INTRODUCTION
The coronavirus disease-2019 (COVID-19) pandemic has profoundly affected global workforces, particularly within high-demand sectors such as healthcare and information technology (IT) [1]. These professions have endured significant alterations in work dynamics, characterized by increased workloads, rapid shifts in work environments, and a heightened risk of occupational burnout [2]. The stress induced by these changes has been exacerbated by the pandemic, necessitating the development of targeted and innovative mental health interventions [1-3]. These interventions are vital for supporting the resilience and well-being of these essential workers, who play a pivotal role in managing the crisis.
In response to the escalated mental health needs during the pandemic, virtual reality (VR) technology has emerged as a significant tool in psychological health management. Utilizing immersive simulations, VR offers a sanctuary for relaxation and stress alleviation, simulating tranquil natural environments and relaxation techniques that are pillars of traditional stress management practices [4-6]. The efficacy of VR in delivering such therapeutic experiences, which mimic the calming effects of physical natural settings, has been noted for its potential to reduce symptoms of stress and anxiety [7]. Moreover, the adaptability of VR technology enables its application across various settings, including those limited by pandemic-related social distancing measures, making it an invaluable asset in the continuity of mental health care during these challenging times.
This research project is designed to meticulously evaluate a VR-based stress reduction program, tailored specifically for professionals in the healthcare and IT sectors amid the ongoing pandemic. The program incorporates principles of forest healing, a practice celebrated for its substantial therapeutic benefits and its efficacy in enhancing mental well-being [8-10]. By integrating this approach into a structured VR setting, the study aims to offer a novel, engaging, and adaptive method for managing occupational stress. Through a comprehensive evaluation using both physiological and psychological metrics, the study intends to deepen the understanding of how VR can be strategically utilized to bolster occupational health, particularly under the stringent conditions imposed by global health emergencies.
The anticipated outcomes of this study are expected to provide empirical support for the implementation of VR as a standard practice in occupational health strategies, especially for those in high-stress professions. By expanding the body of knowledge on VR’s capabilities and limitations, the research could potentially influence policy-making and the allocation of resources towards innovative mental health solutions. The ultimate goal is to establish a more robust framework for mental health resilience in the workforce, ensuring that workers in critical sectors are not only protected from undue stress but are also given the tools to thrive in the face of future global challenges.
METHODS
Participants
In light of a comprehensive literature review on occupational stress intensification and role transformations during the COVID-19 pandemic, sectors identified for this study included healthcare professionals and IT specialists. Recruitment for the VR program spanned from October 2022 through January 2023. The study protocol excluded individuals with physical conditions incompatible with VR use or those currently receiving pharmacotherapy for psychiatric conditions.
Variables
To assess the quality of sleep, four different questionnaires were utilized. The Pittsburgh Sleep Quality Index (PSQI) encompasses questions regarding sleep quality, duration, habits, disturbances, medication use, and daytime dysfunction. Scores across seven domains are aggregated to form a total score ranging from 0 to 21, with scores above five indicating poor sleep quality. The Insomnia Severity Index (ISI) comprises seven items related to sleep disturbances, with a total score above 10 suggesting the presence of insomnia. The Epworth Sleepiness Scale measures fatigue levels, where a score above 11 indicates excessive sleepiness. The Stanford Sleepiness Scale (SSS) evaluates an individual’s immediate level of sleepiness through a seven-item tool.
Additionally, seven surveys were employed to measure stress levels. The Hospital Anxiety and Depression Scale (HADS) rates depression and anxiety on a scale up to eight points, with higher scores indicating more severe symptoms. The Depression Anxiety Stress Scales-21 (DASS-21) subdivides into depression (DASS-D), anxiety (DASS-A), and stress (DASS-S) components, each consisting of seven items for a total of 21, with higher scores reflecting increased levels of depression, anxiety, and stress. The Patient Health Questionnaire-9 (PHQ-9) is developed to evaluate life quality changes due to psychological symptoms like depression and anxiety. The Symptom Checklist-90-R measures somatization symptoms, including subjective questions about cardiovascular, pulmonary, and other organ disorders, as well as headaches and other physical dysfunctions. The Perceived Stress Scale (PSS) is structured into two sections comprising 10 items in total, with six items assessing negative stress perceptions and four for positive ones, offering a subjective evaluation of stress experienced over the past month; higher scores denote greater stress levels. The Korean Resilience Quotient-53 aggregates scores to assess resilience, with totals above 200 considered reassuring, while scores below 180 suggest susceptibility to minor negative events. The Health Promotion Behavior scale includes 52 items across categories such as health responsibility, physical activity, nutrition, spiritual growth, relationships, and stress management, evaluated on a 4-point scale to calculate an average score, where higher scores indicate better health-promoting behaviors.
Evaluation of physiological changes
Salivary biomarkers (melatonin, cortisol, and Dehydroepiandrosterone-sulfate [DHEA-S]) were selected based on their established reliability as non-invasive indicators of stress and circadian rhythm regulation. Melatonin serves as a primary marker for sleep-wake cycle evaluation, while cortisol and DHEA-S offer complementary insights into hypothalamic-pituitary-adrenal axis function under stress conditions. These measurements provide objective physiological data that complement the subjective psychological assessments, creating a more comprehensive evaluation framework for our intervention. Additionally, electrocardiogram (ECG) and Galvanic Skin Response (GSR) measurements were included to capture real-time autonomic nervous system responses to stress, offering immediate physiological feedback on intervention effectiveness.
Physiological indicators of the participants were continuously measured using a wearable device (Mi Band), including average step count, total daily step count, heart rate, and heart rate variability. Additionally, biological markers such as melatonin, cortisol, and DHEA-S were analyzed using saliva samples from the participants. GSR was measured using the Physiolab P400 device (EDG Amp, Physiolab, Korea).
To collect objective data on stress levels and sleep quality, saliva samples were collected four times daily. This was done before and during the forest healing program. Saliva samples were collected immediately upon waking in the morning for cortisol testing, and in the evening, right before bedtime, for melatonin measurement. Sample collection times were at 8:00 AM (after the participant had woken up), and sleep-related data were collected at 9:00 PM.
This study was conducted in accordance with the ethical principles outlined in the Declaration of Helsinki. The research protocol was approved by the relevant ethics review board, identified by protocol number #IS22OISE0013 (IRB NO. CKU-22-01-0110). Prior to participation, all individuals provided informed consent.
The VR program
The VR program implemented in this study spans a 2-week period with sessions scheduled for the morning, afternoon, and evening. Each session is designed around the concept of forest healing, proven in clinical studies to alleviate stress and enhance sleep quality [11-13]. This structured approach aims to harness the therapeutic benefits of immersive forest environments, which have been effectively translated into the virtual setting.
Participants experience a variety of forest scenes throughout the program, each crafted to provide a unique and engaging environment. This diversity helps mitigate monotony and maintains participant interest and engagement throughout the duration of the program. By rotating the virtual settings, the program aims to continually stimulate the participants’ interest and maximize the therapeutic impact of each session. Figure 1 presents representative screenshots of the forest environments experienced by participants during the VR intervention program, illustrating the immersive natural settings designed to promote relaxation and stress reduction.
Program contents
The VR program specifically designed for stress reduction consists of various therapy content which the participants can select and use based on their symptoms or preferences. For the daytime sessions, there are the two mindfulness-based physical exercises, and three active stress-relieving techniques such as Residual Tension Relaxation Therapy, Forehead Deep Pressing Technique, and Progressive Muscle Relaxation with Mental Imagery. The evening sessions consist of the three different passive stress-relieving techniques such as body scan, slow-paced breathing technique, and autogenic training, while the bedtime sessions consist of the six different relaxation therapies including mindfulness meditation, sleep aid sounds, and stretching exercises.
Each content has been delivered to the participants in a combined form of VR forest healing. All of the content used in this program has been produced and guided by a certified facilitator for the mind-body interventions, Brainer Jay, and endorsed by a healthcare venture company, S-OMNI, Inc.
Statistics
The collected data were analyzed using SPSS software, version 22.0 (IBM Corp.). Descriptive statistics, including frequencies, percentages, means, and standard deviations, were utilized to analyze the general characteristics of the participants. To assess the changes pre- and post-intervention, a paired t-test was employed, while differences between groups before and after the intervention were analyzed using repeated measures analysis of variance.
RESULTS
General characteristics
Table 1 displays the general characteristics of the study participants. A total of 54 individuals participated, comprising 46 healthcare and 8 IT professionals. The average age of the participants was 32.9 years with a standard deviation of 9.5 years. The gender distribution included 30 males and 24 females, making up 55.6% of the participant pool as males. The average height and weight of the participants were 166.2±8.6 cm and 64.8±15.3 kg, respectively. Statistical analysis showed no significant differences between the IT and healthcare groups in terms of gender, age, height, weight, smoking status, alcohol consumption, and underlying health conditions, confirming homogeneity across the groups.
Changes pre- and post-VR program
As shown in Table 2, there were no statistically significant changes in sleep-related indices such as the SSS, daytime sleepiness, the ISI, and the holiday PSQI before and after the application of the VR program. However, there was a statistically significant increase in the weekday PSQI scores, rising from an average of 5.52 before the program to 6.28 after the program (p=0.028). This suggests a deterioration in sleep quality during weekdays post-intervention.
Regarding psychological indices, there were no significant changes observed in depression (PHQ-9), somatization, or HADS depression scores following the VR program. Nonetheless, there was a significant reduction in the overall stress levels, with the PSS scores decreasing from an average of 17.15 before the program to 15.13 after (p=0.001). Additionally, anxiety levels, as measured by HADS anxiety, significantly decreased from an average of 6.37 pre-program to 5.33 post-program (p=0.002).
The results also indicated that there were no significant changes in the physiological and biochemical markers measured before and after the VR program. These findings suggest that while the VR intervention may not significantly impact certain sleep and biochemical markers, it could effectively reduce psychological stress and anxiety among participants.
Homogeneity comparison between IT and healthcare groups before VR program application
Table 3 presents the results from the comparative analysis of the IT and healthcare professionals before the application of the VR program. The analysis showed no significant differences between the two groups in terms of sleep-related indicators, psychological indicators, and physiological markers such as DHEA, melatonin, cortisol, ECG, and GSR, establishing homogeneity between the groups.
However, there were notable exceptions in certain physiological measurements. The maximum ECG values were significantly higher in the healthcare group, averaging 0.8673 (p<0.001), and the minimum ECG values were significantly lower, averaging -0.6590 (p<0.001). Additionally, the maximum GSR values were significantly higher in the healthcare group, recorded at 0.6293, compared to the IT group (p=0.042). These findings suggest that while both groups were generally homogeneous in terms of most evaluated parameters, certain physiological responses differed significantly between the groups, potentially indicating variations in stress or physical responses inherent to their professional environments.
Changes in sleep-related and psychological indicators in IT and healthcare groups following VR program application
As shown in Table 4, the weekday PSQI scores in the healthcare group showed a statistically significant increase from a mean of 5.39 points before VR program application to a mean of 6.33 points after the program (p=0.013). Similarly, the holiday PSQI scores in the healthcare group demonstrated a statistically significant increase from a mean of 5.00 points before VR program use to a mean of 5.74 points after the program (p=0.028). In the IT group, the weekday PSQI scores increased from a mean of 6.00 points before VR program application to a mean of 6.25 points after application, but this change was not statistically significant. The holiday PSQI scores in the IT group also showed an increasing trend from a mean of 5.13 points before VR program application to a mean of 5.50 points after application, but this change was not statistically significant.
Regarding insomnia severity (ISI), the healthcare group showed a statistically significant decrease from a mean of 8.46 points before VR program application to a mean of 6.98 points after application (p=0.015). However, the IT group showed an increasing trend from a mean of 6.50 points before VR program application to a mean of 8.38 points after application, but this change was not statistically significant. The difference between the IT and healthcare groups after VR program application was statistically significant (p=0.026), with the IT group showing an average increase of 1.88 points and the healthcare group showing an average decrease of -1.48 points.
For stress (PSS), the healthcare group demonstrated a statistically significant decrease from a mean of 16.72 points before VR program application to a mean of 14.76 points after application (p=0.001). The IT group showed a decreasing trend from a mean of 19.63 points before VR program application to a mean of 17.25 points after application, but this change was not statistically significant. The differences in pre-and post-program scores between the IT and healthcare groups were not statistically significant.
Anxiety (HADS anxiety) in the healthcare group showed a statistically significant decrease from a mean of 6.26 points before VR program application to a mean of 5.02 points after application (p<0.001). Depression also demonstrated a significant decreasing trend in the healthcare group, with scores decreasing from a mean of 4.52 points before VR program application to 3.91 points after application, showing a statistically significant reduction (p=0.012).
In the IT group, anxiety scores showed a slight increasing trend from a mean of 7.00 points before VR program application to a mean of 7.13 points after application, but this change was not statistically significant. Similarly, depression scores in the IT group showed a slight increase from a mean of 5.25 points before VR program application to a mean of 5.75 points after application, but this change was also not statistically significant.
There were no significant differences between the two occupational groups after the application of the VR program for both anxiety and depression scores.
Changes in physiological indicators in IT and healthcare groups following VR program application
As shown in Table 5, melatonin levels did not show significant changes before and after VR program application in both IT and healthcare groups. However, after VR program application, the IT group showed a significantly lower mean (2.0750) compared to the healthcare group (10.4883) (p=0.001).
ECG mean values did not show significant changes before and after VR program application in both groups. However, after VR program application, the IT group showed a significantly lower mean (-0.0055) compared to the healthcare group (-0.0155) (p=0.031).
ECG maximum values did not show significant changes before and after VR program application in both groups. However, after VR program application, the IT group showed a significantly lower mean (0.4147) compared to the healthcare group (0.8050) (p=0.030).
Photoplethysmogram mean values did not show significant changes before and after VR program application in both groups. However, after VR program application, the IT group showed a significantly higher mean (0.2707) compared to the healthcare group (-0.3249) (p=0.029).
The minimum value of active compression decompression out in the IT group showed a statistically significant increase from 0.0088 before VR program application to 0.0259 after application (p=0.022). Comparing the groups after VR program application, the IT group showed a significantly higher mean (0.0259) compared to the healthcare group (0.0152) (p=0.002).
DISCUSSION
The present study aimed to evaluate the effectiveness of a VR based stress reduction program tailored for healthcare and IT professionals during the COVID-19 pandemic. Our findings reveal a complex picture of the program’s impact, with notable differences between the two professional groups. In the healthcare group, we observed significant reductions in stress (PSS), anxiety (HADS anxiety), and depression (HADS depression) scores following the VR intervention. These results align with previous studies that have demonstrated the efficacy of VR-based interventions in reducing psychological distress among healthcare workers [14,15]. The positive outcomes in the healthcare group suggest that VR forest therapy could be a valuable tool for managing occupational stress in this sector, particularly during high-pressure periods such as a global pandemic.
Interestingly, the IT group showed different trends, with slight increases in anxiety and depression scores, although these changes were not statistically significant. This divergence from the healthcare group’s results warrants further investigation. It’s possible that the nature of IT work, which often involves extended periods of screen time, might interact differently with VR interventions. Previous research has indicated that the effectiveness of VR interventions can vary depending on individual factors and the specific work environment [16]. The contrasting results between the two groups highlight the importance of tailoring VR interventions to specific occupational contexts and underscore the need for more targeted research in this area.
One unexpected finding was the increase in PSQI scores for both weekdays and holidays in the healthcare group, indicating a potential deterioration in sleep quality. This result contrasts with some previous studies that have found improvements in sleep quality following VR-based relaxation interventions [17,18]. The discrepancy might be attributed to the ongoing stressors of the pandemic or other external factors not controlled for in this study. It’s crucial to consider that sleep patterns can be influenced by a multitude of factors beyond the scope of the VR intervention, including work schedules, personal life stressors, and the general anxiety associated with the pandemic [19,20]. This paradoxical finding of worsened sleep quality alongside improved psychological measures (decreased stress, anxiety, and depression) warrants further examination. Several methodological factors may have contributed to this outcome. First, our post-intervention assessment coincided with a period of changing pandemic restrictions, which might have introduced additional stressors affecting sleep. Second, the 2-week duration of our intervention may have been insufficient for meaningful sleep improvements, as sleep patterns often require longer periods to stabilize following intervention. Third, participants may have experienced adaptation effects to the VR technology itself, potentially leading to increased cognitive arousal that interfered with sleep onset or maintenance. Recent literature has documented similar sleep disturbances among healthcare workers during the pandemic despite various interventions, suggesting that sleep may be particularly resistant to improvement during ongoing crisis periods [19,20]. This finding highlights the complex relationship between psychological well-being and sleep quality, and indicates that specialized approaches targeting sleep specifically may be necessary alongside stress reduction programs, particularly for healthcare workers who continue to face pandemic-related challenges.
The present study aimed to investigate the effects of VR programs on sleep improvement across different occupational groups. Our recruitment process was intentionally limited to daytime workers due to the study’s primary focus on sleep patterns. This methodological decision, while necessary for controlling variables related to sleep cycles, resulted in some unforeseen challenges in participant recruitment. A notable disparity emerged in the sample sizes between the two occupational groups studied: IT workers and healthcare professionals. The recruitment of IT workers proved particularly challenging due to the often flexible nature of their work schedules. During the study period, we encountered significant difficulties in identifying IT professionals who strictly adhered to regular daytime working hours. Consequently, the sample size for the IT worker group was considerably smaller than that of the healthcare professionals. Despite this imbalance in group sizes, we proceeded with a comparative analysis between the occupational groups, as we believed such a comparison could yield valuable insights. Our analysis, therefore, included both an overall evaluation of the total sample and separate analyses for each occupational group.
Our study provides valuable insights into VR-based interventions for occupational stress management, despite its limitations. The differential effects observed between healthcare and IT professionals highlight the need for occupation-specific stress reduction strategies. Future research should focus on developing and testing VR programs tailored to the unique stressors of different professions, as well as conducting longer-term studies to assess the durability of effects and the relationship between VR interventions and sleep quality.
The primary limitation of this study is the unequal sample sizes between the two groups, which may impact the generalizability of our findings. To address this, future research should implement more robust recruitment strategies, such as extending the recruitment period, broadening inclusion criteria while maintaining a focus on sleep patterns, or employing stratified sampling techniques.
Despite this limitation, the consistency in stress reduction effects observed across both groups suggests a potentially universal benefit of VR interventions that warrants further investigation. As the global workforce faces unprecedented challenges, innovative approaches like VR-based stress reduction programs may play an increasingly important role in maintaining employee well-being and productivity.
In conclusion, our findings contribute meaningfully to the growing body of literature on VR applications in stress management and sleep improvement. Future studies should strive for more balanced sample sizes to validate and expand upon these initial findings, potentially leading to more effective, occupation-specific stress management strategies.
In conclusions, despite the noted limitations, our study contributes valuable insights to the growing body of literature on VR-based interventions for occupational stress management. As the global workforce continues to face unprecedented challenges, innovative approaches like VR-based stress reduction programs may play an increasingly important role in maintaining employee well-being and productivity. Our findings provide a foundation for future research to further validate and expand upon these initial results, potentially leading to more effective, occupation-specific stress management strategies.
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