Psychophysiological Responses of College Students to Audio-Visual Forest Trail Landscapes

Abstract

Forest trails provide urban residents with contact with nature that improves health and well-being. Vision and hearing are important forms of environmental perception, and visual and auditory stimuli should not be overlooked in forest trail landscapes. This study focused on the health benefits of the audio-visual perception of forest trail landscapes. Forest density (FD) and forest sounds (FS) in forest trail landscapes were examined as visual and auditory variables, respectively. FD was divided into three levels: high (Hd), medium (Md), and low density (Ld). FS were divided into four levels: quiet natural and anthropogenic sounds (QnQa), quiet natural and loud anthropogenic sounds (QnLa), loud natural and quiet anthropogenic sounds (LnQa), and loud natural and loud anthropogenic sounds (LnLa). The levels of these two variables were combined to create 12 conditions. A total of 360 college students were randomly assigned to 12 groups (mapping onto the 12 conditions; N=30 per group). All subjects performed the same 5-min high-pressure task indoors, followed by a 5-min recovery period of experiencing a simulated forest trail landscape (viewing pictures and listening to sounds). Brain waves, blood pressure, blood oxygen saturation (SpO₂, measured with a finger monitor), the pulse rate, and mood indicators were collected to analyse the physiological and psychological responses to the audio-visual forest trail landscapes. The results indicated that higher FD and lower FS improved health benefits. The interaction between FD and FS revealed a pattern of combinations that facilitated stress reduction and positive mood recovery. These results are of theoretical value in that they indicate important audio-visual characteristics of forest trail landscapes. In terms of practical applications, these findings support the construction of urban forest trails to provide health benefits.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11524-023-00757-4.

Introduction

Forest Trail Landscapes Provide Health and Well-being Benefits

The extensive urbanization process has provided more information and resources but also has alienated people from the natural environment [1]. Ecological degradation and increased work stress have led to a rise in chronic diseases (e.g., hypertension) and mental illnesses (e.g., anxiety disorders) that pose a serious threat to people’s physical and mental health [2–4]. In recent years, young people have reported more complex and numerous health problems [5]. These issues are most prevalent in the college student population, as this population is exposed to academic and employment pressures, leading to concern about their physical and mental health. Stress leads to sleep, mood, and other problems, and further reduces mental and physical health. Relevant studies have shown that green spaces, such as forest environments, have the potential to restore the health of college students [6, 7]. Additionally, urban forestry in China has evolved over the past 30 years, with urban forests providing diverse benefits [8]. In cities, people can access urban forests through trails. People engage in healthy activities using these trails, and forest trails have become essential infrastructure for experiencing nature and improving health and well-being [9].

In recent years, experiential activities in forest environments have become popular with the public. Forest trails are one of the most important spaces for such experiential activities [10]. Many studies have confirmed that walking in a forest provides rehabilitative, therapeutic, preventive, health care, and science education benefits [11, 12]. The health benefits include alleviating symptoms of chronic diseases such as autism spectrum disorder, dementia, and mild cognitive impairment [13], aiding in the treatment of diseases such as hypertension, coronary heart disease, and obesity [14–16], and improving mental health [17]. In addition, studies have shown that frequent walks in a forest environment may be associated with a higher life expectancy and lower risks of chronic diseases (hypertension, heart disease, etc.) [18]. Moreover, urban forests also have a nature education function [19], and the forest experience fosters contact and communication between people and nature, promoting a harmonious relationship [20].

Visual and Auditory Stimuli of Forest Landscapes

Forest visitors experience the external environment through sensory processing. While vision is the body’s primary perceptual modality [21], auditory perception is also an essential part of the forest experience [22].

Researchers have studied visual preferences, visually induced psychophysiological responses, and visual elements or structures of forest landscapes using real or simulated forest landscapes. In the field of visual perception, research has focused on the importance of specific visual elements, such as forest density (FD), in people’s visual preferences for natural landscapes [23, 24]. Grinde [25] concluded that the absence of visible vegetation has a negative impact on people’s physical and mental health. Chiang [26] demonstrated that high FD improves attention recovery, although people prefer to view scenes with medium FD. However, the relationship between FD and human visual preference is nonlinear [27]. In another study, when FD increased from 1.7 to 24%, the psychophysiological benefits increased. However, when FD increased further, there was no corresponding increase in these benefits [28].

The urban forest contains a comprehensive sound environment formed by a mixture of various sound sources in space and transmitted through different media; such a sound environment is also called a soundscape [29, 30]. Sounds with different characteristics induce different physiological and psychological effects for the listener and may trigger corresponding emotional responses. Anthropogenic and natural sounds are mixed in urban forests. In Aletta’s [31] study, the sound source was manipulated; subjects reported that hearing birdsong (a natural sound source) neutralized noise. A mixture of sounds creates a pleasant soundscape, as natural sound sources alleviating noise. The present study further discusses the importance of soundscapes and visual landscapes. However, one study suggested that soundscapes may exert a greater influence on people’s preferences [32]. Although the mechanisms and causes of the human response to both sounds and sights are not clear, it is widely believed that the loudness of sounds is important. Decibels (dB) are often used to describe the loudness of sounds in the acoustic environment [31].

Moreover, different senses may interact and affect the forest experience. The close relationship between vision and hearing is considered to be an important basis for landscape evaluation. Some research results suggest that sounds congruent with the outdoor environment can enhance people’s visual evaluation [33]. Similarly, visual stimuli may also affect the auditory experience of sound [34]. To understand the physiological and psychological response of the human body to changes in visual and auditory stimuli, we have carried out continuous studies. In a previous study, we explored the effects of the interaction between sound and plant density on neuroemotional indicators in humans [35]. However, findings based on a single indicator are not enough to determine a pattern. In general, FD and forest sounds (FS) are important forest trail landscape factors that need to be further explored. However, there have been few studies on the health benefits of audio-visual interactions of forest stimuli, and in-depth studies assessing more abundant physiological and psychological responses to these interactions have not yet been performed.

Landscape Stimulation and Psychophysiological Responses

In studies on the health benefits of forest landscapes, landscape stimuli need to be designed according to the experimental purpose. The landscape stimulus can be a real environment or a simulated image. Real environments are mainly suitable for conditions where the study variables are relatively simple and there are few environmental distractions. Simulated images are used to carefully manipulate variables of interest to determine the underlying mechanisms of effects [23, 24, 36–38].

Landscape stimuli can exert positive or negative effects on physiological and psychological variables. Previous studies have often focused on physiological responses by assessing indicators of the nervous and cardiovascular systems. With the development of electroencephalography (EEG), the accuracy and ease of capturing brain waves has improved. Increasingly, studies are using brain waves for neurological assessments, with β/α and θ/β often used to assess levels of stress and attention, respectively [39, 40]. Blood pressure, the heart rate (or pulse rate), and blood oxygen saturation (SpO₂) are commonly used physiological indicators of cardiovascular health [9, 41, 42]. Blood pressure and the pulse rate have also been used as indicators of stress in previous studies [9, 43]. Psychological assessments, such as the Profile of Mood States (POMS) [44], perceived restorative scale (PRS) [45], perceived stress scale (PSS) [46], and State-Trait Anxiety Inventory (STAI) [47], are commonly used to assess forest health benefits.

Research Objectives and Hypotheses

In summary, the aim of our study was to explore the effects of different combinations of FD and FS in forest trail landscapes on the physical and mental health of a college-student population. To achieve better experimental control, simulated images were used as landscape stimuli. We evaluated physiological stress according to physiological indicators (EEG β/α, blood pressure, SpO₂, and the pulse rate) and mood states according to psychological indicators (POMS scores). These results were used to determine characteristic patterns of health effects based on the audio-visual perception of forest trail landscapes. The results of the study were expected to provide scientific support for the construction of forest trails given their health benefits.

Considering the results of previous studies, we proposed the following research hypotheses for the main effects of FD and FS in the present study: (1) forest trail landscapes with medium or high FD are more likely to provide health benefits, and (2) forest trail landscapes are more likely to provide health benefits when natural sounds predominate over anthropogenic sounds.

Methods and Materials

Variable and Grouping

In this study, FD and FS in forest trail landscapes were used to manipulate visual and auditory perception, respectively. FD had three levels: high (Hd), medium (Md), and low density (Ld). FS had four levels: quiet natural and anthropogenic sounds (QnQa), quiet natural and loud anthropogenic sounds (QnLa), loud natural and quiet anthropogenic sounds (LnQa), and loud natural and loud anthropogenic sounds (LnLa). The levels of these two variables were combined to generate 12 experimental conditions as follows: high forest density–quiet natural and anthropogenic sounds (Hd-QnQa), high forest density–quiet natural and loud anthropogenic sounds (Hd-QnLa), high forest density–loud natural and quiet anthropogenic sounds (Hd-LnQa), high forest density–loud natural and loud anthropogenic sounds (Hd-LnLa), medium forest density–quiet natural and anthropogenic sounds (Md-QnQa), medium forest density–quiet natural and loud anthropogenic sounds (Md-QnLa), medium forest density–loud natural and quiet anthropogenic sounds (Md-LnQa), medium forest density–loud natural and loud anthropogenic sounds (Md-LnLa), low forest density–quiet natural and anthropogenic sounds (Ld-QnQa), low forest density–quiet natural and loud anthropogenic sounds (Ld-QnLa), low forest density–loud natural and quiet anthropogenic sounds (Ld-LnQa), and low forest density–loud natural and loud anthropogenic sounds (Ld-LnLa) (Fig. 1).

Fig. 1.

Variables used to generate the 12 experimental conditions

Forest trail landscape images were created in Photoshop, and the original images were taken in a real urban forest environment. Each image was of a forest trail environment. Only FD differed among images. Differences in other visual elements were controlled to reduce the interference of nonmanipulated variables. The high, medium, and low FDs used in this study refer to relative rather than absolute FDs.

FS were collected from real urban forest trails and included natural sounds (the sound of water in various forms in nature, the sound of wind blowing through leaves, and the sounds of birds, mammals, and insects) and anthropogenic sounds (the sounds of footsteps and people talking on the trail, and the sounds of vehicles and airplanes). We standardized the way sounds were mixed and the frequencies of sound in the 12 conditions, manipulating only the sound pressure level (an acoustic indicator that distinguishes the intensity of sound). We referred to the International Organization for Standardization (ISO) and China’s “Sound Environmental Quality Standard (GB 3096-2008)” to determine the recommended sound pressure level. The amplitude of loud (high intensity) sounds in this study was set at 65 dB, and the amplitude of quiet (low intensity) sounds was set at 45 dB. Using these simulated images and generated sounds, we generated 12 visual-auditory conditions.

Subjects

We randomly recruited 360 college students (with a male to female ratio of 1:1.2) by advertising with campus posters. Participants had a mean age of 21.5 years (range: 18–26 years) and normal or corrected-to-normal vision and hearing. We informed each participant in writing about the procedure and the equipment to be worn during the experiment. All participants signed informed consent forms. A series of research projects related to this study were approved by the relevant ethics committee.

Experiment Site and Time

The experiment was conducted in an environmental simulation room on a university campus (103°51'39″E, 30°42'22″N, 512 m). The room was 10 m in length and width and 4 m in height. To avoid any influence of external environment, the door to the room was kept closed during the test. The test was taken while facing the wall, and possible interference in the room was kept to a minimum. During the experiment, the room was kept quiet to ensure that the volunteers were not disturbed by external stimuli. The experiment was performed in March 2021 in two time windows: 8–12 am and 1–5 pm. The room temperature and humidity were maintained at 20−25°C and 52–68%, respectively.

Procedure

A pretest stress assessment and a posttest evaluation were the main stages of this experiment. Before the experiment started, a staff member guided the subjects to the testing area in the classroom and helped them to don the Emotiv EPOC X device, which was used to collect EEG data. First, subjects were administered a 5-min high-pressure task (the pretest). In this task, subjects were given a series of difficult mathematical problems and were instructed to “calculate the results accurately.” This procedure generates uniform stress in subjects, as the difficulty of the questions far exceeds university standards. Five minutes of EEG data were collected and transferred to a computer. After the pretest, the subjects’ blood pressure, SpO₂, and heart rate were measured, and their mood was assessed via the POMS. The posttest consisted of a 5-min recovery period of experiencing simulated forest trail landscapes (simultaneously viewing pictures and listening to audio) that varied across condition. As in the pretest, 5 min of EEG data were collected in the posttest, followed by measurements of blood pressure, SpO₂ (using a finger monitor), heart rate, and mood. The experiment finished once the posttest had ended (Fig. 2).

Fig. 2.

Flowchart of the experimental procedure

Collection of Physiological and Psychological Data

Physiological indicators included EEG, blood pressure, SpO₂, and the pulse rate. EEG signals were collected using the Emotiv EPOC X EEG device. The accuracy and safety of this device have been demonstrated in many previous studies [48, 49]. It uses 14 channels (AF3, et al.) that cover the 4 brain regions [50] (Fig. 3). The frontal regions support motor and language functions, occipital regions support visual functions, parietal regions support sensory processing and language functions, and temporal regions support auditory processing and language functions; this study focused on four electrodes related to audio-visual processing (O1 and O2 in the occipital lobe as well as T7 and T8 in the temporal lobe) (Fig. 3). Different EEG frequency bands reflect specific states. The α (8–13 Hz) frequency band represents complete relaxation during an active and awake state. The β (14–30 Hz) frequency band is higher, reflecting mental alertness and concentration as well as the emotions of anxiety, anger, tension, and excitement [48]. Considering numerous related studies, this study used the β/α indicator to assess physiological stress [26, 39, 51]. The higher the β/α value, the more anxious and agitated an individual is, and conversely, the lower this value, the more relaxed and calm and individual is.

Fig. 3.

Distribution of the 14 electrodes (channels) of the Emotiv EPOC X device across brain regions

Systolic blood pressure (SBP) and diastolic blood pressure (DBP) were measured using an Omron sphygmomanometer (HEM-7011). SpO₂ and the pulse rate were measured using a Yuwell finger pulse oximeter (YX301). The data were averaged over three measurements. Blood pressure and the pulse rate are commonly used to evaluate stress in the cardiovascular system. The higher the indicator is, the more stressed the individual [52]. SpO₂ reflects the amount of oxygenated hemoglobin in the blood as a percentage of all hemoglobin and is a physiological parameter related to the respiratory system; normal values should exceed 95%.

Psychological indicators mainly involved emotional state as assessed with the POMS. This study adopted the POMS model developed by Grove and Prapavessis [53] and further simplified by Zhu [54] for Chinese individuals. The POMS contains 40 feelings, and the subjects mark the frequency with which they have experienced these feelings on a 5-point scale (from not at all too extremely). Scores on the 40 feelings are used to calculate 7 mood scores: stress, anger, fatigue, depression, panic, energy, and self-esteem. These 7 mood scores are further combined into 3 indicators: positive mood (energy + self-esteem), negative mood (tension + anger + fatigue + depression + panic), and total mood disturbance (TMD = negative mood − positive mood + 100) [44].

Statistical Analysis

SPSS 22.0 was used for the analysis. All indicators were analysed using analyses of covariance (ANCOVAs) to observe group differences. The data from the pretest were used as covariates in the ANCOVAs, and FD and FS were independent variables. We examined the main effects of FD and FS as well as the effects of the interaction (FD × FS). If the interaction was significant, post hoc simple effects analysis was performed.

Results

EEG results

As mentioned previously, this study focused on EEG data from the occipital (visual function) and temporal (auditory function) regions; thus, data from four channels (O1, O2, T7, and T8) were analysed. There were significant main effects of FD and FS in multiple channels according to the ANCOVA results (Fig. 4, Table S1). A significant main effect of SD on β/α values was observed in the O1 and T7 channels. In the O1 channel, low FD (M=0.411) had significantly higher β/α values than medium FD (M=0.357) and high FD (M=0.349). In channel T7, low FD (M=0.684) had significantly higher β/α values than high FD (M=0.524) and medium FD (M=0.465). Additionally, a significant main effect of FS on β/α values was observed in channels O1 and T7. In channel O1, QnLa sounds (M=0.417) had significantly higher β/α values than QnQa sounds (M=0.331). In channel T7, LnLa sounds (M=0.653) had significantly higher β/α values than QnQa sounds (M=0.457).

Fig. 4.

β/α values in the main effects analysis (N=30 per group). Data are shown as the mean ± standard error;*p<0.05 and **p<0.01

A significant FD × FS interaction effect was observed only in the O1 channel (Table S1). Simple effects analysis revealed the following results (Fig. 5, Table S2). At medium FD, LnQa sounds (M=0.395) had significantly higher β/α values than QnQa sounds (M=0.279), and LnLa sounds (M=0.397) had significantly higher β/α values than QnQa sounds (M=0.279). At low FD, QnLa sounds (M=0.500) had significantly higher β/α values than LnQa sounds (M=0.279), and QnLa sounds (M=0.380) had significantly higher β/α values than LnLa sounds (M=0.342). For QnQa sounds, low FD (M=0.422) had significantly higher β/α values than high FD (M=0.293), and low FD (M=0.422) had significantly higher β/α values than medium FD (M=0.279). For QnLa sounds, low FD (M=0.500) had significantly higher β/α values than high FD (M=0.395), and low FD (M=0.500) had significantly higher β/α values than medium FD (M=0.356).

Fig. 5.

β/α values in the simple effect analysis (N=30 per group). Data are shown as the mean ± standard error; *p<0.05 and **p<0.01

Blood Pressure, SpO₂, the Pulse Rate, and Mood States

The blood pressure, SpO₂, pulse rate, and mood state results of the ANCOVAs are shown in Fig. 6 and Table S3. We observed a significant main effect of FD on positive mood scores. Mood scores were significantly higher at both high FD (M=26.882) and low FD (M=26.251) than at medium FD (M=24.487). Additionally, there was a significant FD × FS interaction effect on DBP, the pulse rate, positive mood scores, and TMD (Table S3). Simple effects analysis revealed the following results (Fig. 7, Table S4).

Fig. 6.

Blood pressure, SpO₂, pulse rate, and mood data in the main effects analysis (N=30 per group). Data are shown as the mean ± standard error; *p<0.05 and **p<0.01

Fig. 7.

DBP, the pulse rate, positive mood scores, and TMD scores in the simple effect analysis (N=30 per group). Data are shown as the mean ± standard error; *p<0.05 and **p<0.01

For LnQa sounds, medium FD (M=65.928) had significantly higher DBP values than low FD (M=59.523). At high FD, LnLa sounds (M=66.415) had significantly higher DBP values than QnLa sounds (M=61.418). At low FD, QnLa sounds (M=65.574) had significantly higher DBP values than LnQa sounds (M=59.523), and LnLa sounds (M=65.140) had significantly higher DBP values than LnQa sounds (M=59.523).

For QnLa sounds, high FD (M=77.713) induced significantly higher pulse rates than medium FD (M=72.403); for LnQa sounds, low FD (M=77.784) induced significantly higher pulse rates than high FD (M=72.882).

At high FD, LnQa sounds (M=28.649) induced significantly higher positive mood scores than QnLa sounds (M=23.865), and LnLa sounds (M=29.232) induced significantly higher positive mood scores than QnLa sounds (M=23.865). At low FD, QnQa sounds (M=29.337) induced significantly higher positive mood scores than LnQa sounds (M=25.045), and QnQa sounds (M=29.337) induced significantly higher positive mood scores than LnLa sounds (M=24.780). For QnQa sounds, low FD (M=29.337) induced significantly higher positive mood scores than medium FD (M=24.900). For LnLa sounds, high FD (M=29.232) induced significantly higher positive mood scores than medium FD (M=23.514), and high FD (M=29.232) induced significantly higher positive mood scores than low FD (M=24.780).

At low FD, LnQa sounds (M=116.558) induced significantly higher TMD scores than QnQa sounds (M=105.329); for LnLa sounds, medium FD (M= 117.260) induced significantly higher TMD scores than high FD (M=108.127).

Discussion

Higher Forest Density Is More Likely to Provide Health Benefits

The main effect of FD revealed that higher FDs (high or medium FD) elicited EEG and positive mood score values indicating lower physiological stress and more positive emotional states, i.e., higher FDs are more likely to provide health benefits. These results support hypothesis 1 and are consistent with Jiang’s [28] findings on the relationship between vegetation density and stress recovery, i.e., that low vegetation density is more likely to induce anxiety and that high vegetation density provides better stress recovery in green space. Similarly, Li [55] found that the amount of greenery seen from the classroom (vegetation density) significantly influenced student performance on attention tests and facilitated student recovery from stressful experiences. In another of her studies, she used land cover datasets, satellite imagery, and other techniques to explore the positive effects of vegetation cover and density in natural landscapes to adolescent mood [56]. Burgess [27] found that higher vegetation density was associated with better physical and mental health and that people preferred green spaces with higher levels of vegetation density. In a study conducted by Bjerke [57] in Norway on public perceptions of park vegetation density, medium-density urban park landscapes were most preferred by the public.

In ecological terms, a too low FD may lead to lower biodiversity. Many previous studies have shown that forest interiors with a more uniform vegetation structure and richer biodiversity provide a better sense of relaxation [58]. Attention recovery theory (ART) considers “being away” a condition for natural health benefits [59, 60]. “Being away” is the experience of creating distance between oneself and the context of mental fatigue. ART notes that the feeling of being away does not necessarily require physical distance; for example, exposure to the natural environment under virtual conditions can also create psychological distance between the individual and the stressor [61]. Therefore, higher FD of forest trail landscapes in this study may be more likely to give the impression of being away from the urban built environment.

Quieter Forest Sounds Are More Likely to Reduce Physiological Stress

The significant main effects of FS on EEG data (the O1 and T7 channels) indicate that forest trail landscapes with quieter natural and anthropogenic (QnQa) sounds induce lower physiological stress. In contrast, forest trail landscapes with louder anthropogenic sounds (the QnLa or LnLa conditions) induced greater physiological stress. These results are not consistent with hypothesis 2. Previous studies on nature sounds have focused on their correlation with pleasant feelings [31]. However, pleasant feelings are not exactly the same as reduced physiological stress. Previous research has shown that sound stimulation at high amplitudes (i.e., loud sounds) can cause unpleasant sensations (annoyance) and increase physiological stress [62]. The quieter soundscape of the natural environment is widely considered a very important aspect; thus, high-amplitude natural sounds may also affect the quietness of the forest environment [63]. Thus, from the physiological stress perspective, it appears that quieter sounds are key.

What Forest Density and Forest Sound Combinations Are More Likely to Reduce Stress?

The effects of the FD × FS interaction on EEG data (the O1 channel), DBP, and the pulse rate all indicate that combinations of higher FD (medium or high FD) and quieter natural sounds (the QnQa or QnLa conditions) reduced physiological stress when experiencing forest trail landscapes. The combination of low FD and loud natural sounds (the LnQa condition) also reduced physiological stress. Are there shared aspects of these two results? Stress reduction theory (SRT) suggests that people recover from psychophysiological stress through deeper contact with the natural environment that lowers blood pressure and reduces stress hormone levels [64]. Guo [65] examined the restorative effects of audio-visual stimuli in parks and showed that visual landscape characteristics mediated the relationship between soundscape pleasantness and restorability (explaining 19.3% and 28.3% of the total variance, respectively). This suggests that landscape effects are not based purely on a single modality and that visual and auditory stimuli interact during landscape assessment.

Based on these findings, we speculate that there may be additive effects of natural elements and perceptual complementation involved in the reduction of physiological stress in the natural environment. According to SRT, exposure to an adequate number of natural elements is necessary for beneficial effects [64, 66]. The natural elements perceived through sight and sound may have additive effects. Visual perception is the primary information modality in humans [67]. In this study, participants tended to favour quiet sounds in dense forest paths (with abundant natural elements). Conversely, in sparse forests (insufficient natural elements), louder natural sounds complement the visual elements in landscape evaluation. The above two types of results both lead to greater reductions in physiological stress.

What Forest Density and Forest Sound Combinations Are More Likely to Elicit Positive Emotions?

The significant effects of the FD × FS interaction on emotions (positive mood and TMD) revealed that the combination of high FD and loud natural sounds (the LnQa or LnLa conditions) elicited positive emotions and reduced negative emotions. Additionally, low FD combined with quiet natural sounds (the QnQa condition) elicited positive emotions. We found that both of these two types of results were consistent across modalities (visual and auditory) in terms of FD (both high and low). Carles’s [68] work on combinations of sounds and landscape photographs suggested that people’s evaluation of a sound is largely determined by how well it matches the setting in which it occurs (such as natural sounds in a natural setting). When sounds are not congruent with the environment and the environment does not provide the same readable information, sounds are considered “noise” and rated negatively. Another of his studies showed that the congruency of sounds and visual images in urban green spaces influenced the perceived preferences of the public [68]. Anderson’s research also demonstrated that when people hear expected sounds, the visual reality of the environment is enhanced [33].

The three studies described above all used subjective evaluation methods, similar to the POMS adopted in our study. We speculate that a possible explanation as to why visual and auditory congruency positively influences subjective evaluations is that subjective evaluations are based on common sense. People therefore prefer combinations consistent with common sense or expectations, such as high FD and loud natural sounds or low FD and quiet natural sounds in this study. The combination that elicits positive emotions differs from the combination that reduces stress. Such differences reflect the complexity of interactions between visual and auditory perception.

Applications

Based on the main effects in our analyses, we suggest that the proportion of trees in urban forest trail landscapes should be increased and that increased density of three-dimensional space will be particularly beneficial. Additionally, forest trails should be planned away from noisy urban areas, creating a quieter soundscape that is more conducive to health.

Based on the interaction effects, we suggest that urban forest trails should be constructed according to local conditions to improve health. As grey spaces (e.g., buildings and roads) and green spaces (e.g., urban forests) are intricately intermingled in urban areas, the selection of areas for forest trails in the planning stage should evaluate both the visual and auditory environments, considering the multisensory interaction effects.

Furthermore, some common patterns of the effects of combinations were found, such as a reduction in stress or an increase in positive emotions. However, no combination improved multiple indicators at once. This led to another important insight, namely, that multisensory interaction-based forest trail landscapes do not always elicit consistent physiological and psychological responses. In the real environment, or even in future development and application of virtual products, it is important to plan and build according to health needs or to make targeted choices.

Limitations and Future Research

The limitations of this study, as an exploratory study assessing multiple senses, lie first and foremost in the use of simulated scenarios. Although simulated images provide better experimental control, the findings still need to be validated in a real forest trail landscape. Second, although the selection of study participants was broad and randomized, the ability of university students to represent the general population is limited, and future studies should be more systematic in terms of assessing perceived benefits in different groups of people. In addition, the audio-visual perceptual interactions suggest differences between indicators. While we have provided logical speculations, but there is an urgent need to conduct more in-depth research on the causes or mechanisms of these interactions in the future.

Conclusion

Based on visual and auditory perceptual modalities, this study explored the physiological and psychological responses of participants when experiencing simulated forest trail landscapes that varied in two variables (FD and FS). The main effects of FD and FS indicated that higher FD or quieter FS were more conducive to health. The interaction between the two variables revealed specific combinations that promoted stress reduction and positive emotions. However, the optimal audio-visual combinations were not identical across indicators, which is another important finding. The results of our study are of theoretical value for the construction of urban forest trail landscapes that improve health.

Funding

This research was funded by the Talent Initiation Program of the Scientific Research Development Fund of Zhejiang A&F University (grant nos.: 2022LFR040 and 2021LFR041).