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Virtual Reality Is Sexist: But It Does Not Have to Be

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The aim of this study was to assess what drives gender-based differences in the experience of cybersickness within virtual environments. In general, those who have studied cybersickness (i.e., motion sickness associated with virtual reality [VR] exposure), oftentimes report that females are more susceptible than males. As there are many individual factors that could contribute to gender differences, understanding the biggest drivers could help point to solutions. Two experiments were conducted in which males and females were exposed for 20 min to a virtual rollercoaster. In the first experiment, individual factors that may contribute to cybersickness were assessed via self-report, body measurements, and surveys. Cybersickness was measured via the simulator sickness questionnaire and physiological sensor data. Interpupillary distance (IPD) non-fit was found to be the primary driver of gender differences in cybersickness, with motion sickness susceptibility identified as a secondary driver. Females whose IPD could not be properly fit to the VR headset and had a high motion sickness history suffered the most cybersickness and did not fully recover within 1 h post exposure. A follow-on experiment demonstrated that when females could properly fit their IPD to the VR headset, they experienced cybersickness in a manner similar to males, with high cybersickness immediately upon cessation of VR exposure but recovery within 1 h post exposure. Taken together, the results suggest that gender differences in cybersickness may be largely contingent on whether or not the VR display can be fit to the IPD of the user; with a substantially greater proportion of females unable to achieve a good fit. VR displays may need to be redesigned to have a wider IPD adjustable range in order to reduce cybersickness rates, especially among females.
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ORIGINAL RESEARCH
published: 31 January 2020
doi: 10.3389/frobt.2020.00004
Frontiers in Robotics and AI | www.frontiersin.org 1January 2020 | Volume 7 | Article 4
Edited by:
David Swapp,
University College London,
United Kingdom
Reviewed by:
Xueni Pan,
Goldsmiths University of London,
United Kingdom
Anne-Emmanuelle Priot,
Institut de Recherche Biomédicale des
Armées (IRBA), France
*Correspondence:
Kay Stanney
kay@designinteractive.net
Specialty section:
This article was submitted to
Virtual Environments,
a section of the journal
Frontiers in Robotics and AI
Received: 03 June 2019
Accepted: 09 January 2020
Published: 31 January 2020
Citation:
Stanney K, Fidopiastis C and Foster L
(2020) Virtual Reality Is Sexist: But It
Does Not Have to Be.
Front. Robot. AI 7:4.
doi: 10.3389/frobt.2020.00004
Virtual Reality Is Sexist: But It Does
Not Have to Be
Kay Stanney 1
*, Cali Fidopiastis 1and Linda Foster 2
1Design Interactive, Inc., Orlando, FL, United States, 2Lockheed Martin Corporate, Washington, DC, United States
The aim of this study was to assess what drives gender-based differences in the
experience of cybersickness within virtual environments. In general, those who have
studied cybersickness (i.e., motion sickness associated with virtual reality [VR] exposure),
oftentimes report that females are more susceptible than males. As there are many
individual factors that could contribute to gender differences, understanding the biggest
drivers could help point to solutions. Two experiments were conducted in which males
and females were exposed for 20 min to a virtual rollercoaster. In the first experiment,
individual factors that may contribute to cybersickness were assessed via self-report,
body measurements, and surveys. Cybersickness was measured via the simulator
sickness questionnaire and physiological sensor data. Interpupillary distance (IPD) non-fit
was found to be the primary driver of gender differences in cybersickness, with motion
sickness susceptibility identified as a secondary driver. Females whose IPD could not be
properly fit to the VR headset and had a high motion sickness history suffered the most
cybersickness and did not fully recover within 1 h post exposure. A follow-on experiment
demonstrated that when females could properly fit their IPD to the VR headset, they
experienced cybersickness in a manner similar to males, with high cybersickness
immediately upon cessation of VR exposure but recovery within 1 h post exposure. Taken
together, the results suggest that gender differences in cybersickness may be largely
contingent on whether or not the VR display can be fit to the IPD of the user; with a
substantially greater proportion of females unable to achieve a good fit. VR displays
may need to be redesigned to have a wider IPD adjustable range in order to reduce
cybersickness rates, especially among females.
Keywords: virtual reality, cybersickness, gender differences, interpupillary distance, head mounted displays,
motion sickness
INTRODUCTION
In general, those who have studied cybersickness (i.e., the motion sickness associated with VR
exposure) and other forms of motion sickness oftentimes report that females are more susceptible
than males. Cooper et al. (at sea; 1997), Kaplan (on trains; 1964), Lawther and Griffin (at sea;
1986; sea, 1988), Lederer and Kidera (on planes; 1954), Lentz and Collins (general susceptibility;
1977), Munafo et al. (in VR; 2017); Park and Hu (in a rotating drum; 1999), Stanney et al. (in
VR; 2003); Turner and Griffin (in automobiles; 1999), and Turner et al. (on planes; 2000) all
found females more susceptible to motion sickness as compared to males across diverse motion
platforms. Yet when Lawson (2014) reviewed 46 studies examining gender differences in motion
sickness, he reported that only 26/46 (56.5%) found higher levels of susceptibility in females as
Stanney et al. Virtual Reality Is Sexist
compared to males. Further, in immersive environments,
there are many individual factors that could contribute to
gender differences, including previous experience with virtual
motion, field of view (FOV), IPD, field dependence, postural
stability, female hormonal cycle, state/trait anxiety, migraine
susceptibility, ethnicity, aerobic fitness, body mass index, among
others (see Tables 1,2). Researchers have yet to identify which of
these factors are the primary drivers of susceptibility differences
between the genders. Of the studies that do exist, generally
only a few variables were considered at one time rather than
examining across a large number of potential drivers (c.f.
Parkman et al., 1996; Stanney et al., 2003; Klosterhalfen et al.,
2005). In addition, gender differences in susceptibility have been
speculated to be attributed to differences in symptom awareness
and willingness to report symptomatology. However, past studies
have shown a 5:3 female to male risk ratio for vomiting,
which is an objective measure of motion sickness (Lawther and
Griffin, 1986). Examining differences from a physiological level
can address any such reporting differences. Yet, even from a
physiological level conflicting data exist. While females have
been shown to have higher emetic response rates (Kennedy
et al., 1995; Golding, 2006), as well as greater sensitivity in
peripheral alpha- and beta- adrenergic receptors (Girdler et al.,
1990; Kajantie and Phillips, 2006), which increases autonomic
responses associated with motion sickness (Finley et al., 2004),
Jokerst et al. (1999) found no significant differences between
the genders in gastric tachyarrhythmia during exposure to an
optokinetic drum, and Cheung and Hofer (2002) found no
significant gender-based physiological differences during coriolis
cross-coupling stimulation. Thus, while females are generally
thought to have higher susceptibility to cybersickness than males,
this relationship has not been well-characterized, especially for
the latest generation of VR headsets.
Why do gender differences matter? VR technology is
anticipated to fill many enterprise roles in the coming decades,
from training to maintenance to operational support to design,
and more. Currently >150 companies in multiple industries,
including >50 Fortune 500 companies, are testing and/or
deploying VR solutions (Kaiser and Schatsky, 2017; Morris,
2018). As VR-based real-time guidance systems driven by
artificial intelligence advance, persons who cannot tolerate these
delivery systems may be left out of job advancement. We
cannot create a divide, with those who can handle VR exposure
advancing due to better, more immersive training, more effective
repair jobs aided by real-time augmented guidance, more creative
designs that evolve from a mesh of digital and physical worlds,
etc., while those who are susceptible to cybersickness are left
on the sidelines watching this new era of VR empowered
productivity pass them by. Further, if the design of VR headsets
is discriminative to females, they, in particular, may experience
challenges when trying to harness the bevy of performance
enhancing potential of VR enterprise applications.
The main goal of this study was to determine what the primary
drivers of gender-based differences in cybersickness susceptibility
within VR environments are so that potential countermeasures to
better accommodate females can be identified. To this end, two
experiments were conducted. The first study examined potential
drivers of cybersickness, which are summarized in Table 1, to
identify those that may be contributing the most to gender
differences. It was anticipated that a subset of these factors would
be identified as particularly influential in driving higher levels of
cybersickness among females.
EXPERIMENT 1
Materials and Methods
The purpose of Experiment 1 was to determine how well males
and females are able to tolerate VR exposure and what factors
might be driving any differences in the cybersickness they may
experience. Based on the studies summarized in Table 1, it
was anticipated that females would experience higher levels of
cybersickness than males, with the goal of the experiment being
to identify which factors drive any such differences.
Participants
Adults aged 18–30 years, balanced between genders participated
in this study. Participants were recruited through a market
research firm. A total of 46 participants participated in the
study and were randomized to either an experimental group (VR
headset; n=30 [15 male/15 female]) or a control condition
(flatscreen television; n=16 [8 male/8 female]). This research
complied with the American Psychological Association Code of
Ethics and was approved by the Institutional Review Board at
Copernicus Group. Informed consent was obtained from each
participant and all participants were compensated for their time
in the experiment.
Equipment and Display Content
The displays used in this study included the HTC Vive VR
headset (which does not fit, on average, 35% of females
and 16% of males based on the adjustable IPD range)
and a flatscreen television. The HTC Vive has OLED display
technology, a resolution of 2,160 ×1,200 (1,080 ×1,200 per eye),
a refresh rate of 90 Hz, a field of view of 110 degrees, weight
of 555 g (1.22 lbs), and an IPD range adjustable from 60.5 to
74.4 mm. The flatscreen television was a Samsung H6350 Smart
LED TV with a screen size of 60.0 measured diagonally and a
resolution of 1,920 ×1,080.
Steam platform was used to develop a virtual rollercoaster of
20 min duration (see Figure 1). In order to create provocative
content that would instigate cybersickness, the following factors
were incorporated into the virtual rollercoaster ride:
Off-vertical axis rotation (visual OVAR; e.g., rollercoaster
wraparounds, spinning track), as OVAR can be expected to
lead to extreme levels of nauseogenicity (Golding et al., 2009);
Variable velocity, forward acceleration, and vertical
acceleration via humps in the track that provided visual
oscillation, as these motions are known to be provocative
(Alexander et al., 1947; Lawther and Griffin, 1986);
High level of optic flow (implemented via movement through
support structures, maintenance gangways, ground tunnels,
and other visual details), which tends to drive visually induced
motion sickness (Smart et al., 2014);
Frontiers in Robotics and AI | www.frontiersin.org 2January 2020 | Volume 7 | Article 4
Stanney et al. Virtual Reality Is Sexist
TABLE 1 | Traits and factors that may affect cybersickness susceptibility in females.
Traits and factors Gender difference Impact
Underlying physiological
mechanisms
Females tend to exhibit greater effects of Neurokinin-1 (NK-1) signaling (receptor
involved in nausea; Arslanian-Engoren and Engoren, 2010), have higher activation
of the limbic system (involved in the generation of nausea; Wang et al., 2007), and
exhibit different gastric dysrhythmia based on menstrual cycle phase (Parkman
et al., 1996) than males.
Females may be physiologically “hard-wired” for motion
sickness susceptibility (Golding, 2006), which can be
upregulated via hormonal fluctuations.
Previous experience In U.S., 59% of males and 41% of females self-report as gamers [Entertainment
Software Association (ESA), 2016]. Yet, hardcore gamers who play >5 h per week
remain primarily male (NPD Group, 2014) and early adopters of VR technology are
primarily hardcore gamers (Leibach, 2015). Thus, one can speculate that to date,
those who have experienced VR are mostly male.
If females have less VR experience than males, it may
predispose them to cybersickness as motion sickness is
postulated to be due to sensory conflicts between
expected patterns of afferent signals (reafference)
established through previous experiences and what is
being experienced in a novel and sensorially altered
environment (Reason and Brand, 1975; Oman, 1998).
Field of view Females have slightly larger peripheral vision fields (Burg, 1966), slightly higher
vertical field of view (Williams and Thirer, 1975), and more active dorsal visual
stream and thus better peripheral vision (Becker-Bense et al., 2012; Amen et al.,
2017) than males (Lawson, 2014).
If females have a wider FOV and are more sensitive to
the peripheral color pallet than males, this may drive
higher levels of vection, which in turn may drive higher
levels of cybersickness (Webb and Griffin, 2003; Diels
and Howarth, 2013).
Interpupillary distance
(IPD)
IPD (the distance between the pupils of both eyes) has been found to vary by
gender (Fledelius and Stubgaard, 1986; Gordon et al., 2014), with adult females
ranging from an IPD of 51–74.5 mm, with a mean of 61.7 mm, and adult males
ranging from an IPD of 53–77.5 mm, with a mean of 64mm. When one matches
these IPD ranges to the IPD ranges supported by current VR headsets, it becomes
evident that some of today’s VR headsets may not fit upwards of 30% or more of
females (see Table 2).
IPD range facilitates the correct positioning of VR
headset lenses, as there are specific points on the lenses
which have to coincide with the center of the pupil (visual
axis) of each eye in order for the display image to be in
focus. If a VR headset does not allow for such eye-lens
alignment, which is much more likely in females (Fulvio
et al., 2018), eyestrain and headaches can be expected
(Ames et al., 2005), as well as incorrect perception of
displayed imagery (Priot et al., 2006).
Field dependence Gender-based differences have been found in field dependence (FD), perception of
veridical vertical with body tilt, perception of the morphological horizon, and mental
rotation ability, with males generally far out-performing females (order of one
standard deviation higher; Witkin and Goodenough, 1977; Harris, 1978; Darlington
and Smith, 1998; Parsons et al., 2004).
If females are more likely to be FD and have difficulty with
visuo-spatial tasks than males, this may predispose
them to higher motion sickness susceptibility (Parker and
Harm, 1992).
Postural stability The spatial magnitude of postural sway and the control of posture differs between
genders, with females demonstrating more multifractality of postural sway
(Koslucher et al., 2016).
If females are less able to control and stabilize their
bodily activity than males, this may predispose them to
higher motion sickness susceptibility according to the
ecological theory (Riccio and Stoffregen, 1991).
Female hormonal cycle Motion sickness susceptibility fluctuates throughout the menstrual cycle, with this
fluctuation in susceptibility across the cycle accounting for approximately one-third
of the overall difference between the genders in motion sickness susceptibility
(Golding et al., 2005).
If females are more susceptible to cybersickness during
certain phases of the hormonal cycle, this may render
them less capable of tolerating VR exposure as
compared to males during these peaks.
State and trait anxiety Females report higher trait-anxiety (Robin et al., 1987), with incidence in females
>2x as high as males (Donner and Lowry, 2013), which may in-turn drive increased
cortisol levels (Meissner et al., 2009), and affect neuronal activity within the
amygdala (Sandi et al., 2008), with phasic activation in the amygdala being shown
to precede strong nausea (Cha et al., 2012; Napadow et al., 2013). State anxiety
may drive disorientation and vertigo (Brandt, 1996), which can drive motion
sickness.
Heightened anxiety in females may render them more
susceptible to motion sickness than males, as
heightened state- (Tucker and Reinhardt, 1967) and trait-
anxiety (Paillard et al., 2013) are strongly related to
cybersickness (Ling et al., 2011).
Migraine susceptibility Females are susceptible to migraines, 3x more prone to than males (Rasmussen
et al., 1991), and migraine sufferers have a global hypersensitivity to different
sensory stimuli (Granziera et al., 2006), anomalies in early motion processing
pathways underlying contrast sensitivity (Singh and Shepherd, 2016), and vestibular
abnormalities (e.g., vestibulo-ocular reflex dysfunction; Kim et al., 2005), which may
contribute to spatial disorientation (Cho et al., 1995).
If females are more predisposed to migraines and
associated vestibular abnormalities than males, this may
predispose them to higher motion sickness susceptibility
(Golding, 1998; Marcus et al., 2005).
Ethnicity Genetic factors account for half of variation in motion sickness susceptibility
(Reavley et al., 2006), with Asians being more susceptible than African Americans
(Stern et al., 1993) and Caucasians (Klosterhalfen et al., 2005). The “gg” phenotype
is 5.8x more common in Chinese than in European Caucasians, as well as 1.6x
more common in those susceptible to motion sickness (Liu et al., 2002).
When comparing differential effects of ethnicity and
gender, ethnicity may be the strongest intrinsic factor
contributing to motion sickness, with gender playing a
more modest role (Klosterhalfen et al., 2006); or there
may be an interaction effect (Stern et al., 1993).
Body mass index (BMI) Higher BMI may somewhat moderate motion sickness (Stanney et al., 2003; Yi
et al., 2017), as adiposity may be protective against emetic responses in that it is
associated with diminished activity of the gastrointestinal system (Kohl, 1990).
If females have a higher proportion of adipose tissue as
compared to males (Hellstroèm et al., 2000), this
difference may lead to males being more susceptibility to
motion sickness than females.
(Continued)
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Stanney et al. Virtual Reality Is Sexist
TABLE 1 | Continued
Traits and factors Gender difference Impact
Aerobic fitness Males have been shown to have higher aerobic capacity than females with
comparable training (Sandbakk et al., 2012), which has also been associated with
increased motion sickness susceptibility (Banta et al., 1987). Rawat et al. (2002)
suggested that vasomotor susceptibility (e.g., epigastric discomfort, nausea,
vomiting, headache) may be higher in aerobically fit individuals susceptible to
motion sickness. Yet, Jennings et al. (1988) found no such association.
If females have less aerobic capacity than males, this
difference may lead to males being more susceptibility to
motion sickness than females.
Past motion sickness
history
Females are generally more inclined to be aware of and admit subjective
symptoms, as well as more likely to remember past motion sickness experiences
as compared to males (Jokerst et al., 1999; Park and Hu, 1999; Cheung and Hofer,
2002; Flanagan et al., 2005; Golding et al., 2005). Dobie (1974) found little evidence
that men are more reticent to report motion sickness as compared to females.
If females are more inclined to report and be aware of
motion sickness symptomatology as compared to males,
this could lead to an overestimation of gender differences
that are not corroborated via physiological assessment.
TABLE 2 | IPD gender range vs. current virtual reality headsets.
Headset IPD range
Sony PlayStation Adjustable via software between 48 and 78 mm (VR Heads, 2017), and would be expected to fit the entire adult population
Samsung Gear VR Fixed at 62 mm (Samsung, 2016), and would only b e expected to fit individuals with an IPD of 62 mm, which is 10% of both males and females
(Gordon et al., 2014)
Oculus Rift Adjustable between 58 and 72 mm (Carbotte, 2016), and thus would not be expected to fit the smallest 15% of females and the largest 1% of
both males and females
Oculus Rift S Adjustable between 61.5 and 65.5 mm (Heaney, 2019), and thus would not be expected to fit the smallest 45% of females, the largest 15% of
women, the smallest 20% of males, and the largest 30% of males
Oculus Quest Adjustable between 56 and 74 mm (Heaney, 2019), and thus would not be expected to fit the smallest 7% of females and the largest 1% of
males
HTC Vive Adjustable from 60.5 to 74.4 mm (HTC Vive, 2017), and thus would not be expected to fit the smallest 35% of females, smallest 15% of males,
and largest 1% of males
HTC Vive Pro Adjustable from 60.9 to 74 mm (HTC Vive Pro, 2018), and thus would not be expected to fit the smallest 40% of females, smallest 18% of
males, and largest 1% of males
Females IPD Range: 51–74.5 mm (mean IPD =61.7 mm; S.D. =3.6 mm); Male IPD Range: 53–77.5 mm (mean IPD =64.0 mm; S.D. =3.4 mm). Percentages based on US Army
Anthropomorphic Survey (ANSUR) database (Gordon et al., 2014).
Anchoring to the lead rollercoaster car with no car in front
to focus on, as a fixed-horizon or stable vehicle dashboard
reduces cybersickness (Prothero and Parker, 2003);
Constant, rhythmic, and repetitive sound that simulated
movement along the track so that participants were visually
and aurally convinced they were moving when they were
actually sitting still in a chair, as such sounds can drive nausea
and disorientation (Dawson, 1982); and
No control by the participant over virtual motion, as lack
of viewpoint control has been demonstrated to be very
nauseogenic (Stanney and Hash, 1998).
To maintain consistency in the visual stimulus across groups,
the SteamVR format was exported to a video format to run in
flatscreen television format.
Procedure
The experiment involved the following phases— pre-screening,
screening, pre-testing, immersive exposure, and post-testing.
In the pre-screening phase, a participant recruiter called
potential participants and reviewed inclusion requirements
with them to identify candidate participants. Any participant
reporting affirmative to any exclusion criteria (neurological
impairments, musculoskeletal problems of the knee, ankle,
shoulder, and/or elbow, loss in depth perception, <20/20
corrected visual acuity, inner-ear anomalies, history of seizures,
pregnancy) was not asked to participate in the study. Participants
who met pre-screening eligibility and inclusion requirements
were scheduled for on-site screening. During the on-site
screening: (1) upon arrival, participants were welcomed,
and provided with informed consent documentation; (2) all
participants were provided with a 3-digit number based on
order of participation and experimental condition that was
used for data collection; (3) a Simulator Sickness Questionnaire
(SSQ; Kennedy et al., 1993) was administered electronically and
participants that scored >12 were thanked for their willingness
to participate and excluded from the study; (4) a visual acuity test
was administered and participants who did not have corrected
20/20 vision were thanked for their willingness to participate
and excluded from the study; and (5) the Titmus Stereotest was
administered to assess depth perception and participants that
scored <6/9 were thanked for their willingness to participate
and excluded from the study. Participants who met screening
eligibility proceeded to pre-testing.
During the pre-testing phase, participants completed a
demographics form via which they reported their previous
VR and gaming experience, phase of the menstrual cycle
(female only), and ethnicity, as well as other demographic
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Stanney et al. Virtual Reality Is Sexist
FIGURE 1 | Views from VR rollercoaster.
data. Participants FOV (i.e., open observable area an individual
can see through their eyes, which covers both central foveal
and peripheral vision; Strasburger et al., 2011) was then
measured via a vision protractor, their IPD (i.e., distance
between the center of their pupils; Dodgson, 2004) was
measured via a digital pupilometer (binocular pupillary
range: 45–80 mm), their weight and height were measured
to assess body mass index, their aerobic fitness (i.e., peak
expiratory flow) was assessed via the Philips Respironics HS755
Personal Best Full Peak Flow Meter, and postural stability
was assessed via the Sharpened Rhomberg Test (Johnson
et al., 2005) using a Polhemus G4 wireless magnetic motion-
tracking device with the sensor mounted via a naval strap.
Participants then filled out surveys, including the State–Trait
Anxiety Inventory (STAI; Spielberger et al., 1970), Motion
History Questionnaire (MHQ; Kennedy et al., 1992), Cube
Comparison Survey (Ekstrom et al., 1976), and Migraine
Susceptibility Survey based on the International Headache
Society [International Headache Society (IHS), 2017] Criteria for
Diagnosing Migraine.
During the immersive exposure phase, participants
were randomized to a control (i.e., flatscreen television)
or experimental group (i.e., VR headset) and fitted with
physiological sensors of electrocardiogram (ECG; to assess
alterations to cardiovascular activity, i.e., heart rate),
electrogastrography (EGG; to assess abnormal gastric rhythms,
including tachygastria and bradygastria), and electrodermal
activity (EDA; to assess skin conductance level [SCL]). Following
a 5 min baselining of the physiological measures, participants
were exposed to immersive content (virtual rollercoaster)
for 20 min. The IPD of participants in the VR group was
entered into the headset software and adjusted on the headset
prior to viewing the rollercoaster stimuli to the best match
available based on the IPD range of the HTC Vive. Those
participants with an IPD smaller or larger than the HTC
VIVE range were, respectively, given the value at the lowest
or highest value available (60.5 or 74.4 mm). Participants
were monitored via the physiological measures throughout
VR exposure.
During the post-testing phase, the SSQ Total Score was
assessed immediately following the immersive exposure (AE
[aftereffects] 1), and in 15 min increments for a total of 60 min
(AE2–AE5) post exposure. Participants were then debriefed,
thanked, and paid for participation.
Experimental Design
The experiment was a mixed design, with 2 (gender) ×2 (display
type) between factors and a 5 (post exposure measurement
time) within factor. The display types were VR headset and
flatscreen television and gender types were male and female.
The post exposure measurement times were 0, 15, 30, 45,
and 60 min.
Predictor Variables
The cybersickness predictor variables (see Table 3) included self-
report measures (previous VR and gaming experience, female
hormonal cycle, ethnicity—all gathered via demographics form),
survey assessed measures (field dependence, state/trait anxiety,
migraine susceptibility, past motion sickness history), and body
measures (FOV, IPD, postural stability, aerobic fitness, body mass
index, ECG, EGG, EDA).
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Stanney et al. Virtual Reality Is Sexist
TABLE 3 | Predictor variables investigated.
Variable Description Experiment 1 inclusion in regression
analysis*
Experiment 2 inclusion in regression
analysis*
Gender Participant gender; Coded as Male =0;
Female =1
Included in model Included in model
Physiological mechanisms Measurement of EGG, heart rate, and
skin conductance level during exposure
Not included in model, no significant
differences in EGG, heart rate, and skin
conductance level between genders
Included EGG (bradygastria) in model; on
average females had higher levels of
bradygastria than males (female mean =
29.72 [S.D.=2.47]; male mean =19.17
[S.D. =2.16], F=10.37, p=0.002)
Previous experience A demographics questionnaire was used
to gather previous experience VR and
gaming
Included in model; on average, females
had less previous VR (female mean =
1.24 h [S.D. =3.16]; male mean =8.36 h
[S.D. =24.85]; F=3.22; p=0.077) and
gaming (female mean =6.63 h [S.D. =
10.13]; male mean =12.11 h [S.D. =
14.16]; F=3.97; p=0.056) experience
than males
Included in model; on average, females
had less previous VR experience (female
mean =4.33 h [S.D. =12.77]; male mean
=7.11 h [S.D. =19.03]; F=0.848; p=
0.359 and gaming (female mean =7.03 h
[S.D. =14.11]; male mean =12.53 h [S.D.
=10.77]; F=5.03; p=0.023) than males
FOV Participant field of view Included in model; on average, males had
significantly wider FOV (female mean =
152.63 degrees [S.D. =14.37]; male
mean =158.82 [S.D. =11.12]; F=3.97;
p=0.039) than females
Included in model; on average, males had
wider FOV (female mean =155.25
degrees [S.D. =32.12]; male mean =
160.52 [S.D. =12.79]; F=1.23; p=
0.270) than females
IPD Fit Measurement of how well the
interpupillary distance could be matched
to the worn VR device; Coded as IPD Fit
=1; IPD Non-Fit =2
Included in model; on average, males had
significantly wider IPD (female mean =
62.63 mm [S.D. =3.52]; male mean =
65.33 mm [S.D. =2.99]; F=5.12; p=
0.031) than females; 5/15 females had an
IPD non-fit; while no males had an IPD
non-fit
Included in model; on average, males had
significantly wider IPD (female mean =
60.80 mm [S.D. =3.92]; male mean =
62.88 mm [S.D. =4.37]; F=7.12; p=
0.001) than females
Field dependence Measured via cube comparison; Coded
as field dependent =1; Field
intermediate =2; Field independent =3
Included in model; on average, males had
higher field independence (female mean =
0.56 [S.D. =10.87]; male mean =6.33
[S.D. =14.65]; F=2.99; p=0.088) than
females
Not included in model; no significance
between genders (female mean =1.92
[S.D. =0.836]; male mean =1.95 [S.D. =
0.80]; F=0.047; p=0.83) than females
Postural stability Measurement of pre exposure postural
stability
Not included; no significance between
genders (female mean =0.527 [S.D. =
1.601]; male mean =0.671 [S.D. =
1.535]; F=0.452; p=0.50)
Not included; no significance between
genders (female mean =4.99 [S.D. =
8.76]; male mean =3.57 [S.D. =3.87]; F
=1.089; p=0.299)
Female hormonal cycle Report of stage in hormonal cycle;
Coded as: Male-0; Premenstrual-1;
Menstruation-2; Postmenstrual-3;
Ovulation-4; Menopause- 5
Included in model Included in model
State anxiety Measured by the STAI State anxiety included in model; on
average, males had higher state anxiety
pre-exposure (female mean =26.82 [S.D.
=7.01]; male mean =31.27 [S.D. =
10.19]; F=4.67; p=0.03) than females
State anxiety included in model; on
average, females had significantly higher
state anxiety pre-exposure (female mean
=26.55 [S.D. =6.26]; male mean =
25.81 [S.D. =5.83]; F=0.44; p=0.51)
than females
Trait anxiety Measured by the STAI Not included; no significance between
genders (female mean =31.84 [S.D. =
10.18]; male mean =33.63 [S.D. =8.67];
F=0.837; p=0.36)
Not included; no significance between
genders (female mean =31.15 [S.D. =
10.19]; male mean =29.96 [S.D. =7.24];
F=0.497; p=0.482)
Migraine susceptibility Indicates an individual’s susceptibility to
migraine; Coded as migraine
non-susceptible =0; Migraine
susceptible =1
Not included; no significance between
genders (females- 13/15 not migraine
sufferers; males- 15/15 not migraine
sufferers; F=0.11; p=0.74)
Not included; no significance between
genders (females- 48/60 not migraine
sufferers; males- 48/58 not migraine
sufferers)
Past motion sickness
history
Measured via MHQ, which assess an
individual’s exposure to various forms of
motion and the occurrence of illness
associated with such motion
Included in model; on average, females
had higher past motion sickness history
(female mean =3.21 [S.D. =2.43]; male
mean =2.47 [S.D. =1.87]; F=1.91; p=
0.17) than males
Included in model; on average, females
had significantly higher past motion
sickness history (female mean =3.32
[S.D. =2.94]; male mean =2.30 [S.D. =
1.99]; F=4.49; p=0.036) than males
(Continued)
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Stanney et al. Virtual Reality Is Sexist
TABLE 3 | Continued
Variable Description Experiment 1 inclusion in regression
analysis
Experiment 2 inclusion in regression
analysis
Ethnicity Sample size per group was too low
resulting in low power, so non-Caucasian
was collapsed and coded as
non-Caucasian =0; Caucasian =1
Included in model; Participant pool was
16% African American, 4% Asian,
55% Caucasian, 14% Hispanic, and
11% of multiple ethnicities
Included in model; Participant pool was
6% African American, 4% Asian,
69% Caucasian, 6% Hispanic, 2%
American Indian or Alaskan Native, and
13% of multiple ethnicities
Aerobic fitness Measured via peak expiratory flow.
Coded as low =0; normal =1; high =2
Included in model; males had higher
aerobic capacity (female mean =290.19
[S.D. =78.40]; male mean =378.44 [S.D.
=99.37]; F=13.85; p<0.0001) than
females
Included in model; males had higher
aerobic capacity (female mean =329.58
[S.D. =79.91]; male mean =467.40
[S.D.=110.23]; F=58.41; p=0.001)
than females
Body mass index Ratio of an individual’s height vs. weight Not included; no significance between
genders (female mean =27.0 [S.D. =
8.56]; male mean =27.0 [S.D. =7.21]; F
=0.039; p=0.84)
Not included; no significance between
genders (female mean =25 [S.D. =5.40];
male mean =26 [S.D. =6.62]; F=0.281;
p=0.597)
VR exposure duration Length of time participant spent in VR
rollercoaster
Not included Included in model; males had higher
exposure duration (female mean =18.31
[S.D. =4.17]; male mean =19.48 [S.D. =
1.96]; F=3.73; p=0.056) than females
*p<0.27 level (Bursac et al., 2008).
Dependent Measure
The dependent measure was cybersickness as measured by
the SSQ Total Score (TS; Kennedy et al., 1993) at 0, 15, 30,
45, and 60 min post exposure. The time component after VR
exposure is critical to understanding the sustained negative
effects of exposure on an individual (Stanney and Hash, 1998).
Thus, for the purposes of regression analysis, cybersickness was
operationalized as a “recovery” SSQ Total Score (TS), which
was defined by the average SSQ TS 45 min post exposure and
SSQ TS 1 h post exposure normalized by the Baseline (BL).
Given a 20 min VR exposure duration and 1 h post exposure
measurement period (i.e., 3x exposure duration), participants
would be expected to have “recovered” to BL SSQ TS levels at
the conclusion of the experiment.
Data Analysis
A mixed-model analysis of variance (ANOVA) was used to
identify main and interaction effects among Gender, Display
Type, and Post Exposure Measurement Time on cybersickness.
A regression analysis was then used to characterize what might
be driving any differences. Several steps were taken to determine
which of the pool of candidate predictive variables (i.e., previous
VR and gaming experience, FOV, IPD, field dependence, postural
stability, female hormonal cycle, state/trait anxiety, migraine
susceptibility, ethnicity, aerobic fitness, body mass index,
physiological mechanisms) should be included in the regression
analysis. First a univariate ANOVA was performed to evaluate
significant gender differences among the potential predictive
variables. The selection criterion chosen was whether or not each
possible predictor variable was significantly different between the
genders; those variables that were significantly different (set at p
<0.27 for univariate analysis; the more traditional 0.05 level can
fail to identify important variables; Bursac et al., 2008) between
the genders were included in the regression analysis. Next, a
zero-order correlation analysis determined the strength of linear
association among the predictor variables, as well as with the
“recovery” SSQ TS metric. High correlation among predictor
variables suggests redundant variable inclusion.
To further increase the predictability of the variables,
especially given that an appropriate predictor to sample size ratio
is 1:15, independent variables with the highest zero-correlation
with the recovery SSQ TS metric were included first in the model.
All categorical variables were dummy coded with males who fit
the VR headset as the comparator. The IPD Fit metric classified
males and females as out of and below the IPD range of the
headset (<60.5 mm), within the range (60.5–74.4 mm), or out
of and above the range (>74.4 mm). A binary classification of
IPD Fit was then determined, which signified participants in IPD
range for the HTC Vive or out of range (either below or above).
The regression coefficient (β) of each predictor variable on
recovery SSQ TS was calculated using SPSS version 24 multiple
linear regression analysis. Models were evaluated for significant
R2 change using an F-test and an a priori αlevel of 0.05, as well
as multicollinearity using 0.20 as a cut off for tolerance and a
variance inflation factor cutoff of 4.
Results
The results revealed that there were significant differences in the
cybersickness experienced between the flatscreen TV and VR
conditions. While there was a main effect of Gender [F(1, 42) =
4.13, p<0.049], and a main effect of Display Type [F(1, 42) =
8.29, p<0.006], there was also a significant interaction between
Gender and Display Type [F(1, 42) =4.85, p<0.033]; with
Gender differences found for the VR display but not flatscreen
TV. As expected, for both genders low levels of cybersickness
were experienced with exposure to flatscreen TV immediately
after exposure (female AE1 SSQ TS mean =7.95; S.D. =18.15;
male AE1 SSQ TS mean =5.61; S.D. =8.48; see Table 4)
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Stanney et al. Virtual Reality Is Sexist
TABLE 4 | Experiment 1 SSQ total score values at baseline (BL), immediately
following exposure (aftereffect; AE1), and 1h post exposure (aftereffect; AE5).
Device Gender BL- Mean (SD) AE1-Mean (SD) AE5-Mean (SD)
VR (HTC Vive) Female 2.24 (3.69) 52.11 (41.63)* 31.42 (40.65)*
VR (HTC Vive) Male 2.99 (3.22) 24.93 (32.69)* 3.24 (6.76)
Flatscreen TV Female 1.40 (1.94) 7.95 (18.15) 0.94 (2.64)
Flatscreen TV Male 1.87 (2.83) 5.61 (8.48) 3.74 (4.90)
*p<0.05 (different from BL).
and these low levels continued throughout the post exposure
measurement periods (female AE5 SSQ TS mean =0.94; S.D.
=2.64; male AE5 SSQ TS mean =3.74; S.D. =4.90; see
Table 4). On the other hand, VR exposure proved problematic
to both genders, but with some clear differences (see Table 4
and Figure 2-Top). Specifically, immediately after VR exposure
the adverse effects in females (AE1 SSQ TS 52.11; S.D. =41.63)
were, on average, more than 2x that of males (SSQ TS mean =
24.93; S.D. =32.69); this difference was significant [F(1, 28) =
4.104, p=0.05]. Further, these adverse effects persisted long after
VR exposure for females (AE5 SSQ TS mean =31.42; S.D. =
40.65), while males recovered much more quickly (AE5 SSQ TS
mean =3.24; S.D. =6.76); this difference was significant [F(1, 28)
=7.27, p=0.012]. Females in the VR condition, on average,
never returned to BL levels (see Table 4 and Figure 2-Top, AE5),
while males, on average, recovered to BL within 30 min post
exposure (see Figure 2-Top, AE3). A regression analysis was
conducted to characterize these gender differences and identify
which predictor variables may be driving them.
Table 3 summarizes the results from the ANOVA analysis
for identifying variables significantly contributing to gender
differences. Based on these results, the variables targeted for
inclusion in the regression analysis were VR and gaming
experience, FOV, IPD Fit, field dependence, female hormonal
cycle, state anxiety, ethnicity, aerobic fitness, and past motion
sickness history, as each of these variables demonstrated
significant differences between genders (see Table 3). The
variables excluded from the regression analysis were postural
stability, trait anxiety, migraine susceptibility, body mass index,
and physiological mechanisms, all of which were not significantly
different between the genders (see Table 3).
As shown in Table 5, FOV was highly correlated with the
IPD Fit measure, so FOV was removed from the model, as IPD
Fit was correlated with Recovery SSQ TS but FOV was not.
As well, Hormonal Cycle was highly correlated with Gender,
so Hormonal Cycle was removed from the model, as Gender
was correlated with Recovery SSQ TS but Hormonal Cycle was
not. All other predictor variables targeted for inclusion were
systematically added and removed from the model based on the
F-statistic and multicollinearity until the model could no longer
be significantly improved.
Table 6 shows the results from the multiple linear regression
analysis. Of the 30 participants in the VR headset condition,
26 participants had complete data for the regression analysis.
The results show that IPD Fit was a strong and significant (p=
0.009) predictor of cybersickness. Past motion sickness history
FIGURE 2 | Experiment 1 (Top) and Experiment 2 (Bottom) group mean SSQ
total score at baseline (BL) and each aftereffects (AE) measurement period for
IPD fit and non–fit and motion sickness history low and high.
also contributed to the explanatory power of the model. The
resulting model, which had an R2 =0.469, Adjusted R2 =0.420,
RMSE =27.77, F(2, 23) =9.70, p=0.001, was as follows:
E(Recovery SSQ TS)= −2.51 +47.45IPD Fit
+4.66Motion Sickness History
This model suggests that IPD non-fit and motion sickness history
are positively correlated with cybersickness, with IPD non-fit
being the most influential variable. This model accounted for
42.0% of the variability in cybersickness. Follow-up analyses
indicated that the model passed the assumptions of multiple
regression including normality and independence of residuals.
Experiment 1 Summary
The primary finding from Experiment 1 is that the most
significant driver of gender differences in cybersickness was IPD
non-fit, with motion sickness history also contributing. The IPD
differences found in the sample population under evaluation in
this study are summarized in Table 7. The table includes the
number of individuals in each condition for which the HTC Vive
IPD adjustable range could not be fit to the participant’s IPD. The
average male IPD (mean =65.33; S.D. =2.99) was 4.1% wider
than females (mean =62.63; S.D. =3.52) and this difference
was significant [F(1, 28) =5.13, p=0.031]. Within the female
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Stanney et al. Virtual Reality Is Sexist
TABLE 5 | Experiment 1 predictor variable correlations.
Gender VR
experience
Gaming
experience
FOV IPD fit Field
dependence
Hormonal
cycle
State
anxiety
Ethnicity Aerobic
fitness
Motion sickness
history
Gender
VR experience 0.266
Gaming experience 0.208 0.113
FOV 0.221 0.002 0.070
IPD fit 0.447* 0.127 0.040 0.589*
Field dependence 0.121 0.299 0.281 0.045 0.047
Hormonal cycle 0.894* 0.242 0.110 0.115 0.320 0.117
State anxiety 0.133 0.103 0.131 0.127 0.016 0.126 0.056
Ethnicity 0.067 0.180 0.088 0.165 0.211 0.287 0.060 0.300
Aerobic fitness 0.387 0.043 0.022 0.013 0.046 0.220 0.389* 0.256 0.124
Motion sick history 0.282 0.198 0.123 0.124 0.378* 0.079 0.162 0.307 0.062 0.415*
SSQ (Dependent) 0.475* 0.144 0.074 0.045 0.489* 0.113 0.332 0.085 0.311 0.175 0.453*
*p<0.05.
TABLE 6 | Summary of multiple linear regression model of cybersickness for
Experiment 1.
Predictor variables in
cybersickness model
βSE t P >|t|
(Intercept) 2.51 8.38 0.30 0.767
IPD Fit 47.45 16.63 2.85 0.009*
Past motion sickness history (MHQ) 4.66 2.62 1.78 0.103
Gender 0.250 1.47 0.156
VR experience 0.012 0.07 0.391
Gaming experience 0.137 0.877 0.941
Field dependence 0.081 0.490 0.629
State anxiety 0.120 0.722 0.473
Ethnicity 0.252 1.67 0.109
Aerobic fitness 0.302 1.90 0.072
*p<0.05.
group, 5 of 15 or 33.3% (in line with expectations based on the
US Army Anthropomorphic Survey [ANSUR] database; Gordon
et al., 2014; see Table 2) of the females had an IPD that could
not be properly fit to the VR headset, while all of the males fit.
Of the five females whose IPD could not be fit, one had a low
motion sickness history (MHQ 2). This individual had low
sickness immediate post VR exposure (AE1 SSQ TS =14.96)
and recovered completely within 1 h post-VR exposure (AE5
SSQ TS =0). The other four IPD non-fit females had a high
motion sickness history (MHQ >2) and these four females were
profoundly sick immediate post VR exposure (AE1 SSQ TS mean
=74.8; S.D. =48.76) and were not able to recover by AE5 (SSQ
TS mean =67.32; S.D. =55.05). As all males could fit their IPD to
the headset, no effects of IPD non-fit could be assessed for males.
These results suggest that those for which a VR headset cannot be
fit to their IPD and who have a high motion sickness history will
be the most susceptible to cybersickness.
Why would IPD non-fit drive higher levels of cybersickness.
There are plenty of online blogs and developer sites that claim
that a little bit of a blurred image in a VR headset due to
TABLE 7 | Experiment 1 interpupillary distance differences between the genders.
Gender Mean S.D. Min Max HTC vive IPD
Non-fit
F Stat p-value
Females 62.63 3.52 57 70 5/15 5.13 0.031*
Males 65.33 2.99 61 71 0
*p<0.05.
a mismatched IPD is no problem (c.f. SteamVR, 2016, 2018).
Yet, even if the IPD non-fit results in a small loss of visual
acuity, this can have a substantial negative impact (Skrbek
and Petrov, 2013). IPD non-fit can lead to increased fusional
difficulty (Rolland and Hua, 2005), binocular stress, increased
near point convergence, an esophoric (inward) shift in distance
heterophoria, and a drop in visual acuity, as well as asthenopia
(i.e., fatigue, eye pain, blurred vision, double vision, headache,
general malaise, nausea; Mon-Williams et al., 1993; Regan and
Price, 1993; Best, 1996). These adverse effects occur because IPD
non-fit leads to misalignment of the VR headset optics and/or
inappropriate binocular overlap, resulting in perceptual issues.
Regan and Price (1993) found that only those with an IPD less
than the interocular distance (IOD), which refers to the distance
between the optical centers of the lens systems installed in the VR
headset, experienced such visual discomfort, with the greater the
mismatch between the two measures (IPD and IOD) resulting in
greater reported side-effects.
In this study, the IOD or distance between the HTC Vive
lenses was set to coincide with the participant’s IPD whenever
possible. This alignment is anticipated to mitigate misalignment
between optics of the eyes and that of the VR headset. However,
as researchers note, when an alignment cannot be achieved this
will result in viewing the VR headset lens system on an off-center
axis, which will in turn lead to prismatic distortions that drive
eyestrain and visual discomfort (Regan and Price, 1993; Costello,
1997; Peli, 1999; Lee et al., 2008). It is thus not surprising that in
the current study, females experienced higher levels and longer
lasting cybersickness than males, as one third of females had a
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Stanney et al. Virtual Reality Is Sexist
smaller IPD than the VR headset. This mismatch between the
IOD and IPD for woman is not correctible in software as it is
a hardware issue and may lead to a higher likelihood over males
of experiencing the taxing effects of a divergence demand such as
visual fatigue (Costello, 1997).
Theoretical research suggests that the mismatch between
inter-screen distance (ISD) and IOD is a driver of
accommodation-convergence issues (Howarth, 1999). Choosing
the correct eye-point for rendering computer generated graphics
potentially diminishes these negative effects, especially depth
errors, since choosing the correct eye-point will account for
near- or far- field headset screen settings and aligns the center
of the display with the optics and the correct eye-point of the
end-user (Rolland et al., 2004). By adjusting the IPD of the end
user and setting the IPD in the system software, the displays
can be aligned to the eye-point of the participant if the headset
IPD adjustable range allows. However, because the VR headset
did not represent the full range of IPDs of the participants (i.e.,
while all males could properly fit their IPD to the headset, a
third of the females could not be properly fit), there is a potential
that the negative effects reported could be due to other types of
interactions between the technology and rendered images.
If IPD non-fit is the main driver of gender differences
in cybersickness, then females whose IPD fit the VR headset
should experience cybersickness in a manner similar to males.
Specifically, they should experience cybersickness at comparable
levels upon immediate post VR exposure, and then they should
recover at a rate similar to males. To test these assumptions, a
second experiment was run.
EXPERIMENT 2
Materials and Methods
The purpose of Experiment 2 was to determine if females whose
IPD could be fit to the VR headset experienced cybersickness in
a manner similar to males. Based on the results of Experiment
1, it was anticipated that females would experience higher levels
and longer lasting cybersickness than males only when their IPD
could not be fit to the headset; and potentially only when their
IPD was smaller than the IOD. It was also expected that both
females and males with high motion sickness histories would
experience cybersickness at higher levels as compared to those
with low histories.
Participants
Adults aged 18–30 years, balanced between genders participated
in this study. Participants were recruited through a market
research firm. A total of 120 participants were recruited for the
study based on their fit to one of eight experimental groups,
which were defined according to gender (male vs. female), IPD
(fit vs. non-fit), and motion sickness history (low vs. high).
MHQ was defined as follows: Low Motion Sickness History =
MHQ <=2; High Motion Sickness History =MHQ >2. This
research complied with the American Psychological Association
Code of Ethics and was approved by the Institutional Review
Board at Copernicus Group. Informed consent was obtained
from each participant and all participants were compensated for
their time in the experiment. Data from the 30 VR participants
from Experiment 1 were also included in the Experiment 2 data
analysis, and the IPD Fit/Non-Fit and MHQ Low/High were
identified for each Experiment 1 VR participant.
Experimental Design
The experiment was a mixed design, with 2 (gender) ×2 (VR
headset IPD fit type) ×2 (motion sickness history type) between
factors and a 5 (post exposure measurement time) within factor.
Gender types were either male or female. VR headset IPD fit type
was either IPD Fit or IPD Non-Fit. Motion sickness history type
was either Low or High. The post exposure measurement times
were 0, 15, 30, 45, and 60 min.
Beyond the randomization of participants to groups, the
Equipment and Display Content, Procedure, Dependent
Measures, and Data Analysis were the same as in Experiment 1.
One additional Predictor Variable was added to Experiment 2,
which was Exposure Duration. This was added to address any
potential differences in drop-out rates.
Results
Complete datasets from 117 of the 120 participants in
Experiment 2 were obtained and combined with the 30 VR
participants from Experiment 1 to run the ANOVA, providing
a total sample size of 147 participants. The combined data led
to a total of: 40 female IPD Fit participants (19 were low MHQ;
21 were high MHQ), 45 male IPD Fit participants (25 were low
MHQ; 20 were high MHQ), 34 female IPD Non-Fit participants
(15 were low MHQ; 19 were high MHQ), and 28 male IPD Non-
Fit participants (15 were low MHQ; 13 were high MHQ). Thus,
when combining Experiment 1 and 2 data, there were a total of 85
IPD Fit VR participants and 62 IPD Non-Fit VR participants. The
mixed-model ANOVA results revealed significant main effects
for Gender [F(1, 139) =7.36, p=0.008] and MHQ, [F(1, 139) =
5.40, p=0.022], as well as a significant interaction of Gender
×MHQ ×IPD Fit [F(1, 139) =4.24, p=0.008]. The results
revealed that, as expected, females whose IPD fit the VR headset
experienced cybersickness in a manner similar to males (see
Table 8 and Figure 2-Bottom). Specifically, immediately after VR
exposure, the average Recovery SSQ TS levels for those in the
IPD Fit condition were high in both those with a high motion
sickness history (females: AE1 SSQ TS mean =40.25; S.D. =
35.99; males: AE1 SSQ TS mean =32.91; S.D. =36.78) and those
with a low motion sickness history (females: SSQ TS mean =
28.94; S.D. =30.25; males: AE1 SSQ TS mean =23.49; S.D. =
28.98). There were no significant gender differences in the IPD
Fit groups at AE1 (n=85), regardless of motion sickness history
[F(3, 81) =1.029, p=0.384]. All those who could fit their IPD to
the VR headset recovered within 1 h post VR exposure, regardless
of motion sickness history (high motion sickness history females:
AE5 SSQ TS mean =7.48; S.D. =11.47; males: AE5 SSQ TS
mean =5.98; S.D. =13.64; low motion sickness history females:
AE5 SSQ TS mean =4.53; S.D. =19.13; males: AE5 SSQ TS
mean =0.45; S.D. =5.10. There were no significant gender
differences in the IPD Fit groups at AE5 (n=85), regardless of
motion sickness history [F(3, 81) =1.29, p=0.283]; further these
groups had no significant differences from BL at AE5 [t(84) =
1.656, p=0.104].
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Stanney et al. Virtual Reality Is Sexist
TABLE 8 | Experiment 2 self-reported SSQ total score values at baseline (BL),
immediately following exposure (aftereffect; AE1), and 1 h post exposure
(aftereffect; AE5).
Gender IPD Motion
sickness
history
BL- Mean (SD) AE1-Mean (SD) AE5-Mean (SD)
Female Fit Low 1.77(2.89) 28.94(30.25)* 4.53 (19.13)
High 1.96 (3.26) 40.25 (35.99)* 7.48 (11.47)
Non-Fit Low 1.50 (2.76) 46.87(50.17)* 4.99 (9.76)
High 2.76 (3.26) 53.54 (41.08)* 29.53 (48.31)*
Male Fit Low 2.09 (2.87) 23.49 (28.98)* 0.45 (5.10)
High 2.06 (3.09) 32.91 (36.78)* 5.98 (13.64)
Non-Fit Low 0.50 (1.32) 22.19 (22.18)* 1.99 (5.45)
High 3.16 (3.69) 31.65 (32.63)* 0.86 (7.19)
*p<0.05 (different from BL).
Immediately after VR exposure, the adverse effects in those
that had an IPD non-fit were, on average, high in both those
with a high motion sickness history (female AE1 SSQ TS mean
=53.54; S.D. =41.08; male AE1 SSQ TS mean =31.65; S.D.
=32.63) and those with a low motion sickness history (females
AE1 SSQ TS mean =46.87; S.D. =50.17; males AE1 SSQ TS
mean =22.19; S.D. =22.18). There were no significant gender
effects in the IPD Non-Fit groups at AE1 (n=62), regardless
of motion sickness history [F(3, 58) =2.25, p=0.092]. There
was a statistically significant difference in AE5 SSQ TS among
the groups [F(3, 58) =4.19, p=0.009; n=62]. A Tukey post-
hoc analysis showed that these adverse aftereffects persisted long
after VR exposure only for those females with an IPD non-fit and
high motion sickness history (females AE5 SSQ TS mean =29.53;
S.D. =48.31) as compared to females with low motion sickness
history (AE5 SSQ TS mean =4.99, S.D. =9.76, p=0.047),
males with low motion sickness history (AE5 SSQ TS mean =
1.99, S.D. =5.45, p=0.033), or males with high motion sickness
history (AE5 SSQ TS mean =0.86, S.D. =7.19, p=0.034). Both
females (female AE5 SSQ TS mean =4.53; S.D. =19.13) and
males (male AE5 SSQ TS mean =1.99; S.D. =5.45) with an IPD
non-fit and low motion sickness history recovered by the final
measurement period. Females in the IPD Non-Fit, High Motion
Sickness History condition also had, on average, less VR exposure
duration (i.e., tended to drop-out; Mean Exposure Duration
=15.95 min; S.D. =6.42) as compared to both males (Mean
Exposure Duration =20 min; S.D. =0.00, i.e., no dropouts) and
females (Mean Exposure Duration =19.81 min; S.D. =0.544) in
the IPD Non-Fit, Low Motion Sickness History conditions; this
difference was significant [F(7, 139) =2.71, p=0.012].
Table 9 shows the results from the multiple linear regression
analysis from Experiment 2. Of the 120 participants in
Experiment 2, 109 participants had complete data for the
regression analysis. These data were combined with the 26
participants from Experiment 1 with complete data sets for the
regression analysis, providing 135 complete data sets. The first
step in the analysis was the same, the univariate analysis of each
possible predictor variable, with the results mostly replicating the
Experiment 1 findings (i.e., VR and gaming experience, FOV,
IPD fit, female hormonal cycle, state anxiety, ethnicity, aerobic
fitness, and past motion sickness history demonstrated significant
differences between genders and were targeted for inclusion in
the regression analysis), with the addition of EGG (bradygastria)
and exposure duration also demonstrating significant gender
differences (see Table 3) and thus these two additional variables
were targeted for regression analysis inclusion. The variables
excluded from the regression analysis were postural stability,
trait anxiety, migraine susceptibility, and body mass index, which
were the same as Experiment 1, with the addition of field
dependence, all of which were not significantly different between
the genders (see Table 3).
As in Experiment 1, in Experiment 2 Hormonal Cycle
had a very high linear association with Gender, so Hormonal
Cycle was removed from the model, as Gender was correlated
with Recovery SSQ TS but Hormonal Cycle was not (see
Table 9). All other predictor variables targeted for inclusion were
systematically added and removed from the model based on the
F-statistic and multicollinearity until the model could no longer
be significantly improved.
Table 10 shows the results from the multiple linear regression
analysis. The resulting model, which had an R2 =0.322, Adjusted
R2 =0.301, RMSE =20.06, F(4,130) =15.40, p=0.001, was
as follows:
E(Recovery SSQ TS)=28.44 +8.22IPD Fit
+2.62Motion Sickness History
+.421EGG Bradygastria
2.20Exposure Duration
This model suggests that IPD non-fit, motion sickness history,
and bradygastria are positively correlated with cybersickness,
while exposure duration (i.e., how long an individual was able
to remain in VR) is negatively correlated to cybersickness. As in
Experiment 1, IPD non-fit was found to be the most influential
variable, followed by motion sickness history. This model
accounted for 32.2% of the variability in cybersickness. Follow-
up analyses indicated that the model passed the assumptions
of multiple regression including normality and independence
of residuals.
Experiment 2 Summary
Similar to Experiment 1, Experiment 2 found that the
primary driver of cybersickness is IPD non-fit, followed by
motion sickness history. Experiment 2 also found higher EGG
(bradygastria) and higher dropout rates (i.e., lower exposure
duration) associated with higher levels of cybersickness. In terms
of EGG, previous research has indicated that bradygastria is a
correlate of motion sickness (Lang et al., 1999) and changes
to bradygastria immediately precede nausea (Kim et al., 2005;
Dennison et al., 2016); this associated objective physiological
response, in effect, validates the subjective SSQ TS results in
the current study. In terms of exposure duration, increased
cybersickness has been previously associated with higher drop-
out rates (Stanney et al., 1999), and the negative correlation for
exposure duration mirrors this finding. Further, the results from
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Stanney et al. Virtual Reality Is Sexist
TABLE 9 | Experiment 2 predictor variable correlations.
Gender VR
experience
Gaming
experience
FOV IPD fit Hormonal
cycle
State
anxiety
Ethnicity Aerobic
fitness
Motion
sickness
history
Exposure
duration
EGG
bradygastria
Gender
VR experience 0.133
Gaming experience 0.208* 0.168*
FOV 0.127 0.027 0.127
IPD fit 0.083 0.034 0.028 0.147
Hormonal cycle 0.811* 0.117 0.214* 0.048 0.061
State anxiety 0.061 0.062 0.016 0.098 0.005 0.096
Ethnicity 0.060 0.051 0.089 0.035 0.027 0.039 0.108
Aerobic fitness 0.521* 0.092 0.057 0.294* 0.103 0.413* 0.002 0.167*
Motion sickness
history
0.169* 0.138 0.122 0.147 0.115 0.076 0.164* 0.025 0.021
Exposure duration 0.173* 0.039 0.005 0.021 0.012 0.086 0.150 0.091 0.072 0.177*
EGG bradygastria 0.199* 0.007 0.068 0.050 0.031 0.196* 0.067 0.081 0.073 0.041 0.098
SSQ (Dependent) 0.211* 0.048 0.095 0.014 0.192* 0.141 0.061 0.001 0.032 0.360* 0.365* 0.230*
*p<0.05.
TABLE 10 | Summary of multiple linear regression model of cybersickness for
Experiment 1 and Experiment 2 combined data.
Predictor variables in
cybersickness model
βSE t P >|t|
(Intercept) 28.44 10.52 2.70 0.008*
IPD fit 8.22 3.53 2.32 0.022*
Past motion sickness history (MHQ) 2.62 0.696 3.763 0.000*
Exposure duration 2.20 0.511 4.30 0.000*
EGG bradygastria 0.421 0.120 3.51 0.001*
Gender 0.065 0.845 0.400
VR exposure 0.022 0.293 0.770
Gaming experience 0.122 1.66 0.098
Field of view 0.081 1.10 0.271
Female hormonal cycle 0.044 0.589 0.557
State anxiety 0.004 0.050 0.960
Ethnicity 0.006 0.084 0.933
Aerobic fitness 0.032 0.410 0.682
*p<0.05.
Experiment 2 demonstrated that females whose IPD could be fit
to the VR headset experienced cybersickness in a manner similar
to males, while those females who could not be fit experienced
more severe and more persistent cybersickness. For females and
males whose IPD could be fit to the VR headset, they experienced
high levels of cybersickness immediately after VR exposure but
fully recovered within 1 h post exposure, regardless of motion
sickness history (all AE5 SSQ TS not significantly different than
BL; see Table 8 and Figure 3-Top).
In the high motion sickness history conditions, IPD non-fit
did not affect males to the same degree as females, as males were
able to recover to baseline while females were not (see Table 8
and Figure 3-Bottom). This may be because males’ IPD non-fit
FIGURE 3 | Experiment 2 group mean SSQ total score at baseline (BL) and
each aftereffects (AE) measurement period for the IPD fit (Top) and non–fit
(Bottom) and motion sickness history low and high experimental groups.
was not as severe as females’ and a greater degree of mismatch has
been associated with more severe adverse effects (Mon-Williams
et al., 1993; Regan and Price, 1993; Best, 1996). In general, all
but one female in the IPD Non-Fit condition had an IPD smaller
than the adjustable IPD range, and the average IPD of this group
was 57.52 mm (S.D. =1.77). All but two males in the IPD Non-
Fit condition had an IPD smaller than the adjustable IPD range,
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Stanney et al. Virtual Reality Is Sexist
and the average IPD of this group was 58.87 mm (S.D. =1.14).
There was a significant difference [F(1, 60) =7.48, p=0.008],
in the severity of the IPD non-fit between males and females (n
=62), with females having a more severe non-fit. This greater
degree of IPD non-fit was associated with a significantly higher
level of cybersickness for females vs. males immediately following
VR exposure (see Tables 5,8and Figures 2,3).
GENERAL DISCUSSION
This study sought to identify the main drivers of gender
differences associated with the adverse effects of VR exposure.
Two experiments were conducted, the first to investigate the
many variables that could contribute to gender differences and
the second to validate and further explore the findings of the first.
In both experiments, IPD non-fit was found to be the main driver
of cybersickness, with motion sickness history a secondary driver.
Interpupillary Distance
Quite interestingly, it was not an inherent characteristic of
females but rather a characteristic of the VR headset itself, IPD
non-fit, that was found to be the primary driver of cybersickness
in both experiments. To properly view objects in a virtual
environment, most VR headsets have a variable IPD range that
allows an individual to align the center of their pupils with
the center of the VR lenses. Any deviation between IPD and
IOD can cause a host of visual issues, as well as asthenopia
(Mon-Williams et al., 1993; Regan and Price, 1993; Best, 1996;
Rolland and Hua, 2005). To resolve this issue and allow both
females and males to be able to properly center their pupils to
the lenses, the IPD range needs to be adjustable from 50 to
77 mm (Dodgson, 2004; Gordon et al., 2014). As can be seen in
Table 2, the Sony PlayStation headset accommodates this range
but many other VR headsets on the market today do not (e.g.,
Samsung Gear VR, Oculus Rift, Oculus Rift S, Oculus Quest,
HTC Vive, HTC Vive Pro). Another option is to custom fit
VR headsets to an individual, much like eyeglasses (Luckey,
2019). Software IPD adjustment helps with scale issues but does
not address issues with hard to fuse imagery, blurry images,
distortion, or VOR mismatches (Luckey, 2019), thus it may not
be the panacea many believe it to be. Other possible alternatives
to resolve this issue include gaze-contingent and adaptive focus
displays (Padmanaban et al., 2017), yet these solutions still pose
challenges to the human visual system (Rolland et al., 2000;
Mercier et al., 2017). Until such modifications to VR headsets
are made, females will be at a particular disadvantage with regard
to cybersickness because in general the IPD range in current VR
headsets accommodates substantially fewer females as compared
to males (see Table 2) and their IPD mismatch will likely be more
severe than males.
Beyond widening the IPD range, there are a number of
design parameters that need to be considered to ensure the
design of VR headsets better accommodates human physiology.
Specifically, Robinett and Rolland (1992) noted that VR headsets
are cross compared using engineering techniques that use a
reduced eye model that sets average human constraints based on
male specific measures, such as performing tests using a static
TABLE 11 | VR headset parameters compared to human visual system.
VR headset parameters*
(typical values)
Human visual system
(typical values)
Monocular field of view:
20–65
Binocular field of view:
20–110
Monocular field of view: 160horizontal
Binocular field of view: 200horizontal
Image quality: 2.0–4.1 arc
min
Visual acuity: .8 arc min in fovea (20/20 Snellen)
Exit pupil: 12 mm Pupil: 2–10 mm depends on lighting conditions
Luminance range: 50–1,000
cd/m2
Brightness: Scoptic 106to 3 cd/m2
Photopic >3 cd/m2
Image focus: Fixed Accommodation: Dynamic and Cornea +Lens
have a combined power of 58.6 Diopters
Eye relief ( ER): 15–50 mm Eyeglasses imposed ER 17 mm
Optical separation between
L and R lenses, 58–72mm,
widest range available**
based on IPD of user
Interpupillary eye distance (IPD): distance
between the two eyes, 50–77 mm for 95% of
Asians, Caucasians, and African Americans
*VR headset parameters adapted from Rolland and Hua (2005) and Cakmakci and Rolland
(2006).**see Table 2.
IPD of 64 or 65 mm (note, Male mean IPD is 64.0 mm [S.D.
=3.4 mm]; Gordon et al., 2014). These model simplifications
ignore performance limitations compared to the human eye
(e.g., FOV, resolution). Specifically, the VR headset parameters of
resolution, image focus, contrast, brightness, and frame rate are
interdependent parameters of a VR headset that affect viewability
of complex, dynamic VR imagery. At the same time, the human
eye is an optical system that is functionally limited much like the
VR headset in such parameters as display resolution and image
quality. Clearly explicating these limitations and avoiding making
display choices that do not match human visual capabilities (see
potential mismatches in Table 11) will reduce cybersickness. By
understanding these challenges, VR headset design can be much
improved to better accommodate the human visual system.
The results of Experiment 2 suggest that if an individual’s
IPD can be properly fit to the VR headset, gender differences in
cybersickness are not expected (see Table 8 and Figure 3-Top).
Cybersickness is still expected, as was experienced in Experiment
2 (see AE1 in Table 8) due to visual-vestibular mismatches
(Reason and Brand, 1975; Oman, 1998) and vergence-
accommodation conflict (Szpak et al., 2019). Specifically, if
designers create content with a great deal of vection (Webb and
Griffin, 2003) associated with high levels of visual-vestibular
mismatches and/or content with a large conflict between
vergence and focal distances (Hoffman et al., 2008), these
conflicts are expected to precipitate sickness (see Figure 4).
Based on the results of Experiment 2, sickness levels upon
immediate post VR exposure are expected to be higher in those
with a high motion sickness history. However, regardless of
motion sickness history, these adverse effects are expected to
dissipate once adaptations (e.g., avoiding visual dominance,
adopting postural control strategies such as through active
viewpoint control, and cuing off a rest frame to minimize visual-
vestibular mismatches) and habituation with repeat exposures
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Stanney et al. Virtual Reality Is Sexist
FIGURE 4 | Mechanisms of cybersickness.
kick-in (see Figure 4), as was experienced in Experiment 2 (see
AE5 in Table 8), and in proportion to exposure duration for
those individuals that can properly fit their IPD (Kennedy et al.,
2000; Murata, 2004). Note that the VR content used in this
study was designed to induce cybersickness by creating content
that was intended to be provocative. Yet, even with potent VR
content, the results from Experiment 2 demonstrated that when
the IPD could be properly fit to the VR headset, both males
and females recovered from the adverse effects of VR exposure
within 1 h post VR exposure, regardless of motion sickness
history (see Figure 3-Bottom). It is only when an individual
has the provoking factor of an IPD that cannot be properly fit,
specifically when the IPD of an individual is smaller than the
IOD, and the individual has the predisposing factor of a high
motion sickness history that the individual is expected to enter
a perpetuating loop that does not allow cybersickness recovery
and habituation (see Figure 4).
Motion Sickness History
Many individuals experience motion sensitivity during activities
such as reading when being a passenger in an automobile (Turner
and Griffin, 1999), riding on a boat (Lawther and Griffin, 1986;
1988; Cooper et al., 1997), riding on a train (Kaplan, 1964),
and flying (Lederer and Kidera, 1954; Turner et al., 2000),
with these activities leading to feelings of dizziness, general
malaise, nausea, blurry vision, and other such adverse effects.
Individuals with such a history of motion sensitivity may be
more susceptible to cybersickness in virtual environment than
those without such history. Motion sensitivity has been suggested
to be caused by vestibular dysfunction (Akin and Davenport,
2003) and/or an over-reliance on the visual system with a
residual deficit of the vestibular system (Akiduki et al., 2003).
Daily, short (5 min) vestibular adaptation exercises in those
with vestibular dysfunction have been shown to be effective
in reducing symptomatology (Alyahya et al., 2016). In fact,
habituation (i.e., desensitization with repeat exposures) has been
suggested to be the most effective countermeasure to motion
sickness, even more so than anti-motion sickness drugs (Cowings
and Toscano, 2000). Specifically, over repeat exposures to VR
environments, habituation may occur in which symptomatology
decreases (Kennedy and Graybiel, 1965; Biocca, 1992; McCauley
and Sharkey, 1992; Regan, 1995; Domeyer et al., 2013; Welch,
2014). Habituation is oftentimes highly effective (Golding, 2017),
perhaps as high as 85% effective (Benson, 1999). Habituation
protocols would involve designing VR applications with stepwise
increments in stimulus intensity coupled with frequent exposures
of slowly increasing duration, which may allow motion sensitive
individuals to acclimate to the experience, enable initial faster
recovery and more sessions to be tolerated. Based on Welch
(2014), important elements of a habituation protocol would
include: (1) active (not passive) interaction within the VR
environment, coupled with the visual consequences associated
with these actions (reafference), (2) immediate feedback to this
interaction (any transport delays, response lags, etc. will hinder
adaptation, however, if these lags are consistent, then adaptation
may still be achieved), (3) incremental (rather than massed)
exposure, with progressive VR stimulus strength (e.g., start with
mild, slow movements, constant velocity, etc.), and (4) the
use of distributed practice (e.g., 2–5 day intersession intervals).
However, based on the results of these studies, should the VR
headset pose an IPD non-fit, such habituation protocols may not
prove effective.
If IPD fit is achieved, such habituation protocols hold great
promise in addressing gender differences, as females are generally
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Stanney et al. Virtual Reality Is Sexist
more disposed, as compared to males, to benefit from such
conditioning countermeasures (Rohleder et al., 2006; Stockhorst
et al., 2007). For example, Rohleder et al. (2006) demonstrated
that physiological habituation to a repetitive rotation experience
was demonstrated only in females via habituation of a rotation-
induced cortisol response, whereas males continued to show
cortisol sensitivity. Thus, even though females oftentimes report
being more highly susceptible to motion sickness than males
(Lentz and Collins, 1977; Park and Hu, 1999; Dobie et al., 2001;
Graeber and Stanney, 2002; Stanney et al., 2003; Wilson and
Kinsela, 2017), and this susceptibility may lead to higher levels
of cybersickness in virtual environments, susceptibility difference
for both females and males can be counteracted via appropriate
habituation practices.
It is also interesting to note that when IPD was properly fit,
even those with a high motion sickness history could recover
within 1 h post exposure (see Figure 3-Top). Thus, the impact of
motion sickness history is not as profound as that of IPD non-fit,
which the regression model confirmed.
Limitations
Given the vast range of motion sensitivity in the general
population, which varies by about 10-1 (Lackner, 2014), a larger
sample would have been desirable. Further, while the SSQ
(Kennedy et al., 1993) is a standard measure of motion sickness
that has been used for decades (Bulk et al., 2013), future research
should add objective measures of the adverse aftereffects of VR
exposure to confirm subjective reports of cybersickness, e.g.,
measures of ataxia, VOR shift, kinesthetic position sense shift
(Kennedy et al., 1998).
CONCLUSIONS
In summary, Experiment 1 identified that IPD non-fit is
a primary driver of gender differences in cybersickness.
Experiment 2 confirmed this finding and further demonstrated
that when an individual’s IPD could be properly fit to the
VR headset, females experienced cybersickness in a manner
similar to males, with high levels immediately post VR exposure
and recovery within 1 h post exposure following a 20 min
provocative VR exposure. As more females were unable to
properly fit their IPD to currently available VR headsets, and
any IPD non-fit experienced was more extreme in females
than males, VR technology was indeed found to be sexist,
but it does not have to be. If VR headset manufacturers
implement an IPD adjustable range of 50 to 77 mm to
capture >99% of both females and males, it is anticipated
that a far greater number of females will be able to harness
the performance enhancing potential of VR technology. In
addition, motion sickness susceptibility contributes to higher
levels of cybersickness and this can be counteracted via
habituation protocols.
DATA AVAILABILITY STATEMENT
The datasets generated for this study are available on request to
the corresponding author.
ETHICS STATEMENT
The studies involving human participants were reviewed and
approved by Copernicus Group. The participants provided their
written informed consent to participate in this study.
AUTHOR CONTRIBUTIONS
KS conducted the literature review, designed the experiments,
directed the study, and was the lead author of the paper. CF
led the data analytics. LF reviewed and provided feedback on all
aspects of this research.
FUNDING
The authors declare that this study received funding from
Lockheed Martin Corporation. The funder was not involved in
the study design, collection, analysis, interpretation of data, the
writing of this article or the decision to submit it for publication.
The funder did review the manuscript prior to publication.
ACKNOWLEDGMENTS
The authors would like to thank Erin Baker for helping to run the
study, Charles Ortiz for developing the virtual rollercoaster, Scott
Christian, Peyton Bailey, Caroline Bates, Haden Eckbert, and
Jason English for their assistance in conducting the experiment,
David Campbell for his support of the measurement tools, and
Joanna Chiang for assistance with data analysis. Much gratitude
is also extended to Robert S. Kennedy for his mentorship in
the area of motion sickness and beyond throughout the past
several decades.
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Conflict of Interest: KS and CF are employed by Design Interactive, Inc. LF is
employed by Lockheed Martin Corporate.
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Frontiers in Robotics and AI | www.frontiersin.org 19 January 2020 | Volume 7 | Article 4
... In several studies, sex differences in susceptibility did not reach statistical significance (Munafo et al., 2017, Experiment 1;Clifton and Palmisano, 2020;Curry et al., 2020). In part for this reason, the true extent of sex differences in cybersickness is the subject of lively debate (e.g., Grassini and Laumann, 2020;Stanney et al., 2020a;Stanney et al., 2020b). Additional research is needed to understand the circumstances under which cybersickness differs between women and men (Munafo et al., 2017;Curry et al., 2020). ...
... 2.2.2 Prevention through hardware re-design 2.2.2.1 Inter-pupillary distance Stanney et al. (2020a), suggested that cybersickness in HMDs may be related to poor fit of the HMD headsets, in terms of interpupillary distance. They noted that existing headsets often cannot be set at an inter-pupillary distance that is appropriate for many female users. ...
... 04 measured before users donned an HMD that induced sex differences in cybersickness (see Section 4.1.2). It is not clear whether the hypothesis of Stanney et al., 2020a, can explain such effects. More broadly, it remains to be seen whether reengineering of headset ergonomics can yield significant reduction in cybersickness. ...
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In this article, we discuss general approaches to the design of interventions that are intended to overcome the problem of cybersickness among users of head-mounted display (HMD) systems. We note that existing approaches have had limited success, and we suggest that this may be due, in part, to the traditional focus on the design of HMD hardware and content. As an alternative, we argue that cybersickness may have its origins in the user’s ability (or inability) to stabilize their own bodies during HMD use. We argue that HMD systems often promote unstable postural control, and that existing approaches to cybersickness intervention are not likely to promote improved stability. We argue that successful cybersickness interventions will be designed to promote stability in the control of the body during HMD use. Our approach motivates new types of interventions; we describe several possible directions for the development of such interventions. We conclude with a discussion of new research that will be required to permit our approach to lead to interventions that can be implemented by HMD designers.
... The only individuals who experienced significant side effects were women. This result seems to be corroborated by the literature [23,34]. Analysis of the different SSQ items seems to show a vision effect [35]. ...
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Background and objective: Duration of rehabilitation and active participation are crucial for gait rehabilitation in the early stage after stroke onset. Virtual reality (VR) is an innovative tool providing engaging and playful environments that could promote intrinsic motivation and higher active participation for non-ambulatory stroke patients when combined with robot-assisted gait training (RAGT). We have developed a new, fully immersive VR application for RAGT, which can be used with a head-mounted display and wearable sensors providing real-time gait motion in the virtual environment. The aim of this study was to validate the use of this new device and assess the onset of cybersickness in healthy participants before testing the device in stroke patients. Materials and Methods: Thirty-seven healthy participants were included and performed two sessions of RAGT using a fully immersive VR device. They physically walked with the Gait Trainer for 20 min in a virtual forest environment. The occurrence of cybersickness, sense of presence, and usability of the device were assessed with three questionnaires: the Simulator Sickness Questionnaire (SSQ), the Presence Questionnaire (PQ), and the System Usability Scale (SUS). Results: All of the participants completed both sessions. Most of the participants (78.4%) had no significant adverse effects (SSQ < 5). The sense of presence in the virtual environment was particularly high (106.42 ± 9.46). Participants reported good usability of the device (86.08 ± 7.54). Conclusions: This study demonstrated the usability of our fully immersive VR device for gait rehabilitation and did not lead to cybersickness. Future studies should evaluate the same parameters and the effectiveness of this device with non-ambulatory stroke patients.
... There have been contrasting reports regarding gender differences with females being more susceptible to cybersickness, but recent findings have begun to suggest that this may have been due to improper fitting of the HMDs to the inter-pupillary distance (IPD) of females, as well as differences in sensitivity to motion parallax and 3D visual acuity (Allen et al., 2016;Fulvio et al., 2021;Stanney et al., 2020). Another interesting factor is gaming experience, in which it appears that people with more gaming experience may be less susceptible and recover faster from sickness (Curry et al., 2020;da Silva Marinho et al., 2022). ...
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The malaise symptoms of cybersickness are thought to be related to the sensory conflict present in the exposure to virtual reality (VR) content. When there is a sensory mismatch in the process of sensory perception, the perceptual estimate has been shown to change based on a reweighting mechanism between the relative contributions of the individual sensory signals involved. In this study, the reweighting of vestibular and body signals was assessed before and after exposure to different typical VR experiences and sickness severity was measured to investigate the relationship between susceptibility to cybersickness and sensory reweighting. Participants reported whether a visually presented line was rotated clockwise or counterclockwise from vertical while laying on their side in a subjective visual vertical (SVV) task. Task performance was recorded prior to VR exposure and after a low and high intensity VR game. The results show that the SVV was significantly shifted away from the body representation of upright and towards the vestibular signal after exposure to the high intensity VR game. Cybersickness measured using the fast motion sickness (FMS) scale found that sickness severity ratings were higher in the high intensity compared to the low intensity experience. The change in SVV from baseline after each VR exposure modelled using a simple 3-parameter gaussian regression fit was found to explain 49.5% of the variance in the FMS ratings. These results highlight the aftereffects of VR for sensory perception and suggests a potential relationship between the susceptibility to cybersickness and sensory reweighting.
... Until now, side effects such as nausea, vertigo, and impaired coordination have been considered objective physiological aspects of simulation disorder, in contrast with vection as a subjective phenomenon [15]- [18]. ...
... A second limitation is related to the gender distribution of the Estonian sample, in which 93% of the participants were women. Currently, the relationship between gender and subjective variables during VR experiences is debated, with some works reporting no differences and some others showing that females are more susceptible to sickness and more prone to feel present [66,67]. Nonetheless, in the previous experience, we had with the Virtual Supermarket, we found no differences depending on gender, with the only exception of spatial presence and only in the group with subjective cognitive decline. ...
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Mild cognitive impairment (MCI) is an early stage of cognitive abilities loss and puts older adults at higher risk of developing dementia. Virtual reality (VR) could represent a tool for the early assessment of this pathological condition and for administering cognitive training. This work presents a study evaluating the acceptance and the user experience of an immersive VR application representing a supermarket. As the same application had already been assessed in Italy, we aimed to perform the same study in Estonia in order to compare the outcomes in the two populations. Fifteen older adults with MCI were enrolled in one Rehabilitation Center of Estonia and tried the supermarket once. Afterwards, they were adminis