Background: With the influx of medical virtual reality (VR) technologies, cybersickness has transitioned from a nuisance experienced during leisure activities to a potential safety and efficacy concern for patients and clinicians. To improve health equity, it is important to understand any potential differences in cybersickness propensity among demographic groups, including racial groups.
Objective: This study aims to explore whether cybersickness propensity differs across racial groups.
Methods: We collected self-reported cybersickness ratings from 6 racially diverse independent samples within 1 laboratory group (N=931). In these studies, the participants were asked to perform tasks in VR such as traversing environments, pointing at and selecting objects, and interacting with virtual humans.
Results: Significant racial differences in cybersickness were found in 50% (3/6) of studies. A mini meta-analysis revealed that, on average, Black participants reported approximately one-third of SD less cybersickness than White participants (Cohen d=−0.31; P<.001), regardless of the nature of the VR experience. There was no overall difference in reported cybersickness between the Asian and White participants (Cohen d=−0.11; P=.51).
Conclusions: Racial differences in cybersickness indicate that researchers, practitioners, and regulators should consider patient demographics when evaluating VR health intervention outcomes. These findings lay the groundwork for future studies that may explore racial differences in cybersickness directly.
Cybersickness is a common negative physiological effect of exposure to virtual reality (VR), with symptoms similar to motion sickness, including disorientation, nausea, headache, and eye strain . Recent technological advances have led to the widespread use of low-cost VR technologies. In turn, VR technologies that were developed and primarily used for gaming and entertainment have been applied broadly across areas such as education, industry, and medicine. Medicine, in particular, is a rapidly expanding application space for VR technologies, including new developments in a range of areas such as medical education and training [ ], physical therapy and rehabilitation [ ], surgical planning [ ], pain management [ - ], psychotherapy [ , ], and treatment for ophthalmic disorders [ ]. As medical VR technologies have become more commonplace, cybersickness has transitioned from a nuisance to a potential safety and efficacy concern. Cybersickness concerns have prompted researchers [ - ], professional groups [ ], standards organizations [ ], and the US Food and Drug Administration [ ] to address prevention, assessment, and mitigation strategies.
When researchers and clinicians are making benefit-risk determinations for emerging VR technologies, they should consider cybersickness propensity. Although cybersickness is a well-known response to VR exposure, the full range of causes and risk factors are not well understood. This knowledge gap may be a barrier in assessing the safety and effectiveness of VR technologies for all users. Thus far, the scientific literature has identified several factors that are linked to differences in cybersickness risk. It is well known that elements of VR content, VR hardware, and the interface between them influence cybersickness outcomes [, ]. Certain demographic and within-person factors have also been connected with propensity to experience cybersickness, such as age [ , - ], sex and gender [ , , - ], BMI [ ], and health and health history [ - ]. A large meta-analysis of existing literature (k=137) recently found that various individual differences predict cybersickness propensity, including gender, real-world experience, technological experience, possessing a neurological disorder, and possessing a relevant phobia [ ]. However, this meta-analysis did not consider race as a potential moderator.
Racial Differences in Cybersickness and Motion Sickness
Studies investigating potential cybersickness differences by user race could provide valuable new insights into potential inequities in VR accessibility, which is critical for ensuring that this emerging technology is accessible to all in the future. Currently, such studies are lacking in the cybersickness literature. However, early research has suggested that there are racial differences in motion sickness propensity. For example, a series of studies conducted in the United States found that Asian participants reported more motion sickness symptoms than White and Black participants [- ]. This racial difference was maintained regardless of whether the Asian participants were born in the United States or were recent immigrants [ ]. This led the authors to posit an evolutionary and genetic basis for these differences. However, this conclusion is at odds with the modern understanding that race is a social construct rather than a biological or genetic one. As such, the identified differences in motion sickness reporting by race may alternatively reflect cultural and social differences that result, in part, from systemic differential treatment. Various other sociocultural factors may contribute to racial differences in reporting of discomfort such as language, acculturation, learning and cultural conditioning, and attention to uncomfortable stimuli (refer to Lasch [ ]). More recent research conducted in Germany found that Asian participants reported less motion sickness than White participants [ ]. However, this study found that Asian participants had a shorter tolerance for rotation despite reporting less motion sickness, which may indicate differences in motion sickness reporting that are separate from physical experience. Overall, the existing research on racial differences in motion sickness is limited. Moreover, cybersickness, although related to motion sickness and simulator sickness, is a distinct phenomenon, with disorientation being more common and oculomotor symptoms being less common [ ]. Given these differences between motion sickness and cybersickness, racial variability in cybersickness warrants investigation.
In anticipation of evaluating VR-based medical product efficacy alongside VR-associated risks across patient demographics, it is important to understand any potential underlying differences between groups related to these outcomes. Addressing this knowledge gap aligns with an increasing regulatory focus on health equity  as well as related efforts to promote diversity in study populations and evaluate potential differential product outcomes by patient demographics [ ]. The potential for race-related variability in the performance of medical technologies was recently illustrated by a safety communication on the limitations of pulse oximeter devices, which highlighted the potential accuracy differences between patients with dark and light skin pigmentation [ ]. Similarly, medical use of VR may be susceptible to racial inequities in ways that have not yet been uncovered. Thus, although theory and previous literature do not provide a clear path toward hypothesizing racial differences in cybersickness, it is important to explore existing data associated with VR use to determine whether racial variability in cybersickness exists. Understanding the differences in cybersickness propensity based on race is critical to ensuring that this emerging technology is accessible to all in the future.
Currently, studies exploring racial differences in cybersickness are lacking. To address this gap in the literature, we reported data from 6 independent samples collected within 1 laboratory group. In these studies, participants were asked to perform various tasks in VR such as traversing environments, pointing at and selecting objects, and interacting with virtual humans. The analyses compared self-identified Black and Asian participants’ reporting of cybersickness to that of self-identified White participants. Comparisons between Black and Asian participants were also included in individual studies, where feasible. These 3 racial groups were chosen for comparison because they were well represented across all study samples and represent groups of interest for potential disparities when evaluating VR health care devices for use in the United States. White participants were chosen as the comparison group in the analyses because they are the most represented racial group in the existing literature. We also report a mini meta-analysis to illustrate the overall trends across all 6 studies. Together these studies are intended to reveal any differences in reported cybersickness between racial groups and lay the groundwork for future studies that may explore these differences directly. To the best of our knowledge, this is the first report of racial differences in cybersickness in the literature and is therefore a critical first step that should be explored in future research. Ultimately, addressing racial differences in cybersickness will help to move toward greater health equity.
This analysis included data from 6 experimental trials conducted for other purposes (). All studies were conducted between 2009 and 2020 through the Immersive Simulation Program at the National Human Genome Research Institute, National Institutes of Health. All research participants were recruited from the local community. VR was used at the program’s laboratory facility.
Each study used one of 2 types of VR settings: a buffet restaurant environment called the VR buffet  or a clinical examination room environment. Both VR programs were created using the Vizard VR platform [ ]. Studies were selected for inclusion because they administered measures of participant cybersickness symptoms using a variant of the Short Symptoms Checklist (SSC) [ ], because they recorded participants’ self-reported race, and because data were available for analysis. In all studies, the possibility of experiencing cybersickness was communicated to participants both in the consent form and by the research assistant during the study. Participants were told that they were welcome to stop the study if they experienced any cybersickness symptoms. This rarely occurred in practice. More details about each study are available in the original publications [ - ].
|Year||Content||Locomotion||Headset||Aim of the study|
|Study 1||2017||VR buffet||Walking||HTC Vive||Measure the influence of messages about children’s diet on parents’ feeding behavior|
|Study 2||2011||VR buffet||Walking||nVisor SX60||Measure the influence of children’s risk information provision on parents’ feeding behavior|
|Study 3||2009||Virtual clinic||Walking||nVisor SX60||Assess medical students’ reaction to a virtual patient’s weight in a clinical scenario|
|Study 4||2020||Virtual clinic||Seated||HTC Vive Pro||Assess medical students’ use of a virtual patient’s genomic risk information in a clinical scenario|
|Study 5||2014||Virtual clinic||Seated||nVisor SX60||Assess reaction of women with overweight to virtual provider’s messages|
|Study 6||2012||Virtual clinic||Seated||nVisor SX60||Assess reaction of women with overweight to virtual provider’s messages|
The VR Buffet
The VR buffet is a simulated buffet restaurant in which parental food choices for their child are assessed by tracking the parents’ virtual food selections. Outcomes for the VR buffet are a validated measure of parental food choices . The participants’ physical movements drive the viewpoint in the virtual world, such that walking around the physical room corresponds to walking around the virtual buffet. Participants made food selections in the virtual buffet using a controller. Once all food and drink selections were made, participants selected a virtual cash register to indicate completion. shows the VR buffet environment.
VR Clinical Simulations
Several VR clinical simulations are included in which participants are immersed in a virtual medical examination room as either the provider or patient and asked to interact verbally with a virtual human playing the opposite role. When medical students (as opposed to patients) are the users, they are also asked to read information about the virtual patient’s medical records on a virtual computer monitor or tablet situated within the VR environment. A research assistant controlled the prerecorded statements of the virtual human interaction partner. In most cases, users are seated in this virtual environment, although there is also a version in which users can walk around and approach their virtual interaction partner.shows a sample VR clinical simulations.
All studies were conducted within the same physical laboratory environment, which consisted of a room fitted with a 6-dof VR headset system. The headset and equipment used differed across studies (provides information on the system used in each study). The earlier VR system included an NVIS nVisor SX60 headset with a WorldViz Precision Point Tracking System. A handheld presentation pointer was modified to provide hand control of the selection tool in the VR buffet environment. Later systems included an HTC Vive headset with an integrated tracking system or an HTC Vive Pro headset with an integrated tracking system. In both cases, the relevant Vive or Vive Pro controllers were used for hand control when needed.
Study Inclusion and Exclusion Criteria
Several inclusion and exclusion criteria (eg, gender, age, and parental status) varied between studies based on the content of the specific research study. All studies also had exclusion criteria related to the use of the VR equipment. In all studies, potential participants were excluded if they reported having epilepsy, seizures, or a vestibular disorder or if they reported having vision or hearing that was neither normal nor corrected to normal. In most studies, a known pregnancy was also an exclusion criterion. Potential participants were excluded if they reported higher levels of propensity to motion sickness. Participants were asked the following question: “How easily would you say that you get motion or car sickness on a scale of 1 to 7 where 1 would be that you ‘never get motion sick’ and 7 would be that you ‘get sick easily’?” Those who answered 6 or 7 on a 7-point scale were deemed ineligible for participation. As such, all participants in the included studies were individuals who did not identify themselves as being particularly vulnerable to motion sickness symptoms.
All participants were encouraged to adjust the VR headset themselves while viewing the VR environment. For example, participants were asked to move the headset up and down on their face and then to use the adjustment that would “move the lenses closer together and further apart, allowing [them] to focus.” They were repeatedly asked whether the virtual room looked blurry and were instructed to make adjustments until they felt that their view of the room was clear. After completing the VR experience, the participants were asked to rate their level of cybersickness as part of a larger questionnaire. Demographic information including self-reported race was collected at pretest or during the laboratory visit.
All studies included in this analysis administered a variant of the Simulator Sickness Checklist, the SSC . The SSC is a commonly used self-report measure of cybersickness that contains a subset of symptoms used in the longer Simulator Sickness Questionnaire [ ]. Most of the included studies used a 5-item version of the SSC that assessed headache, blurred vision, dizziness with eyes open, dizziness with eyes closed, and nausea. Clinical studies involving women with higher weight as participants (studies 5 and 6) used a 6-item version that additionally assessed eyestrain. In all studies, each item was measured on a 4-point Likert-type scale; some studies began this scale at zero, whereas others began at one. The lowest end point was labeled none or not at all, and the highest end point was labeled severe ( ). For each study, the composite cybersickness score was calculated by summing the responses for each item on the scale. In all analyses, we retained all original scale items and response options (without transformation), as these may have influenced participant responses [ ]. Therefore, we caution readers that it is not possible to compare raw cybersickness scores among the included studies. We performed a mini meta-analysis for this purpose.
The primary predictor in our analysis was the race of participants included in the study. We considered the number of White, Black, and Asian participants based on each participant’s self-reported racial background. For a racial group to be considered for analysis within a given study, at least 10 participants in the study needed to self-identify with that racial group.
Additional variables assessed included self-reported gender and age. BMI was calculated from weight and height, which was self-reported except in the case of the 2 studies of medical students (studies 3 and 4), where it was measured in the laboratory. The time spent in the VR environment was automatically calculated using the VR environment software. The year of the study was determined as the year in which the last participant data collection visit occurred.provides a summary of the demographic variables for each study.
|Scale||Racial group||Severity rating of cybersickness symptoms for each item, mean (SD)||Composite cybersickness, mean (SD)|
|Headache||Eyestrain||Blurred vision||Dizzy (eyes open)||Dizzy (eyes closed)||Nausea|
|Study 1 (0=none, 1=slight, 2=moderate, 3=severe)a|
|White||0.15 (0.44)||N/Ab||0.48 (0.61)||0.39 (0.63)||0.09 (0.33)||0.13 (0.37)||1.23 (1.68)|
|Black||0.04 (0.20)||N/A||0.34 (0.60)||0.13 (0.45)||0.11 (0.38)||0.00 (0.00)||0.57 (1.13)|
|Asian||0.22 (0.42)||N/A||0.26 (0.45)||0.26 (0.45)||0.07 (0.27)||0.04 (0.19)||0.85 (1.10)|
|Total||0.13 (0.39)||N/A||0.40 (0.58)||0.29 (0.57)||0.09 (0.33)||0.07 (0.29)||0.98 (1.49)|
|Study 2 (0=none, 1=slight, 2=moderate, 3=severe)a|
|White||0.18 (0.43)||N/A||0.44 (0.63)||0.33 (0.56)||0.19 (0.46)||0.09 (0.30)||1.23 (1.37)|
|Black||0.11 (0.32)||N/A||0.29 (0.48)||0.19 (0.39)||0.05 (0.22)||0.06 (0.24)||0.69 (0.94)|
|Total||0.15 (0.39)||N/A||0.38 (0.58)||0.27 (0.50)||0.13 (0.38)||0.08 (0.28)||1.01 (1.23)|
|Study 3 (0=none, 1=slight, 2=moderate, 3=severe)a|
|White||0.12 (0.36)||N/A||0.40 (0.57)||0.36 (0.59)||0.12 (0.43)||0.14 (0.40)||1.14 (1.68)|
|Black||0.06 (0.24)||N/A||0.38 (0.55)||0.38 (0.55)||0.00 (0.00)||0.03 (0.17)||0.86 (1.05)|
|Asian||0.27 (0.54)||N/A||0.38 (0.61)||0.42 (0.68)||0.15 (0.36)||0.21 (0.46)||1.42 (2.03)|
|Total||0.15 (0.40)||N/A||0.39 (0.57)||0.38 (0.61)||0.10 (0.37)||0.14 (0.39)||1.16 (1.69)|
|Study 4 (0=none, 1=slight, 2=moderate, 3=severe)a|
|White||0.12 (0.41)||N/A||1.00 (0.78)||0.12 (0.41)||0.00 (0.00)||0.00 (0.00)||1.19 (1.19)|
|Black||0.00 (0.00)||N/A||0.81 (0.91)||0.00 (0.00)||0.00 (0.00)||0.00 (0.00)||0.73 (0.88)|
|Asian||0.08 (0.28)||N/A||0.56 (0.65)||0.08 (0.28)||0.00 (0.00)||0.00 (0.00)||0.78 (0.90)|
|Total||0.08 (0.32)||N/A||0.81 (0.78)||0.08 (0.32)||0.00 (0.00)||0.00 (0.00)||0.96 (1.05)|
|Study 5 (1=not at all, 4=severe)c|
|White||1.10 (0.31)||1.56 (0.66)||1.44 (0.56)||1.08 (0.27)||1.00 (0.00)||1.01 (0.11)||7.19 (1.27)|
|Black||1.03 (0.18)||1.33 (0.50)||1.36 (0.55)||1.03 (0.18)||1.03 (0.18)||1.00 (0.00)||6.81 (1.05)|
|Total||1.07 (0.25)||1.45 (0.59)||1.40 (0.56)||1.06 (0.23)||1.02 (0.13)||1.01 (0.08)||7.01 (1.18)|
|Study 6 (1=not at all, 4=severe)c|
|White||1.14 (0.39)||1.36 (0.58)||1.32 (0.54)||1.03 (0.18)||1.03 (0.18)||1.02 (0.13)||6.91 (1.39)|
|Black||1.13 (0.51)||1.24 (0.49)||1.24 (0.49)||1.06 (0.28)||1.04 (0.19)||1.05 (0.25)||6.75 (1.56)|
|Total||1.13 (0.47)||1.28 (0.52)||1.27 (0.51)||1.05 (0.25)||1.04 (0.19)||1.04 (0.22)||6.81 (1.50)|
aScale minimum: 0; scale maximum: 15.
bN/A: not applicable.
cScale minimum: 6; scale maximum: 24.
|Study||Racial group||Sample, n||Age (years), mean (SD)||Gender (female), n (%)||BMI (kg/m2), mean (SD)||Time in virtual reality (seconds), mean (SD)|
|White||88||38.08 (5.72)||60 (68)||25.87 (5.51)||318 (323)|
|Black||48||36.31 (6.35)||33 (69)||31.63 (9.32)||301 (294)|
|Asian||27||39.12 (4.76)||18 (67)||25.64 (5.90)||306 (214)|
|White||105||38.89 (5.33)||105 (100)||30.18 (4.78)||409 (491)|
|Black||75||35.81 (5.62)||75 (100)||31.10 (5.18)||389 (452)|
|White||104||26.55 (2.25)||49 (47)||23.92 (2.85)||414 (119)|
|Black||34||26.56 (3.74)||21 (62)||26.08 (4.43)||433 (125)|
|Asian||48||25.77 (2.48)||24 (50)||22.95 (3.74)||414 (119)|
|White||34||26.41 (2.66)||20 (59)||23.26 (3.63)||664 (252)|
|Black||16||26.06 (1.84)||12 (75)||25.56 (4.57)||663 (218)|
|Asian||25||25.96 (1.57)||15 (60)||23.53 (4.29)||721 (185)|
|White||88||35.24 (9.65)||88 (100)||31.25 (5.25)||253 (21)|
|Black||85||35.55 (8.16)||85 (100)||35.55 (8.16)||255 (22)|
|White||58||35.35 (8.71)||58 (100)||36.33 (7.66)||423 (60)|
|Black||109||36.07 (11.24)||109 (100)||32.07 (6.03)||427 (70)|
For each individual study, we conducted an ANOVA to examine the relationship between participant race (2 or 3 groups depending on whether Asian participants were present in sufficient numbers to be included) and cybersickness. When there were 3 racial groups, we examined planned contrasts to assess the differences between individual racial groups. We also examined zero-order correlations between cybersickness and 3 person-level variables: age, BMI, and time spent in the VR environment (, Tables S1-S5). When these variables demonstrated significant relationships with cybersickness, they were included as covariates in an additional analysis of covariance.
We also conducted a random effects meta-analysis  using Comprehensive Meta-Analysis V3 software [ ] to determine the overall difference in self-reported cybersickness between racial groups in our 6 studies. Analyses were performed using Cohen d with weighted averages of the effect sizes.
Participants were compensated for participation in all studies, and all studies were approved by the relevant institutional review boards. IRB review approval numbers are as follows: 08HG0122, 10HG0076, 11HG0238, 13HG0125, and 16HG0026.
Cybersickness Levels Overall
Self-reported cybersickness was very low in all racial groups (). On average, participants reported no to slight symptoms, with blurred vision and eyestrain being reported the most and nausea and dizziness (with eyes closed) being reported the least.
Relationships Between Race and Cybersickness in Individual Studies
Study 1: Buffet With Parents
ANOVA revealed a significant difference in cybersickness by racial group (). Pairwise comparisons showed that Black participants reported lower levels of cybersickness than White participants. There was no significant difference between White and Asian participants. No other person-level variables showed a significant relationship with cybersickness ( , Table S1).
|Effect of racial group, omnibus analysis||Pairwise comparisons|
|F test (df)||P value||White vs Black||White vs Asian|
|Mean difference||P value||Bonferroni corrected P value||Mean difference||P value||Bonferroni corrected P value|
|Study 1||3.34 (2,157)||.03||0.84||.004||.02||0.44||.19||.38|
|Study 2||7.75 (1,180)||.006||N/Aa||N/A||N/A||N/A||N/A||N/A|
|Study 3||1.12 (2,183)||.33||0.29||.38||.76||−0.27||.36||.72|
|Study 4||2.07 (2,72)||.13||0.42||.18||.84||0.52||.06||.12|
|Study 5||5.18 (1,173)||.02||N/A||N/A||N/A||N/A||N/A||N/A|
|Study 6||0.36 (1,166)||.55||N/A||N/A||N/A||N/A||N/A||N/A|
aN/A: not applicable (pairwise comparisons are only reported for studies with more than 2 racial groups).
Study 2: Buffet With Mothers Only
The ANOVA revealed a significant difference in cybersickness by racial group (), wherein Black participants reported lower levels of cybersickness than White participants. There was also a significant relationship between age and cybersickness, with older participants reporting more cybersickness. However, the main effect of race on cybersickness was maintained when age was added as a covariate (F1,175=5.33; P=.02). No other person-level variables showed a significant relationship with cybersickness ( , Table S2).
Study 3: Clinical With Medical Students
The ANOVA did not show a significant effect of race on cybersickness. Among the person-level variables, there was a significant relationship between age and cybersickness, with older participants reporting greater cybersickness (, Table S3). However, the analysis of covariance also did not show a main effect of race on cybersickness (F1,180=2.32; P=.44).
Study 4: Clinical With Medical Students
The ANOVA did not show a significant effect of race on cybersickness. No other person-level variables showed a significant relationship with cybersickness (, Table S4).
Study 5: Clinical With Women Only
The ANOVA revealed a significant difference in cybersickness by racial group (), wherein Black participants reported lower levels of cybersickness than White participants. No other person-level variables showed a significant relationship with cybersickness ( , Table S5).
Study 6: Clinical With Women Only
The ANOVA did not show a significant effect of race on cybersickness. No other person-level variables showed a significant relationship with cybersickness (, Table S6).
Although the results of each individual study reported above are revealing, determining whether there are racial differences in cybersickness, based on whether individual studies report a statistically significant difference, is inherently flawed. By conducting a mini meta-analysis, we were able to determine the size of these effects. In addition, by combining data in a meta-analysis, we reduced the impact of random error and increased the precision of our estimate. This increased precision allowed us to detect racial differences in cybersickness that individual studies may lack the power to detect.
In our mini meta-analysis (), effect sizes indicated the difference in reported cybersickness between White participants and Black and Asian participants. Positive effect sizes indicate that Black and Asian participants report more cybersickness than White participants, whereas negative effects indicate that Black and Asian participants report less cybersickness than White participants. Moderator analyses were conducted using mixed-effect models.
Overall, Black participants reported significantly less cybersickness than White participants (Cohen d=−0.31; P<.001; κ=6;). On average, Black participants reported approximately one-third of an SD less cybersickness than White participants. Asian participants did not report significantly different cybersickness levels compared with White participants (Cohen d=−0.11; P=.51; κ=3; ).
To explore whether the racial differences in reported cybersickness between Black and White participants may be exaggerated or attenuated in certain situations, we conducted exploratory moderator analyses. Specifically, we evaluated the VR environment (buffet vs clinical), movement (seated vs standing), headset type (nVisor SX60 vs Vive), duration of experience, and year of data collection. Asian participants were excluded from these moderator analyses. This exclusion was a conservative approach to ensure that White participants (the comparison group) were included only once in the analysis. This approach prevented artificial inflation of N and the overestimation of the precision of the effect.
The moderator analyses did not reveal any variables that attenuated racial differences in cybersickness. Black participants reported less cybersickness than White participants regardless of the nature of the VR experience. Specifically, the magnitude of the racial difference was not significantly different based on whether participants engaged with the VR buffet or the clinical VR scenario (Q1=2.155; P=.14). Racial differences were also unchanged regardless of whether the participants were seated or standing (Q1=0.79; P=.37). Racial differences in cybersickness were also consistent regardless of the type of headset (Q1=0.700; P=.40) and duration of the VR experience (B=−0.0001; 95% CI −0.002 to 0.002; Z=−0.14; P=.88). In addition, the year of study did not moderate racial differences in reported cybersickness (B=−0.0195, 95% CI −0.068 to 0.030; Z=−0.78; P=.44).
Among the studies included in this analysis, racial differences in cybersickness appear robust and consistent across various VR experiences and experimental designs. Overall, Black participants reported less cybersickness than White participants regardless of the nature of the VR experience.
This study presents the first known examination of racial differences in cybersickness. We found that, on average, Black participants reported less cybersickness than White participants, and our analyses did not reveal any moderators that attenuated this racial difference. In contrast to previous research on motion sickness [- , ], we found no differences in reported cybersickness between White and Asian participants. However, this comparison should be interpreted with caution, as cybersickness differs from other forms of motion sickness [ ] and as we excluded potential participants who reported that they were particularly prone to motion sickness. Although our results require replication, they indicate that researchers, practitioners, and regulators may need to consider the potential for racial differences in cybersickness when evaluating VR applications for their side effects.
Potential Explanations for Racial Differences in Cybersickness
The data reported here do not allow us to determine why different racial groups reported varying levels of cybersickness. There are a multitude of factors that could influence pathways through which individuals differ in their propensity for and reporting of cybersickness. Some of these factors are discussed below. Although our data do not support any individual causal mechanism, we highlight where theories are consistent or inconsistent with our findings. Importantly, it is likely that multiple causal forces influence cybersickness experience and reporting simultaneously, working together or in opposition with one another. Further research is needed to determine the possible causal mechanisms behind racial differences in cybersickness.
One reason that people differ in their propensity for cybersickness is their previous experience with VR [, ]. Previous research has found that people who have never used VR report more cybersickness than people who rarely use VR, and both groups report more cybersickness than those who use VR weekly [ ]. Familiarity with VR is therefore associated with lower levels of cybersickness. There is no reason to believe that familiarity with VR explains the results of our studies, as most of the research was conducted before VR became a common consumer device, and we saw no evidence that racial differences in cybersickness were attenuated or augmented in more recent years. Nevertheless, familiarity with VR technology may be a piece of this puzzle going forward. A recent survey of British internet users found that people of color are overrepresented in the VR consumer market [ ]. This is supported by market research findings that Black and Hispanic consumers are more aware of and interested in VR than White consumers [ ]. Therefore, familiarity-related method equivalence [ ] should be monitored in future VR studies.
Another reason people differ in their propensity for cybersickness is differences in body size and proportions. Oculomotor cybersickness has been shown to decrease with higher BMI . However, in these studies, racial differences largely remained when BMI was entered as a covariate, with the exception of study 1 ( , Table S7). Another potential factor is participants’ interpupillary distance (IPD) and goodness of headset fit. Previous research has shown that people with an IPD that is poorly accommodated by standard VR headsets are more likely to experience cybersickness [ ] and that the inability to accommodate diverse bodies may lead to apparent demographic differences in cybersickness. In 2 studies, researchers demonstrated that women, for whom a VR headset built for men’s IPD specifications did not fit properly, reported higher cybersickness than men. However, when women’s headsets were fitted to them correctly, they experienced cybersickness at a rate similar to that of men [ ]. As we did not measure the IPD of our participants or the goodness of headset fit, we cannot rule out the possibility of variation within our sample and thus cannot explore whether these are a potential explanation for our results. These limitations should be addressed in future research. Previous research has suggested that in some cases, self-reported racial identity is associated with differences in average IPD measurements (refer to the study by Dodgson [ ]); however, it is unknown how this might influence the fit of VR headsets or subsequent cybersickness.
Individuals may also differ in their likelihood of reporting cybersickness, just as there are individual differences in reporting other types of pain and discomfort. With some exceptions, people of color generally report higher levels of pain than White patients [- ] (refer to the study by Plesh et al [ ] for contrasting results). Systemic barriers to adequate pain management and a long history of discrimination and dehumanization likely explain these trends [ ]. Various other cultural factors may also contribute to differences in reporting, including language, acculturation, learning and cultural conditioning, degree of expressiveness, heightened attention to painful stimuli, and coping styles (refer to the study by Booker [ ]). Given that our results contradict much of the existing research on pain and discomfort, further research is needed to understand how sociocultural factors may specifically influence the reporting of cybersickness.
Another factor relevant to cybersickness reporting is the individual differences in how people respond to questionnaires, particularly Likert-type scales. Scales that range from 1=strongly disagree to 5=strongly agree or from 1=poor to 5=excellent are often used in health research but are troublesome because responses are influenced not only by the content of the question but also by general approaches to answering such questions. Researchers have documented that certain response styles are more common among specific racial and ethnic groups. For example, Black and Latino study participants are especially likely to use the extreme positive end of rating scales , whereas East Asian participants are more likely to select scale midpoints and avoid extreme responses compared with North American participants [ , ]. It has been hypothesized that these response styles reflect various cultural values on which people of color generally differ from White participants [ ]. In particular, manifestations of social desirability may differ across cultures [ ] in ways that result in different response styles [ ]. It is difficult to know how such response styles would manifest on the cybersickness scale used in this study, which ranges from none to severe. Nevertheless, it is certainly possible that racial differences in response styles may explain the differences in cybersickness reporting between Black and White participants in our research. Future research would benefit from using more objective, passive physiological approaches to measuring cybersickness such as electroencephalography [ ] to overcome difficulties with self-reporting. Nevertheless, it is important to understand potential racial differences in self-reported cybersickness, for example, when evaluating novel VR medical interventions.
In addition to the limitations of our design that prevent us from examining why racial differences in cybersickness occur, there are other important limitations to our sample that should be considered when interpreting the results of these studies. First, we recruited participants from Washington, District of Columbia area. Participants were aware that they were volunteering for a VR experiment, and we excluded individuals who reported a high propensity for motion sickness. Therefore, the resulting samples are more likely to be interested in VR and may be less likely to experience cybersickness than the general population. In practice, exclusion because of motion sickness propensity was rare. For example, in one included sample , only 1.47% of potential study participants were ineligible because of this factor. Therefore, we believe that these results are a useful starting point, particularly for designing and evaluating VR medical interventions for use with populations that are not especially susceptible to cybersickness. It is worth noting that American participants represent a society that is not typical of the world’s population, which limits its representativeness [ , ]; therefore, there is no reason to expect that these same racial differences would be found outside of the US context.
Second, we excluded participants who did not identify as Asian, Black, or White from this analysis. This decision was made to ensure that we had sufficient power to detect racial differences in cybersickness. However, we were unable to draw any conclusions regarding racial groups that were not well represented in our samples. Future research should attempt to oversample people of color to achieve a sufficient sample size for other racial comparisons. Another limitation is that we excluded individuals who identified as more than one race. This may have artificially created distinct racial groups that in reality are much less coherent and discrete. In addition, we did not find many of the demographic correlations with cybersickness that have been reported in previous literature (ie, age, BMI, and time spent in VR). This is likely because of the limited range in age, BMI, and exposure time in our reported studies.
Another limitation is that we assessed cybersickness following the use of only 2 types of VR environments, neither of which is characteristic of the types of VR environments that typically elicit significant cybersickness (eg, sensory conflict and imposed motion ). Therefore, perhaps unsurprisingly, cybersickness ratings across all racial groups were very low. Although we anticipate that many health- and medicine-oriented VR applications will be designed to minimize cybersickness, mild cybersickness may still be of practical significance. Many medical VR experiences are designed for repeated use over time (eg, exposure therapy and pain management), and mild cybersickness may increase attrition. A recent meta-analysis of attrition in VR exposure for anxiety disorders found that attrition ranged from 2% to 41% [ ]. Unfortunately, these studies rarely reported the reasons for dropout. Nevertheless, when reasons were given, cybersickness was among the top 5, accounting for 6.5% of dropouts. The most common reason given was the inability to immerse, which accounted for 42% of dropouts. A sense of presence is negatively related to cybersickness [ ], and feeling cybersickness has been proposed as a reason for failure to immerse [ - ]. It is possible that in addition to being a direct cause of attrition itself, cybersickness may also indirectly influence attrition by reducing immersion in and enjoyment of the VR environment. Unfortunately, our study design did not allow us to investigate how cybersickness influences attrition because we used a single session of VR exposure and the discontinuation rates were very low. Research with a wider variety of VR environment types is needed, to understand how cybersickness severity relates to study attrition, intervention adherence, and the efficacy of medical VR.
Here, we present data demonstrating a potentially important racial difference in cybersickness that we believe should be explored in future research. The first step in continuing this investigation would be to conceptually replicate these findings with different VR experiences, headsets, and study populations. Once replicated, research should then be conducted on the potential explanations and mechanisms. We have discussed some potential causal factors in this manuscript, but we acknowledge that there are likely other important factors that we have not considered. If future research suggests that racial differences in cybersickness are primarily in reporting as opposed to experience, it would suggest the need for the development of more objective measures of cybersickness. Such a result would also constitute an important consideration for those designing and evaluating medical VR interventions to promote health equity. Notwithstanding the caveats above, the research presented here underscores the importance of testing VR applications with a diverse group of participants to move toward achieving equitable access to emerging medical VR devices.
The authors would like to acknowledge the contributions of all the original authors of the studies reported in this analysis. This research was funded by the Intramural Research Program of the National Human Genome Research Institute.
The mention of commercial products, their sources, or their use in connection with the material reported herein is not to be construed as either an actual or implied endorsement of such products by the Department of Health and Human Services.
EB and SP conceptualized the project. SP, SHT, and APD were involved in data collection. SP and AJM conducted formal analysis of the data. All authors contributed to the drafting of the manuscript.
Conflicts of Interest
Supplementary data tables.DOCX File , 18 KB
- Nesbitt K, Nalivaiko E. Cybersickness. In: Lee N, editor. Encyclopedia of Computer Graphics and Games. Cham, Switzerland: Springer; 2018.
- Kyaw BM, Posadzki P, Paddock S, Car J, Campbell J, Tudor Car L. Effectiveness of digital education on communication skills among medical students: systematic review and meta-analysis by the digital health education collaboration. J Med Internet Res 2019 Aug 27;21(8):e12967 [FREE Full text] [CrossRef] [Medline]
- Howard MC. A meta-analysis and systematic literature review of virtual reality rehabilitation programs. Comput Human Behav 2017 May;70(C):317-327. [CrossRef]
- Hammoudeh JA, Howell LK, Boutros S, Scott MA, Urata MM. Current status of surgical planning for orthognathic surgery: traditional methods versus 3D surgical planning. Plast Reconstr Surg Glob Open 2015 Feb;3(2):e307 [FREE Full text] [CrossRef] [Medline]
- Eijlers R, Utens EM, Staals LM, de Nijs PF, Berghmans JM, Wijnen RM, et al. Systematic review and meta-analysis of virtual reality in pediatrics: effects on pain and anxiety. Anesth Analg 2019 Nov;129(5):1344-1353 [FREE Full text] [CrossRef] [Medline]
- Indovina P, Barone D, Gallo L, Chirico A, De Pietro G, Giordano A. Virtual reality as a distraction intervention to relieve pain and distress during medical procedures: a comprehensive literature review. Clin J Pain 2018 Sep;34(9):858-877. [CrossRef] [Medline]
- Kenney MP, Milling LS. The effectiveness of virtual reality distraction for reducing pain: a meta-analysis. Psychol Conscious 2016;3(3):199-210. [CrossRef]
- Rizzo AS, Koenig ST. Is clinical virtual reality ready for primetime? Neuropsychology 2017 Nov;31(8):877-899. [CrossRef] [Medline]
- Rizzo A, Thomas Koenig S, Talbot TB. Clinical results using virtual reality. J Technol Hum Serv 2019 May 17;37(1):51-74. [CrossRef]
- Mishra S, Kim YS, Intarasirisawat J, Kwon YT, Lee Y, Mahmood M, et al. Soft, wireless periocular wearable electronics for real-time detection of eye vergence in a virtual reality toward mobile eye therapies. Sci Adv 2020 Mar;6(11):eaay1729 [FREE Full text] [CrossRef] [Medline]
- Halbig A, Latoschik ME. A systematic review of physiological measurements, factors, methods, and applications in virtual reality. Front Virtual Real 2021 Jul 14;2:694567. [CrossRef]
- Hussain R, Chessa M, Solari F. Mitigating cybersickness in virtual reality systems through foveated depth-of-field blur. Sensors (Basel) 2021 Jun 10;21(12):4006 [FREE Full text] [CrossRef] [Medline]
- Li G, McGill M, Brewster S, Pollick F. A review of electrostimulation-based cybersickness mitigations. In: Proceedings of the 3rd IEEE International Conference on Artificial Intelligence and Virtual Reality. 2020 Presented at: AIVR '20; December 14-18, 2020; Virtual p. 151-157. [CrossRef]
- Stanney K, Fidopiastis C, Foster L. Virtual reality is sexist: but it does not have to be. Front Robot AI 2020 Jan 31;7:4 [FREE Full text] [CrossRef] [Medline]
- IEEE Standard for Head-Mounted Display (HMD)-Based Virtual Reality(VR) Sickness Reduction Technology. Institute of Electrical and Electronics Engineers Standards Association. 2020. URL: https://standards.ieee.org/standard/3079-2020.html [accessed 2022-01-18]
- Ergonomics of human-system interaction — Part 393: Structured literature review of visually induced motion sickness during watching electronic images (ISO/TR 9241-393:2020). International Organization for Standardization. 2020. URL: https://www.iso.org/standard/73225.html [accessed 2022-01-18]
- Medical Extended Reality Program: Research on Medical Extended Reality-Based Medical Devices. U.S. Food & Drug Administration. 2021 Mar 24. URL: https://www.fda.gov/medical-devices/medical-device-regulatory-science-research-programs-conducted-osel/medical-extended-reality-program-research-medical-extended-reality-based-medical-devices [accessed 2021-10-28]
- Petri K, Feuerstein K, Folster S, Bariszlovich F, Witte K. Effects of age, gender, familiarity with the content, and exposure time on cybersickness in immersive head-mounted display based virtual reality. Am J Biomed Sci 2020 Apr;12(2):107-121. [CrossRef]
- Weech S, Kenny S, Barnett-Cowan M. Presence and cybersickness in virtual reality are negatively related: a review. Front Psychol 2019 Feb 4;10:158 [FREE Full text] [CrossRef] [Medline]
- Arns LL, Cerney MM. The relationship between age and incidence of cybersickness among immersive environment users. In: Proceedings of 2005 IEEE Annual International Symposium Virtual Reality. 2005 Presented at: VR '05; March 12-16, 2005; Bonn, Germany p. 267-268. [CrossRef]
- Knight MM, Arns LL. The relationship among age and other factors on incidence of cybersickness in immersive environment users. In: Proceedings of the 3rd Symposium on Applied Perception in Graphics and Visualization. 2006 Jul Presented at: APGV '06; July 28-29, 2006; Boston, MA, USA p. 162. [CrossRef]
- Park GD, Allen RW, Fiorentino D, Rosenthal TJ, Cook ML. Simulator sickness scores according to symptom susceptibility, age, and gender for an older driver assessment study. Proc Hum Factors Ergon Soc Annu Meet 2006 Oct 1;50(26):2702-2706. [CrossRef]
- Saredakis D, Szpak A, Birckhead B, Keage HA, Rizzo A, Loetscher T. Factors associated with virtual reality sickness in head-mounted displays: a systematic review and meta-analysis. Front Hum Neurosci 2020 Mar 31;14:96 [FREE Full text] [CrossRef] [Medline]
- Gonçalves G, Melo M, Bessa M. Virtual reality games: a study about the level of interaction vs. narrative and the gender in presence and cybersickness. In: Proceedings of the 2018 International Conference on Graphics and Interaction. 2018 Presented at: ICGI '18; November 15-16, 2018; Lisbon, Portugal p. 1-8. [CrossRef]
- Jun H, Miller MR, Herrera F, Reeves B, Bailenson JN. Stimulus sampling with 360-videos: examining head movements, arousal, presence, simulator sickness, and preference on a large sample of participants and videos. IEEE Trans Affective Comput 2020 Jun 24:1-1. [CrossRef]
- Shafer DM, Carbonara CP, Korpi MF. Modern virtual reality technology: cybersickness, sense of presence, and gender. Media Psychol Rev 2017;11(2):1.
- Stanney KM, Hale KS, Nahmens I, Kennedy RS. What to expect from immersive virtual environment exposure: influences of gender, body mass index, and past experience. Hum Factors 2003;45(3):504-520. [CrossRef] [Medline]
- Pot-Kolder R, Veling W, Counotte J, van der Gaag M. Anxiety partially mediates cybersickness symptoms in immersive virtual reality environments. Cyberpsychol Behav Soc Netw 2018 Mar;21(3):187-193. [CrossRef] [Medline]
- Rebenitsch L, Owen C. Estimating cybersickness from virtual reality applications. Virtual Reality 2020 May 28;25(1):165-174. [CrossRef]
- Solimini AG, Mannocci A, Di Thiene D, La Torre G. A survey of visually induced symptoms and associated factors in spectators of three dimensional stereoscopic movies. BMC Public Health 2012 Sep 13;12:779 [FREE Full text] [CrossRef] [Medline]
- Howard MC, Van Zandt EC. A meta-analysis of the virtual reality problem: unequal effects of virtual reality sickness across individual differences. Virtual Reality 2021 Dec;25(4):1221-1246. [CrossRef]
- Stern RM, Hu S, LeBlanc R, Koch KL. Chinese hyper-susceptibility to vection-induced motion sickness. Aviat Space Environ Med 1993 Sep;64(9 Pt 1):827-830. [Medline]
- Stern RM, Hu S, Uijtdehaage SH, Muth ER, Xu LH, Koch KL. Asian hypersusceptibility to motion sickness. Hum Hered 1996;46(1):7-14. [CrossRef] [Medline]
- Stern RM, Koch KL. Motion sickness and differential susceptibility. Curr Dir Psychol Sci 1996 Aug 1;5(4):115-120. [CrossRef]
- Muth ER, Stern RM, Uijtdehaage SH, Koch KL. Effects of Asian ancestry on susceptibility to vection-induced motion sickness. In: Chen JZ, McCallum RW, editors. Electrogastrography, Principles and Applications. New York, NY, USA: Raven Press; 1994:227-233.
- Lasch KE. Culture, pain, and culturally sensitive pain care. Pain Manag Nurs 2000 Sep;1(3 Suppl 1):16-22. [CrossRef] [Medline]
- Klosterhalfen S, Pan F, Kellermann S, Enck P. Gender and race as determinants of nausea induced by circular vection. Gend Med 2006 Sep;3(3):236-242. [CrossRef] [Medline]
- Stanney KM, Kennedy RS, Drexler JM. Cybersickness is not simulator sickness. Proc Hum Factors Ergon Soc Annu Meet 1997 Oct 1;41(2):1138-1142. [CrossRef]
- Araojo R. Working to advance health equity: the U.S. Food and Drug Administration Office of Minority Health and Health Equity. J Natl Med Assoc 2021 Jun;113(3):359-362 [FREE Full text] [CrossRef] [Medline]
- Enhance EQUITY Initiative. U.S. Food & Drug Administration. 2021 Sep 12. URL: https://www.fda.gov/consumers/minority-health-and-health-equity/enhance-equity-initiative [accessed 2021-10-28]
- Pulse Oximeter Accuracy and Limitations: FDA Safety Communication. U.S. Food & Drug Administration. 2021 Feb 19. URL: https://www.fda.gov/medical-devices/safety-communications/pulse-oximeter-accuracy-and-limitations-fda-safety-communication [accessed 2022-01-02]
- Persky S, Goldring MR, Turner SA, Cohen RW, Kistler WD. Validity of assessing child feeding with virtual reality. Appetite 2018 Apr 01;123:201-207 [FREE Full text] [CrossRef] [Medline]
- Vizard. WorldViz. 2008. URL: https://www.worldviz.com/vizard-virtual-reality-software [accessed 2022-01-15]
- Cobb SV, Nichols S, Ramsey A, Wilson JR. Virtual reality-induced symptoms and effects (VRISE). Presence (Camb) 1999 Apr;8(2):169-186. [CrossRef]
- McBride CM, Persky S, Wagner LK, Faith MS, Ward DS. Effects of providing personalized feedback of child's obesity risk on mothers' food choices using a virtual reality buffet. Int J Obes (Lond) 2013 Oct;37(10):1322-1327. [CrossRef] [Medline]
- Persky S, Eccleston CP. Medical student bias and care recommendations for an obese versus non-obese virtual patient. Int J Obes (Lond) 2011 May;35(5):728-735 [FREE Full text] [CrossRef] [Medline]
- Persky S, Street Jr RL. Evaluating approaches for communication about genomic influences on body weight. Ann Behav Med 2015 Oct;49(5):675-684. [CrossRef] [Medline]
- Persky S, Ferrer RA, Klein WM. Nonverbal and paraverbal behavior in (simulated) medical visits related to genomics and weight: a role for emotion and race. J Behav Med 2016 Oct;39(5):804-814 [FREE Full text] [CrossRef] [Medline]
- Persky S, Ferrer RA, Klein WM, Goldring MR, Cohen RW, Kistler WD, et al. Effects of fruit and vegetable feeding messages on mothers and fathers: interactions between emotional state and health message framing. Ann Behav Med 2019 Aug 16;53(9):789-800 [FREE Full text] [CrossRef] [Medline]
- Kennedy RS, Lane NE, Berbaum KS, Lilienthal MG. Simulator sickness questionnaire: an enhanced method for quantifying simulator sickness. Int J Aviat Psychol 1993 Jul;3(3):203-220. [CrossRef]
- Rivers DC, Meade AW, Lou Fuller W. Examining question and context effects in organization survey data using item response theory. Organ Res Methods 2009;12(3):529-553. [CrossRef]
- DerSimonian R, Laird N. Meta-analysis in clinical trials. Control Clin Trials 1986 Sep;7(3):177-188. [CrossRef] [Medline]
- Borenstein M, Hedges LV, Higgins JP, Rothstein HR. Comprehensive meta-analysis. Version 3.0. National Institutes of Health. 2006. URL: https://www.meta-analysis.com/downloads/Meta-Analysis%20Manual%20V3.pdf [accessed 2022-05-17]
- McCauley ME, Sharkey TJ. Cybersickness: perception of self-motion in virtual environments. Presence (Camb) 1992 Aug 1;1(3):311-318. [CrossRef]
- Welch RB. Perceptual Modifications: Adapting to Altered Sensory Environments. New York, NY, USA: Academic Press; 1978.
- Shahid Anwar M, Wang J, Ahmad S, Ullah A, Khan W, Fei Z. Evaluating the factors affecting QoE of 360-degree videos and cybersickness levels predictions in virtual reality. Electronics 2020 Sep 18;9(9):1530. [CrossRef]
- Allen C. Immersion Promotion. 2021 Jan. URL: https://www.immersivepromotion.com/understanding-the-vr-market-in-2021 [accessed 2022-01-18]
- Burch A. VR and Consumer Sentiment. Touchstone Research. 2016 Jan 28. URL: https://touchstoneresearch.com/vr-and-consumer-sentiment/ [accessed 2022-01-18]
- Landrine H, Corral I. Advancing research on racial-ethnic health disparities: improving measurement equivalence in studies with diverse samples. Front Public Health 2014 Dec 22;2:282 [FREE Full text] [CrossRef] [Medline]
- Dodgson NA. Variation and extrema of human interpupillary distance. In: Proceedings of the International Society for Optics and Photonics. 2004 Presented at: SPIE '04; January 18-22, 2004; San Jose, CA, USA p. 36-46. [CrossRef]
- Booker SQ. African Americans’ perceptions of pain and pain management: a systematic review. J Transcult Nurs 2016 Jan;27(1):73-80. [CrossRef] [Medline]
- Edwards CL, Fillingim RB, Keefe F. Race, ethnicity and pain. Pain 2001 Nov;94(2):133-137. [CrossRef] [Medline]
- Kim HJ, Yang GS, Greenspan JD, Downton KD, Griffith KA, Renn CL, et al. Racial and ethnic differences in experimental pain sensitivity: systematic review and meta-analysis. Pain 2017 Feb;158(2):194-211. [CrossRef] [Medline]
- Meints SM, Cortes A, Morais CA, Edwards RR. Racial and ethnic differences in the experience and treatment of noncancer pain. Pain Manag 2019 May;9(3):317-334 [FREE Full text] [CrossRef] [Medline]
- Sheffield D, Biles PL, Orom H, Maixner W, Sheps DS. Race and sex differences in cutaneous pain perception. Psychosom Med 2000;62(4):517-523. [CrossRef] [Medline]
- Plesh O, Adams SH, Gansky SA. Racial/Ethnic and gender prevalences in reported common pains in a national sample. J Orofac Pain 2011;25(1):25-31 [FREE Full text] [Medline]
- Anderson KO, Green CR, Payne R. Racial and ethnic disparities in pain: causes and consequences of unequal care. J Pain 2009 Dec;10(12):1187-1204. [CrossRef] [Medline]
- Lee JW, Jones PS, Mineyama Y, Zhang XE. Cultural differences in responses to a Likert scale. Res Nurs Health 2002 Aug;25(4):295-306. [CrossRef] [Medline]
- Chen C, Lee SY, Stevenson HW. Response style and cross-cultural comparisons of rating scales among East Asian and North American students. Psychol Sci 1995;6(3):170-175. [CrossRef]
- Dudley NM, McFarland LA, Goodman SA, Hunt ST, Sydell EJ. Racial differences in socially desirable responding in selection contexts: magnitude and consequences. J Pers Assess 2005 Aug;85(1):50-64. [CrossRef] [Medline]
- Krokos E, Varshney A. Quantifying VR cybersickness using EEG. Virtual Reality 2021 May 31;26(1):77-89. [CrossRef]
- Henrich J, Heine SJ, Norenzayan A. The weirdest people in the world? Behav Brain Sci 2010 Jun;33(2-3):61-135. [CrossRef] [Medline]
- Rad MS, Martingano AJ, Ginges J. Toward a psychology of Homo sapiens: making psychological science more representative of the human population. Proc Natl Acad Sci U S A 2018 Nov 06;115(45):11401-11405 [FREE Full text] [CrossRef] [Medline]
- Stanney K, Lawson BD, Rokers B, Dennison M, Fidopiastis C, Stoffregen T, et al. Identifying causes of and solutions for cybersickness in immersive technology: reformulation of a research and development agenda. Int J Hum Comput Interact 2020 Nov 04;36(19):1783-1803. [CrossRef]
- Benbow AA, Anderson PL. A meta-analytic examination of attrition in virtual reality exposure therapy for anxiety disorders. J Anxiety Disord 2019 Jan;61:18-26. [CrossRef] [Medline]
- Witmer BG, Singer MJ. Measuring presence in virtual environments: a presence questionnaire. Presence (Camb) 1998 Jun;7(3):225-240. [CrossRef]
- Usoh M, Catena E, Arman S, Slater M. Using presence questionnaires in reality. Presence (Camb) 2000 Oct;9(5):497-503. [CrossRef]
- Nichols S, Haldane C, Wilson JR. Measurement of presence and its consequences in virtual environments. Int J Hum Comput Stud 2000 Mar;52(3):471-491. [CrossRef]
|IPD: interpupillary distance|
|SSC: Short Symptoms Checklist|
|VR: virtual reality|
Edited by R Kukafka; submitted 27.01.22; peer-reviewed by S Weech, B Gibson; comments to author 10.02.22; revised version received 22.03.22; accepted 20.04.22; published 01.06.22Copyright
©Alison Jane Martingano, Ellenor Brown, Sydney H Telaak, Alexander P Dolwick, Susan Persky. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 01.06.2022.
This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on https://www.jmir.org/, as well as this copyright and license information must be included.