Effects on Heart Rate Variability and EEG of Type of Participation in Health Tourism Programs Involving Stays in Hot Springs and Forests in Asan

Article information

J. People Plants Environ. 2024;27(6):675-683
Publication date (electronic) : 2024 December 31
doi : https://doi.org/10.11628/ksppe.2024.27.6.675
1Associate Professor, Department of Recreaction and Leisure Sports, DanKook University, Chungcheongnam-do 31116, Republic of Korea
2Research Professor, Sports Science Institute, DanKook University, Chungchengnam-do 31116, Republic of Korea
3Visiting Professor, Department of Sports Healthcare, DanKook University, Chungcheongnam-do 31116, Republic of Korea
*Corresponding author: Jae Heon Son, 12201026@dankook.ac.kr
First authorKi Hong Kim, bodykim@dankook.ac.kr
Received 2024 September 25; Revised 2024 November 1; Accepted 2024 December 20.

Abstract

Background and objective

Health tourism, which involves activities for health promotion and therapeutic travel, is gaining attention as a means of stress and fatigue relief. While therapeutic programs involving forests and hot springs have been reported to have positive effects, there is a lack of research on the combined benefits of these environments, as well as comparative studies of static and dynamic health tourism. Therefore, this study aimed to examine changes in stress levels by analyzing heart rate variability (HRV) and electroencephalograms (EEG) taken from participants in different types of health tourism programs involving stays in hot springs and forests.

Methods

A total of 20 adults (9 males and 11 females) were randomly assigned to either the Relax Tour program (N=10) or the Activity Tour program (N=10). Both health tour programs, which included forest walking and aquatic exercise in hot springs, were implemented. To ensure homogeneity between the groups, physical characteristics, EEG, and HRV were assessed using independent samples t-tests. The effects of the health tourism participation type on EEG and HRV were analyzed using a two-way mixed-design ANOVA. When significant differences were found, post-hoc analyses were conducted using the Bonferroni method.

Results

An HRV analysis showed no significant differences between the groups; however, significant changes were observed based on the time points measured. An EEG analysis showed a significant increase in alpha waves in the Activity Tour group, while the Relax Tour group also showed a trend toward improvement in alpha waves.

Conclusion

The effects of a health tourism program combining forest and hot spring activities on the autonomic nervous system and EEG were analyzed. The Activity Tour group showed an increase in alpha and beta waves, while both the Activity and Relax Tour groups showed a decrease in RMSSD and SDNN in HRV. This suggests that physical fatigue and mental activation may occur simultaneously. Therefore, it seems necessary to consider long-term effects and optimization in program design to reduce physical fatigue and maximize psychological stability and cognitive recovery to increase effectiveness.

Introduction

In the 21st century, global economic growth has improved living standards and contributed to an aging population, leading to the emergence of new lifestyles focused on quality of life and a growing interest in health and well-being. Furthermore, with a large portion of the population residing in urban areas, people are increasingly exposed to high levels of stress and fatigue, which negatively affect their health. As a result, health tourism has gained attention as a mean to address these matters (Kebbede, 2017; Roh, 2009).

Health tourism is a rapidly growing industry that consists of health-promoting activities or therapies for physical and mental well-being at hot springs and spas, in beautiful natural landscapes, or at hotels and resorts away from residential areas (Smith and Puczkó, 2015). It is known to have effects such as relaxation and stress reduction (Pessot et al., 2021). Therapeutic activities utilizing forest resources include: forest walks, in which visitors engage their five senses—sight, sound, touch, smell, and taste—while appreciating the natural surroundings; forest experiences; meditation (Lee et al., 2017); and therapeutic treatments such as spa programs with different water temperatures (e.g., warm baths and hot springs) and physical activities (Šrámek et al., 2000). The forest environment has the effects of increasing parasympathetic nerve activity and decreasing sympathetic nerve activity, decreasing cortisol concentration, decreasing blood pressure and heart rate, and relieving depression, anxiety, and stress. On the other hand, spa programs using hot springs are physical activities that utilize the physical properties of water to provide physiological and psychological effects, including pain and fatigue reduction, depression relief, physical relaxation, and lowered blood pressure and heart rate (Castro-Sánchez et al., 2012; Park et al., 2009; Ohe et al., 2017; Vujcic and Tomicevic-Dubljevic, 2018).

As programs utilizing such therapeutic elements can consist of both static programs focused on relaxation and dynamic programs involving physical activities, their effects may differ accordingly. Lee et al. (2018) found that stress levels were decreased and positive emotions increased after a program that combined both static and dynamic activities. These activities included forest walks, barefoot walks, stretching, hammock rest, meditation, foot baths, water gymnastics, and massage over the course of 2 nights and 3 days. Eom and Whang (2015) reported that just 15 minutes of appreciating natural scenery improved the balance of the autonomic nervous system and promoted psychological stability. Even in aquatic environments, such as hot springs, dynamic activities were effective in reducing stress and enhancing psychological well-being (Jeong, 2021; Kim and Kim, 2018; Lim, 2011), but static activities, such as soaking in hot springs (e.g., hydrotherapy and balneotherapy), also contributed to similar effects on psychological stability (Koroglu, S and Yıldız, 2024). Given this, there is a lack of comparative studies that incorporate both dynamic and static activities in the design of effective health tourism programs.

Moreover, the main assessment tools used in previous studies have been heart rate variability (HRV), which indirectly measures psychological changes by assessing heart rate fluctuations in physiological responses (Lee, 2016), or psychological surveys based on participants’ subjective perspectives (Lee et al., 2012). These appear insufficient to investigate the direct changes in the brain that control psychological responses. In this study, we compared and analyzed the differences in the effectiveness of programs consisting of static and dynamic activities using HRV and electroencephalogram (EEG), with the aim of providing data that can serve as a reference for the development of hot spring and forest health tourism programs.

Research Methods

Experimental design

This study was approved by the Research Ethics Committee of D University Hospital (2024-04-040-008). Participants were fully informed of the research procedures in accordance with the Bioethics and Safety Act. They were advised to refrain from drinking alcohol or smoking during the experiment, and were informed of their right to withdraw from the study at any time. Among the participants who provided voluntary consent, only those who met the eligibility criteria visited the Healthcare and Spa Industry Promotion Agency, where their pre-test HRV and EEG were measured. Participants were randomly assigned to either the Relax Tour or Activity Tour programs, which were based on subjective cognitive intensity (Borg scale). After a day of rest at a glamping facility, their posttest HRV and EEG were measured, as shown in Fig. 1.

Fig. 1

Research process.

Participants

The participants in this study consisted of 20 adults (nine males and eleven females) who were physically able to engage in forest walking and aquatic exercises in hot springs, and had no history of cardiovascular or pulmonary diseases. They were randomly assigned to either the Relax Tour or Activity Tour programs, with 10 participants in each group. The physical characteristics of the participants were assessed using a body composition analyzer (Inbody720, Inbody, Korea), and the mean and standard deviation (SD) were estimated to confirm homogeneity. An independent samples t-test was then conducted, with the results of the physical characteristics analysis presented in Table 1, and those of the EEG and HRV analyses shown in Table 2.

Characteristics of participants

Participant homogeneity test

Relax tour program

The Relax Tour Program utilized a forest walking route leading to the entrance of the Younginsan Arboretum, Jandi Square, Mu-jang-ae Nanum-gil, and the forest restoration zone in the Younginsan Natural Forest. The exercise intensity included approximately 1 hour of walking at a heart rate (HR) of 120–130 bpm and an RPE of 12–13 on the Borg’s rating of perceived exertion (RPE) scale, as shown in Fig. 1. This was followed by approximately 1 hour of aquatic activities at Spavis, a hot spring facility in Asan, as detailed in Table 3 and Fig. 2.

Tour program format

Fig. 2

Forest walking courses in Yeonginsan National Forest.

Activity tour program

The Activity Tour Program involved a forest trail connecting Sangtu-bong, Yeonhwa-bong, Gitdae-bong, and Shinsung-bong in the Yeonginsan Natural Forest. The exercise intensity consisted of approximately 1 hour of walking at a HR of 130–150 bpm and an RPE of 13–15 on the Borg RPE scale. The walking trail is depicted in Fig. 1. This was followed by approximately 1 hour of aquatic activities at Spavis, as shown in Table 3 and Fig. 2.

Physiological assessment tools

Heart Rate Variability (HRV)

Heart rate variability (HRV) was measured using an HRV analyzer (SA-3000P, Medicore Co., Seoul, Korea). Participants sat comfortably and wore the patch for 5 minutes while their HRV was measured. The equipment automatically analyzed key HRV metrics, including the SD of normal intervals (SDNN), the root mean square of successive differences (RMSSD), and the low frequency/high frequency (LF/HF) ratio. These measurements were taken twice: once before and once after the participants engaged in the tour program. The frequency ranges measured included the LF component (0.04–0.15 Hz), the HF component (0.15–0.4 Hz), and the LF/HF ratio. Measurements were taken between 9 and 11 a.m. to control circadian variations in HRV.

Electroencephalography (EEG)

To measure EEG, the ProComp Infiniti System w/BioGraph Infiniti Software (T7500M, Thought Technology, Montreal, Canada) was used. This multi-channel EEG device is employed for psychophysiological analysis, neurofeedback training, and data collection (Chakrabarti and Chakrabarti, 2017). To ensure accurate measurements, Nuprep EEG Skin Prep Gel was applied to the left and right earlobes and the crown of the head to remove dead skin cells, thereby enhancing conductivity and reducing resistance. Next, the ground electrode was placed on the right earlobe, the negative electrode on the left earlobe, and the positive electrode on the crown, using Ten20 conductive paste. Participants then sat comfortably for 5 minutes while the measurements were taken. This was done once before and once after participation in the tour program.

Statistical analysis

The data for this study were analyzed using SPSS Statistics version 22.0 (IBM Co., Armonk, NY, USA). An independent samples t-test was conducted to assess the homogeneity of the participants. The mean and SD of HRV and EEG, based on participation type (Relaxation or Activity Tour programs) and measurement time points (MTPs; i.e., pretest and post-test), were estimated and analyzed using a two-way mixed-design ANOVA. When significant differences were found, post-hoc analysis was performed using the Bonferroni method, with the statistical significance level set at α = .05.

Results

Changes in Heart Rate Variability (HRV)

The mean, SD, and two-way mixed-design repeated measures ANOVA for HRV by group (the Relax and Activity Tour (Program) groups) are presented in Table 4. No significant difference in RMSSD was found between the groups (p = .453), but a significant difference in RMSSD was observed between MTPs (p < .001). No interaction effect was found (p = .628). Post-hoc analysis showed that RMSSD decreased at the post-test for both the Relax Tour group (p = .001) and the Activity Tour group (p = .004). There was no significant difference in SDNN between the groups (p = .765), but a significant difference was found between MTPs (p = .005). No interaction effect was found (p = .328). Post-hoc analysis showed a decrease in SDNN at the post-test for the Relax Tour group (p = .008). No significant difference in the LF/HF ratio was found between the groups (p = .783), nor was there a difference between MTPs (p = .159). No interaction effect was found (p = .413).

Results of two-way mixed-design repeated measures ANOVA on HRV

Changes in Electroencephalography (EEG)

The mean, SD, and two-way mixed-design repeated measures ANOVA for EEG by group are shown in Table 5. There were no significant differences in delta waves between groups (p = .104), nor was there a significant difference between MTPs (p = .333). No interaction effect was found (p = .174) either. Theta waves did not show a significant difference between groups (p = .349), nor was there a significant difference between MTPs (p = .499). No interaction effect was found (p = .152). Alpha waves also did not differ significantly between groups (p = .360), nor was there a significant difference between MTPs (p = .201). However, an interaction effect was found (p = .009). Post-hoc analysis revealed an increase in alpha waves at the post-test for the Activity Tour group (p=.008). Beta waves did not differ significantly between groups (p = .390) or between MTPs (p = .389), but an interaction effect was observed (p = .050). Post-hoc analysis revealed that the Activity Tour group exhibited higher beta wave activity at the post-test (p = .049). Gamma waves did not show a significant difference between groups (p = .607) or between MTPs (p = .516). An interaction effect was found (p = .006), and post hoc analysis showed that the Relax Tour group had lower gamma wave activity at the post-test (p = .015).

Results of two-way mixed-design repeated measures ANOVA on EEG

Discussion

Heart rate variability (HRV) reflects the activity of the autonomic nervous system and is affected by environmental factors such as temperature and weather, as well as by physical and psychological health (Farrow & Washburn, 2019; Michael et al., 2018). Furthermore, it has been reported that the effect is amplified when physical activities, such as walking, are incorporated (Kobayashi et al., 2018; Lee et al., 2014; Song et al., 2015). This study found no significant difference in HRV between the Relax and Activity Tour groups. Both groups showed a decrease in RMSSD and SDNN. A decrease in RMSSD and SDNN may indicate increased physical fatigue (Gratze et al., 2005), potentially resulting from the accumulation of physical fatigue caused by the program’s demand for more exercise than participants’ usual daily activities, coupled with continuous activity without adequate rest. These results suggest that both the Relax and Activity Tour programs contributed to autonomic nervous system fatigue. On the other hand, it seems difficult to clearly demonstrate the positive effects of the programs in this study beyond the physical fatigue experienced by the participants, based on the HRV results alone. Murad et al. (2012) suggested that improvements in HRV through physical activity require an intervention lasting more than 12 weeks, while Wang et al. (2024) reported that a single session of physical activity changed HRV, but it returned to the previous state (baseline) after 6 hours. Therefore, in order to maximize the positive effects of the programs implemented in this study on the autonomic nervous system, it appears necessary to design a program that involves longer program participation and stays at the program sites. Moreover, given the potential physical fatigue experienced by the participants, it is important to increase the interval between sessions and incorporate adequate rest into the program structure.

On the other hand, EEG analysis in this study revealed a significant increase in alpha and beta waves for the Activity Tour group. The rise in alpha waves indicates a state of relaxation and a reduction in anxiety (Petruzzello et al., 1991), while the increase in beta waves suggests improved attention and concentration (Nielsen & Nybo, 2003). The increase in alpha waves shows the positive effects of the physical activity programs, which seems to be attributed to differences in exercise intensity depending on the type of activity performed during the forest exercise. It is well-established that a single session of physical activity increases cerebral blood flow (Herholz et al., 1987) and activates the brain’s arousal mechanisms, thereby improving implicit information processing (Audiffren et al., 2008). This aligns with the findings of Moraes et al. (2011), who reported an increase in alpha and beta waves in a group that engaged in moderate-intensity aerobic exercise. The results of the two indicators, HRV and EEG, seem to be contradictory, as they reflect the effects of physical activity from different perspectives. While HRV highlights physical fatigue and the strain on the autonomic nervous system, EEG captures the neurophysiological changes induced by physical activity. This suggests that although physical fatigue was experienced in both the Relax and Activity Tour program structures, the higher exercise intensity in the Activity Tour group had a positive effect on brain arousal and concentration. Bailey & Kang (2022) further support this finding by reporting that changes in alpha waves were greater in the outdoor walking group compared to the sedentary group. Considering the HRV and EEG results comprehensively, it seems that implementing long-term programs that involve participants’ stay at program sites, along with structuring the program activities, may help improve their overall health. To address the discrepancies between HRV and EEG, follow-up studies that examine additional indicators, such as psychological factors and subjective fatigue, are also considered necessary.

Conclusion

In this study, the effects of health tourism programs combining activities in forests and hot springs on EEG and HRV were analyzed. There was no significant difference in HRV between the Relax and Activity Tour groups, although both showed a decrease in RMSSD and SDNN. However, EEG analysis showed a significant increase in alpha and beta waves in the Activity Tour group. Although these results may appear contradictory, they suggest that physical fatigue and positive brain activation can occur simultaneously. Physical activity induces fatigue by placing short-term stress on the autonomic nervous system, but the combination of a forest environment and moderate-intensity exercise appears to promote both positive brain activation and relaxation effects. In other words, while the body may experience fatigue, mental and cognitive recovery and activation appear to occur simultaneously. This suggests that natural environmental stimulation may have contributed to alleviating physical fatigue while enhancing psychological stability and concentration. Future studies should consider refining program details, such as duration and frequency, to minimize participant fatigue.

Notes

This study was conducted with the support of the ‘Hot Spring Medical Efficacy Verification Systemization Project (2024 Hot Spring City Development Project)’ funded by Asan City in 2024.

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Article information Continued

Fig. 1

Research process.

Fig. 2

Forest walking courses in Yeonginsan National Forest.

Table 1

Characteristics of participants

Group (N) Age (year) Weight (kg) Height (cm) Muscle Mass (kg) Body Fat (%)
Relax tour program (n = 10) 41.80 ± 9.47 67.03 ± 12.52 165.81 ± 7.45 28.48 ± 6.56 23.53 ± 4.50
Activity tour program (n =10) 36.30 ± 11.52 63.68 ± 17.84 167.03 ± 8.90 26.26 ± 8.83 25.37 ± 4.77
t 1.166 .442 −.332 .638 −.886
p .259 .633 .743 .531 .387

M±SD

Table 2

Participant homogeneity test

Group HRV EEG


RMSSD SDNN LF/HF ratio Delta wave Theta wave Alpha wave Beta wave Gamma wave
Relax tour program 42.66 ± 23.17 45.75 ± 16.92 1.12 ± 2.14 9.19 ± 1.56 9.30 ± 2.46 10.50 ± 5.12 7.52 ± 1.51 2.14 ± 0.31
Activity tour program 35.80 ± 12.78 41.47 ± 13.52 0.80 ± 0.48 7.47 ± 2.29 7.73 ± 2.70 11.29 ± 4.98 6.30 ± 2.39 1.76 ± 0.85
t .820 .636 .465 1.961 1.367 −.349 1.360 1.348
p .423 .539 .652 .066 .188 .731 .191 .194

Table 3

Tour program format

Relax tour program Activity tour program


Action Time(min) Exercise Repetition/Set Time (min)
Walking in water 10 Water walking and running 50 m × 2 set 20

Neck shower 10 Shoulder horizontal abduction 10 Reps/2 Set 30
Hydro jet 5 Shoulder flexion and extension

Stretching Gastrocnemius and soleus 20 Shoulder abduction and adduction
Quadriceps muscles Elbow flexion/Extension
Hamstring muscles Squat
Trunk muscles (Front, Side) Jumping lunge
Shoulder muscles Knee flexion and extension
Trapezius and scalene muscles Hip flexion and extension

Hot spring bath 15 Hip abduction and adduction

Table 4

Results of two-way mixed-design repeated measures ANOVA on HRV

Time Relax tour group Activity tour group F p
RMSSD (ms) Pre 42.66 ± 23.17 35.80 ± 12.87 Group .589 .453

Post 31.99 ± 19.99** 27.00 ± 12.78** Time 26.352 .000
G × T .244 .628

SDNN (ms) Pre 45.75 ± 16.92 41.47 ± 13.52 Group .092 .765

Post 35.99 ± 15.94** 36.38 ± 14.38 Time 10.184 .005
G × T 1.012 .328

LF/HF ratio Pre 1.12 ± 2.14 0.80 ± 0.48 Group .078 .783

Post 1.48 ± 1.55 2.12 ± 2.43 Time 2.162 .159
G × T .703 .413

M±SD,

*

Between Time (p < .05),

**

Between Time (p < .01)

Table 5

Results of two-way mixed-design repeated measures ANOVA on EEG

Time Relax tour group Activity tour group F p
Delta Wave Pre 9.19 ± 1.56 7.47 ± 2.29 Group 2.935 .104

Post 9.07 ± 1.78 8.17 ± 1.58 Time .991 .333
G × T 1.999 .174

Theta Wave Pre 9.30 ± 2.46 7.73 ± 2.70 Group .926 .349

Post 8.98 ± 2.56 8.60 ± 2.01 Time .477 .499
G × T 2.238 .152

Alpha Wave Pre 10.50 ± 5.12 11.29 ± 4.98 Group .884 .360

Post 9.81 ± 4.82 13.16 ± 5.15** Time 1.763 .201
G × T 8.462 .009

Beta Wave Pre 7.52 ± 1.51 6.30 ± 2.39 Group .776 .390

Post 7.23 ± 1.77 7.01 ± 1.78* Time .778 .389
G × T 4.417 .050

Gamma Wave Pre 2.14 ± 0.31 1.76 ± 0.85 Group .274 .607

Post 1.85 ± 0.38* 1.95 ± 0.82 Time .439 .516
G × T 9.798 .006

M±SD,

*

Between Time (p < .05),

**

Between Time (p < .01)