International Journal of
Environmental Research
and Public Health
Article
The Effect of Forest Video Using Virtual Reality on the Stress
Reduction of University Students Focused on C University
in Korea
Seong-Hee Jo 1 , Jin-Seok Park 2 and Poung-Sik Yeon 2,*
1 Forest Welfare Research Center, Korea Forest Welfare Institute, Youngju 36043, Korea; jorent@chungbuk.ac.kr
2 Department of Forest Therapy, Chungbuk National University, Cheongju 28644, Korea; f-welfare@naver.com
* Correspondence: well@chungbuk.ac.kr; Tel.: +82-43-261-3412
Citation: Jo, S.-H.; Park, J.-S.; Yeon,
P.-S. The Effect of Forest Video Using
Virtual Reality on the Stress
Reduction of University Students
Focused on C University in Korea. Int.
J. Environ. Res. Public Health 2021, 18,
12805. https://doi.org/10.3390/
ijerph182312805
Academic Editor: Paul B. Tchounwou
Received: 10 November 2021
Accepted: 2 December 2021
Published: 4 December 2021
Publisher’s Note: MDPI stays neutral
with regard to jurisdictional claims in
published maps and institutional affil-
iations.
Abstract: The purpose of this study is to study the effect of forest videos using virtual reality (VR)
on the stress of college students. The study subjects were 60 college students who watched two-
dimensional (2D) and VR videos, and we compared their control heart rate variability (HRV) and
electroencephalogram (EEG). As a result, it was found that the VR group had a positive effect on high
frequency (HF), standard deviation of all NN intervals (SDNN), and root-mean-square of successive
differences (RMSSD) compared with the control group, and the VR group had a positive effect on HF
compared with the 2D group. Second, EEG, a physiological indicator, showed statistical differences
in Relative Alpha Power (RA), Relative Beta Power (RB), and Ratio of SMR–Mid Beta to Theta (RSMT)
in VR groups in intra-group analysis. Among them, it was investigated that watching forest videos
on VR became a state of concentration and immersion due to the increase in RSMT. As a result of
the above, it was investigated that forest videos using VR had a positive effect on the physiological
stress on college students. Therefore, it is expected that a positive effect will occur if VR is used as an
alternative to stress management for college students.
Keywords: forest video; virtual reality; stress; HRV; EEG
1. Introduction
According to the Korea National Statistical Office [1], 7.9% of adolescents aged 20 to
29 said they felt suicidal, which is the highest figure compared to other age groups [2].
In fact, the suicide rate of college students was 44.2% in 2019, 44.2% in 2018, 42.2% in
2017, and 42.9% in 2016 among suicides of adolescents aged 20 to 29 [1]. Suicide research
is conducted on all age groups, but it is necessary to pay particular attention to college
students. According to statistics on causes of death over the past four years, as shown in
Table 1, suicide is the number one cause of death for college students in their 20s [1]. It is a
sad reality for young people who will lead the future society, and it has a negative impact
on modern society.
Table 1. 2016–2019 Statistics on the cause of death in the 20s (unit: Death rate (per 100,000 people), N).
Copyright: © 2021 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
Year
Category
Suicide
Traffic accident
Malignant neoplasm
Heart disease
2016
16.4
5.7
4.2
1.5
1097
379
284
100
2017
16.4
5.1
4.0
1.5
1106
347
273
102
2018
17.6
4.3
3.9
1.5
1192
293
267
103
2019
19.2
3.7
4.2
1.4
1306
253
283
92
Int. J. Environ. Res. Public Health 2021, 18, 12805. https://doi.org/10.3390/ijerph182312805
https://www.mdpi.com/journal/ijerph
Int. J. Environ. Res. Public Health 2021, 18, 12805
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Modern college students are greatly influenced by anxiety about their career path [3]
and difficulties living in reality [4]. The stressful environment appears as anxiety [5,6],
depression [5–7], suicide [8], social isolation, etc., and the severity is increasing.
In their 20s, the static influence on suicidal thoughts is influenced by various factors.
According to a study by Kim [9], life stress in college students has a positive effect on
depression and suicidal thoughts, and various suicide thoughts such as stress with family
and friends, suicide with no sense of shame, and defeat [10–13]. The main reasons for the
influence on college students’ suicidal thoughts were various stresses and depression.
Forest landscapes can have a healing effect; they improve health by reducing and
recovering human psychological and physiological stress [14,15]. Psycho-evolutionary
theory (PET), asserted by Ulrich [14], is a theory that explains how the natural environment
reduces response to stress. According to the theory, stress is a physiological reaction to
all situations that threaten a healthy life, which causes negative emotions and activation
of the sympathetic nervous system. Various natural environments help recover stress,
develop an appropriate state of interest, and feel pleasure and tranquility. In the previous
situation, negative emotions change to positive emotions, negative emotions soften, and
the condition of the sympathetic nervous system decreases. The Attention Restoration
Theory (ART), advocated by Kaplan [15], is one of the representative theories that explain
the physiological and psychological comfort of the forest environment along with PET. In
order for us to continue our daily lives, we must listen to the surroundings and special
information. However, attention is gradually reduced due to stimuli generated by external
environmental factors or internal psychological factors. In other words, there are external
factors that reduce attention in our daily lives, and there is a limit to human attention, so if
attention is excessively focused due to external environmental or internal psychological
factors for a long time, the capacity of attention is reduced [16]. When shown images of
forests, valleys, and trees as well as the sound of wind, water, and birds that can be heard
in the forest, the stress condition recovered faster than the urban environment [17]; another
study found that viewing natural paintings is more effective in reducing stress [18]. A study
by Woo [19] found that indirect forest experience activities and forest image walking, when
examining the effect on the mental health of female and humanities high school students,
showed improved self-esteem, and humanities high school students had reduced stress
and improved psychological well-being. Through many studies, it has been found that the
natural environment such as vegetation and water systems becomes an environment that
causes stress recovery [20–23], and the indirect use of forests has a positive effect on the
human body [24–26].
We are currently living in the era of the Fourth Industrial Revolution. Various modern
technologies such as augmented reality (AR), 3D industry, autonomous driving, and virtual
reality (VR) are emerging. Virtual reality is a “virtual world that feels real” and is also called
an artificial environment or a synthetic environment. It is a space that aims to provide
an “as if it is real” experience by taking advantage of the fact that human visual, smell,
hearing, and tactile experiences are eventually processed in the brain [27]. The ultimate
goal of virtual reality is to make the senses felt by humans in the real world feel the same
in the virtual world [28]. One of the most practical technologies among VR is Virtual
Reality Therapy (VRT). It provides an opportunity for users to immerse themselves in
a realistic but non-realistic treatment environment. Thanks to virtual reality technology,
patients can be exposed to situations that are difficult to reproduce, and stimuli that can
shift attention from pain can be presented [27]. It is used as various intervention methods
using VR. VR is used in various ways such as rehabilitation [29–31], sleep [32,33], and pain
reduction [34–37].
The preceding studies above suggest the direction of applying virtual reality tech-
nology. In order to obtain the effect of using forests, there is a spatial constraint in that
you have to go directly to the mountains or forests. For those who cannot easily go to the
mountains or forests, they do not have the effect. Virtual reality, a modern technology that
will compensate for these shortcomings, is developing, but previous studies that applied
Int. J. Environ. Res. Public Health 2021, 18, 12805
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virtual reality to forests are insufficient. Therefore, this study is necessary to study ways to
expand opportunities for forest health utility by overcoming the limitations of space to go
to the space directly and applying new modern technologies and ways to manage stress,
and it is differentiated from existing studies.
The purpose of this study is to investigate the effect of watching forest videos using
virtual reality (VR), a modern technology, on the stress reduction of college students and
to suggest a stress management plan for college students. The specific objectives of this
study are as follows. First, the effect of watching forest videos using VR on heart rate
variation (HRV) changes in college students is investigated. Second, the effect of watching
forest videos using VR on the electroencephalogram (EEG) change of college students is
investigated.
2. Materials and Methods
2.1. Subjects and Scope of Research
This study conducted a study on college students to study the effect of forest videos
using VR on the stress of college students. On 11 November 2020, IRB research approval
(CBNU-2011-HR-0176) was obtained from Chungbuk National University’s Institutional
Bioethics Committee, and the subjects of the study were college students in Cheongju.
The study subjects recruited subjects who had no cardiovascular disease, physical disease,
mental illness, and audiovisual abnormalities in watching videos, and the study was
conducted with the consent of the study subjects.
Groups were randomly created after the homogeneity of the study subjects was
verified. A total of 60 subjects were counted: 20 in the control group without any treatment,
20 in the experimental group watching 2D videos, and 20 in the experimental group
watching VR videos. The demographic characteristics are shown in Table 2. All participants
conducted the study without dropouts, and 60 subjects were statistically processed. The
spatial scope was conducted in the research laboratory of University C in Cheongju-si. It
was conducted from 11 November 2020 to 25 December 2020 in the time range, and a total
of 4 sessions were conducted in the form of once a week.
Table 2. Demographic characteristics.
Division
Category
N
VR
20
Group
2D
Control
20
20
All
60
Man
44
Sex
Woman
16
All
60
1
1
2
13
Grade
3
16
4
30
All
60
2.2. Research Progress Procedure
As for the group of this study, a total of three groups were divided into a group that
watches VR videos, a group that watches 2D videos, and a control group that does not
watch anything. The group watching VR videos watched a forest video taken by GoPro
Fusion with GoPro Studio that was converted it into a video that can be viewed 360 degrees.
The 2D group viewed their video through a general monitor. The control group had no
treatment related to the forest.
All three groups had their heart rate variation (HRV) and electroencephalogram
(EEG) measured to verify homogeneity between groups, and a questionnaire on general
characteristics was conducted. HRV was tested for 2 min and 30 s, and EEG was measured
Int. J. Environ. Res. Public Health 2021, 18, 12805
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for 5 min. The group who watched VR and 2D videos watched the video once a week for a
total of four times a week for about 5 min per viewing, and the control group proceeded
without any treatment. After all experiments were over, the three groups conducted
post-examinations (HRV, EEG, and survey), and the procedure for this study is as shown
in Figure 1.
Figure 1. Research process.
2.3. Research Tool
2.3.1. 360◦ Camera
Using the GoPro Fusion (GoPro Inc., San Mateo, CA, USA) camera (Figure 2), 360◦
images were taken and processed by GoPro Fusion Studio (GoPro Co. Ltd.) Using America,
a 360-degree image was produced by stitching two 180-degree images. The direct forest
video was filmed with a light walk on the forest-bound deck path for about five minutes,
and the filming period was filmed from September to October 2020. The videos used in
the study were videos of a total of four locations, and HMD was used and driven through
GoPro’s VR player.
’
Figure 2. The 360◦ camera.
2.3.2. HMD VR Device
A Samsung Odyssey+ VR HMD VR device XQ800ZBA-HC2KR (Samsung Inc. Seoul,
Korea) was used. It is worn as a headset on the head, and after connecting the HMD device
to the computer, the video was played through Window Mixed Reality.
2.3.3. Physiological Examination
Heart rate variability (HRV)
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The heart rate variation measurement product used in this study was Ubiomacpa/
UBioClip v40 (Biosence Creative Inc., Seoul, Korea) equipment. Using Ubiomacpa (Biosence
Creative Co. Ltd., Korea) software, data result values were stored on the computer in real
time. Clip-type heart rate variation measuring equipment is equipped with a technology
to measure heart rate through light reflection changes in the hemoglobin of fingertip
capillaries. The test was conducted in a stable state for about 2 min and 30 s after wearing
equipment on the subject’s index finger. To measure changes in autonomic nervous system
activity, standard deviation of heart rate and heart rate (standard deviation of all NN
intervals, SDNN) and high frequency (HF) and low frequency (LF) of the parasympathetic
and sympathetic nervous system were used. Additionally, root-mean-square of successive
differences (RMSSD) was used to determine cardiac stability.
Electroencephalogram (EEG)
EEG is a physiological monitoring method that records brain activity from electrodes
attached to a person’s scalp. It is non-invasive, and the electrodes are placed along the scalp.
It is usually referred to as “EEG”, and it is used to study overall brain-related functions
such as sleep research, anesthesia monitoring, and emotional measurement tools. EEG
analysis used in this study stored data in real time on a computer through Cygnus (BioBrain
Co. Ltd. Korea) software, and the EEG device used was BIOS-ST (BioBrain Inc.) Daejeon,
Korea) equipment. Currently, the basic analysis method used is the frequency series power
spectrum analysis through Fast Fourier Transformation (FFT). Power spectrum analysis
is an analysis method used to determine the pattern of signals according to the degree of
frequency change by converting time series signals that change over time into frequency
domains [38]. Each equipment, wearing appearance, and international electrode law are as
shown in Figure 3.
Figure 3. Appearance of wearing experimental equipment and international electrode placement
method.
2.4. How to Analyze Data
The statistics of the collected data were analyzed using the statistical program SPSS
ver. 18.0.
Frequency analysis was conducted on the general characteristics of the study subjects.
Kruskal–Wallis test and Mann–Whitney’s U-test were performed to verify homogene-
ity between g–roups and to find out d–ifferences’between pre and post.
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Wilcoxon code ranking test was performed to compare the differences between the
pre- and post-group.
For all analyses, the statistical significance level was set to p < 0.05.
3. Results
3.1. HRV (LF, HF, SDNN, RMSSD)
HRV tests were performed before and after group treatment, and Mann–Whitney’s
U-test was performed by analyzing the results between groups. The results are shown
in Table 3 and Figure 4. The VR group showed a more positive effect on HF, SDNN, and
RMSSD than the control group, and they showed statistical significance (p < 0.05). The
2D group showed a greater difference in positive effects on SDNN and RMSSD than the
control group (p < 0.05), and the VR group showed a more positive effect on HF than the
2D group (p < 0.05).
Table 3. HRV pre–post result.
–
VR–Control
Pre-Test
Post-Test
LF
HF
SDNN RMSSD LF
HF
SDNN RMSSD
U
140.5
191.5
175.5
199.0
173.5
61.0
124.0
122.0
Z
−1.613 −0.231 −0.663 −0.663 −0.718 −3.772 −2.056 −2.110
p
0.107
0.818
0.507
0.978
0.473 0.000 * 0.040 * 0.035 *
−
−
−
−2D–Control −
−
−
−
Pre-test
Post-test
LF
U – 176.5
HF
166.5
SDNN RMSSD
189.0
183.0
LF
196.0
HF
139.5
SDNN RMSSD
119.5
110.0
Z
−0.637 −0.907 −0.298 −0.460 −0.108 −1.641 −2.178 −2.435
p
0.524
0.364
0.766
0.646
0.914
0.101 0.029 * 0.15 *
VR–2D
Pre-test
Post-test
−
−
U
−
LF
154.5
1H6−4F.5
S1D8N3.5N−
RMSSD
186.0
−
LF
179.5
1H2−5F.0
S1D9N0.5N−
RMSSD
190.5
Z
−1.233 −0.962 −0.446 −0.379 −0.556 −2.036 −0.257 −0.257
p
0.218
0.336
0.655
0.705
0.578 0.042 * 0.797
0.797
*: p < 0.05; LF: low frequency; HF: high frequency; SDNN: standard deviation of all NN intervals; RMSSD: root
mean square of successive differences.
Figure 4. HRV results by group. *: p < 0.05.
–
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3.2. EEG (RA, RB, RSMT)
The descriptive statistics of EEG by group are shown in Table 4. In each electrode
indicator, the power value of the Relative Alpha Power Spectrum (RA) decreased in all
groups. The power values of the Relative Beta Power Spectrum (RB) and Ratio of SMR–Mid
Beta to Theta (RSMT) showed an increase. Among them, the RB and RSMT power values
of the VR group increased more than those of the other groups.
The Wilcoxon code ranking test was performed to compare the pre- and post-mortem of
VR groups by index within the group, and the results are shown in Table 5 below. In the VR
group, the indicators of RB showed statistical significance overall (p < 0.05) and partial statistical
significance in RA and RSMT (p < 0.05). In the VR group, RA tends to decrease, while RB and
RSMT tend to increase, as shown in Figure 5, which is mapped. The gauge in Figure 5 means
the power value. The red color indicates a higher power value, and the blue color indicates a
lower power value. In the case of ‘RA’, the power value is lower than before the experiment,
indicating blue. The power values of ‘RB’ and ‘RSMT’ increase to indicate red. The activation of
‘RA’ was lowered, and ‘RB’ and ‘RSMT’ were further activated.
Category
RA
RB
RSMT
Figure 5. VR group EEG mapping.
Table 4. EEG descriptive statistics.
Group
VR
Fp1
0.317
Fp2
0.319
T3
0.240
Pre-Test
T4
O1
0.286
0.452
O2
0.478
Fz
0.345
Pz
0.284
Fp1
0.226
Fp2
0.223
T3
0.176
Post-Test
T4
O1
0.192
0.386
O2
0.403
2D
0.292
0.282
0.221
0.254
0.388
0.429
0.339
0.268
0.229
0.224
0.162
0.182
0.353
0.373
C
0.241
0.236
0.212
0.234
0.362
0.378
0.271
0.315
0.228
0.228
0.176
0.199
0.345
0.368
VR
0.244
0.242
0.283
0.288
0.228
0.212
0.259
0.269
0.322
0.328
0.359
0.368
0.288
0.281
2D
0.282
0.284
0.313
0.321
0.268
0.269
0.270
0.305
0.320
0.320
0.341
0.362
0.301
0.290
C
0.298
0.316
0.327
0.330
0.292
0.285
0.333
0.318
0.306
0.310
0.341
0.343
0.290
0.272
VR
0.891
0.894
1.403
1.492
2.011
1.901
1.143
1.690
1.733
1.787
2.542
3.333
2.604
2.700
2D
1.346
1.538
1.495
1.886
2.368
2.700
2.006
2.360
1.465
1.530
2.067
2.357
2.651
2.694
C
1.868
1.937
2.182
3.020
2.631
2.838
2.194
2.380
1.789
1.730
2.265
2.536
2.553
2.491
RA: Relative Alpha Power Spectrum; RB: Relative Beta Power Spectrum; RSMT: Ratio of SMR–Mid Beta to Theta.
Fz
0.254
0.280
0.251
0.336
0.309
0.313
1.742
1.493
1.951
Pz
0.227
0.190
0.233
0.373
0.387
–0.339
3.110
3.069
2.734
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Table 5. VR group post-test EEG analysis.
Category
Fp1
Fp2
T3
T4
O1
O2
Fz
Pz
RA
Z −3.621a −3.696a −3.323a −3.808a −2.389a −2.688a −3.211a −1.792a
p 0.000 * 0.000* 0.001 * 0.000 * 0.017 * 0.007 * 0.001 *
0.073
RB
Z −3.472a −3.696a −3.285a −3.621a −3.248a −3.472a −2.875a −2.875a
p 0.001 * 0.000 * 0.001 * 0.000 * 0.001 * 0.001 * 0.004 * 0.004 *
RSMT
Z −3.024a −3.099a −2.987a −2.875a −1.829a −2.128a −2.539a −2.501a
p 0.002 * 0.002 * 0.003 * 0.004 * 0.067 0.033 * 0.011 * 0.012 *
*: p < 0.05; RA: Relative Alpha Power Spectrum; RB: Relative Beta Power Spectrum; RSMT: Ratio of SMR–Mid
Beta to Theta; a: Based on the positive number.
4. Discussion
First, heart rate variability was measured first to find out the effect on physiological
stress. Comparison between groups showed that the VR group had a more positive
significant difference in HF, SDNN, and RMSSD than the control group (p < 0.05), indicating
that the VR group had a more significant difference in HF increase than the 2D group
(p < 0.05). As the VR group appears to have a more positive effect on HF, SDNN, and
RMSSD than the control group [39], it is considered that the forest video viewed as an image
has a physiologically positive effect. This showed similar results to previous studies [40–45]
in which forest activities reduce stress.
Second, EEG was measured for the second time to find out the effect of physiological
stress, and indicators of RA, RB, and RSMT were used. As an intra-group comparison, RA
and RB showed statistical significance in overall indicators in the VR group (p < 0.05) and
partial statistical significance in RSMT (p < 0.05). Among them, the increase in beta waves
and the increase in RSMT showed that immersion and concentration were concentrated
on images physiologically while watching forest videos on VR, similar results to previous
studies [46,47], and the decrease in alpha waves decreased when attention was being
focused [48].
Forest activities have a positive effect on the psychological aspect of college stu-
dents [49], as shown in studies in which intervention through forests plays a beneficial
mediating role in mental health such as stress and anxiety [50,51]. In addition, a study
showed increases in physiological well-being when walking after appreciating bamboo
forest paths for 15 min [52], and a study showed that forest rest environments using virtual
reality have a physiological positive effect on the human body [53]. Compared to urban
areas, it was confirmed through previous studies that forests have a positive effect on the
human body, including increased positive moods such as vitality and decreased negative
moods [54], and this study confirmed that virtual forests applied with modern technology
have an indirect effect on the human body [55].
As a result of the previous study, it was found that watching forest videos on VR
provided college students with a state of immersion and concentration, that is, attention,
and had a physiological positive effect on stress, and I would like to propose them as a way
to reduce stress.
This study has the characteristics of a preliminary study, and as a limitation of this
study, the number of samples of the study subjects is small, so it cannot be generalized.
There are control variables for the external environment of the study subjects. The period
during which this study was conducted can affect the experimental results due to the test
period of college students, weather changes, and the increase in COVID-19-confirmed
patients, and the period and place of the study should be determined in consideration
of external environmental factors. In future studies, directions for increasing reliability
through control of the number of samples and the external environment of the study
subjects are presented.
Int. J. Environ. Res. Public Health 2021, 18, 12805
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5. Conclusions
The discussion through the conclusion of this study is as follows.
First, the effect of virtual reality forest videos using VR on the stress of college students
is to reduce and have a positive effect on the stress of college students, and I would like to
suggest a forest experience using VR as a stress management plan for college students. It
was found that the stress of college students is affected in various ways and there is more
stress caused by employment. Stress management is necessary because excessive stress
increases depression and degrades the quality of college life and daily life.
Second, unlike this study, which moves freely only in sight with a simple 360-degree
video, it is expected that producing content that can interact with virtual reality will further
feel interest, immersion, and reality and relieve stress.
Third, if a person who can receive the health promotion effectiveness of forests using
virtual reality is applied to a different class than only college students, it will have a positive
effect on physical disability or elderly care facilities with a lot of indoor life.
Fourth, I would like to propose identifying the mechanisms of beneficial advantages
of forests to humans using VR. In this study, vision and hearing were used among human
senses. In subsequent studies, it is expected to help verify the utility of forests using sight
and hearing.
Fifth, since the effect was verified only with VR images and 2D images, that is, the
same forest video, more accurate verification of the effectiveness of the forest video is
required. In order to see the effect of forest videos, a more detailed research plan is needed
to compare forest videos with other natural videos (sea, sky, etc.) in VR videos to check the
effect of forests more accurately, or between groups that directly go through forests and
groups that view the same forest as VR videos.
Author Contributions: S.-H.J. performed data acquisition, statistical analysis, interpretation of the
results, and manuscript preparation. J.-S.P. was involved with acquisition of data. P.-S.Y. had an
important role in the overall performance of this research, particularly in experimental design and
research ideas. All authors have read and agreed to the published version of the manuscript.
Funding: This research was supported by the Chungbuk National University Korea National Uni-
versity Development Project (2020). (Funding number: 2020100127).
Institutional Review Board Statement: This study was approved by the Institutional Review Board
of Chungbuk National University (IRB number: CBNU-2011-HR-0176).
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.
Data Availability Statement: The data presented in this study are available on request from the
corresponding author. The data are not publicly available due to privacy.
Conflicts of Interest: The authors declare no conflict of interest.
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