INTRODUCTION
Cognitive behavioral therapy for insomnia (CBTi) is widely recognized as a validated and highly recommended treatment for chronic insomnia [1]. Nevertheless, the conventional CBTi delivery faces significant challenges due to constraints related to time and location [2]. To address these issues, digital platforms have been suggested as alternatives for providing CBTi. According to previous studies, internet-based CBTi is effective in improving insomnia symptoms and comparable in efficacy to traditional face-to-face CBTi [3]. The Metaverse is emerging as a particularly promising option in the realm of internet-based therapy. It offers unique advantages for telehealth treatments [4], including the elimination of geographical limitations, offering flexible scheduling that accommodates user’s lifestyle, and creating captivating, interactive environments that enhance understanding and implementation of CBTi principles. Harnessing the potential of the Metaverse for cognitive behavioral therapy leverages its advantages, providing a distinctive and immersive experience to address conventional constraints [5].
This study aimed to assess the feasibility and demand for providing CBTi through the Metaverse for individuals with poor sleep quality.
METHODS
Study design and participants
A single-arm, retrospective design was used in the study. This study was conducted with 49 individuals who participated in the CBTi program utilizing Metaverse. Recruitment occurred through an online community from March 3, 2023, to March 11, 2023. A total of 127 individuals volunteered to participate the program. Due to server limitations of the online platform used to implement the Metaverse, participation was capped at 100. Therefore, registrations were accepted on a first-come, first-served basis. Finally, a total of 59 participants actually participated in the program, excluding those who withdrew from the program in advance, those with contact errors, and those who were unable to show up on the day.
For conducting this retrospective study, a total of 49 participants excluding those under the age of 19 years (1 participant) and who did not experience sleep problems such as difficulty initiating and maintaining sleep (9 participants). Specific inclusion criteria were: 1) an adult aged 20 years or older, 2) individuals who have consented to their questionnaire responses being used in research, and 3) individuals who experienced sleep problems at least once in the past three months.
Participants received individualized links and access dates for the program. Following the program, they completed an online survey addressing their sleep problems, sleep satisfaction and sleep patterns. Additionally, 2 weeks after the program, participants completed the additional online survey asking about their change in sleep satisfaction and sleep patterns to ensure the maintenance of program effects. A total of 40 participants completed the online survey, which was conducted 2 weeks follow-up.
This study was approved by the International St. Mary’s Hospital, the Catholic Kwandong University College of Medicine in Korea (IS24RISI0011). Informed consent was waived as the study was deemed minimal risk, data fully anonymized before analysis.
The CBTi program utilizing Metaverse
The Metaverse-based CBTi program was conducted as a single-session group training program for 2 hours. The program was provided through Ifland, a Metaverse community platform developed by SK Telecom in the Republic of Korea (SK Telecom Corp., Seoul, Republic of Korea).
The CBTi curriculum in Metaverse includes sleep education, cognitive restructuring, relaxation techniques, and sleep hygiene guided by CBTi principles. Within the Metaverse setting, participants engaged in discussions about insomnia-related difficulties and interacted with others experiencing similar difficulties. Specifically, virtual classrooms or interactive tutorials were designed within the Metaverse to educate individuals about the importance of sleep hygiene and the methods used in CBTi. In addition, a virtual sleep environment was provided to participants with a calm and relaxing atmosphere conducive to sleep. A virtual sleep coach guided participants in practicing sleep-promoting behaviors and adopting healthy sleep habits.
The specific procedures for the CBTi program utilizing Metaverse were as follows. First, at the beginning of the program, participants underwear a brief training session to adapt to the Metaverse environment and learn how to navigate the Ifland platform. In this phase, participants created their avatars to use in the Metaverse and practiced manipulating them and moving freely around the virtual environment. Next, CBTi-based sleep education was provided in a classroom setup in the Metaverse (e.g., sleep hygiene education.) Third, participants shared their sleep problems through group interaction using chat and received coaching from an expert to develop appropriate sleep habits. Finally, after a 15-minute break, participants guided mindfulness-based meditation and practiced it during the session. The sleep coaches explained the purpose and principles of mindfulness-based meditation and guided participants through the practice. Those experiencing difficulty focusing on meditation interacted with experts to try alternative mindfulness-based relaxation therapies (e.g., deep pressure therapy, postural deep breathing, body scan combined with autogenic training).
The program was led by one neurologist and one internationally certified sleep coach (Integrative Adult Sleep Coach, International Parenting & Health Institute, Ojai, CA, USA).
Statistical analysis
Frequency analysis was conducted to identify demographic characteristics and to investigate the percentage of participants reporting sleep improvements at each time point. A paired sample t-test was performed to compare post-program results with 2-week follow-up data. All statistical analyses were conducted with SPSS version 21.0 (IBM Corp., Armonk, NY, USA).
Measurements
Demographic data collected included participants’ gender, age range, duration of sleep problems, and subjectively perceived severity of sleep issues. Sleep problem severity was assessed with the question as follows: “How severe did you feel the symptoms of your sleep problem were?” Participants responded on a scale ranging from very mild to very severe.
Participants were asked to report sleep problems experienced in the past three months, selecting all that applied from the following list: 1) difficulty initiating sleep, 2) difficulty maintaining sleep, 3) narcolepsy, 4) nightmares, 5) snoring and sleep apnea, 6) restless legs syndrome, 7) paralysis, 8) REM sleep behavior disorder, 9) other (ETC).
Subjective sleep indicators were assessed as follows: sleep satisfaction, sleep onset latency (SOL), number of wakings (NWAK), and feeling refreshed upon waking (FRESH).
Participants were asked questions after the program (e.g., “How was your sleep satisfaction on the day you participated in the program compared to over the past two weeks?”) and 2 weeks follow-up (e.g., “How is your sleep satisfaction now compared to before you participated in the program?”). Responses were rated on a 5-point scale ranging from 1 (Much better or much shorter) to 5 (Much better or much longer).
RESULTS
Participant’s characteristic
A total of 49 individuals, including 41 females were recruited in the study. The majority of participants were young, with 42.9% in their twenties and 42.9% in their thirties. Most participants reported experiencing sleep problems for 1 and 5 years (51.0%). Nearly half rated their sleep problems as severe (46.9%). Detailed demographic data of participants were presented in Table 1.
Common sleep problems in individuals
The results of the frequency analysis of multiple responses were presented in Table 2. Participants were asked to identify all sleep problems experienced over the past three months to explore the prevalence of sleep disturbances among individuals interested in Metaverse-based interventions. Every individual who has experienced sleep problems checked more than one item which indicates specific sleep problems. “The sum of the response ratios for each item” represents the ratio of the total number of responses. The results of multiple-response analysis not only indicate the percentage of cases in which each multiple-response item was mentioned but also inform what percentage of the total responses these mentions are. For instance, 81.6% of the respondents reported experiencing “difficulty initiating sleep” within the last three months, accounting for 28.8% of total responses.
In total, the analysis included 49 participants, but allowed duplicate responses, resulting in a total frequency of 139 responses was. The cumulative response ratio across all items was 283.7%.
As a result, the most frequent sleep problem was initiating sleep (percentage=28.8%, percentage of cases=81.6%). Subsequently, difficulty maintaining sleep (percentage=25.9%, percentage of cases=73.5%) and narcolepsy (percentage=10.8%, percentage of cases=30.6%) were common issues that individuals reported as sleep related problems. Additionally, snoring and sleep apnea symptoms, which are one of the significant factors contributing to sleep disruption, were reported by only 12 of 58 participants (percentage=8.6%, percentage of cases=24.5%).
Percentage of individuals reporting improvements in sleep
The results showed that most participants reported improvements in their sleep after the program. Specifically, increases were observed in sleep satisfaction (75.5%), SOL (65.3%), NWAK (65.3%) and FRESH (62.7%) compared to the previous 2 weeks (Table 3).
Two weeks after the program, many participants continued to report improved sleep satisfaction (40.8%), shorter SOL (34.7%), fewer NWAK (38.8%), and better FRESH (40.8%) after the program.
Maintaining the improvements until a 2-week follow-up
The paired sample t-test results were presented in Table 4. There was a significant decrease in subjective sleep satisfaction in 2 weeks of follow-up (M=3.60, SD=0.77) compared to the day after the program (M=4.03, SD=0.71), t (34)=2.77, p=0.009.
However, there were no significant differences in SOL between the day after the program (M=3.83, SD=0.89) and 2 weeks of follow-up (M=3.54, SD=0.82), t (34)=1.71, p=0.096. There were no significant differences in NWAK between the day after the program (M=3.83, SD=0.79) and 2 weeks of follow-up (M=3.74, SD=0.95), t (34)=0.53, p=0.597. There were no significant differences in FRESH between the day after the program (M=3.86, SD=0.81) and 2 weeks of follow-up (M=3.57, SD=0.74), t (34)=1.89, p=0.067.
DISCUSSION
The CBTi program utilizing Metaverse indicates potential improvements in the sleep quality, specifically by reducing sleep onset latency and enhancing overall sleep satisfaction.
One possible explanation for these results is the immersive and interactive characteristics of the Metaverse, which may have successfully engaged individuals in the program. Active participation is essential for the effectiveness of CBTi, as it strongly depends on their engagement in learning and implementing new sleep habits [6]. Especially, the inclusion of a virtual sleep coach likely contributed to creating an individualized and engaging learning environment, hence enhancing the program’s effectiveness.
The demographic profile of participants, consisting primarily young adults in their twenties and thirties, may have contributed to a more open and accepting attitude towards this innovative treatment strategy. In the previous study, young adults were more positive about Metaverse use than older [7,8]. While the impact of age on Metaverse acceptability remains inconsistent, younger adults are generally more positive and receptive to digital intervention, making this platform a suitable choice for this age group.
Additionally, this demographic group is often affected by significant sleep disruptions due to lifestyle factors, stress levels, and heightened exposure to screens and digital devices [9,10]. Providing CBTi via a digital platform such as the Metaverse caters to their regular use of technology and offers an easily accessible and appealing treatment option. Tailoring digital health interventions to age groups more accustomed to digital devices could significantly enhance the success rate of the program.
In sum, this retrospective study demonstrates the potential of the Metaverse platform as an innovative and accessible alternative to in-person therapy sessions.
Nevertheless, it is crucial to recognize the constraints of the research.
First, this study recruited fewer cases for men than for women, and a majority of younger participants in their 20s and 30s.
This participant bias limits the generalizability of the study’s findings. Therefore, future studies should aim to recruit more male participants and study individuals of a wider age range.
Second, this retrospective study was limited in collecting data on a variety of confounding variables that may have influenced participants’ sleep (e.g., illness status, medication history, physical activity index, etc.). For this reason, future study designs should employ more stringent methodologies to account for these variables.
Third, the absence of a control group hinders the capacity to conclusively credit enhancements in sleep quality solely to the Metaverse-based CBTi program. Subsequent studies should include a comparison group to more precisely evaluate the effectiveness of the Metaverse platform.
Fourth, the study’s dependence on self-reported evaluations of sleep quality introduces the possibility of subjective bias, emphasizing the necessity for objective sleep evaluations in future research.
Fifth, we observed improvements in sleep parameters immediately after the intervention and at a 2-week follow up, the long-term effects of the Metaverse-based CBTi program remain unclear. Extended follow-up periods in future studies would provide valuable insights into the durability of the intervention’s benefits.
Sixth, we were unable to utilize all of the content of CBTi in our Metaverse-based study, and the interpretation of the results may be limited due to the heterogeneity of the study population, which included both patients with insomnia and the general population with subjectively poor sleep quality.
Finally, there was a high number of dropouts at the 2-week follow-up. This result makes the potential of a Metaverse-based CBTi program unclear. One possible explanation is that the short duration of the intervention and the dependence on online surveys may have contributed to the high dropout rate. Participants did not receive any feedback during the 2 weeks after the single 2-hour session and were only asked to complete an online survey 2 weeks later. Being a retrospective study, it was not possible to control for these factors. Future research should design more refined Metaverse-based CBTi programs and validate their effectiveness, taking into account dropout rates, which is an important issue in non-face-to-face interventions.
These limitations underscore the need for cautious interpretation of the findings. Despite these challenges, this study provides a foundation for future research into Metaverse-based CBTi, highlighting the need for more robust, controlled studies in this promising area of digital health interventions. Future research should address these limitations by employing larger, more diverse samples, incorporating objective sleep measures, utilizing full CBTi content, and conducting longer follow-ups with appropriate control groups.
Although this study has certain limitations, the findings suggest that the Metaverse platform holds promise as a supplementary digital health tool for addressing sleep-related challenges, particularly among younger adults. However, as the inclusion criteria did not exclusively target individuals with diagnosed insomnia, the generalizability of these findings to insomnia management remains uncertain. Further studies with well-defined populations, including patients diagnosed with insomnia and more diverse age and gender groups, are necessary to better evaluate the Metaverse platform’s potential for managing insomnia and related mental health challenges.
PDF Links
PubReader
ePub Link
XML Download
Full text via DOI
Download Citation 
Print 
