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Article

Human Physiological Responses to Sitting and Walking in Green Spaces with Different Vegetation Structures: A Seasonal Comparative Study

by
Yifan Duan
1,
Hua Bai
1,* and
Shuhua Li
2,*
1
College of Architecture, Chang’an University, Xi’an 710061, China
2
College of Architecture, Tsinghua University, Beijing 100084, China
*
Authors to whom correspondence should be addressed.
Forests 2024, 15(10), 1759; https://doi.org/10.3390/f15101759
Submission received: 10 September 2024 / Revised: 25 September 2024 / Accepted: 3 October 2024 / Published: 7 October 2024
(This article belongs to the Section Urban Forestry)
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Abstract

:
This study seeks to address the gap in knowledge regarding the varying effects of vegetation on human perception and preference, and to comprehend how green spaces can better serve community needs. The research assessed the impact of different vegetation structures on physiological responses during two types of on-site perceptions: sitting and walking, in both winter and summer. The green spaces included single-layer grassland, single-layer woodland, tree-shrub-grass composite woodlands, and tree-grass composite woodlands, and a non-vegetated square. The findings indicated the following. (1) The physiological recovery effect of walking in green spaces is relatively greater than that of sitting; walking in green spaces with different vegetation types was found to enhance participants’ pNN50 values (p = 0). (2) During the summer, sitting and observing provided a better physiological recovery effect (p < 0.05), whereas in the winter, walking was more beneficial (p < 0.05). (3) Green spaces with vegetation were more beneficial for physiological recovery than the non-vegetated square, which could not sustain recovery effects for more than 1 min. Single-layer grassland and tree-shrub-grass composite woodlands had the most significant physiological recovery effects on health (p < 0.01). (4) Based on these conclusions, it is suggested that a combination of sitting and walking can lead to improved recovery outcomes. Therefore, when visiting parks during extreme weather conditions, individuals should adjust the duration of their sitting and walking experiences to enhance their overall experience.

1. Introduction

Understanding public views on urban green spaces is key to enhancing well-being [1,2]. Despite the real-world use of perception research, doubts exist about the reliability of strategies, risking misalignment with public needs and urban design [3]. Visual experiences largely shape landscape perception, yet other factors complicate comparisons [4]. These factors such as site characteristics, the time of access (season), and the method of perception, making comparisons difficult.

1.1. The Impact of Seasonal Variability in Horticultural Green Spaces on Physiological Recovery

The visual appearance of green spaces varies significantly across seasons, influencing people’s perceptions and preferences [5]. Intra-seasonal weather variations can also modulate these preferences [5]. However, few researchers have considered seasonal dynamics [6,7,8]. Most studies present participants with landscapes typical of warmer seasons, such as spring and summer [9]. Landscape perception research has two primary limitations. Firstly, while short-term exposure to natural images is beneficial, prolonged interaction with nature may potentially detract from human well-being [10,11]. Secondly, the preponderance of studies is conducted during summer or utilizes summer landscape photographs, leaving unclear whether the health benefits of nature contact are season-independent [12,13,14].
Seasonal variations in natural landscapes significantly shape people’s preferences. The seasons portrayed in landscapes greatly influence the perception of landscape legibility [15], and the presence of seasonal trees affects visual preferences [16]. Furthermore, changes in foliage across seasons enhance the perceived restorative quality of landscape environments [17]. Research indicates that seasonal landscapes can strongly affect human psychological and physiological responses, suggesting that the design of urban green spaces should take into account the changing characteristics of different seasons to maximize their beneficial effects on well-being.
For instance, one study found no significant difference in the forest bathing experience between summer and autumn forest landscapes [18]. In Da et al. [19], stress recovery was notably more effective during the autumn and winter seasons, with a significant interaction observed between seasonal changes and vegetation structures. The tree-shrub configuration was found to be optimal for stress recovery, whereas the tree-shrub-herb configuration was the least effective overall but showed the best performance in winter [20]. Winter, a quintessential season, impacts outdoor activities, with a general decline in physical exercise participation, leading to a decrease in public physical activity levels [21]. Nonetheless, the public’s desire for physical and mental health recovery remains strong. The limited understanding of perceptions and preferences during winter constrains the improvement of recreational quality in urban green spaces [6]. For Duan et al. [22], research on urban green spaces demonstrated that environments rich in greenery, especially single-layer grasslands, significantly boost stress recovery, with autumn and winter showing the most notable effects. The study highlighted a pronounced preference for single-layer woodland landscapes across all seasons, suggesting these spaces are particularly conducive to rest and social interaction in the summer, while being more suitable for physical activities in the winter [23]. This underscores the importance of aligning green space activities with seasonal changes and the specific benefits of various vegetation types and configurations [22,23]. Da et al. [19] demonstrated interactions between seasonal, structural, and configuration factors. They found that tree-shrub configurations were most effective for stress recovery overall, but tree-shrub-herb configurations were particularly beneficial in winter. Additionally, rich color and stable green cover were identified as especially helpful for psychological and physiological stress recovery. The study also indicated that the optimal vegetation types and configurations for stress recovery can vary depending on the viewing periods within each season.

1.2. The Impact of Perception Modes in Garden Green Spaces on Physiological Recovery

Perceptions of garden green spaces can be mediated by various means, including photographs, videos, and virtual reality (VR) technology, which provide visual representations of plant landscapes for sensory experiences that are purely ocular. Such visual-only perceptions do not replicate the interactivity and multisensory richness of on-site experiences. However, on-site perception offers the benefits of robust interactivity and multisensory engagement, allowing for spatial awareness that more effectively aligns with individuals’ preferences for green spaces [22,23]; on-site perception experiences are diverse, encompassing encounters in different seasons and through various observational modes, such as static and dynamic observations.
Studies have demonstrated that contact with nature or plants can enhance physiological responses [24,25,26]. Many scholars primarily employ one mode of perception—either observation or walking observation—for their experiments. Participants are instructed to sit still or walk at a constant speed while observing the green space in front of them, and their physiological and psychological indicators are monitored. For instance, Ulrich et al. [27] reported that sitting observation of natural environments can lead to faster and more complete physiological recovery. Compared to environments lacking natural elements, environments rich in natural elements can more quickly and effectively assist college students in recovering from stress, and these students also exhibit more positive emotions, such as calmness and happiness [26,28,29,30,31]. Browning et al. [31] reported that a 6 min exposure to an outdoor natural environment is beneficial for achieving positive emotions and has restorative effects. Zhao Renlin et al. [32] found that walking at a constant speed in green spaces to observe plant landscapes can alleviate tension and reduce autonomic nervous system excitability. Chen et al. [17] showed that walking and playing in urban parks during different seasons have a relaxing effect and positively affect healthy individuals. Wang et al. [33] demonstrated significant differences between winter and summer natural landscapes, with summer landscapes promoting greater physiological recovery than winter landscapes. Zhu et al. [34] reported that planted landscapes in winter and summer have the same effect on attention restoration, and visual changes in seasonal plants can enhance restorative benefits. When designing for winter, designers should consider plant hierarchy and coverage, and how seasonal changes can be utilized to enhance the restorative value of parks. Zhou et al. [35] reported that green spaces significantly enhance physical activity during the summer months, with no observable effect in winter. Conversely, blue spaces exert a more pronounced inhibitory effect on physical activity during the summer. Da et al. [19] indicate that colorful landscapes are particularly beneficial for psychological stress recovery, while stable and lush areas are most conducive to physiological restoration. It is noteworthy that the most effective vegetation types and configurations for stress alleviation vary depending on the specific viewing times within each season. Zhang et al. [36] also studied the two modes of perception and found that observing forest landscapes and walking in a forest environment can reduce cortisol levels, pulse rate, and blood pressure and increase the high-frequency component of heart rate variability (HRV) while reducing the low-frequency to high-frequency ratio. Despite the application of observation and walking perception modes in landscape perception research, questions remain about which mode is more reliable and whether they are applicable to any space. For urban dwellers, urban parks are the closest approximation of nature. Further research is needed to determine which mode of perception is most effective in different seasons.
In previous research, stress recovery and attention restoration served as key indicators for many studies aiming to measure the relationship between green spaces and health. Scholars have conducted corresponding research from various perspectives, such as different research methodologies, diverse landscape scenarios, and distinct seasons (spring and summer). However, the combination and arrangement of green spaces with different vegetation structures are among the critical factors influencing people’s perceptual experiences [22,23,37,38], which is essential for shaping a diverse array of urban green spaces. Nevertheless, many studies have selected different urban green spaces, primarily based on the type of green space (e.g., woodlands, parks, gardens, wetlands, etc.) to ascertain people’s opinions and preferences [39]. The scale and characteristics of each type of garden green space vary significantly, which may lead to difficulties in providing detailed guidance for urban garden green space planning and planting design in practice [40]. Research suggests that visual perception and emotions are influenced by the presence of green space, as well as its structures, configurations, and seasons [41]. However, there is a scarcity of research on the impact of green spaces with different vegetation structures on human physical and mental well-being.
As the most frequently encountered form of urban green space, areas with different vegetation structures not only enhance land use efficiency and foster greater daily contact with nature but also positively influence public health. Accordingly, this study employs on-site perception methods, including sitting and walking observations, to examine the physiological recovery effects of participants across two seasons—summer and winter—on four typical vegetation structure types: single-layer grassland, single-layer woodland, tree-shrub-grass composite woodlands, and tree-grass composite woodlands, and a non-vegetated square. The findings from this research can inform urban planners and landscape architects, enabling them to make more informed decisions regarding the planning and management of green spaces. The ultimate goal is to enhance the visual appeal, functionality, and popularity of public spaces.
We aimed to answer the following questions:
(1)
Do seasonal changes affect participants’ perception modes?
(2)
What is the impact of walking and viewing perception modes on participants’ physiological recovery in green spaces with different vegetation structures?
(3)
Does the seasonal change in green spaces with different vegetation structures have a differential impact on perception modes?

2. Materials and Methods

2.1. Study Area

This study was conducted in Xi’an, the capital of Shaanxi Province, China (34°16′ N, 108°54′ E), which spans an area of 10,752 km2 and is home to a population of 12.6 million. The city boasts a green coverage rate of approximately 33.5% [42]. The study area was selected from green spaces within a park in Xi’an, each characterized by distinct vegetation structures. To ensure that the study focused on the effects of vegetation structure rather than other landscape attributes on perception and preference, green spaces containing trees with colored foliage, which might be subject to seasonal color changes, were deliberately excluded from the selection.
Due to the fact that Gleditsia sinensis Lam. and Prunus cerasifera ‘Atropurpurea’ are deciduous plants, while the others are evergreen plants or perennial herbs, the density of branches and leaves of the vegetation types in winter is lower than that in summer.
Based on the morphological and spatial attributes of the plant landscape space, four green spaces with different vegetation structures and one group of plant-free squares (control group) were selected: single-layer grassland, single-layer woodland, tree-shrub-grass composite woodlands, and tree-grass composite woodlands, with the plant-free square located within the park. The four green spaces with vegetation structures were chosen as common types in Xi’an (Table 1 and Figure 1).
Classification of the green spaces was based on the canopy cover of trees and shrubs, as well as their size and horizontal structure. The criteria are as follows: single-layer grasslands were characterized by a tree and shrub canopy cover of less than 10%; single-layer woodlands had a canopy cover of 30%–40%; tree-shrub-grass composite woodlands featured a canopy cover between 40%–50%; and tree-grass composite woodlands had a canopy cover exceeding 70%. The sizes of the green spaces were standardized to an area of 25–50 m2, with the tree layer height ranging from 3–6 m, the shrub layer height from 1.5–2.5 m, and the herbaceous layer height from 0.2–0.5 m.

2.2. Participants

A total of 400 university students, with a mean age of 21.85 years (SD = 3.34, range = 17–25 years), were recruited for this study. All participants were healthy and proficient in Mandarin. They were provided with detailed information about the study procedures, potential risks, and confidentiality measures. Written informed consent was obtained from each participant prior to their involvement in the experiment. The study was conducted in compliance with the Declaration of Helsinki. Participants were randomly assigned to one of ten groups, each comprising 40 individuals, corresponding to the different plant communities and seasons.
The use of college student populations in studies by Tian [40], Browning [31] and Duan [22,23,37] has consistently demonstrated their stability as experimental subjects. College students are also recognized as a group with broad generalizability in a wide range of research studies.

2.3. Physiological Measures

2.3.1. Skin Conductance Level

Electrodermal activity (EDA), which encompasses skin conductance level (SCL) and skin conductance response (SCR), measures the fluctuating resistance and conductance of the skin due to the functionality of eccrine sweat glands. EDA serves as a sensitive and rapid indicator of an individual’s response to stimulating events. These skin electric responses, originating from autonomic activation of sweat glands, are closely linked to emotional states, arousal, and attention. As a result, EDA is a sensitive measure of emotional responses and is widely utilized in the physiological response system. In this study, EDA was employed to monitor participants’ SCL, reflecting their arousal, attention, and stress levels. The physiological stress and comfort levels of the subjects were recorded in real-time during both stress-inducing tasks and exposure to plant communities [23]. SCL data were collected utilizing the ErgoLAB EDA wireless skin conductance sensor.
Research indicates that skin conductance is positively correlated with the activity of the human sympathetic nervous system [27]. Skin conductance levels are often utilized as an index of stress, reflecting the degree to which an individual experiences stress [33]. A notable elevation in skin conductance suggests a transition from a stable to a stressful and tense state, typically characterized by increased sweating, whereas a decrease implies a reduction in stress and a return to a relaxed state [37,43]. Consequently, higher skin conductance level (SCL) values are associated with heightened negative emotions and impaired physiological recovery.

2.3.2. Heart Rate Variability

Heart rate variability (HRV) refers to the variations in the time intervals between consecutive heartbeats, serving as a robust indicator of cardiovascular regulation. An HRV analysis quantifies the interplay and balance between cardiac sympathetic and parasympathetic nervous activities, shedding light on their collective impact on the cardiovascular system. This analysis is typically conducted using time series data of pulse intervals obtained from electrocardiogram (ECG) or photoplethysmogram (PPG) pulse signals. By computing and transforming these data, indices such as heart rate (HR), the root mean square of the successive differences (RMSSD), and the proportion of adjacent R-R intervals greater than 50 milliseconds (pNN50) are derived [23]. These indices provide insights into an individual’s thoughts, emotions, and behaviors that correlate with HRV, thereby facilitating a more nuanced understanding of emotional states, fatigue, stress, and other psychological conditions.
In this study, heart rate (HR) and the root mean square of successive differences (RMSSD) were employed as indicators of the autonomic nervous system’s overall balance. HR, expressed in beats per minute (bpm), is a standard measure of the resting heart rate in healthy individuals, typically increasing during periods of excitement or tension and decreasing during calm or relaxation [44,45]. HRV metrics, including these indicators, were collected using the ErgoLAB smart wearable ear clip sensor. The pNN50, an HRV metric, quantifies the proportion of successive R-R intervals that vary by more than 50 milliseconds from the mean R-R interval. An elevated pNN50 value suggests a shift toward a calm and relaxed physiological and psychological state [46]. The R-R interval, representing the time between two consecutive heartbeats, is a fundamental component of HRV analyses. RMSSD, which reflects the variability of these intervals, is considered an index of parasympathetic modulation; a higher RMSSD value indicates greater HRV. It is widely recognized that higher HRV signifies a greater ability to withstand stress and enhanced stress recovery capabilities [47].
Accordingly, higher HR values are indicative of increased negative emotional states and impaired physiological recovery. Conversely, greater pNN50, RMSSD, and R-R interval values are associated with more positive emotional responses and enhanced physiological recovery.
In outdoor versus controlled laboratory settings, it is important to be as quiet as possible to minimize interference from external factors [22,23,37]. Additionally, when using physiological measurement tools (e.g., skin conductance and heart rate variability), these tools must be calibrated before starting the measurement. The benchmarks in this study serve this purpose. During data processing, the value of the benchmark subtracted is removed from the remaining three phases (Stress-induced, Seated observation, Walking observation)—please refer to Section 2.6 for detailed equations.

2.4. Experimental Design

At the outset, participants were provided with an overview of the experimental protocol and gave their written informed consent. Following this, they completed a brief demographic questionnaire. Participants were then guided to the experimental area, where electrodes were affixed to their skin to measure skin conductance level (SCL) and heart rate variability (HRV). SCL and HRV were continuously monitored and recorded throughout the experiment. Before the experiment began, participants were instructed to relax for a 3 min baseline period. The average SCL and HRV values recorded during this period served as the calibration baseline (M0). These baseline measurements were utilized as a reference for assessing the subsequent effects of the experiment (Figure 2a).
Participants then engaged in a 3 min mathematical computation test under constant noise conditions to elicit stress responses [48]. The mean SCL and HRV values recorded during this challenge were utilized to assess their physiological recovery effects under stress, designated as M1 (Figure 2b). These values also served as a basis for tracking changes in the participants’ psychological states following stress induction. Subsequently, each participant was randomly assigned to one of five scenarios. For scenarios involving static observation, participants donned homemade viewfinder glasses to constrain their visual field. They were instructed to sit quietly and observe the designated scene for 3 min [14], during which the average SCL and HRV values were measured and documented. This phase of the study was designated as M2 (Figure 2c).
After the static observation phase, participants were given a 1 min break before commencing the walking observation. Following this, their physiological recovery post-walking observation was measured and recorded, designated as M3 (Figure 2d). Upon completion of the walking observation, participants removed the physiological monitoring devices and concluded their participation in the experimental session.
The winter experiment was conducted from 1 November to 30 November 2020, under an average temperature of 11.2 °C (11.2 ± 1.27), with consistently clear weather conditions throughout the trial period. The summer experiment took place from 1 June to 30 June 2021, with an average temperature of 26.8 °C (26.8 ± 2.75), and similarly experienced clear weather throughout. Given that Gleditsia sinensis Gleditsia sinensis Lam. and Prunus cerasifera ‘Atropurpurea’ are deciduous species, the vegetation types exhibited lower branch and leaf density in winter compared to summer, when the majority of the plants are either evergreen or perennial herbs.
To minimize the influence of confounding variables, we standardized the environmental conditions within the landscape area, ensuring that lighting, temperature, humidity, and wind speed were consistent. Additionally, a quiet surrounding environment was maintained. Prior to the commencement of the experiment, reminder signs were placed within a 2-m radius around the experimental site to notify visitors of its designated purpose. This measure was taken to reduce potential interference from external factors, such as visitor activities and noise.

2.5. Stress Tasks

To elicit stress, participants were tasked with completing a 3 min mathematical computation test. They were informed that the experiment aimed to evaluate their numerical performance, which would be graded and ranked. This procedure was designed to more accurately reflect their physiological recovery subsequent to the stressor. To enhance the stress-inducing effect, the math test was conducted in a noisy environment, intended to stimulate participants’ physiological responses. Previous studies have validated the use of noise as a stressor, demonstrating its effectiveness in increasing skin conductance level (SCL) or suppressing mood, as indicated by changes in heart rate variability (HRV) [49]. These physiological markers are crucial for assessing the impact of stress on the autonomic nervous system and for evaluating the subsequent recovery processes in the context of our study.

2.6. Data Processing

Data obtained were processed using IBM SPSS Statistics 26.0 (IBM, Inc., Armonk, NY, USA). The formulas for calculating changes in physiological indicators during various phases were as follows:
Changes in physiological recovery at each stage were calculated as follows:
  • Stress induced change (ΔM1) = M1 − M0,
  • Seated observation change (ΔM2) = M2 − M0,
  • Walking observation change (ΔM3) = M3 − M0.
  • The change between seated and walking observations (ΔM4) = ΔM3 − ΔM2,
In this study, we define ‘M‘ as the mean value of each physiological indicator. ‘M0’ denotes the baseline mean, ‘M1’ represents the mean during the stress phase, ‘M2’ is the mean during the seated observation, and ‘M3’ corresponds to the mean during the walking observation. ‘ΔM’ indicates the change in these indicators.
To evaluate the impact of seasonal perception methods, perception modes (seated viewing and walking observation), and four distinct plant communities on physiological recovery, we conducted a one-way analysis of variance (ANOVA). This analysis aimed to compare the variance in changes between green spaces across two different seasons.
Furthermore, to ascertain the influence of the two perception modes of the plant community landscape on physiological indicators, we utilized paired sample t-tests. These tests were designed to assess the differences in physiological responses between the seated and walking observation modes.

3. Results

3.1. Factors Affecting Green Spaces with Different Vegetation Structures and Perception Modes

3.1.1. Variations across Seasons

Results from a one-way ANOVA indicated that seated viewing promotes physiological recovery in summer, while the opposite is true in winter. Importantly, post-observation walking viewings of green spaces with different vegetation structures significantly altered pNN50 values (p = 0), suggesting that moderate physical activity, moderate vegetation, and optimal comfort during the summer months positively affect physiological recovery (Table 2).

3.1.2. Impact of Perception Modes on Green Spaces with Different Vegetation Structures

One-way ANOVA revealed significant differences in pNN50 values among participants observing five distinct green spaces, including a non-vegetated square, during walking sessions (p = 0.01). These findings imply that the active perception of green spaces has a marked influence on physiological recovery functions. To further explore the nuances of pNN50 value changes, we performed multiple comparison analyses, which confirmed significant variations in pNN50 values associated with different vegetation structures during walking observations (p = 0.01). Notably, significant differences were observed between the non-vegetated square and the four vegetated green spaces: single-layer grassland (p = 0.004), single-layer woodland (p = 0.034), tree-shrub-grass composite woodlands (p < 0.001), and tree-grass composite woodlands (p = 0.006). The results indicate that green spaces with varying vegetation structures significantly influence perception. The substantial differences in the pNN50 index following walking observations within green spaces that feature diverse vegetation suggest that the impact of walking observation on physiological recovery is considerable, and potentially linked to the spatial arrangement of vegetation and the extent of visible greenery (Table 3).

3.2. Impact of Different Perception Modes on Participants’ Physiological Recovery

3.2.1. Influence of Two Perception Modes on Participants’ HRV Indicators

(1) HR
Analysis of heart rate (HR) changes during seated (ΔM2) and walking (ΔM3) observations in green spaces with varying vegetation structures, including a non-vegetated square, showed a decrease in HR values after walking in the summer. This decrease suggests improved physiological recovery (Figure 3A and Table 4). Comparatively, the tree-grass composite woodlands had the greatest HR reduction (10.85), indicating the most significant recovery, followed by single-layer grassland (7.425), single-layer woodland (6.625), tree-shrub-grass composite woodlands (6.425), and the non-vegetated square (6.075 beats per minute). Significant differences were noted between seated and walking observations for single-layer grassland, single-layer woodland, tree-grass composite woodlands, and the non-vegetated square (p < 0.05). The results show that walking observations in single-layer grasslands, single-layer woodlands, and tree-grass composite woodlands help reduce heart rate (HR) more effectively than in squares without vegetation.
In winter, heart rate (HR) values significantly decreased more after walking than after seated observations. Among the green spaces, the tree-shrub-grass composite woodlands exhibited the most pronounced reduction in HR (8.3), followed by single-layer grassland (5.15), tree-grass composite woodlands (3.925), the non-vegetated square (2.05), and single-layer woodland (1.675). Significant differences were noted for single-layer grassland and tree-shrub-grass composite woodlands between seated and walking observations (p < 0.05). These findings suggest that walking observations in green spaces, particularly in tree-shrub-grass composite woodlands, enhance physiological recovery more than seated observations. In contrast to summer, winter seems to favor more complex vegetation structures, such as tree-shrub-grass composite woodlands, for their positive impact on physiological responses (Figure 3B and Table 4).
A comparative analysis of heart rate (HR) between the two observation methods revealed that the effect of walking observation was significantly greater than that of seated observation. According to the differential comparison, the tree-grass composite woodland exhibited the greatest change (7.388), followed by the tree-shrub-grass composite woodland (7.363), single-layer grassland (4.063), and another single-layer grassland (2.875). The differences between seated and walking observations for the single-layer grassland, tree-shrub-grass composite woodlands, tree-grass composite woodland, and non-vegetated square reached significance (p < 0.05). This indicates that the tree-grass composite woodland shows the greatest change in HR values, both in terms of the magnitude of change and variability, compared to single-layer grassland. Regardless of seasonal variations, the perceptual effect of walking observation is superior to that of seated observation. Furthermore, the smaller difference in HR between seated and walking observations in single-layer grassland suggests that in China, the frequency of activities conducted in single-layer grassland is significantly greater than that in other types of vegetated green spaces, both in summer and winter (Figure 4 and Table 5).
(2) pNN50
An analysis of pNN50 values for seated and walking observations in green spaces, including a non-vegetated square, showed that walking observations led to greater increases in pNN50 values than seated observations in summer. This suggests that walking is more effective for physiological recovery (Figure 5A and Table 6). The tree-grass composite woodlands had the highest ΔM4 change in pNN50 values (30.31), followed by the tree-shrub-grass composite woodlands (29.67), single-layer grasslands (29.45), and the non-vegetated square (29.16). Single-layer grasslands also showed a notable change (23.4). Significant differences were found between seated and walking observations in all green spaces, including the non-vegetated square (p < 0.001).
In winter, the pNN50 values after walking observation in the four types of vegetated green spaces increased more significantly than those after seated observation, while the pNN50 values in the non-vegetated square showed the opposite trend. A comparison of the walking and seated observations revealed that the tree-shrub-grass composite woodlands had the greatest change (22.28), followed by the tree-grass composite woodlands (17.19), single-layer grasslands (12.97), and single-layer grasslands (9.83), and the non-vegetated squares decreased (−2.64). Significant differences were found between seated and walking observations for single-layer grassland and tree-shrub-grass composite woodlands (p < 0.05). This suggests that in summer, walking observation in green spaces had a greater physiological recovery effect than seated observation, and all green spaces, including non-vegetated squares, had physiological recovery effects. In winter, except for the non-vegetated square, the other vegetated green spaces also had physiological recovery effects on the human body (Figure 5B and Table 6).
Comparative analysis of pNN50 values between walking and seated observation methods revealed that walking observations significantly outperformed seated ones in enhancing physiological recovery. Among different green spaces, tree-shrub-grass composite woodlands exhibited the greatest increase in pNN50 values (25.97), followed by tree-grass composite woodlands (23.75), single-layer grassland (21.21), and the non-vegetated square (13.26). The single-layer grassland also showed a notable increase (16.62). Significant differences were observed between seated and walking observations across all green spaces, including the non-vegetated square (p < 0.001). These findings suggest that the perceptual benefits of walking over seated observation are consistent regardless of seasonal variations (Figure 6 and Table 7).
(3) RMSSD
Analysis of RMSSD values for seated and walking observations across various green spaces, including a non-vegetated square, indicated that walking observations led to significantly greater increases in RMSSD values than seated observations during summer (Figure 7A and Table 8). This suggests that walking observations are more effective in enhancing physiological recovery. Among the vegetation types, tree-shrub-grass composite woodlands showed the highest increase in RMSSD values (668.57), followed by tree-grass composite woodlands (510.15), and two different single-layer grasslands with increases of 402.9 and 342.31, respectively. The non-vegetated square showed the least increase (106.03). Significant differences were noted between seated and walking observations for the vegetated green spaces (p < 0.05), whereas the non-vegetated square did not exhibit a significant change (p > 0.05).
In winter, RMSSD values increased more significantly after walking than seated observations across green spaces with various vegetation structures, with a greater increase observed in winter compared to summer. The tree-shrub-grass composite woodlands showed the most pronounced change (589.7), followed by single-layer grassland (574.18), the non-vegetated square (554.75), tree-grass composite woodlands (386.53), and another single-layer grassland (335.03). Significant differences were noted between seated and walking observations for all green spaces, including non-vegetated squares (p < 0.05). These findings indicate that different vegetation structures in green spaces influence physiological recovery differently in summer and winter. Notably, observing tree-shrub-grass composite woodlands had a more substantial impact on participants’ physiological function recovery in both seasons (Figure 7B and Table 8).
Comparative analysis of RMSSD values between walking and seated observation methods showed that walking observations significantly enhanced physiological recovery more than seated observations. The tree-shrub-grass composite woodlands had the highest increase in RMSSD (629.13), followed by single-layer grassland (458.25), tree-grass composite woodlands (448.34), and the non-vegetated square (330.39). Another single-layer grassland also showed a notable increase (368.97). Significant differences were found between seated and walking observations across all green spaces, including the non-vegetated square (p < 0.05). These results suggest that walking observations in green spaces, regardless of vegetation structure, promote greater physiological recovery. This indicates that active engagement with green spaces through walking is more beneficial for the restoration of physical function and enhancement of vitality (Figure 8 and Table 9).
(4) R-R interval
An analysis of R-R interval values across different vegetation structures in green spaces, including a non-vegetated square, showed that in summer, these values increased more after walking observations than after seated ones (Figure 9A and Table 10). The tree-grass composite woodlands had the greatest increase (206.88), followed by single-layer grasslands (188.52 and 147.94), tree-shrub-grass composite woodlands (137.49), and the non-vegetated square (105.83). Significant differences between seated and walking observations were noted for single-layer grasslands and tree-grass composite woodlands (p < 0.05), while the tree-shrub-grass composite woodland and non-vegetated square did not show significant differences (p > 0.05).
In winter, R-R interval values increased with walking observations across green spaces of varying vegetation structures, but the increment was significantly lower than in summer. The tree-grass composite woodlands showed the most significant change (96.54), followed by tree-shrub-grass composite woodlands (70.94), the non-vegetated square (65.58), and single-layer grasslands and single-layer woodland with changes of 25.33 and 16.1, respectively. No significant differences were observed between seated and walking observations for any of the green spaces, including the non-vegetated square (p > 0.05). This indicates that while both observation methods showed an increasing trend in R-R interval values, the effect of physical activity on these values was not statistically significant during winter (Figure 9B and Table 10).
A comparative analysis of the R-R intervals between walking and seated observation methods indicated that walking observation had a significantly greater effect on heart rate variability than seated observation. The differential comparison between seated and walking observations (ΔM4) revealed the most pronounced change in the tree-grass composite woodlands (151.71), followed by the tree-shrub-grass composite woodlands (104.21), single-layer grassland (102.31), another single-layer grassland (86.63), and the non-vegetated square (85.7). Statistically significant differences were detected between seated and walking observations for green spaces with varying vegetation structures, including the non-vegetated square (p < 0.05). These findings suggest that the disparities between the two observation methods surpass those attributable to seasonal variations. Consequently, the choice of observation method should be tailored to the participants’ specific needs and the season in which the observation takes place (Figure 10 and Table 11).

3.2.2. Impact of Two Observation Modes on Participants’ Skin Conductance Level (SCL)

(1) Two Observation Modes
A comparative analysis of skin conductance level (SCL) between seated and walking observation methods demonstrated that walking observation was more effective in promoting physiological recovery than seated observation. The differential comparison (ΔM4) between the two observation methods revealed a decrease in SCL for single-layer grasslands (−0.41), another single-layer grassland (−0.11), and tree-grass composite woodlands (−0.009), indicating a trend towards reduced physiological arousal. Conversely, an increase in SCL was observed in tree-shrub-grass composite woodlands (0.13) and the non-vegetated square (0.22), suggesting a trend of increased physiological arousal. Notably, the SCL difference between seated and walking observations in the non-vegetated square was statistically significant (p< 0.05), suggesting that non-vegetated environments may not be as conducive to physiological recovery as vegetated spaces. These findings indicate that participants experienced improved physiological recovery in single-layer grasslands and tree-grass composite woodlands, regardless of the observation method used, although the magnitude of the effect was modest. In contrast, the increase in SCL within the non-vegetated square suggests a diminished capacity for physiological recovery, highlighting a preference for green spaces that incorporate vegetation. This preference underscores the importance of vegetation in enhancing the restorative qualities of urban green spaces (Figure 11 and Table 12).
The study’s findings indicate that vegetation, whether in the form of grasslands or composite woodlands, significantly calms participants, as evidenced by reduced SCL values. In contrast, the non-vegetated square, which showed an increase in SCL, did not promote physiological recovery to the same extent. This suggests that the presence of plants is crucial for enhancing the restorative effects of green spaces.
These findings underscore the pivotal role of vegetation in urban green spaces for enhancing physiological recovery and overall well-being. The marked preference for spaces rich in plant life over non-vegetated areas highlights the importance of integrating diverse vegetation structures in landscape planning. This integration can optimize the health benefits of green spaces for the community.
(2) Seasonal changes in viewing patterns
Analysis of SCL values for seated and walking observations across vegetation structures in green spaces, including a non-vegetated square, revealed distinct seasonal patterns. These patterns suggest that physiological responses to different green spaces may vary significantly depending on the season, which has implications for the design and use of urban green spaces throughout the year.
During summer, a significant reduction in SCL was observed in participants following walking observations in single-layer grasslands, compared to seated observations. In contrast, for other vegetation types and the non-vegetated square, SCL values tended to increase. However, no significant differences were noted between the two observation methods across all green spaces, suggesting that single-layer grasslands may offer superior physiological recovery effects in summer, regardless of the observational approach (Figure 12A and Table 13).
In winter, SCL values decreased after walking in all vegetated green spaces, with a more pronounced reduction compared to summer observations. Notably, single-layer grasslands showed a significant difference between walking and seated observations, with a change value of −0.8 (p < 0.05), suggesting a substantial impact on physiological recovery (Figure 12B and Table 13).These results indicate that the presence of vegetation, particularly in single-layer grasslands, enhances physiological recovery, as reflected by SCL values. The significant SCL reduction during walking observations in single-layer grasslands during winter underscores the restorative capacity of vegetated green spaces. In contrast, the non-vegetated square exhibited a less pronounced effect on physiological recovery, suggesting a preference for vegetated areas in stress recovery scenarios.
This analysis highlights the pivotal role of vegetation in augmenting the restorative capabilities of green spaces. It also underscores the potential benefits of active engagement with these spaces, such as walking, which can foster physiological relaxation and enhance well-being.

4. Discussion

4.1. Effects of Seasonal Changes in Different Green Spaces on Participants’ Physiological Recovery

The two seasonal perception methods generally show differences in variation and exhibit significant differences in pNN50 values. This indicates that viewing in the summer can relax participants’ moods and soothe their emotions, while viewing in the winter has less of an effect on alleviating participants’ stress levels. Seasons may greatly impact landscape perception experiences, possibly due to changes in the biological characteristics of plants with the seasons, altering the appearance and ecological characteristics of vegetation structures, such as color, shape, density, and biodiversity, thereby affecting visual perception experiences and psychological responses [5,50].
There is no significant impact of viewing the four types of vegetation structure green spaces on participants’ physiological recovery. The differences between green spaces with different vegetation structures may be due to different planting methods and layouts that form various enclosed spaces, thus providing different spatial perceptions and viewing experiences [37,51]. Changes in the climate affect people’s preferences for the landscape environment. In summer, individuals prioritize shade and cooling, whereas in winter, they focus on exercise and maintaining body temperature, supporting previous findings [23]. The findings of this study are in line with prior research on photostimulation and field surveys, which have established that single-layer grasslands offer superior physiological recovery benefits for humans when compared to single-layer woodlands and tree-shrub-grass composite woodlands [22]. People’s aesthetic and leisure preferences for forest landscapes have a great impact [52], and it has also been found that people prefer single-layer grasslands in winter than in summer [23]. In China, single-layer grasslands in winter are mostly composed of pine and cypress plants, and the volatile substances of these plants promote human health [53]. Previous research [23] has confirmed that individuals prefer single-layer grasslands for physical exercise activities during winter. It also noted that participants favored the winter landscape of single-layer grasslands over that of summer. Tree-shrub-grass composite woodlands provide space for people to be alone and communicate, ensuring people’s safety, but overly dense trees can limit the field of vision and reduce the sense of security [54,55]. We need to ensure that the tree-shrub-grass composite woodlands are sparse and well arranged, with a moderate green view rate, to ensure their healing effect on the participants’ physical and mental recovery [56]. Single-layer grasslands are open spaces that do not block the participants’ line of sight [37,57]. Researchers have proposed that people are attracted to this habitat because it is where humans have evolved over tens of thousands of years and have a ‘homely feeling.’ In other habitats, such as mountains and forests, humans will look for grasslands [58]. Tree-grass composite woodlands are similar to single-layer grasslands, and the difference between the two is that single-layer grasslands enable people to experience the environment empathetically, and the vertical space formed by single-layer grasslands conforms to the scenes described in the ‘prospect-refuge’ theory. Tree-grass composite woodlands bring people a sense of distance [55]. In tree-grass composite woodlands, people feel only the foreground that provides a good view and lack the shelter that provides a place to stay. Tree-grass composite woodlands lack the activity space under the forest and do not meet people’s psychological needs for shelter, thus affecting the participants’ experience and feelings [48]. Street greening, representing a variety of urban green spaces with different vegetation structures, enjoys the most frequent public contact [19]. It optimizes land utilization, fosters greater daily engagement with the natural environment, and exerts a positive influence on public health [41]. Consequently, the diversity in vegetation structures of urban green spaces is essential for the enhancement of street greening and for fulfilling a range of public demands associated with nature, as well as for bolstering public health.

4.2. Differences in the Physiological Recovery of Participants by Ornamental Mode of Green Space with Different Plant Structures

We categorized the observation methods into two types—seated observation and walking observation. First, the four vegetation structures and green spaces both exhibited good physiological recovery effects under these two observation methods. Second, the non-vegetated square also influenced physiological recovery in these methods. However, compared to the four types of vegetation structure green spaces, the recovery effect of the non-vegetated square was less than that of the vegetated green spaces. Additionally, the physiological recovery effects obtained from different plant vegetations and structures using various observation methods in different seasons differed. Due to changes in the climate environment, people’s preferences for the landscape environment also change, focusing on shade and cooling in summer and exercise and warmth maintenance in winter. For instance, in summer, the greatest physiological recovery effect was experienced by participants during seated observation, while in winter, the greatest effect was experienced during walking observation.
This may arise due to two factors. One is climate differences, which are mostly manifested in temperature, humidity, and light exposure. Sensory reactions to climate change stem from people’s sensitivity to changes in temperature, humidity, and light. In the hot summer climate, green spaces have a cooling and humidifying effect, forming a certain microclimate around them, which effectively aids human comfort. Participants are more suited to seated observation in summer, such as resting, meditating, and being alone, and an increase in autonomous physiological responses is observed, which is related to a decrease in recovery from mental fatigue [27]. In a cold winter climate, people are more willing to experience green spaces through walking or running. The withering and yellowing of herbaceous plants in winter affect participants’ visual experience and psychological response [50]. The physiological recovery effect obtained by participants through walking observation in the winter perception of single-layer grassland is stronger than that of seated observation. Due to the cold climate in winter, the withered leaves, dull color, and open environment of single-layer grasslands greatly reduce their attractiveness to participants. In summer perception, choosing a combination of seated and walking observation can effectively improve participants’ physiological recovery [59]. In winter, single-layer grassland is mainly experienced through walking observation, with seated observation as a supplement. Tree-shrub-grass composite woodlands can be affected by both observation methods in both winter and summer, as rich plant species and high spatial perception, along with maintaining a moderate plant density, can ensure favorable recovery [60]. Browning et al. [31] described the perceptual experiences associated with walking and sitting observations in natural environments yet did not provide an extensive comparative analysis of these methods. Building on this, Duan et al. [37] explored how green spaces with diverse vegetation structures impact human health in different seasons but did not scrutinize the effects of distinct perceptual methods. This research addresses this gap by conducting a detailed analysis and discussion of the comparative perceptual experiences between walking and sitting observations, thereby enhancing the understanding of how green spaces can be designed to better serve the needs of residents across seasons.
Another reason may be the difference in plant community types. Different vegetation structures form different spaces, changing the effects of spatial perception for people, and the differences in physiological recovery between accessible and inaccessible spaces formed by different vegetation structures vary greatly. Even in non-vegetated areas, walking observation can also have brief physiological recovery effects on people, but compared with that in spaces with plants, the physiological recovery effect is smaller. This indicates that seated and walking observations of the four vegetation structures and green spaces can effectively alleviate stress. Although there is a brief relief in the non-vegetated square, long-term exposure can lead to a continuous increase in stress levels. The physiological recovery effect of single-layer grassland is prominent in walking observation, possibly because seated observation limits the participants’ field of view. Some scholars believe that the area of green space per capita and the method of green space observation are important factors affecting people’s physiological and psychological responses. Adopting different behavioral patterns for observation in urban green spaces of different sizes can effectively improve people’s physiological and psychological responses [4,61]. The combination of seated and walking observation in single-layer grasslands has a good physiological recovery effect on participants, and people’s aesthetic and activity preferences for forest landscapes have a large impact [52]. The combination of the two observation methods enables people to experience a comfortable environment. Tree-shrub-grass composite woodlands provide private space for people to be alone and communicate, but overly dense trees can limit the field of view and reduce the sense of security [55]. We need to maintain the appropriate sparsity of tree-shrub-grass composite woodlands to ensure their healing effect on the participants’ physical and mental recovery [62]. The physiological recovery effect of tree-grass composite woodlands on participants was greater in walking observation than in seated observation, which may be due to the tall trees and plant density in seated observation, resulting in greater attention restoration [60,63,64]. Walking observation enables participants to experience the landscape from different angles, thus relieving stress and gradually improving physiological recovery effects in tree-grass composite woodlands. Therefore, in terms of the seasonal changes in different vegetation structure types of green spaces, appropriate observation methods should be carefully chosen to maximize the recovery effect. The implications of these findings for landscape design are profound, suggesting that designers must consider not only the type of vegetation but also the seasonality of the landscape to create spaces that foster physical and mental well-being [23]. The study’s results suggest that a dynamic approach to landscape design, which adapts to the changing seasons and user preferences, could significantly enhance the restorative benefits of green spaces. This study contributes to the existing body of research by providing empirical evidence on how different vegetation structures and observation methods can influence physiological recovery across seasons [22,37]. It highlights the need for a more nuanced understanding of the relationship between green spaces, human health, and seasonal changes, offering valuable insights for urban planning and public health interventions [19].

4.3. Limitations

Selecting college students as participants in research is often a deliberate choice, reflecting two primary considerations. First, college students comprise a substantial and accessible demographic in urban settings, frequently facing elevated stress levels stemming from academic pressures and lifestyle changes. Their mental health issues, including conditions such as depression, anxiety, and agitation, are not only common but also increasingly recognized as significant challenges for young adults [65]. The stress from academic and early professional demands contributes to an earlier onset of chronic conditions and cardiovascular diseases among young adults [66]. Second, college students are often more willing to participate in research, providing a reliable and engaged participant base. Their accessibility and willingness to participate in scientific studies render them ideal candidates for understanding how urban green spaces and other environmental factors can impact public health [22,23,37].
College students offer a valuable perspective, but their experiences may not fully represent those of the general public, particularly given the unique stressors and developmental stages they encounter [67]. Therefore, future research should aim to include a more diverse range of participants to fully understand the impact of urban green spaces on different segments of the population.
Additionally, potential biases in physiological measurements must be considered when conducting studies in outdoor environments. Consequently, it is essential to conduct a series of pilot trials prior to initiating formal experimental procedures. The outdoor environment presents a multitude of influences, which must be carefully considered during the experimental design and data collection phases. Efforts should be made to mitigate or minimize the impact of these external factors to ensure the accuracy and reliability of the study’s findings.

5. Conclusions

We investigated landscape perception experiences through seated and walking observation for green spaces with different vegetation structures in the summer and winter seasons by monitoring the physiological recovery of participants. Our aim was to provide a scientific basis for the assessment of the effects of landscape perception experience in the future and to offer theoretical references for landscape design. First, the physiological recovery effect of the walking observation is greater than that of the seated observation method, and the walking observation in green spaces with different vegetation structures can enhance participants’ pNN50 values. Second, in green spaces with different vegetation structures in summer, the seated observation method is better for participants’ physiological recovery, while in winter, the walking observation is better. Third, in terms of the green spaces with different vegetation structures, vegetated green spaces are better for physiological recovery, and non-vegetated squares cannot sustain recovery effects for a long time (less than 1 min). Compared to other types of green spaces with vegetation structures, single-layer grasslands and tree-shrub-grass composite woodlands have better physiological recovery effects on the human body. Finally, based on the conclusions, three suggestions are proposed. In the practical guidance of green space design, research findings can provide specific guidance for urban green space design. For instance, it is recommended to integrate dynamic and static observation methods in green spaces, as well as to adjust the duration of observation activities according to seasonal changes. These suggestions can assist designers in creating more effective green spaces that meet the recovery needs of residents. The research emphasizes the impact of seasonal changes on the experience of using green spaces. When designing, it is important to consider the climatic conditions and vegetation characteristics of different seasons to ensure that green spaces provide restorative benefits throughout the year. In optimizing vegetation structure, the study points out that single-layer grasslands and tree-shrub-grass composite woodlands have a positive impact on physiological recovery. This can guide urban planners in selecting vegetation types and designing green space structures, taking into account their potential benefits to the health and well-being of residents. Therefore, the integration of dynamic and static observation methods, as well as the adjustment of activity durations based on seasonal changes, can lead to the creation of green spaces that are not only aesthetically pleasing but also contribute to the well-being of the community. The research findings further confirm the biophilic design theory, which posits that human well-being is closely linked to the presence of nature in the built environment. The study also demonstrates that urban green spaces with diverse vegetation patterns play a significant role in enhancing both physical and mental health, as well as in boosting the social welfare associated with these green areas. This knowledge can guide urban planners in selecting vegetation types and designing green space structures that promote the health and well-being of residents, thereby creating healthier and more sustainable cities.
However, this study also has several limitations. First, all participants were college students, so the results may not reflect other social groups. In addition, the age of the participants can be further studied as an effect modification variable. In the research, the selection of the target audience is not diversified, and the audience group is relatively single, which limits the help that the research results can provide to the audience. Therefore, in future research and design, more consideration should be given to a variety of audience groups, such as focusing on age or occupation, etc., so as to better serve the general public. Second, although the four green spaces with vegetation structures are typical and representative, the green spaces are not comprehensive enough. To fully explore the differences in green spaces with different vegetation structures among participant groups, we should consider research on other green spaces or other plant species. At the same time, in future research, more consideration should be given to the diversification of plant species, and in-depth research and discussion should be carried out. Finally, the use of seated and walking observation in green spaces with different vegetation structures provides only short-term physiological recovery and mental fatigue recovery. To fully illustrate the effects of plant community landscapes on physical and mental health, we should consider the frequency of repeated perception experiences to verify whether the recovery effect will become more critical with increasing time and frequency. Importantly, the physiological and mental fatigue recovery effects observed through seated and walking observations in green spaces with varying vegetation structures are short-term. To comprehensively understand the long-term impact of plant community landscapes on health, future studies should examine the frequency and repetition of such experiences. This examination could reveal whether the restorative effects strengthen with chronic exposure, providing invaluable data and insights for the advancement of healthy landscape design and research.

Author Contributions

Conceptualization, Y.D.; methodology, Y.D.; software, Y.D.; validation, Y.D.; formal analysis, Y.D.; investigation, Y.D.; resources, H.B.; data curation, Y.D.; writing—original draft preparation, Y.D.; writing—review and editing, Y.D.; visualization, Y.D.; supervision, H.B. and S.L.; project administration, Y.D., H.B. and S.L.; funding acquisition, Y.D. All authors have read and agreed to the published version of the manuscript.

Funding

The research was funded by the General Special Project (Humanities and Social Sciences) (23JK0056) of the Shaanxi Provincial Department of Education.

Institutional Review Board Statement

The study was conducted in accordance with the Declaration of Helsinki, and the protocol was approved by the Ethics Committee of the College of Architecture, Chang’an University.

Data Availability Statement

All subjects gave their informed consent for inclusion before they participated in the study.

Acknowledgments

We would like to thank our 5 volunteers for helping us prepare for the experiment and the 400 college students who participated in our survey. We are also very grateful to the “Scientific Research Support” project provided by Kingfar International, Inc., for the research’s technical support.

Conflicts of Interest

The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of the data; in the writing of the manuscript; or in the decision to publish the results.

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Figure 1. Study Area and Research Target [23,37].
Figure 1. Study Area and Research Target [23,37].
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Figure 2. Experimental process.
Figure 2. Experimental process.
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Figure 3. Comparison of HR between two types of summer viewing and winter viewing ((A). summer viewing and (B). winter viewing). p < 0.05 indicates that the difference is statistically significant, as shown by * in the figure. p < 0.01 indicates that the difference is more statistically significant, as shown by ** in the figure.
Figure 3. Comparison of HR between two types of summer viewing and winter viewing ((A). summer viewing and (B). winter viewing). p < 0.05 indicates that the difference is statistically significant, as shown by * in the figure. p < 0.01 indicates that the difference is more statistically significant, as shown by ** in the figure.
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Figure 4. Comparison of HR between two ornamental viewing styles. p < 0.05 indicates that the difference is statistically significant, as shown by * in the figure. p < 0.01 indicates that the difference is more statistically significant, as shown by ** in the figure. p < 0.001 indicates that the difference is very statistically significant, as shown by *** in the figure.
Figure 4. Comparison of HR between two ornamental viewing styles. p < 0.05 indicates that the difference is statistically significant, as shown by * in the figure. p < 0.01 indicates that the difference is more statistically significant, as shown by ** in the figure. p < 0.001 indicates that the difference is very statistically significant, as shown by *** in the figure.
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Figure 5. Comparison of pNN50 between two types of summer viewing and winter viewing ((A). summer viewing and (B). winter viewing). p < 0.05 indicates that the difference is statistically significant, as shown by * in the figure. p < 0.01 indicates that the difference is more statistically significant, as shown by ** in the figure. p < 0.001 indicates that the difference is very statistically significant, as shown by *** in the figure.
Figure 5. Comparison of pNN50 between two types of summer viewing and winter viewing ((A). summer viewing and (B). winter viewing). p < 0.05 indicates that the difference is statistically significant, as shown by * in the figure. p < 0.01 indicates that the difference is more statistically significant, as shown by ** in the figure. p < 0.001 indicates that the difference is very statistically significant, as shown by *** in the figure.
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Figure 6. Comparison of pNN50 between two ornamental viewing styles. p < 0.001 indicates that the difference is very statistically significant, as shown by *** in the figure.
Figure 6. Comparison of pNN50 between two ornamental viewing styles. p < 0.001 indicates that the difference is very statistically significant, as shown by *** in the figure.
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Figure 7. Comparison of RMSSD between two types of summer viewing and winter viewing ((A). summer viewing and (B). winter viewing). p < 0.05 indicates that the difference is statistically significant, as shown by * in the figure. p < 0.001 indicates that the difference is very statistically significant, as shown by *** in the figure.
Figure 7. Comparison of RMSSD between two types of summer viewing and winter viewing ((A). summer viewing and (B). winter viewing). p < 0.05 indicates that the difference is statistically significant, as shown by * in the figure. p < 0.001 indicates that the difference is very statistically significant, as shown by *** in the figure.
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Figure 8. Comparison of RMSSD between two ornamental viewing styles. p < 0.05 indicates that the difference is statistically significant, as shown by * in the figure. p < 0.001 indicates that the difference is very statistically significant, as shown by *** in the figure.
Figure 8. Comparison of RMSSD between two ornamental viewing styles. p < 0.05 indicates that the difference is statistically significant, as shown by * in the figure. p < 0.001 indicates that the difference is very statistically significant, as shown by *** in the figure.
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Figure 9. Comparison of R-R interval between two types of summer viewing and winter viewing ((A). summer viewing and (B). winter viewing). p < 0.05 indicates that the difference is statistically significant, as shown by * in the figure. p < 0.01 indicates that the difference is more statistically significant, as shown by ** in the figure. p < 0.001 indicates that the difference is very statistically significant, as shown by *** in the figure.
Figure 9. Comparison of R-R interval between two types of summer viewing and winter viewing ((A). summer viewing and (B). winter viewing). p < 0.05 indicates that the difference is statistically significant, as shown by * in the figure. p < 0.01 indicates that the difference is more statistically significant, as shown by ** in the figure. p < 0.001 indicates that the difference is very statistically significant, as shown by *** in the figure.
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Figure 10. Comparison of R-R interval between two ornamental viewing styles. p < 0.05 indicates that the difference is statistically significant, as shown by * in the figure. p < 0.001 indicates that the difference is very statistically significant, as shown by *** in the figure.
Figure 10. Comparison of R-R interval between two ornamental viewing styles. p < 0.05 indicates that the difference is statistically significant, as shown by * in the figure. p < 0.001 indicates that the difference is very statistically significant, as shown by *** in the figure.
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Figure 11. Comparison of SCL Between Two Observation Modes. p < 0.01 indicates that the difference is more statistically significant, as shown by * in the figure.
Figure 11. Comparison of SCL Between Two Observation Modes. p < 0.01 indicates that the difference is more statistically significant, as shown by * in the figure.
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Figure 12. Comparison of SCL between two types of summer viewing and winter viewing ((A). summer viewing and (B). winter viewing). p < 0.01 indicates that the difference is more statistically significant, as shown by * in the figure. p < 0.001 indicates that the difference is very statistically significant, as shown by ** in the figure.
Figure 12. Comparison of SCL between two types of summer viewing and winter viewing ((A). summer viewing and (B). winter viewing). p < 0.01 indicates that the difference is more statistically significant, as shown by * in the figure. p < 0.001 indicates that the difference is very statistically significant, as shown by ** in the figure.
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Table 1. Different vegetation types in green spaces.
Table 1. Different vegetation types in green spaces.
Vegetation TypesPlant Species
single-layer grasslandAxonopus compressus (Sw.) Beauv.
single-layer woodlandPinus tabuliformis Carr.
tree-shrub-grass composite woodlandsGleditsia sinensis Lam., Ligustrum lucidum Ait., Prunus cerasifera ‘Atropurpurea’, Nandina domestica Thunb., Euonymus japonicus ‘Aureo-marginatus’, Axonopus compressus (Sw.) Beauv.
tree-grass composite woodlandsGleditsia sinensis Lam., Axonopus compressus (Sw.) Beauv.
non-vegetated squareplant-free square
Table 2. Impact of Seasonal Variations on Physiological Responses of College Students during Sitting and Walking Observation Experiences.
Table 2. Impact of Seasonal Variations on Physiological Responses of College Students during Sitting and Walking Observation Experiences.
Indicator TypeViewing StyleWinterSummerFp
SCLSitting0.031 ± 0.1450.028 ± 0.1800.994
Walking−0.267 ± 0.1680.251 ± 0.1041.40.238
HRSitting−6.62 ± 1.045−3.15 ± 1.473.7120.55
Walking−10.17 ± 1.137−10.63 ± 1.4810.0590.808
RMSSDSitting311.206 ± 45.326358.718 ± 93.4450.2090.648
Walking799.243 ± 60.448764.71 ± 92.2140.0980.754
R-R intervalSitting114.38 ± 16.77766.707 ± 27.8712.1480.144
Walking169.275 ± 21.032224.039 ± 29.2892.3070.13
pNN50Sitting3.908 ± 2.2232.765 ± 2.7670.1040.748
Walking15.836 ± 2.47831.163 ± 2.49219.0220
p < 0.01 indicates that the difference is more statistically significant. p < 0.001 indicates that the difference is very statistically significant. p > 0.05 indicates that the difference is not statistically significant.
Table 3. Influence of Perception Modes in Green Spaces with Different Vegetation Structures.
Table 3. Influence of Perception Modes in Green Spaces with Different Vegetation Structures.
Indicator TypeViewing StyleSingle-Layer GrasslandSingle-Layer WoodlandTree-Shrub-Grass Composite WoodlandsTree-Grass Composite WoodlandsNon-Vegetated SquareFp
SCLSitting−0.68 ± 0.120.4 ± 0.14−0.11 ± 0.150.38 ± 0.10.155 ± 0.10.780.54
Walking−1.09 ± 0.170.29 ± 0.190.01 ± 0.320.37 ± 0.110.37 ± 0.161.640.17
HRSitting−5.0 ± 1.9−6.54 ± 2.03−5.7 ± 2.32−3.18 ± 1.77−4.01 ± 2.080.430.78
Walking−10.84 ± 2.09−9.41 ± 2.12−13.1 ± 2.15−10.56 ± 2.06−8.08 ± 2.020.80.53
RMSSDSitting340.41 ± 100.58422.88 ± 103.45270.24 ± 123.19259.68 ± 108.53381.59 ± 141.210.370.83
Walking798.66 ± 125.95791.84 ± 116.6899.37 ± 151.33708.04 ± 117.07711.97 ± 101.030.40.81
R-R intervalSitting108.13 ± 35.23126.38 ± 36.7799.112 ± 45.4341.47 ± 27.0377.624 ± 35.490.80.53
Walking194.77 ± 39.66228.69 ± 41.4203.33 ± 44.38193.33 ± 39.04163.33 ± 37.780.340.86
pNN50Sitting5.68 ± 3.916.1 ± 4.034.75 ± 4.062.63 ± 3.4−2.49 ± 4.380.790.53
Walking26.9 ± 3.6122.72 ± 4.1730.72 ± 3.626.38 ± 3.6210.78 ± 4.693.730.01
p < 0.01 indicates that the difference is more statistically significant. p > 0.05 indicates that the difference is not statistically significant.
Table 4. Comparison of HR between two types of summer viewing and winter viewing.
Table 4. Comparison of HR between two types of summer viewing and winter viewing.
Vegetation Structure TypeSeasonal Viewing TypesViewing StyleMean Value
single-layer grasslandsummer viewingSitting0.48 ± 0.25
Walking−6.15 ± 3.05
winter viewingSitting−10.38 ± 2.11
Walking−15.53 ± 2.70
single-layer woodlandsummer viewingSitting−3.83 ± 3.04
Walking−11.25 ± 3.66
winter viewingSitting−9.25 ± 2.67
Walking−7.58 ± 2.14
tree-shrub-grass composite woodlandssummer viewingSitting−5.13 ± 4.03
Walking−11.55 ± 3.46
winter viewingSitting−6.35 ± 2.34
Walking−14.65 ± 2.57
tree-grass composite woodlandsummer viewingSitting−1.70 ± 0.71
Walking−12.55 ± 2.03
winter viewingSitting−4.65 ± 2.30
Walking−8.58 ± 2.79
non-vegetated squaresummer viewingSitting−5.55 ± 2.61
Walking−11.63 ± 2.38
winter viewingSitting−2.48 ± 2.10
Walking−4.53 ± 2.12
Table 5. Comparison of HR between two ornamental viewing styles.
Table 5. Comparison of HR between two ornamental viewing styles.
Vegetation Structure TypeViewing StyleMean Value
single-layer grasslandSitting−4.95 ± 1.90
Walking−10.84 ± 2.09
single-layer woodlandSitting−6.54 ± 2.03
Walking−9.41 ± 2.12
tree-shrub-grass composite woodlandsSitting−5.74 ± 2.32
Walking−13.10 ± 2.15
tree-grass composite woodlandSitting−3.18 ± 1.77
Walking−10.56 ± 2.06
non-vegetated squareSitting−4.01 ± 2.08
Walking−8.08 ± 2.02
Table 6. Comparison of pNN50 between two types of summer viewing and winter viewing.
Table 6. Comparison of pNN50 between two types of summer viewing and winter viewing.
Vegetation Structure TypeSeasonal Viewing TypesViewing StyleMean Value
single-layer grasslandsummer viewingSitting0.92 ± 1.95
Walking30.37 ± 4.83
winter viewingSitting10.45 ± 5.03
Walking23.42 ± 5.38
single-layer woodlandsummer viewingSitting3.96 ± 1.42
Walking27.36 ± 6.42
winter viewingSitting8.25 ± 4.94
Walking18.08 ± 5.31
tree-shrub-grass composite woodlandssummer viewingSitting2.53 ± 2.95
Walking32.20 ± 5.50
winter viewingSitting6.96 ± 4.26
Walking29.24 ± 4.71
tree-grass composite woodlandsummer viewingSitting2.34 ± 2.33
Walking32.65 ± 5.25
winter viewingSitting2.93 ± 2.29
Walking20.12 ± 4.86
non-vegetated squaresummer viewingSitting4.07 ± 4.18
Walking33.23 ± 5.95
winter viewingSitting−9.04 ± 4.91
Walking−11.68 ± 5.26
Table 7. Comparison of pNN50 between two ornamental viewing styles.
Table 7. Comparison of pNN50 between two ornamental viewing styles.
Vegetation Structure TypeViewing StyleMean Value
single-layer grasslandSitting5.68 ± 3.91
Walking26.90 ± 3.61
single-layer woodlandSitting6.10 ± 4.03
Walking22.72 ± 4.17
tree-shrub-grass composite woodlandsSitting4.75 ± 4.06
Walking30.72 ± 3.60
tree-grass composite woodlandSitting2.63 ± 3.40
Walking26.38 ± 3.62
non-vegetated squareSitting−2.49 ± 1.38
Walking10.78 ± 4.69
Table 8. Comparison of RMSSD between two types of summer viewing and winter viewing.
Table 8. Comparison of RMSSD between two types of summer viewing and winter viewing.
Vegetation Structure TypeSeasonal Viewing TypesViewing StyleMean Value
single-layer grasslandsummer viewingSitting233.50 ± 172.86
Walking575.81 ± 195.07
winter viewingSitting447.32 ± 102.55
Walking1021.50 ± 153.83
single-layer woodlandsummer viewingSitting428.18 ± 179.20
Walking831.09 ± 198.42
winter viewingSitting417.57 ± 106.04
Walking752.60 ± 125.02
tree-shrub-grass composite woodlandssummer viewingSitting183.17 ± 121.01
Walking851.74 ± 251.16
winter viewingSitting357.31 ± 110.69
Walking947.01 ± 171.97
tree-grass composite woodlandsummer viewingSitting262.02 ± 196.83
Walking772.17 ± 202.00
winter viewingSitting257.37 ± 94.73
Walking643.90 ± 120.47
non-vegetated squaresummer viewingSitting686.72 ± 262.46
Walking792.74 ± 185.46
winter viewingSitting76.46 ± 44.39
Walking631.21 ± 81.36
Table 9. Comparison of RMSSD between two ornamental viewing styles.
Table 9. Comparison of RMSSD between two ornamental viewing styles.
Vegetation Structure TypeViewing StyleMean Value
single-layer grasslandSitting340.41 ± 100.58
Walking798.66 ± 125.95
single-layer woodlandSitting422.88 ± 103.45
Walking791.84 ± 116.60
tree-shrub-grass composite woodlandsSitting270.24 ± 123.19
Walking899.37 ± 151.33
tree-grass composite woodlandSitting259.70 ± 108.53
Walking708.04 ± 117.07
non-vegetated squareSitting381.59 ± 141.21
Walking711.97 ± 101.03
Table 10. Comparison of R-R interval between two types of summer viewing and winter viewing.
Table 10. Comparison of R-R interval between two types of summer viewing and winter viewing.
Vegetation Structure TypeSeasonal Viewing TypesViewing StyleMean Value
single-layer grasslandsummer viewingSitting5.48 ± 25.24
Walking153.42 ± 58.90
winter viewingSitting210.78 ± 37.88
Walking236.11 ± 53.05
single-layer woodlandsummer viewingSitting80.32 ± 53.88
Walking268.84 ± 69.36
winter viewingSitting172.44 ± 49.65
Walking188.54 ± 45.28
tree-shrub-grass composite woodlandssummer viewingSitting104.56 ± 44.70
Walking242.05 ± 79.86
winter viewingSitting93.67 ± 34.42
Walking164.61 ± 39.05
tree-grass composite woodlandsummer viewingSitting21.70 ± 26.75
Walking228.58 ± 56.95
winter viewingSitting61.24 ± 27.46
Walking157.77 ± 53.55
non-vegetated squaresummer viewingSitting121.48 ± 65.09
Walking227.32 ± 61.62
winter viewingSitting33.76 ± 20.70
Walking99.34 ± 42.44
Table 11. Comparison of R-R interval between two ornamental viewing styles.
Table 11. Comparison of R-R interval between two ornamental viewing styles.
Vegetation Structure TypeViewing StyleMean Value
single-layer grasslandSitting108.13 ± 35.22
Walking194.77 ± 39.66
single-layer woodlandSitting126.38 ± 36.77
Walking228.69 ± 41.40
tree-shrub-grass composite woodlandsSitting99.11 ± 45.43
Walking203.33 ± 44.38
tree-grass composite woodlandSitting41.47 ± 27.03
Walking198.18 ± 39.04
non-vegetated squareSitting77.62 ± 35.49
Walking163.33 ± 37.78
Table 12. Comparison of SCL between two ornamental viewing styles.
Table 12. Comparison of SCL between two ornamental viewing styles.
Vegetation Structure TypeViewing StyleMean Value
single-layer grasslandSitting−0.68 ± 0.12
Walking−1.09 ± 0.17
single-layer woodlandSitting0.40 ± 0.14
Walking0.29 ± 0.19
tree-shrub-grass composite woodlandsSitting−0.11 ± 0.05
Walking0.01 ± 0.03
tree-grass composite woodlandSitting0.38 ± 0.10
Walking0.37 ± 0.11
non-vegetated squareSitting0.15 ± 0.08
Walking0.37 ± 0.06
Table 13. Comparison of SCL between two types of summer viewing and winter viewing.
Table 13. Comparison of SCL between two types of summer viewing and winter viewing.
Vegetation Structure TypeSeasonal Viewing TypesViewing StyleMean Value
single-layer grasslandsummer viewingSitting−1.05 ± 0.41
Walking−1.07 ± 0.51
winter viewingSitting−0.31 ± 0.15
Walking−1.11 ± 0.34
single-layer woodlandsummer viewingSitting0.25 ± 0.19
Walking0.53 ± 0.26
winter viewingSitting0.55 ± 0.22
Walking0.05 ± 0.04
tree-shrub-grass composite woodlandssummer viewingSitting0.18 ± 0.16
Walking0.46 ± 0.20
winter viewingSitting−0.41 ± 0.15
Walking−0.43 ± 0.14
tree-grass composite woodlandsummer viewingSitting1.07 ± 0.40
Walking1.36 ± 0.42
winter viewingSitting−0.31 ± 0.17
Walking−0.61 ± 0.12
non-vegetated squaresummer viewingSitting−0.32 ± 0.11
Walking−0.02 ± 0.09
winter viewingSitting0.63 ± 0.30
Walking0.77 ± 0.33
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Duan, Y.; Bai, H.; Li, S. Human Physiological Responses to Sitting and Walking in Green Spaces with Different Vegetation Structures: A Seasonal Comparative Study. Forests 2024, 15, 1759. https://doi.org/10.3390/f15101759

AMA Style

Duan Y, Bai H, Li S. Human Physiological Responses to Sitting and Walking in Green Spaces with Different Vegetation Structures: A Seasonal Comparative Study. Forests. 2024; 15(10):1759. https://doi.org/10.3390/f15101759

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Duan, Yifan, Hua Bai, and Shuhua Li. 2024. "Human Physiological Responses to Sitting and Walking in Green Spaces with Different Vegetation Structures: A Seasonal Comparative Study" Forests 15, no. 10: 1759. https://doi.org/10.3390/f15101759

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