findings are in accordance with previous studies reporting the same trends of PA seasonality (Dėdelė et al., 2019; Ferguson et al., 2021; Kimura et al., 2015; Tucker & Gilliland, 2007; Turrisi et al., 2021; Uitenbroek, 1993). However, our study provides new empirical data that the seasonal variability of PA in natural environments (i.e., working, walking, leisure cycling, etc.) follows the same trend as general PA. A similar PA seasonality trend was observed in a study assessing PA behaviour in urban green spaces (Roberts et al., 2017). An analysis of general PA trends showed that outdoor PA during colder and darker periods is also significantly lower compared to warm periods, and it suggests that PA in natural environments is possibly not substituted by indoor PA. In other words, it seems that the respondents of our sample do not have routines to adapt their PA habits to colder periods. This may be attributed to biological factors and poor weather conditions (Tucker & Gilliland, 2007), but also to a lack of knowledge about winter PAs, the availability of comfortable winter outdoor apparel, a lack of winter sports equipment, a reduced need for physical work around the home (e.g., gardening), and other factors that future studies are recommended to analyze in exploring why people are more sedentary in winter time. In Lithuania, people traditionally enjoy winter recreational activities such as skiing and skating. However, winters have become warmer and snow less frequent, potentially leading to a lack of tradition and knowledge regarding wintertime PA that is a part of Scandinavian countries living in a similar climate (Gatti et al., 2022).
The second, our assumption was also confirmed. We found that lower financial security was associated with less regular and more seasonal PA in natural environments. This finding is in accordance with the results of a previous study, which reported that lower socioeconomic status and financial constraints are associated with greater seasonal variability of exercise (Eisenberg & Okeke, 2009; Kaneva et al., 2024). These results might be explained by evidence that while nature availability in living areas is associated with greater PA in natural environments (Calogiuri & Chroni, 2014; Pyky et al., 2019), socially deprived groups usually live in places that are distant from natural environments and are perceived as less safe (Schüle et al., 2019) and more polluted (European Environment Agency, 2018). Therefore, the PA in natural environments might be lower in individuals with lower financial security. However, our findings confront previous evidence suggesting that socio-economic status is not associated with the frequency of exercising in natural environments (Calogiuri et al., 2016) and a higher seasonal variability of PA (Schüttoff & Pawlowski, 2018). These discrepancies may be due to differences in study methodology. Exercising in natural environments is free of charge, and therefore, it might be more available for socially deprived groups compared to organized sports and gyms. However, the results of our study suggest that it might be true for warmer seasons, but not for dark, wet, and cold periods. Future studies should further analyze this question.
In the present study, we found that those in the group involving the seasonal variability of PA in nature were younger, reporting a lower proximity of nature to their living residence, lower nature exposure and connectedness to nature, and a lower quality of life compared to the group of regular PA in nature. These results are in accordance with previous findings that report that green exercise is more prevalent in older adults (Calogiuri et al., 2016), and the young generation reports lower nature connectedness (Beery, 2013). Previous studies also showed that nature proximity in the living area is related to greater PA (Pyky et al., 2019). Also, these findings are partially in line with previous findings reporting that greater nature exposure is associated with higher PA (Jimenez et al., 2021; Shanahan et al., 2016). Our study adds new knowledge that nature proximity and nature exposure are associated with the lower seasonal variability of PA in nature. However, we observed no significant differences in perceived nature restoration between regular PA in natural environments and seasonal groups. This finding is difficult to explain due to the lack of similar studies. However, one explanation is that poor weather conditions may reduce potential recovery and outweigh the motivation to be physically active in natural environments. However, these assumptions are speculative, and future studies are recommended to further investigate these associations.
Additionally, in the present study, we also explored how the seasonality of PA in nature and other analyzed variables were associated with quality of life. The analysis of the results showed that the main contributor to quality of life is financial security, followed by nature exposure and perceived restoration in nature, nature proximity, living in urban areas, and being younger. Less variability of PA in natural environments during seasons increased quality of life insignificantly. While this was not a primary aim of the study, these results add important insights. Specifically, our findings show that higher socioeconomic status and possibilities to experience nature in the living environment significantly contribute to quality of life. Generally, our results are in accordance with previous studies suggesting that nature exposure is associated with a higher quality of life and well-being (Hartig et al., 2014; Nejade et al., 2022; Ward Thompson & Aspinall, 2011).
Taken together, our findings point to the problem of inequitable access to PA in natural environments during the darker, colder, and wetter seasons. It seems that inequitable access to PA in nature manifests as a higher seasonality of PA in natural environments. This is an important new finding, and it is recommended that future studies assessing the seasonality of PA in natural environments consider socioeconomic status. The study’s practical implications underscore the significance of addressing inequitable access to natural environments in promoting consistent PA during various seasons and enhancing overall well-being. Specifically, there is a need for targeted interventions to improve access to natural environments for individuals with lower financial security. One potential solution is the implementation of subsidized programmes or initiatives, which would facilitate greater access to PA in nature for economically disadvantaged groups. Previous studies concluded that access to green or natural open space is associated with better health of socially deprived populations (Mitchell & Popham, 2008). Programmes that focus on increasing nature exposure and PA among younger adults, particularly those with lower financial security, could help mitigate the negative impacts on their quality of life. Community-based solutions, such as regularly organized local nature clubs or outdoor activity groups, can help bridge the gap in nature exposure and PA for those living in urban areas with limited access to green spaces (Pedersen Gurholt, 2024). Future studies should continue to explore the relationship between socioeconomic status and the seasonal variability of PA in natural environments, considering additional individual and environmental factors to develop more comprehensive strategies.
The present study is subject to several limitations that should be discussed. The primary constraint of this study is its cross-sectional design, which hinders the interpretation of the direction of the associations. Secondly, while the present study explored the seasonality of PA in nature, it did not specifically address the prevalence of indoor PAs across different seasons. Addressing these limitations in future studies is therefore recommended. Thirdly, the present sample exhibited a highly unequal distribution of men and women, with men constituting a mere 26.4% of the sample. Finally, the nature of the data collection method, namely, that it was self-reported rather than objectively measured, might have led to an overestimation or underestimation of PA levels, an issue that has been previously identified (Prince et al., 2008). Also, we did not evaluate the duration and social context of PA in natural environments; however, these factors might influence the results (Wicks et al., 2022).

5. Conclusions

Given the established positive impact of nature exposure on human health and the importance of maintaining regular PA to achieve these benefits, it is essential to identify the factors that affect seasonal variations in PA within natural environments. The findings of the present study showed that the lowest rates of PA in natural environments are in winter and the highest in summer. In the present sample, the seasonal variability of PA in natural environments was associated with lower financial security, younger age, lower proximity of nature to the living residence, lower nature exposure and connectedness, and lower quality of life. These findings point to the problem of inequity and suggest that inequitable access to PA in nature manifests as a higher seasonality of PA in natural environments. The practical implications of the study highlight the importance of addressing inequitable access to natural environments to promote consistent PA throughout seasons and enhance overall well-being. During the dark, wet, and cold periods, it is important to implement targeted interventions that improve access to natural environments for individuals with lower financial security. This could help reduce inequity in physical activity.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/bs15060773/s1, Research data.

Author Contributions

Conceptualization, M.B. and R.J.; methodology, M.B. and R.J.; software, M.B.; validation, M.B. and R.J.; formal analysis, M.B.; investigation, M.B. and R.J.; resources, M.B. and R.J.; data curation, M.B.; writing—original draft preparation, M.B. and R.J.; writing—review and editing, M.B. and R.J.; visualization, M.B. and R.J.; supervision, R.J.; project administration, M.B. and R.J. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

The study was conducted in accordance with the Declaration of Helsinki and approved by the University Social Research Ethics Committee (Protocol No SMTEK-60; date of approval: 24 November 2020).

Informed Consent Statement

Digital informed consent was obtained from all subjects involved in the study.

Data Availability Statement

The original contributions presented in this study are included in the article/Supplementary Material. Further inquiries can be directed to the corresponding author.

Conflicts of Interest

The authors declare no conflicts of interest.

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Table 1. Comparison of sociodemographic and anthropometric characteristics by gender (n = 924).
Table 1. Comparison of sociodemographic and anthropometric characteristics by gender (n = 924).
CharacteristicsMen
(n = 244)		Women
(n = 680)		p
n%n%
Age, years (m ± SD)41.814.039.411.70.016
Educationsecondary4921.6568.5<0.001
in full-time studies2812.3436.6
undergraduate9541.929044.2
postgraduate5524.275.340.7
Marital statussingle5422.811618.50.269
in committed relationship4619.411318.1
married/in partnership13757.839763.4
Place of residenceurban15864.848871.80.041
rural8635.219228.2
Ethnic groupmajority21688.562692.10.124
minority72.9202.9
not sure218.6345.0
Financial securityless secure4217.211617.10.205
average, like most14961.145066.2
more secure5321.711416.8
Body mass
index		underweight (<18.5 kg/m2)52.1304.5<0.001
normal weight (18.5–24.9 kg/m2)9940.741561.1
overweight (25.0–29.9 kg/m2)10242.015923.4
obesity (≥30.0 kg/m2)3715.27511.0
Table 2. Comparison of sociodemographic characteristics by seasonal patterns of physical activity in nature (n = 924).
Table 2. Comparison of sociodemographic characteristics by seasonal patterns of physical activity in nature (n = 924).
GroupsIrregular or no PA in Nature, n = 153Seasonal Variability, n = 201Regular PA in Nature, n = 570Totalχ2, df, p
n%n%n%n%
Gender
Men3514.34819.716166.0244100.02.63, 2, 0.268
Women4817.415322.540960.1680100.0
Education
Secondary1817.12422.96360.0105100.01.51, 6, 0.959
In full-time studies1115.51926.84157.771100.0
Undergraduate6115.88221.324262.9385100.0
Postgraduate5517.16821.119961.8322100.0
Marital status
Single3323.12114.78962.2143100.05.70, 4, 0.223
In a committed relationship3217.74323.810658.5181100.0
Married/in partnership17019.59517.633962.9539100.0
Place of residence
Urban11117.215123.438459.4646100.04.86, 2, 0.088
Rural4215.15018.018666.9278100.0
Financial security
Less secure3924.74327.27648.1158100.022.33, 4, <0.001
Average, like most9215.413422.437362.3599100.0
More secure2213.22414.412172.4167100.0
Note. PA—physical activity.
Table 3. A comparison of the study measures (m ± SD) by the seasonal patterns of physical activity in nature (n = 924).
Table 3. A comparison of the study measures (m ± SD) by the seasonal patterns of physical activity in nature (n = 924).
VariablesIrregular or no PA in Nature
(n = 153)		Seasonal Variability
(n = 201)		Regular PA in Nature (n = 570)p
Age, years38.99 ± 12.1838.11 ± 12.2140.95 ± 12.44 b0.011
Body mass index, kg/m225.02 ± 5.1524.39 ± 4.2324.82 ± 4.610.398
Physical activity in nature1.27 ± 0.352.95 ± 0.69 a4.31 ± 0.77 ab<0.001
Green space proximity3.27 ± 1.213.46 ± 1.253.74 ± 1.12 ab<0.001
Nature exposure3.69 ± 0.873.93 ± 0.72 a4.26 ± 0.63 ab<0.001
Nature connectedness3.60 ± 0.793.78 ± 0.633.88 ± 0.71 a<0.001
Perceived restoration in nature5.22 ± 1.605.32 ± 1.315.47 ± 1.530.141
Quality of life64.59 ± 14.6867.62 ± 13.2370.82 ± 13.47 ab<0.001
Note. PA—physical activity. a: p < 0.05 compared to the irregular or no physical activity in nature group; b: p < 0.05 compared to the seasonal variability group.
Table 4. Multiple regression analysis predicting overall quality of life (n = 924).
Table 4. Multiple regression analysis predicting overall quality of life (n = 924).
CharacteristicsBSEβtp
Women (ref. men)−0.360.96 −0.370.710
Age, years−0.090.04−0.08−2.270.024
Body mass index, kg/m2−0.430.10−0.14−4.46<0.001
Rural residence (ref. urban)−2.820.94 −3.010.003
Education: full-time studies (ref. secondary)0.941.97 0.480.635
Education: undergraduate (ref. secondary)1.361.40 0.970.332
Education: postgraduate (ref. secondary)1.021.47 0.700.487
Marital status: in a committed relationship (ref. single)2.111.32 1.600.110
Marital status: married/cohabiting partnership (ref. single)1.411.09 1.300.194
Financial security: like most (ref. not secure)8.151.14 7.16<0.001
Financial security: more secure (ref. not secure)13.681.44 9.53<0.001
Nature proximity1.220.360.103.37<0.001
Nature exposure3.240.680.174.75<0.001
Nature connectedness1.460.740.081.980.048
Perceived restoration in nature1.610.340.174.75<0.001
Regular PA in nature: all seasons (ref. non or irregular)1.190.89 1.340.181
Model summary: R = 0.55; R2 = 0.30
Note. ref.—reference; PA—physical activity; B—unstandardized regression coefficient; SE—standard error; β—standardized regression coefficient calculated only for continuous variables; t—t−test.
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Jankauskiene, R.; Baceviciene, M. An Analysis of the Factors Associated with the Seasonal Variability of Physical Activity in Natural Environments in a Sample of Lithuanian Adults. Behav. Sci. 2025, 15, 773. https://doi.org/10.3390/bs15060773

AMA Style

Jankauskiene R, Baceviciene M. An Analysis of the Factors Associated with the Seasonal Variability of Physical Activity in Natural Environments in a Sample of Lithuanian Adults. Behavioral Sciences. 2025; 15(6):773. https://doi.org/10.3390/bs15060773

Chicago/Turabian Style

Jankauskiene, Rasa, and Migle Baceviciene. 2025. "An Analysis of the Factors Associated with the Seasonal Variability of Physical Activity in Natural Environments in a Sample of Lithuanian Adults" Behavioral Sciences 15, no. 6: 773. https://doi.org/10.3390/bs15060773

APA Style

Jankauskiene, R., & Baceviciene, M. (2025). An Analysis of the Factors Associated with the Seasonal Variability of Physical Activity in Natural Environments in a Sample of Lithuanian Adults. Behavioral Sciences, 15(6), 773. https://doi.org/10.3390/bs15060773

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