These authors contributed equally to this work.
The prolonged coronavirus disease-2019 (COVID-19) pandemic is likely to cause psychological distress in people. This systematic review aimed to identify the effectiveness of virtual reality (VR)-based psychological intervention among individuals with psychological distress during the COVID-19 crisis. PubMed, Ovid MEDLINE, Cochrane Library, Web of Science, Embase, and PsycINFO databases were searched for articles published until July 2022.
The available citations were deduplicated and screened by two authors using the title and abstract information. Eligibility criteria were constructed according to the PICOT guidelines. Empirical studies of all designs and comparator groups were included if they appraised the impact of an immersive VR intervention on any standardized measure indicative of psychological distress (stress, anxiety, depression, and post-traumatic symptoms) or improvements in quality of life in participants, including COVID-19 patients, medical staff working with COVID-19 patients, and people who had experienced strict social distancing during the COVID-19 pandemic.
The results were discussed using a narrative synthesis because of the heterogeneity between studies. Seven of the studies met the inclusion criteria. There were two randomized controlled trials and five uncontrolled studies on VR interventions.
All studies reported significant improvement in a wide range of psychological distress during COVID-19, ranging from stress, anxiety, depression, and post-traumatic symptoms to quality of life, supporting the efficacy of VR-based psychological intervention. Our results suggest that VR intervention has potential to ameliorate COVID-19-related psychological distress with efficacy and safety.
Coronavirus disease-2019 (COVID-19), caused by the SARSCoV-2 virus, was first identified in Wuhan, China, in late 2019, and swiftly spread to the other countries. Subsequently, the World Health Organization declared the COVID-19 outbreak a global “pandemic” in March 2020 [
The pandemic has had a harmful effect on public mental health, leading to psychological crises [
The number of newly reported COVID-19 cases has been declining globally since the end of March, 2022 [
COVID-19 makes it difficult for people to receive face-toface medical and psychological support, forcing clinicians to deliver treatment via audio/videocalls, e-mail, or the Internet [
Different from traditional face-to-face psychotherapy, a combination of visual and auditory stimuli in VR can be used to create an immersive experience, which may reduce perceived distress and negative affect, and induce relaxation and positive affect [
Prior studies revealed the potential of digital therapeutic technology like as immersive VR that offer cost-effective, scalable, and on-demand solution to address mental problems. Since COVID-19 era has prolonged yet, VR intervention has the potential strength to minimize face-to-face intervention [
This systematic review was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. This review was registered under registration number CRD42022351974 in the International Prospective Register of Systematic Reviews (PROSPERO).
Search strategies were developed in collaboration with a research team and an experienced librarian. Keywords related to VR and COVID-19-related psychological distress were developed by the research team, and a librarian searched the following electronic databases: PubMed, Ovid MEDLINE, Cochrane Library, Web of Science, Embase, and PsycINFO. A database search was conducted on July 22, 2022. We used the following search terms: (COVID 19 OR SARS-CoV-2) AND (psychological distress OR anxiety OR depression OR stress, psychological OR quality of life OR COVID-19 stress syndrome) AND (virtual reality exposure therapy OR virtual reality). Furthermore, a search for registered clinical trials (clinicaltrials. gov), grey literature, and references of the included studies was conducted to identify potentially relevant studies. The search strategy is presented in
Eligibility criteria were constructed according to the PICOT guidelines [
Exclusion criteria included 1) psychological distress unrelated to COVID-19; 2) non-psychological interventions using VR (e.g., VR tour); and 3) studies that did not report the outcome of interest. In addition, the following studies were excluded: reviews, case reports, commentaries, letters to the editor, daily reports, books, protocol registrations, and abstracts without full text.
All relevant citations were saved by the reference manager, EndNote. Two reviewers (SA Lee and S Heo) independently screened each citation according to the inclusion/exclusion criteria stated above. Eligibility of the studies was determined by reading the titles and abstracts of the retrieved articles. If there was insufficient information in the abstract regarding inclusion or exclusion, the full text was reviewed before the final decision. The results were compared to identify inconsistencies, and inter-examiner conflicts were resolved through discussion with a third reviewer (S Kim). A PRISMA flow diagram was used to organize and keep track of the numbers of studies that were included/excluded.
Three reviewers (SA Lee, S Heo, and S Kim) created a detailed table for data extraction using Microsoft Excel. Six evaluators (SA Lee, S Heo, S Kim, C Park, Y Jung, and G Ji) independently extracted data using a prespecified form. The first author, year of publication, study design, country, population characteristics, number of participants, age, intervention, control, and outcome were extracted from the included studies. In addition, data related to the VR intervention included its content, duration of intervention, total number of sessions, and whether it was combined with other treatments. Finally, three evaluators (SA Lee, S Heo, and S Kim) reviewed the data extraction results, and any disagreement regarding the extracted data was resolved by consensus among the reviewers.
Two evaluators (SA Lee and S Heo) independently assessed the quality of all the included studies, and any discrepancies were resolved by a third evaluator (S Kim). As this review included studies using different designs, the relevant quality assessment tools were applied according to the study design. The risk of bias in randomized controlled trials (RCTs) was assessed using the revised Cochrane risk of bias tool for randomized controlled trial (RoB 2.0 [
The NIH quality assessment tool for before-after studies with no control group consisted of 12 questions: 1) study question; 2) eligibility criteria and study population; 3) study participants representative of clinical populations of interest; 4) all eligible participants enrolled; 5) sample size; 6) intervention clearly described; 7) outcome measures clearly described, valid, and reliable; 8) blinding of outcome assessors; 9) follow-up rate; 10) statistical analysis; 11) multiple outcome measures; and 12) group-level interventions and individual-level outcome efforts. Each question was evaluated as “yes,” “no,” “cannot determined,” “not applicable,” or “not reported.”
The studies included in this review used heterogeneous populations (e.g., COVID-19 patients, COVID-19 medical staff, and people who had undergone strict social distancing during the pandemic) and included various outcomes such as stress, anxiety, depression, post-traumatic symptoms, and quality of life. Therefore, quantitative synthesis (meta-analysis) could not be performed due to clinical and methodological diversity in the included articles [
A flowchart of the data selection process is shown in
Among the seven studies included in the systematic review, in terms of population, three [
Regarding study design, five [
Nijland et al. [
Riva et al. [
Beverly et al. [
Groenveld et al. [
Meyer et al. [
A randomized study by Rodrigues et al. [
Vlake et al. [
This systematic review included two RCT studies and five before- and-after studies with no control group, so that the quality assessment tool was mixed and used according to the study design. Five before-and-after studies with no control groups were assessed using the NIH quality assessment tool (
As a result of the quality assessment, most studies included in this review had methodological limitations. First, five of the seven studies were before-and-after studies, which had insufficient designs to rigorously verify the effectiveness of the intervention. In particular, Nijland et al. [
To the best of our knowledge, this is the first systematic review to provide an up-to-date overview of the effectiveness of VR-based psychological interventions in individuals experiencing psychological distress during the COVID-19 crisis. In total, there were seven studies eligible for this systematic review— two RCTs and five uncontrolled studies. We could not synthesize the data for a meta-analysis because of the heterogeneity between the studies; they had different populations, assessment tools, outcomes, and duration of studies. Instead, we conducted a systematic literature search of all accessible resources, minimizing selection bias and subjective selection bias.
The participant groups included COVID-19 patients [
In contrast to Hatta et al.’s [
Taken together, our systematic review suggests future directions for studies that intend to utilize VR for COVID-19-related psychological distress. First, the development of VR content should be based on the current knowledge of core psychopathology and psychological sciences to be more effective. Most existing studies have shown participants peaceful or pleasant images (e.g., natural environments) in immersive VR modalities, lacking an active treatment component. Because nature-related stimuli are easily accessible and safe to most people and can effectively induce general positive emotions [
Second, seven studies included in the present systematic review have shown that VR is a feasible and acceptable innovative method to reduce psychological distress and improve quality of life in the place where the participants stayed (e.g., hospitals, ICU, workplaces, and home). This confirms that VR may serve as a reachable and immersive way to bring practical clinical interventions to hospitalized patients, health-related workers, and patients in the COVID-19 recovery phase, mainly during the ongoing COVID-19 pandemic. Future studies should focus on the fact that VR is necessary for a more traditional style of face-to-face consultation settings [
Third, from a methodological point of view, multiple issues must be addressed to provide higher-quality evidence. A small sample size may restrict the statistical power; therefore, the minimum number of participants per group should be determined in the power calculation analysis prior to data collection. The mean duration of VR intervention was inconsistent; hence, it was difficult to determine the optimal duration to maximize treatment outcomes and minimize cybersickness.
Lastly, existing studies mostly relied on self-reported data for assessing treatment outcomes, which can be criticized for validity. Therefore, diversifying measurements using physiological or behavioral data would be helpful to better capture treatment outcomes at different analytical levels. In addition, a long-term follow-up assessment is necessary to determine whether the treatment effect is enduring.
Overall, the existing studies had low methodological quality, and a meta-analysis was not possible due to high heterogeneity. Currently, the total number of VR-based psychological intervention studies was limited. There were only two RCTs, and their methodological quality was evaluated as “some concern” to “high risk of bias.” Therefore, well-designed RCTs with enhanced methodological quality are needed to test the efficacy of VR interventions for the treatment of COVID-19-related psychological distress. Although the number of final literatures itself is not a deciding factor to conduct a systematic review and there is no minimum number of studies to include in a systematic review [
Despite the limited number and low methodological quality of existing studies, our results suggest that VR intervention has promising potential to ameliorate COVID-19-related psychological distress with efficacy and safety. In a world where the demands for mental health service are steeply increasing in response to unforeseen social and natural disasters including the pandemic, well-designed, evidence-based VR interventions can function as useful supplement or alternatives of traditional face-to-face psychotherapy. And our review suggests the major points that needs to be addressed to turn the potential of VR intervention into real progress in the near future.
The online-only Data Supplement is available with this article at
Search Strategy for Mapping Review
The datasets generated or analyzed during the study are available from the corresponding author on reasonable request.
Ji Sun Kim, a contributing editor of the
Conceptualization: Bin-Na Kim, Ji Sun Kim. Formal analysis: Seul-Ah Lee, Simyang Heo. Investigation: Somin Kim, Chaeyeon Park, Yujin Jung, Garam Ji, Hyeon-Ah Lee, Kibum Kim, Sungkean Kim. Writing—original draft: Seul-Ah Lee, Simyang Heo. Writing—review & editing: Bin-Na Kim, Ji Sun Kim.
This research was supported by a grant of the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health & Welfare, Republic of Korea (grant number: HI22C0619).
This study was also supported by Soonchunhyang University.
Flowchart of the selection strategy and inclusion/exclusion criteria for the systema-tic review according to PRISMA guideline.
Risk of bias assessment using RoB 2.0 for randomized controlled trials.
Characteristics of the included studies
Study | Study design | Sample size | Age (yr) | Country | Population | Setting | Intervention | Outcome measures | Comparator/control | Outcome (VR-based intervention) |
---|---|---|---|---|---|---|---|---|---|---|
Nijland et al. [ |
Within subject pre-post | 326 | Adult | Netherlands | ICU nurses of the UMCG working on one of the four intensive care units for COVID-19 patients | University Medical Centre Groningen (UMCG) | VR relaxation (VRelax) (≥10 min, actual use duration was not collected), including videos of calming natural environments | Perceived stress (VAS-stress: 0–100/PSS-10: 10 items, 5-point) | N/A | Mean VAS-stress before VRelax was 35.2 (standard deviation [SD]=24.3), mean VAS-stress after VRelax was 21.1 (SD=21.1). The mean difference was 14.0 points, SD=13.3; t-test value=8.6; p<0.005; 95% confidence interval (CI)=10.8–17.3. |
Resilience (CD-RISC-10: 10 items, 5-point) | ||||||||||
Riva et al. [ |
Within subject pre-post | 40 | ≥18 | Italy | People who had experienced at least two months of strict social distancing | Recruited through the combined use of traditional strategies and advertisements on a social media platform | VR video “The Secret Garden” (10 min, 1 week): immersive modality+non-immersive modality+a series of social exercises (1 week) | Anxiety and depression (DASS) | N/A | There was a significant effect of time for the primary outcomes of depression [F(3, 115.2)=6.68; p<0.001, η2 p=0.15, 95% CI (0.04, 0.26)], stress [F(3, 115.17)=6.35; p<0.001, η2 p=0.14, 95% CI (0.03, 0.25)], general distress [F(3, 115.15)=6.97; p<0.001, η2 p=0.15, 95% CI (0.04, 0.26)], perceived stress [F(3, 115.17)=5.15; p=0.002, η2 p=0.12, 95% CI (0.02, 0.22)], and hopelessness [F(3, 115.07)=4.80, p=0.003, η2 p=0.11, 95% CI (0.01, 0.21)]. No significant effect of time was found on anxiety [F(3, 115.2)=2.2556, p>0.05, η2 p=0.06, 95% CI (0.00, 0.14)]. |
Perceived stress (PSS-10) | ||||||||||
Hopelessness (BHS) | ||||||||||
Regarding primary outcome measures, participants exhibited improvements from baseline to post-intervention for depression levels, stress levels, general distress, and perceived stress (all ps<0.05) but not for the perceived hopelessness (p=0.110). | ||||||||||
Beverly et al. [ |
Within subject pre-post | 102 | ≥18 | USA | Frontline healthcare workers, including direct care providers, indirect care providers, and support or administrative services | OhioHealth Healthcare System (an emergency department, a medical/surgical unit, and a critical care unit) | Tranquil Cine-VR simulation (3 min, 1 session): depicted a lush, green nature preserve to promote relaxation and peace | Subjective stress (VAS-stress: 10-point) | N/A | A significant reduction in subjective stress scores from pre- to post-simulation (mean change=-2.2±1.7, t=12.749, p<0.001), with a Cohen’s d of 1.08, indicating a very large effect |
Only four (3.9%) participants met the cutoff for high stress after the simulation, and the number of people with high stress differed pre- and post-simulation (χ2=8.582, p=0.003). | ||||||||||
Participants who met the cutoff for high stress pre-simulation showed a greater reduction in subjective stress scores compared to participants who did not meet the cutoff pre-simulation (mean change=3.3±2.0 vs. 1.6±1.2, t=5.403, p<0.001). | ||||||||||
Groenveld et al. [ |
Within subject pre-post | 48 | ≥16 | Netherlands | Patients with post COVID-19- condition referred for physiotherapy to a physiotherapists | Community-based practice or outpatient rehabilitation clinic | Multimodal VR exercises (30 min per session, 6 weeks): physical (SyncVR Fit), cognitive (Koji’s Quest), relaxation and distraction exercises (SyncVR Relax & Distract) | Anxiety and depression (HADS: ≥8) | N/A | The scores of the Positive Health questionnaire and SF-12 were significantly increased after 6 weeks. |
Quality of life (SF-12, The Positive Health questionnaire) | The 1.4 points decrease of total HADS score was not significant (p=0.08) for the total group but reached significance (p=0.01) for the subgroup of patients who used the mental VR applications. | |||||||||
Meyer et al. [ |
Within subject pre-post | 38 | ≥18 | Germany | People who had experienced at least two months of strict social distancing | N/A | Online self-help protocol “COVID Feel Good” | Depressiveness (DASS: 7 of 21 items, 4-point) | N/A | There was a statistically significant effect of the variable Time displaying difference between the 4 measurements (day -7, 0, 7, 21) for general distress [F(3, 111)=11.65, p<0.001, η2=0.061], perceived stress [F(3, 111)=4.74, p=0.004, η2=0.038], as well as the subscales depression [F(3, 111)=7.93, p≤0.001, η2=0.047], anxiety [F(3, 111)=7.80, p<0.001, η2=0.047], and stress [F(3, 111)=6.78, p<0.001, η2=0.052]. |
VR video “Secret Garden” (10 min, 1 week)+daily social or cognitive exercise (1 week) | Anxiety (DASS: 7 of 21 items, 4-point) | |||||||||
General distress (SUDS: 0–100) | ||||||||||
Perceived stress (PSS-10: 10 items, 5-point/DASS: 7 of 21 items, 4-point) | ||||||||||
Hopelessness (BHS: 20 items, true or false) | However, the decrease in hopelessness turned out to be insignificant [F(3, 111)=2.65, p=0.052, η2=0.009]. | |||||||||
Rodrigues et al. [ |
RCT | 44 (22+22) | 18–80 | Brazil | Enrolled from inpatient wards for patients with COVID-19 | Regional hospital or university hospital at four sites in Brazil | Therapeutic VR (10 min, 1 session): VR videos with images of landscapes and/or mindfulness techniques)+Occupational therapy (30 min, 1 session) | Anxiety and depression (HADS) | Non-therapeutic VR + Occupational therapy | Concerning the HADS scale, only the experimental group had a difference from baseline to post-intervention (anxiety: baseline 4.10±2.90, post 2.10±2.23, p=0.001, effect size=0.773245). |
Vlake et al. [ |
RCT | 89 (45+44) | ≥18 | Netherlands | COVID-19 patients in ICU | A university teaching hospital and 3 universityaffiliated secondary care hospitals | ICU-VR (14 min, 1 session) consists of 6 scenes+60-minute-long consultation | PTSD (IES-R: ≥33) | Control (only consultation without VR intervention) | The prevalence and severity of psychological distress were limited throughout follow-up, and no differences in psychological distress or quality of life were observed between the groups. ICU-VR improved satisfaction with (mean score 8.7, SD 1.6 vs. 7.6, SD 1.6 [ICU-VR vs. control]; t64=-2.82, p=0.006) and overall rating of ICU aftercare (mean overall rating of aftercare 8.9, SD 0.9 vs. 7.8, SD 1.7 [ICU-VR vs. control]; t64=-3.25; p=0.002) compared to controls. |
Anxiety (HADS: 7 items, ≥8) | ||||||||||
Depression (HADS: 7 items, ≥8) | ||||||||||
Quality of life (SF-36, EQ-5D) |
ICU, Intensive Care Unit; COVID-19, coronavirus disease-2019; VR, virtual reality; N/A, not available; VAS-stress, Visual Analogue Scale-stress; CD-RISC-10, 10-item Connor-Davidson Resilience Scale; DASS, Depression Anxiety Stress Scales; PSS-10, Perceived Stress Scale 10; BHS, Beck Hopelessness Scale; SF-12, Short Form-12; SUDS, Subjective Units of Distress Scale; HADS, Hospital Anxiety and Depression Score; IES-R, Impact of Event Scale-Revised; SF-36, Short-Form 36; EQ-5D, European Quality of Life 5 dimensions
Risk of bias assessment using NIH quality assessment tool for before and after (pre-post) studies with no control group
Q1 | Q2 | Q3 | Q4 | Q5 | Q6 | Q7 | Q8 | Q9 | Q10 | Q11 | Q12 | |
---|---|---|---|---|---|---|---|---|---|---|---|---|
Nijland et al. [ |
Y | Y | N | Y | CD | N | Y | NA | NA | Y | N | NA |
Riva et al. [ |
Y | Y | N | Y | Y | Y | Y | NA | Y | Y | Y | NA |
Beverly et al. [ |
Y | Y | N | Y | N | Y | N | N | NA | Y | N | NA |
Groenveld et al. [ |
Y | Y | CD | Y | NR | N | Y | NA | N | Y | N | NA |
Meyer et al. [ |
Y | Y | N | Y | Y | Y | Y | NA | NR | Y | Y | NA |
Q1: Was the study question or objective clearly stated?, Q2: Were eligibility/selection criteria for the study population prespecified and clearly described?, Q3: Were the participants in the study representative of those who would be eligible for the intervention in the general or clinical population of interest?, Q4: Were all eligible participants that met the prespecified entry criteria enrolled?, Q5: Was the sample size sufficiently large to provide confidence in the findings?, Q6: Was the intervention clearly described and delivered consistently across the study population?, Q7: Were the outcome measures prespecified, clearly defined, valid, reliable, and assessed consistently across all study participants?, Q8: Were the people assessing the outcomes blinded to the participants’ interventions?, Q9: Was the loss to follow-up after baseline 20% or less? Were those lost to follow-up accounted for in the analysis?, Q10: Did the statistical methods examine changes in outcome measures from before to after the intervention? Were statistical tests done that provided p-values for the pre-to-post changes?, Q11: Were outcome measures of interest taken multiple times before the intervention and multiple times after the intervention (i.e., did they use an interrupted time-series design)?, Q12: If the intervention was conducted at a group level (e.g., a whole hospital, a community, etc.) did the statistical analysis take into account the use of individual-level data to determine effects at the group level?. NIH, National Institutes of Health; Y, yes; N, no; CD, cannot determine; NA, not applicable; NR, not reported