Enhancing Mindfulness-Based Cognitive Therapy in a Virtual Reality: A Prospective Interventional Study

We conducted a prospective interventional study to evaluate the feasibility of the VR-MBCT intervention. After confirming the usability of VR-MBCT, we compared behavioral patterns between the two groups. By analyzing behavioral patterns in both groups, this study aims to provide important insights into the feasibility and potential clinical applicability of VR-MBCT.

VR-MBCT

The two core components of MBCT are mindfulness cultivated through meditation and individual traits related to mindfulness (e.g., self-awareness, emotional acceptance)³². Based on these, VR-MBCT was developed through iterative discussions with mental health professionals (e.g., psychiatrists, therapists). VR-MBCT consists of an introductory mindfulness exercise and a three-session VR-MBCT program (Fig. 1). VR-MBCT is designed to be experienced in a virtual environment featuring the ocean with the goal of harnessing the positive effects of nature on mental health (e.g., stress relief, improved self-esteem)^33,34,35.

Screenshots of Introductory Mindfulness Exercises and three sessions of VR-MBCT: (a) introductory mindfulness exercise, (b) starry expressions, (c) self as context, and (d) acceptance. Due to security restrictions during the Korea Food and Drug Administration (KFDA) clinical trials, we were unable to include screenshots of the actual VR content. Instead, we have used processed VR content screenshots to facilitate understanding of the paper.

Introductory mindfulness exercise

The Introductory Mindfulness Exercise helps users easily adapt to meditation techniques (e.g., focusing on breathing) in a virtual environment and approach VR-MBCT comfortably and naturally. By showing visual changes from sunrise to sunset and various objects encountered at sea, users are guided to focus on the present moment and observe their experiences without critical or negative thought patterns.

Session 1: starry expressions

The Starry Expression session involves users drawing existing constellations in the night sky and then creating their own constellations through “free constellation drawing.” This activity is designed based on research findings that the overload of negative emotions and thoughts experienced by depressed patients can lead to differences in information processing³⁶, which can increase the complexity and depth of the artwork when applied to creations³⁷. Users can enhance self-expression and awareness by focusing on the present moment and their emotions.

Session 2: self as context

The Self as Context session allows users to choose an avatar that reflects their current situation, helping them view their emotions more objectively. This activity is based on the metacognitive process model of decentering^38,39, which focuses on disidentification from internal experience and self-distancing to analyze the meaning of memories and experiences, and can benefit mental health⁴⁰. Users can practice emotional distancing and accept their emotions in a non-critical manner.

Session 3: acceptance

The Acceptance session involves users placing emotion-labeled leaves in their own baskets to understand their emotional state. This activity is based on the symbolic interaction theory^41,42, in which individuals assign meaning to symbols and objects and regulate their behaviors and responses based on these meanings. Users can reflect on and understand their recent emotional states through the emotions written on each leaf, thereby increasing their overall self-awareness and emotional clarity, and ultimately improving their mental health through clear emotional expression.

Study setting

This study was conducted from September 2023 to April 2024 in two VR laboratory units isolated from external factors. The VR laboratory was divided into a dialogue unit to explain the study, answer surveys and complete exit interviews, and a therapeutic unit for experiencing VR-MBCT. The dialogue unit was set up so that researchers and participants could face each other, which could help foster rapport and improve participant engagement and data quality^43,44. The therapeutic unit was equipped with a swivel recliner to ensure participant comfort during the VR-MBCT experience, with all surrounding obstacles removed for safety. These units were located in the same building as the counseling center where participants were recruited, but were physically separate rooms.

Hardware

We chose the Meta Quest Pro standalone VR Head-Mounted Display (HMD) and the Empatica E4 wristband. The Quest Pro has a customizable fit to accommodate a variety of head sizes and shapes, and its built-in sensors track the user’s eye movements, head movements, and facial expressions. The VR content was designed to provide a fully immersive 3D experience through the HMD by placing the user in a 360-degree environment with 4K resolution and a 60 Hz refresh rate to ensure a high level of user immersion. The E4 wristband non-invasively collects electrodermal activity (EDA) data without compromising the user’s immersion during the VR experience.

Software (VR interface tutorial)

Minimizing researcher intervention is crucial to allow participants to explore and resolve their emotions and issues independently through VR-MBCT. To support this goal, we developed a VR interface tutorial (mean = 36 s, min = 25 s, max = 40 s) to familiarize participants with the VR interface prior to performing VR-MBCT. This tutorial was essential to effectively control for exogenous variables, such as behavioral delays due to unfamiliarity with the VR interface during tasks in VR-MBCT. The tutorial demonstrated the appearance of the actual controller within the virtual environment, allowing participants to intuitively understand trigger positions and interact with VR-MBCT through scripts and narration (e.g., pressing the “next” button, selecting objects).

Flowchart of study participants from recruitment to analysis. Participants were recruited from university counseling center-based or online community sources, screened based on inclusion criteria, and assigned to either the IWD or the IWoD.

Study procedure

Screened participants completed three sessions in the experiment: (1) an introductory session that included an explanation of the study and the VR interface tutorial experience, (2) an intervention session in which they experienced VR-MBCT, and (3) a review session that included post-experience surveys. In the introductory session, the researchers introduced the purpose of the study, explained the equipment, allowed sufficient time to become familiar with the virtual environment (e.g., personalized gaze calibration) and equipment, and guided participants through the VR interface tutorial. The intervention session took place in a therapeutic unit, starting with participants wearing the HMD and E4 wristband, followed by the VR-MBCT experience (mean=9.45 minutes, min=6.83 minutes, max=12.72 minutes). The researchers did not intervene during the VR experience, but were always available in case of emergencies. Finally, in the review session, we collected responses to the System Usability Scale (SUS)⁴⁵, the Igroup Presence Questionnaire (IPQ)⁴⁶, and the NASA Task Load Index (NASA-TLX)⁴⁷ to assess the immersion and usability of VR-MBCT (See Supplementary Tables S1, S2, and S3).

Recruitment

Considering the potential for difficulties of using VR technology to act as an exogenous variable influencing experimental results, we restricted the age of participants to between 18 and 40 years to minimize the impact of these factors. This step was taken to more accurately assess the feasibility of VR-MBCT. Initially, we recruited 48 young adults with depression and 49 young adults without depression in the same age range who were screened according to the eligibility criteria for participation in the study. The required sample size was calculated using \(\hbox {G}^*\)Power (version 3.1). For a two-tailed independent samples t-test, with an effect size of d=0.8, an alpha level of 0.05, and a power of 0.95, the analysis indicated a total sample size of 70 participants (35 per group). The sample size recruited for this study significantly exceeded this requirement, ensuring sufficient statistical power for reliable interpretation of the results.

Exclusion criteria included age outside the specified range, failure of the Northstar Digital Literacy Assessment (NDLA)⁴⁸, which assesses basic digital skills (e.g., using document software), comorbid psychiatric conditions (e.g., bipolar disorder, schizophrenia) that affect the ability of decision-making, and refusal to participate in the experiment (n = 18). We also collected responses to the Patient Health Questionnaire-9 (PHQ-9), developed by Kroenke et al. (2001)⁴⁹. The PHQ-9 is a validated self-report measure consisting of 9 items that contain the frequency of depressive symptoms over the past two weeks on a 4-point Likert scale (0 =“Not at all” to 3 =“Nearly every day”). Scores range from 0 to 27, with higher scores indicating greater depressive severity. Among participants with PHQ-9 scores of 20 or higher (n = 3, indicating severe depressive symptoms), two were excluded following consultation with the clinical team, as their symptoms were considered unsuitable for cognitive therapy. Most participants with depression (33 out of 38; 86.84%) had mild to moderate depressive symptoms based on PHQ-9 scores (See Table 1).

Although depressive symptoms can fluctuate episodically^50,51, PHQ-9 was used as a screening tool before the VR-MBCT experience to identify participants currently experiencing depressive symptoms. Participants with a score of 5 or higher (indicating mild depression or above) were grouped as individuals with depression. Applying these exclusion criteria resulted in a final sample of 38 individuals with depression and 35 without depression. Figure 2 shows the flow of study participants from recruitment to analysis.

Demographics

The baseline demographic and clinical characteristics of the study participants are summarized in Table 1. The study included 73 participants who were divided into two groups: the IWD (n = 38) and the IWoD (n = 35). The mean age was similar between groups, with the IWD at 25.36 years (SD = 3.61) and the IWoD at 25.17 years (SD = 4.11). Gender distribution showed 21 females and 17 males in the IWD, and 18 females and 17 males in the IWoD. Depression severity reassessed using the PHQ-9 prior to conducting the study, showed a range of severity within the IWD: 21 with mild, 17 with moderate, 5 with moderately severe, and 1 with severe depression.

Data collection

Measures of user experience and usability

All scales were assessed after the completion of VR-MBCT. These measures were chosen because they comprehensively reflect aspects of the user experience and represent the usability of VR-MBCT. We used SUS to assess the usability of VR-MBCT. This survey consists of 10 items, scored on a 5-point Likert scale ranging from 1 (“Strongly disagree”) to 5 (“Strongly agree”). Scores below 68 are considered inadequate, scores between 68 and 84 are considered acceptable, and scores above 85 are considered excellent (Brooke et al., 1996). To measure the presence experienced by users in the virtual environment, we used the IPQ, which includes one general item and three subscales (spatial presence, involvement, and realness) considered as independent factors. It consists of 14 items using a 7-point Likert scale, with higher scores indicating greater presence in a VR environment (Schubert et al., 2001). To assess the cognitive load of users in VR-MBCT-guided activities, we used the NASA-TLX, which consists of six subscales (mental demand, physical demand, temporal demand, performance, effort, and frustration) with a 7-point Likert scale. Higher scores on mental demand, physical demand, temporal demand, effort, and frustration indicate higher cognitive load, while higher scores on performance reflect better task performance (Hart, 2006). Table 2 shows the types and features of the surveys, sensor data, and user interaction log data used in our study.

Sensor data

Sensor data are passive data collected continuously by sensors that reflect physiological responses or behavior patterns of the user without direct manipulation or conscious input from the user. Sensor data were collected non-invasively and in real-time during the VR-MBCT experience to avoid disrupting the user experience. We used the eye-tracking technology of Meta Quest Pro to collect users’ eye movement data. By projecting a laser from the gaze origin in the direction of the gaze, we confirmed interactions with objects in the virtual environment. When the laser collided with an object, the system recorded the object name and the conflict signal, allowing us to identify the region of interest (ROI) where the user’s gaze was focused. Additionally, we used the E4 wristband to collect EDA data. The E4 wristband detects the user’s autonomic nervous response to stress, emotional changes, and physiological arousal. EDA is widely recognized as a potential digital biomarker for reflecting various mental and emotional states and predicting depression^52,53,54.

User interaction log

We collected user interaction logs during the three VR-MBCT sessions. In the first session, we recorded the sequence of stars drawn by users based on 13 fixed star coordinates. In the second session, we collected the latency and the type of avatar chosen by users among five avatars with different concerns. In the final session, we collected the emotion labels written on the leaves chosen by users (e.g., relaxed, confused) and the number of positive or negative emotion leaves placed in their baskets.

Exit interview

We conducted 10-minute exit interviews to identify participants’ usability experiences and any discomfort during each VR-MBCT session. The interview questions, detailed in Supplementary Table S4, were designed to align with the survey and provide comprehensive qualitative feedback on VR-MBCT. Three authors of this paper independently coded interview transcripts, repeatedly discussing the coding results to categorize themes until the coders reached a consensus. Cohen’s Kappa measurement verified the inter-coder reliability. The scores for each category were higher than 0.70 (mean=0.82, max=0.90), indicating that the inter-coder reliability lies between “substantial” and “perfect”⁵⁵.

Ethics approval

Ethical approval was granted by the Institutional Review Board (IRB) of the authors’ institution (HYUIRB-202308-006). All procedures and methods were explicitly conducted in accordance with the relevant guidelines and regulations as approved by the institution. All participants provided written informed consent and could voluntarily withdraw from the VR-MBCT session at any time without giving a reason. Patients who were considered high risk required hospitalization, or showed other signs that the use of VR-MBCT might be harmful were not offered the use of VR-MBCT. All personally identifiable information was removed from the collected data to protect the privacy of the participants, and code names were assigned prior to analysis.

Statistical analysis

To evaluate VR-MBCT and to investigate unique behavioral patterns in the IWD, we conducted descriptive statistics and statistical tests on post-evaluation results for several measures, including SUS, IPQ, NASA-TLX, ROI-based level of attention, emotional variability from EDA, and session-by-session log data.

Our analysis aimed to identify significant differences between the IWD and the IWoD. To control for potential confounding variables, we included gender, age, VR experience (categorized as 0 times, 1-2 times, 3-5 times, more than 5 times), and the time spent experiencing VR-MBCT as covariates in our statistical model. We used Analysis of Covariance (ANCOVA) to adjust for these covariates and ensure robust comparisons between groups. Before running ANCOVA, we confirmed that all necessary conditions were met, including independence, linearity, homoscedasticity, normality of covariates, and the absence of interaction effects between covariates and treatments. In addition, we used partial eta squared \(\eta _{p}^{2}\) to estimate the effect sizes of our results, interpreting values around 0.01 as small, 0.06 as medium, and 0.14 as large effects.

To evaluate the emotional variability from EDA, we compared pre- and post-session measures (i.e., emotional state, stress levels) across three sessions without controlling for pre-measures as covariates. Since the EDA data did not follow a normal distribution, we used the Wilcoxon Signed-Rank Test. We also compared the entropy-based variability across three sessions by group. We conducted Shapiro-Wilk tests for normality, and when outliers, skewed, or multimodal distributions were detected, we applied the non-parametric Mann-Whitney U test. For the effect size of both tests, we used the coefficient r, interpreting values around 0.1 as small, 0.3 as medium, and 0.5 as large effects.