thesis virtual reality

•   Home
• University of Bedfordshire e-theses
• Masters e-theses

A study on virtual reality and developing the experience in a gaming simulation

Description

Collections.

The following license files are associated with this item:

• Creative Commons

entitlement

Export search results

The export option will allow you to export the current search results of the entered query to a file. Different formats are available for download. To export the items, click on the button corresponding with the preferred download format.

By default, clicking on the export buttons will result in a download of the allowed maximum amount of items.

To select a subset of the search results, click "Selective Export" button and make a selection of the items you want to export. The amount of items that can be exported at once is similarly restricted as the full export.

After making a selection, click one of the export format buttons. The amount of items that will be exported is indicated in the bubble next to export format.

Advertisement

A narrative review of immersive virtual reality’s ergonomics and risks at the workplace: cybersickness, visual fatigue, muscular fatigue, acute stress, and mental overload

• Original Article
• Open access
• Published: 16 July 2022
• Volume 27 , pages 19–50, ( 2023 )

Cite this article

You have full access to this open access article

• Alexis D. Souchet   ORCID: orcid.org/0000-0003-4885-1392 1 , 2 ,
• Domitile Lourdeaux 1 ,
• Alain Pagani 3 &
• Lisa Rebenitsch 4  

16k Accesses

43 Citations

138 Altmetric

17 Mentions

Explore all metrics

This narrative review synthesizes and introduces 386 previous works about virtual reality-induced symptoms and effects by focusing on cybersickness, visual fatigue, muscle fatigue, acute stress, and mental overload. Usually, these VRISE are treated independently in the literature, although virtual reality is increasingly considered an option to replace PCs at the workplace, which encourages us to consider them all at once. We emphasize the context of office-like tasks in VR, gathering 57 articles meeting our inclusion/exclusion criteria. Cybersickness symptoms, influenced by fifty factors, could prevent workers from using VR. It is studied but requires more research to reach a theoretical consensus. VR can lead to more visual fatigue than other screen uses, influenced by fifteen factors, mainly due to vergence-accommodation conflicts. This side effect requires more testing and clarification on how it differs from cybersickness. VR can provoke muscle fatigue and musculoskeletal discomfort, influenced by fifteen factors, depending on tasks and interactions. VR could lead to acute stress due to technostress, task difficulty, time pressure, and public speaking. VR also potentially leads to mental overload, mainly due to task load, time pressure, and intrinsically due interaction and interface of the virtual environment. We propose a research agenda to tackle VR ergonomics and risks issues at the workplace.

Similar content being viewed by others

Potentials and Limitations in the Application of Virtual Ergonomics: A Review of Empirical Studies

Factors of Cybersickness

A meta-analysis of the virtual reality problem: Unequal effects of virtual reality sickness across individual differences

Avoid common mistakes on your manuscript.

1 Introduction

There is increasing consideration of replacing parts of current work involving a PC (using mouse and keyboard) with immersive virtual reality (VR) by both industry and scientists (Filho et al. 2018 , 2019 , 2020 ; Guo et al. 2019a ). Democratization of nomad and remote work is a common argument for office-like experiences in VR (Grubert et al. 2018 ; Ofek et al. 2020 ). Literature contributions started to analyze the transposition to VR from a PC for various tasks. This includes spreadsheets (Gesslein et al. 2020 ), text entry (Knierim et al. 2018 ; Speicher et al. 2018a ), editing and proofreading (Kim and Shin 2018 ; Li et al. 2021 ), reading (Baceviciute et al. 2021 ; Rzayev et al. 2021 ), coding (Castelo-Branco et al. 2021 ), and information retrieval (Schleußinger 2021 ). A growing number of contributions present VR with possible benefits in various fields. However, many scientific challenges remain for VR daily adoption in office-like tasks. One of them is health and safety implications (LaViola et al. 2017 ; Fuchs 2018 ; Khakurel et al. 2018 ; Çöltekin et al. 2020 ; Olson et al. 2020 ; Anses 2021 ; Ens et al. 2021 ). Virtual environment creators and head-mounted display (HMD) manufacturers do not seem to consider human factors/ergonomics enough in their design processes (Dehghani et al. 2021 ; Saghafian et al. 2021 ; Szopa and Soares 2021 ). However, virtual reality-induced symptoms and effects (VRISE) have been documented for more than thirty years (Kennedy et al. 1993 ; Keller and Colucci 1998 ; Cobb et al. 1999 ; Nichols 1999 ; Nichols and Patel 2002 ; Sharples et al. 2008 ; Melzer et al. 2009 ; Fuchs 2017 , 2018 ; Souchet 2020 ; Grassini and Laumann 2021 ). Contributions about VR use for office-like tasks rarely mention or assess possible VRISE risks.

The EU-OSHA has already identified VRISE risks in a brochure on digitalization (EU-OSHA 2019 ) . VRISE are real problems that elicit a negative user experience (Somrak et al. 2019 ; Lavoie et al. 2020 ). Concretely, workers might become sick or suffer from side effects, possibly reducing their task performance (Mittelstaedt et al. 2019 ; Mittelstaedt 2020 ; Park et al. 2021 ). Although the contradicting results show no significant correlation between cognitive performance and VRISE, such as cybersickness (Varmaghani et al. 2021 ), there is a definite need to consider possible VR side effects in everyday work to anticipate normalization or future regulation guidelines. Above all, designers and employers should safeguard workers’ health and safety if they use VR. This can only be achieved if all stakeholders know VR benefits and risks.

We concentrate on typical office-like tasks workers mainly fulfill with a PC. Usually, previous works would concentrate on one possible VRISE at a time. However, possible confusions are maintained between cybersickness (visually induced motion sickness) and visual fatigue. Moreover, other possible issues such as muscle fatigue, acute stress, and mental overload are rarely considered when measuring VRISE. Chen et al. ( 2021 ) gather several ergonomic risks of HMDs, but they do not comprehensively propose a general approach and an exhaustive list of influencing factors. Stanney et al. ( 2020b ) focused on cybersickness and did not separate it from other VRISE.

This article considers five specific risks: cybersickness, visual fatigue, muscular fatigue, acute stress, and mental overload. It is worth noting that we mainly concentrate on acute symptoms, not chronic ones, based on repeated VR use since very few studies directly address medium- to long-term side effects. Yet, Howarth and Hodder ( 2008 ) found that 50% of users no longer had any symptoms after ten sessions every two to seven days. Our purposes are to.

catalog the main VR ergonomic risks at work by referring to recent publications with new HMD generation

point the distinction between cybersickness and visual fatigue

consider other risks than cybersickness

better inform VR users and designers about the risks inherent in this technology if they want to introduce it at the workplace

The article is structured as follows. First, we describe the method to search previous works. Second, we present the results from this search on cybersickness, visual fatigue, muscle fatigue, acute stress, and mental overload. For each VRISE, we draw an overview and occurrence description based on previous meta-analyses, systematic reviews, or overviews when available. For each VRISE, we propose a synthesis of possible factors evoked in the literature as inducing symptoms. The review encouraged us to add a disambiguation section in virtual reality relating to cybersickness. Then, we introduce works assessing office-like tasks in VR about each VRISE that can help better gauge risks. Third, we discuss the results about each VRISE, the limitations of our review, and provide a research agenda for theoretical and experimental works that better define, quantify, and distinguish each ergonomic risk.

2.1 Keywords and database

Initial papers’ selection was made in August 2021 based on the following keywords for each VRISE: “cybersickness” OR “visually induced motion sickness,” “visual fatigue” OR “eyestrain,” “muscle fatigue” OR “musculoskeletal discomfort,” “stress” OR “acute stress,” “mental workload” OR “cognitive load, AND “Virtual reality” AND “work” AND “meta-analysis” OR “Systematic review” OR “Review” in Google Scholar. We used the same keywords listed above to document VRISE at work without adding meta-analysis, systematic review, or review.

2.2 Inclusion and exclusion criteria

Papers were included if.

Published between 2016 and 2021

Language used was English and French

Peer-reviewed or grey literature written by scientists

Mentioning one searched keyword at least in whether the title, abstract or list of keywords

Using HMDs available for the general public

Papers were excluded if.

Using other languages than English or French

Not mentioning keywords, at least in whether the title, abstract, or list of keywords

Not using off-the-shelf HMDs

Subjects in experiments were children

Stimuli are video games that require interactions or stimuli too far from office-like tasks in VR

2.3 Search strategy

We concentrated on articles published between 2016 and 2021. 2016 has been selected as the starting date of literature research because it corresponds to Oculus CV1’s commercial release, making the HMD widely accessible for labs and allowing overview contributions to incorporate new generation HMDs. If the latest review, systematic review, or meta-analysis was published before 2016, we augmented the range to years before 2016 until finding a result. When the latest meta-analysis, systematic review, or review paper was found, we extracted information to write our overview of VRISE and its occurrence. If no meta-analysis, systematic review, or review existed, we draw factors from individual papers proposing them. The procedure described here, including the criteria, mostly refers to the description of VR in the work environment. The general presentation of VRISE mostly mixes latest review papers and older contributions.

3.1 Cybersickness

3.1.1 cybersickness overview.

The following symptoms characterize cybersickness: visual fatigue, headache, pallor, sweating, dry mouth, full stomach, disorientation, dizziness, ataxia (movements coordination), nausea, and tiredness (Lawson 2014 ; Davis et al. 2014 ; Rebenitsch and Owen 2016 ; Bockelman and Lingum 2017 ; Nesbitt and Nalivaiko 2018 ; Descheneaux et al. 2020 ; Chang et al. 2020 ). During the first popularization phase of HMDs in the 90 s, there was optimism about the ability to “cure” cybersickness (Biocca 1992 ). However, thirty years later, the issue still exists, as documented by the latest overviews (Stanney et al. 2020b ). Cybersickness arises no matter the HMD (Yildirim 2020 ). Therefore, despite HMD technical improvements, cybersickness is not likely to disappear anytime soon (Gallagher and Ferrè 2018 ). However, the current HMD generation seems to cause fewer risks than previous generations (Caserman et al. 2021 ).

Several competing theories exist to explain and predict the cybersickness phenomenon: sensory conflict, evolutionary, ecological (postural instability), and multisensory reweighting (Palmisano et al. 2020a ; Stanney et al. 2020b ). The sensory conflict theory of motion sickness (or sensory cues conflict) is the most widely accepted (Lackner 2014 ; Stanney et al. 2020b ). According to this theory, passive movement creates a mismatch between information relating to orientation and movement, provided by the visual and the vestibular systems (Colman 2009 ) .

As Watt (1983) recalls, Reason ( 1978 ) explains that motion sickness results from a mismatch between predicted and actual sensory inputs in his theory description. Constancy is disturbed within the virtual environment due to sensorimotor conflicts (Patterson et al. 2006 ; Patterson 2009 ). “Sensorimotor” represents sensory and motor elements necessary for an individual to interact with their environment (Ehrenbrusthoff et al. 2018 ). Most conflicts in virtual environments are visually induced (Rebenitsch and Owen 2016 ). Our “probabilistic brain” (Pouget et al. 2013 ), which seems to rely on predictive computation to perceive, process, and interact with the natural environment (Diaz et al. 2013 ; Van den Berg et al. 2015 ; Mahani et al. 2017 ; Alais and Burr 2019 ; Walsh et al. 2020 ), faces inconsistent and unreliable cues from virtual environments. An alternative explanation is that our brain, via error minimization, could also reweigh each sensory signal (Gallagher and Ferrè 2018 ) to reduce unpredictability. The main criticism of sensory cue conflict is that the theory is not falsifiable (Stanney et al. 2020b ).

The evolutionary theory states that the resulting illness is derived from prior evolutionary adaptation to the effects of poison (Treisman 1977 ; Stanney et al. 2020b ). The body essentially misinterprets the symptoms caused by inconsistent cues as poison.

The ecological theory states that cybersickness is due to the body's inability to compensate for its posture given the external stimuli properly. An increase in deviation from ideal posture is thought to indicate more significant illness. The primary criticism of the ecological theory is that the severity and type of postural instability vary across VR environments (Munafo al . 2017 ), and illness may occur with no instability (Dennison and D’Zmura 2017 ).

However, the exact psycho-physiological causes and the most parsimonious theories are not sufficient to explain cybersickness (Davis et al. 2014 ; Nesbitt and Nalivaiko 2018 ; Weech et al. 2018 ; Descheneaux et al. 2020 ; Stanney et al. 2020b ; Howard and Van Zandt 2021 ). Therefore, the actual models describing and explaining cybersickness remain under debate.

Two aspects of cybersickness research continue to cause controversy:

A unifying theory is still missing. Hence, more contributions under each competing prediction are needed.

Various strategies exist to tackle cybersickness’s prediction. To deploy and assess each strategy, objective and subjective measurements are necessary.

Cybersickness is one concern for workers using VR. Hereafter, we describe cybersickness occurrence based on the current state of the art.

3.1.2 Cybersickness occurrence

According to Stanney et al. ( 2020b ), at least one-third of users will experience discomfort during VR usage, and 5% will present severe symptoms with the current HMD generation. Although, in some contexts, it can be up to 80% (Kim et al . 2005 ). Rebenitsch and Owen ( 2021 ), following Laviola ( 2000 ) and Davis et al. ( 2014 ), list three types of factors affecting VR experience and cybersickness. See Table 1 for an organized list of the factors.

We rearranged Rebenitsch and Owen’s ( 2021 ) factors into individual (demographics in their contribution), hardware (former device factor), and software categories (former task factor) compared to Davis et al. ( 2014 ) in Table 1 . According to Rebenitsch and Owen ( 2021 ), at least fifty factors could influence cybersickness. It should be noted that perceptual style, which is listed under mental attributes in individual factors (see Table 1 ), is linked to learning style and is criticized as a neuromyth (Willingham et al. 2015 ; Kirschner 2017 ). The documented higher risks of symptoms in women, which is the Gender factor listed in Individual factors (see Table 1 ), in past works could be due to the general ergonomics of current HMDs and higher average motion sickness susceptibility (Stanney et al. 2020a ). However, there is no consensus about gender differences as data acquired by previous works are questionable (Grassini and Laumann 2020 ; MacArthur et al. 2021 ).

Latency or lag, listed under Screen in Hardware factors (see Table 1 ), can impact cybersickness. However, to date, the magnitude is still unclear as experiments are drastically varying (latency measures, paradigms, etc.) (Stauffert et al. 2020 ). Rebenitsch and Owen ( 2021 ) also point out that the initial lag factor had been determined with old apparatuses and argue that it is less likely to occur with new HMDs due to better performances.

Cybersickness increases with exposure time (Dennison et al. 2016 ). The duration factor, listed under experience in individual factors (see Table 1 ), is widely pointed out as one of the main contributors to cybersickness in appearance and magnitude (Dużmańska et al. 2018 ).

Standing rather than sitting increases the chances of provoking cybersickness (Merhi et al . 2007 ), as Rebenitsch and Owen ( 2021 ) mentioned. Therefore, defining whether users should use VR applications while sitting is necessary for work purposes.

Other factors than the list by Rebenitsch and Owen ( 2021 ), mainly individual, might influence cybersickness (Howard and Van Zandt 2021 ). Here is a list pointing at individual factors that could also influence cybersickness (pathologies, neurodiversity):

Emotional personalities reported the highest oculomotor, disorientation, and VR sickness scores (Widyanti and Hafizhah 2021 )

Smoking seems to be a predictor of cybersickness in highly stressed people (Kim et al. 2021 )

Insomnia seems to impact vestibular, oculomotor, and interoceptive functions, leading to more visually induced motion sickness (Altena et al. 2019 ).

Autism spectrum disorders might cause users to suffer from higher adverse symptoms (Schmidt et al. 2021 ).

Multiple sclerosis could affect people differently from the general population as these users have balance impairments (Ferdous et al. 2018 ), less alertness, more stress, and possibly lower attention (Arafat et al. 2018 ).

Age-related macular degeneration (arising starting at 50) seems to result in an increased rate of less perceived vection strength, and those with early manifest glaucoma reported lower perceived vection strength but also lower cybersickness than the “normal” population (Luu et al. 2021a ).

Alcohol (intoxication at a blood alcohol level of approximately 0.07%) seems to alleviate cybersickness (Iskenderova et al. 2017 ).

Prior information or a questionnaire about cybersickness can provoke priming or anchoring effects (Furnham and Boo 2011 ; Weingarten et al. 2016 ; Doherty and Doherty 2018 ). Users report more side effects when expecting them (Almeida et al. 2018 ).

Even though short-term side effects of VR are well known, impacts on cognition and long-term effects are yet to be documented. However, based on questionnaires like the simulator sickness questionnaire (Sevinc and Berkman 2020 ; Hirzle et al . 2021 ), physiological changes that correlate to subjective reports have been documented by Gallagher and Ferrè ( 2018 ). According to Gallagher and Ferrè, cybersickness influences psycho-physiological variables that can be measured with ECG (Electrocardiography, heart), EDA (Electrodermal activity, skin), or EEG (Electroencephalography, brain). Blink rate increase with exposure time and cybersickness (Lopes et al. 2020 ). Therefore, we can also add to Gallagher and Ferrè’s ( 2018 ) list the incidences on the visual system.

During and after VR exposure, users report symptoms correlated with psycho-physiological changes. Rebenitsch and Owen's ( 2021 ) list of factors influencing cybersickness to go beyond classic motion sickness symptoms and vection issues (visually mediated subjective experience of self-motion). However, cybersickness is mainly explained by visually induced motion. Therefore, it is necessary to understand if cybersickness could arise with office-like tasks.

3.1.3 Cybersickness and working in VR

Most experimental contributions on cybersickness use video games (rollercoasters), driving tasks, or dedicated “walking-around” tasks. Those paradigms induce cybersickness symptoms with some confidence to measure psycho-physiological variations attributable to it. However, even if those previous works provide useful information, we narrowed down the literature presented to work-related tasks to match office-like tasks. Ten articles met inclusion/exclusion criteria.

In a collaborative car design environment, Coburn et al. ( 2020 ) experimented with four moving methods: teleport, fade, fly, and manual. (Translation and rotation automatically place users in a predetermined location.) After moving, participants located a particular part of the car. Flying proved to be the best solution for spatial location. But it implied a potentially higher discomfort (cybersickness). In their experiment, teleporting was the worst mode because of disorientation. Coburn et al. ( 2020 ) advocate multiple transition styles (locomotion) for users. We can hypothesize that users in a VR application for office-like tasks will be sitting (Zielasko et al. 2017 ; Zielasko and Riecke 2021 ). Zielasko et al. ( 2019 ) had participants move by leaning (forward and backward). Participants found the shortest path between a pair of red vertices hidden in a node-link visualization. Since participants were sitting in front of a desk, Zielasko et al. ( 2019 ) tested two conditions: a stable virtual desk visually represented in VR and without a virtual desk. The author did not find differences in cybersickness (and task performance) when employing a keyboard and other interfaces instead of a virtual desk.

When analyzing data, the type of locomotion also seems to be impacted by the user’s expertise in data analysis, video games, and spatial orientation ability (Lages and Bowman 2018 ). Some works show no difference in cybersickness symptoms when comparing real desk tasks to a virtual reality desk (Guo et al. 2019a ). Boges et al. ( 2020 ) work (editing and exploring medial axis representations of nanometric scale neural structures) shows that users must take several breaks because of cybersickness after being immersed for fifteen minutes. However, side effects in experimental contributions are not always assessed, such as in works about data visualization in VR, e.g. (Andersen et al. 2019 ). In office-like work in VR, visually induced motion sickness could be less of a problem since fewer tasks or stimuli require continuous locomotion than virtual environments consisting of driving or rollercoaster games. Filho et al . with “VirtualDesk” (data visualization and analytics) show low cybersickness in different experiments (Filho et al. 2018 , 2019 , 2020 ).

Previous works relating to data visualization and office work in VR have shown that cybersickness needs further investigation and acknowledged as a risk of side effects even in this configuration. Locomotion and visual feedback of this locomotion are two crucial factors leading to cybersickness (Caserman et al. 2021 ).

Based on the latest reviews and systematic reviews (Koohestani et al. 2019 ; Descheneaux et al. 2020 ; Kemeny et al. 2020 ; Saredakis et al. 2020 ; Stanney et al. 2020b ), we can infer that contributions regarding cybersickness concentrate on the visual-vestibular-proprioceptive conflicts (like motion sickness) issue. Contributions rarely focus on visual fatigue, i.e., vergence-accommodation conflict (Fuchs 2017 ; Souchet 2020 ; Chang et al. 2020 ; Souchet et al. 2021a ). But Rebenitsch and Owen ( 2017 ) reported no effect of vergence-accommodation conflict on cybersickness.

Since visual fatigue (or oculomotor symptoms in cybersickness-related works) is pointed out as one of the main symptoms in VR side effects, it seems legitimate to focus on it (Chang et al. 2020 ). Nausea may only result in 30% of the instances after withdrawing from VR use (Rebenitsch and Owen 2017 ). Our current focus choice aligns with the agenda of Stanney et al. ( 2020b ) as we contribute to the evaluation and applications research to tackle cybersickness issues. This section shows that oculomotor symptoms are mainly induced by visual motion in VR. But visual fatigue should be considered not only as a symptom related to cybersickness but as a side effect of its own. Therefore, the following section addresses visual fatigue, as cybersickness seems heavily dependent on locomotion, less of a determinant feature in office-like VR applications.

3.2 Visual fatigue

3.2.1 visual fatigue overview.

According to Evans ( 2007 ), visual fatigue (also named asthenopia, eyestrain, visual strain, ocular symptoms, depending on the discipline tackling this issue) generally corresponds to eye fatigue and headaches. Sheppard and Wollfsohn ( 2018 ) quote the list of symptoms by the American Optometric Association : eyestrain, headaches, blurred vision, dry eyes, and pain in the neck and shoulders. The subjective appreciation of these symptoms is visual discomfort (Lambooij et al. 2007 , 2009 ). Visual fatigue is due to a weakness of the eyes or vision, i.e., resulting from a visual or ocular abnormality rather than purely extrinsic (environmental) factors. Lambooij and IJsselsteijn ( 2009 ) define visual fatigue as a “ physiological strain or stress resulting from excessive exertion of the visual system .” Various screen usages induce this excessive exertion. Sheppard and Wollfsohn ( 2018 ) have reviewed the visual fatigue phenomenon linked to digital uses. They have determined that a large part of the population is at risk. However, they did not evaluate HMDs or other devices displaying stereoscopy.

HMDs are absent of most visual fatigue reviews, such as in Coles-Brennan et al. ( 2019 ) for standard displays. One of the main issues regarding visual fatigue is that HMDs are displaying stereoscopic images: depth cues from the environment inferred from the distance between our two eyes (interpupillary-distance) fused by our brain (Parker 1983 , 2016 ; Hodges and Davis 1993 ; Best 1996 ; Reichelt et al. 2010 ; Holliman et al. 2011 ; Urey et al. 2011 ; Rößing 2016 ).

Terzić and Hansard ( 2017 ) reviewed the causes of visual discomfort, pointing to future problems with HMDs since the apparatuses display stereoscopy. Displaying stereoscopy is known to induce visual strain in general (Lambooij et al. 2007 , 2009; Kuze and Ukai 2008 ; Fortuin et al. 2010 ; Kim et al. 2011 ; Karajeh et al. 2014 ; Sugita et al. 2014 ; Sasaki et al. 2015 ). However, the scientific literature is unclear on the mechanisms and predictions distinguishing visual fatigue from cybersickness.

3.2.2 Visual fatigue occurrence

Despite all the excessive exertions on the visual system when using HMDs, they do not seem to induce myopia after 40 min of exposure (Turnbull and Phillips 2017 ). However, HMD use can contribute to near-work factors that induce myopia, and the impact on accommodation and vergence functions also could be a long-time concern (Németh et al. 2021 ). Therefore, we concentrate on visual fatigue rather than other issues that could arise with users’ eyes since we are concerned with what happens while VR is used.

Stereoscopy allows us to reproduce binocular and proprioceptive (or oculomotor) depth cues. Stereoscopy aims to provide clear stimuli for our eyes in HMDs (Rotter 2017 ). Binocular cues mean that the effect can only be seen with two eyes (Blake and Wilson 2011 ). The horizontal distance between the eyes (inter-pupillary distance) is on average 65 mm (Anses 2014 ), ranging from about 50 to 77 mm for the general population (Lambooij et al. 2009 ; Stanney et al. 2020b ). However, this range may vary depending on the country and gender and be wider if children are included (Dodgson 2004 ; Stanney et al. 2020a ). Misadjustments of HMD lenses, in the form of binocular stimuli related to IPD, can provoke visual fatigue (Hibbard et al. 2020 ).

Disparity and blur drive the vergence and accommodation mechanisms (Sweeney et al. 2014 ). According to Schor and his colleagues’ model (Schor and Kotulak 1986 ; Schor and Tsuetaki 1987 ; Schor 1992 ), vergence and accommodation are two dual parallel feedback control systems that interact via cross-links. Lambooij et al. ( 2009 ) summarized that accommodation and vergence interact to provide comfortable and clear, binocular, single vision under natural viewing.

However, stereopsis is only possible for a limited number of positions in space. The brain will only consider the point of vergence as unique despite the binocular disparity if the distance meets certain conditions. This set of merging points can be represented by the human's binocular horizontal field of view of 120°. Fusion without diplopia (double vision) is possible (Patterson 2015 ). Retinal disparity on the horopter is about 0°. Shibata et al. ( 2011 ) assume that the maximum and minimum relative distance of the comfort zone is between 0.8 diopters and 0.3 diopters. Stereoscopy sometimes requires fusion outside of the comfort zone (Lambooij et al. 2009 ; Fortuin et al. 2010 ). When this occurs, the habitual crosslink between accommodation and vergence is mismatched because accommodation applies to the screen’s plane while convergence applies to objects of interest (Emoto et al. 2005 ; Banks et al. 2013 ; Kim et al. 2014 ; Leroy 2016 ; Fuchs 2017 ) (see Fig.  1 ). Several scientific works treat accommodation and vergence mechanisms and conflicts due to stereoscopy in detail (Schor 1992 ; Jiang et al. 2002 ; Hoffman et al. 2008 ; Lambooij et al. 2009 ; Mays 2009 ; Banks et al. 2012 , 2013 ; Kim et al. 2014 ; Leroy 2016 ; Neveu et al. 2016 ; Rößing 2016 ; Fuchs 2017 ).

Comparison of natural binocular viewing and HMD viewing with stereoscopy (near object, negative parallaxes in this example): accommodation and convergence occur on the same plane in natural viewing but in HMD viewing with stereoscopy, there is a mismatch between accommodation and vergence that are crosslinked mechanisms

Stereoscopy induces the vergence-accommodation conflict (Ukai and Howarth 2008 ; Bando et al. 2012 ). This conflict also arises with HMDs (Yuan et al. 2018 ; Matsuura 2019 ). There is no theoretical consensus on which to rely, but this conflict concerns everyday VR uses (Biggs et al. 2018 ). This sensorimotor conflict mainly explains visual fatigue with HMDs (Fuchs 2017 ). A new generation of HMDs still causes visual fatigue (Souchet et al. 2018 , 2019 , 2021a ; Hirota et al. 2019 ; Wang et al. 2019 ; Yoon et al. 2020 ) and visual discomfort (Cho et al. 2017 ; Guo et al. 2017 , 2019b ; Souchet et al. 2018 ; Bracq et al. 2019 ). A lack of contributions to document that effect (beyond merely knowing that it still exists for HMDs) has been pointed out (Szpak et al. 2019 ). Table 2 updates the list of factors proposed by Bando et al. ( 2012 ).

Visual fatigue appears to be time-related: the more prolonged the VR exposure, the higher the visual fatigue. Guo et al. ( 2019b ) find that symptoms are increasingly severe and that the severity increases faster during the first 20 min. Guo et al. ( 2020 ) tested exposures of almost eight hours to VR and reported increasingly impacted accommodative response and pupil size. However, the impact is comparable with VR and 2D screen working tasks (text error corrections) for pupil size. Specific cumulative effect of immersion on eye movement (extraocular muscle excitation) has been observed while calculating a visual fatigue index through ocular biomechanics by Iskander and Hossny ( 2021 )

Apart from the population that is “stereo-blind,” have missing or have non-measurable binocular depth perception, the proportion of concerned individuals varies according to the tested populations and measurement conditions from 2.2% to 32% (Lambooij et al. 2009 ; Bosten et al. 2015 ; Hess et al. 2015 ). Moreover, although not necessarily impacting the discriminating abilities to determine an object’s depth, the precision abilities of stereopsis diminish with age (Schubert et al. 2016 ). It also seems that poor stereo acuity drives higher visual fatigue (Ramadan and Alhaag 2018 ). Therefore, this population seems to present higher risks of visual fatigue.

Blue light might also contribute to visual fatigue, but it remains unclear how significant this factor is since little research has been conducted, especially with HMDs (Heo et al. 2017 ; Lawrenson et al. 2017 ; Priya and Subramaniyam 2020 ; Tu et al. 2021 ). Continuous (chronic) exposure to blue light might damage the retina (Ahmed et al. 2018 ). Since HMDs use OLED and LCD technologies, this suggests that blue light could be a factor of visual fatigue when using VR. Previous stereoscopy and near-work contributions indicate that blue light implies less accommodation (Panke et al. 2019 ).

Several more display features are associated with visual fatigue. The lighter the displayed stimuli, the higher the visual fatigue (Wang et al. 2010 ; Erickson et al. 2020 ). The more frequent the color changes, the higher the visual fatigue (Kim et al. 2016 ). The more dynamism in videos, the more visual fatigue (Kweon et al. 2018 ). An Anses Footnote 1 report about light effects on health includes blue lights (range from 400 to 490 nm). It indicates that the “phototoxicity” range (450 to 470 nm – deep blue) has possible effects (Anses 2019 ) 1) on myopia (positive or negative), and 2) on dry eye syndrome.

Blue light seems to facilitate visual discomfort in general (not restricted to screen use). However, according to the report, proof of effects on humans is limited. Long-term issues include (Anses 2019 ) 1) disturbance of circadian rhythms in the form of disturbance of sleep if exposed to blue lights during the evening, at night before sleep, or even during the day (Wahl et al. 2019 ), and 2) phototoxicity (Youssef et al. 2011 ) on the cornea (Niwano et al. 2019 ; Mehra and Galor 2020 ). However, it is not clear how much blue lights emitted by HMDs influence visual fatigue.

Other possible factors that might influence visual fatigue but that has yet to be further tested in the VR context (therefore, we did not list them in Table 2 ), including the following:

Passive smoking and e-cigarettes have similar negative impacts on tear films, and smoking regular cigarettes is detrimental to the tear film (Miglio et al. 2021 ). This implies that dry eye syndromes would be more likely to arise in those situations, possibly hastening the development of visual fatigue when using VR.

Vergence and accommodation insufficiency is associated with less task engagement and higher cognitive fatigue during complex tasks (Bernhardt and Poltavski 2021 ). Visual fatigue can also negatively impact attention (Yue et al. 2020 ). Therefore, mental workload might influence visual fatigue (Daniel and Kapoula 2019 ; Bernhardt and Poltavski 2021 ).

Usually, visual fatigue is measured before and after HMD use. But, HMDs increasingly implement eye trackers, allowing measurements during immersion (Souchet et al. 2021b ). But, no measurement method for visual fatigue caused by HMDs has reached a consensus. Factors inducing visual fatigue and cybersickness are sometimes similar (see Tables 1 and 2 ). This similarity does not help to clarify the domain of visual fatigue and the domain of cybersickness. In both cases, oculomotor performance seems to be negatively impacted in VR (Valori et al. 2020 ). In the next section, we try to disambiguate cybersickness from visual fatigue.

3.2.3 Disambiguation of cybersickness and visual fatigue

Vi sual fatigue is listed as one of cybersickness’s symptoms (Lawson 2014 ; Davis et al. 2014 ; Rebenitsch and Owen 2016 ; Bockelman and Lingum 2017 ; Nesbitt and Nalivaiko 2018 ; Descheneaux et al. 2020 ; Chang et al. 2020 ). However, visual fatigue and visually induced motion sickness seem different but with a small relation (Bando et al. 2012 ; Yuan et al. 2018 ; Wang et al. 2019 ). Hereafter, we develop each argument advocating for two different VRISE measurements.

3.2.3.1 Visual fatigue and cybersickness intersect theoretically but do not rely on the same theories

Visual fatigue predictions in VR mostly reuse knowledge drawn from stereoscopic images and their perception without discomfort (Lambooij et al. 2009 ; Terzic and Hansard 2017 ). Most contributions point to vergence-accommodation conflict as the main factor explaining visual fatigue with HMDs (see Sect.  3.2.2 ). Patterson proposes the “Dual-process theory” to predict visual fatigue occurrence with stereoscopic images (Patterson 2009 ; Patterson and Silzars 2009 ; Evans and Stanovich 2013 ). However, when describing the vergence-accommodation conflict occurring with HMDs – or only stereoscopy – most peers point to “sensorimotor conflicts” during visual perception (Bando et al. 2012 ; Fuchs 2017 ). They do not rely on Patterson’s proposal or other transparent theoretical backgrounds. This view relies on the sensorimotor approach and sensorimotor contingencies theory, indicating that perception (especially sight) is intimately linked to motor actions (O’Regan and Noë 2001 ; Buhrmann et al. 2013 ; Dell’Anna and Paternoster 2013 ; Bishop and Martin 2014 ).

From the perspective of sensorimotor contingencies, accommodation-vergence conflict is failing our brain’s probabilities. Thus, the accommodation-vergence mismatch can be considered a sensorimotor conflict. The mismatch impacts the crosslink between the accommodation-vergence components resulting in a sensory conflict during depth perception and drives to excessive oculomotor movements.

It should be noted that the concepts of predictive coding and sensorimotor contingencies theory are in debate in the cognition field and are not reaching a consensus (Flament-Fultot 2016 ; Vernazzani 2019 ; Williams 2020 ; Marvan and Havlík 2021 ). Ukai and Howarth ( 2008 ) conclude their review by stating that the theory applying to visual fatigue provoked by vergence-accommodation conflict remains unclear. Therefore, our description of sensorimotor contingencies theory as a possible candidate should be taken with caution because 1) it drifts from the current views in cognition that humans have an internal representation of the outside world in line with current developments of controversial “embodied cognition” (Adams 2010 ; Goldinger et al. 2016 ), and 2) it is not directly used by previous works to explain the accommodation-vergence conflict in VR. However, peers refer to sensorimotor conflicts to explain accommodation-vergence conflicts with HMDs.

In parallel, cybersickness's “evolutionary theory” provides predictions about vergence-accommodation conflict (Stanney et al. 2020b ). However, as introduced in Sect.  3.1.1 , the theory most widely employed is the sensory conflict theory of motion sickness. Vergence-accommodation conflict is usually listed in cybersickness descriptions (Nesbitt and Nalivaiko 2018 ; Descheneaux et al. 2020 ; Chang et al. 2020 ; Stanney et al. 2020b ; Rebenitsch and Owen 2021 ) but without clearly demonstrating if it is predicted by the sensory conflict theory of motion sickness. Like visual fatigue in VR, cybersickness relies on the concept of conflicts between “sensorimotor” systems (Weech et al. 2018 ; Stanney et al. 2020b ).

In summary, it appears that visual fatigue in HMDs could rely on sensorimotor contingencies theory which mainly applies for predicting visual fatigue due to vergence-accommodation conflict: visual-proprioceptive (oculomotor) conflicts. In contrast, cybersickness relies on the sensory conflict theory of motion sickness, mainly predicting visual-vestibular conflicts. However, both theories are debated, and no clear consensus advocates that those theories apply.

3.2.3.2 Visual fatigue and cybersickness intersect in symptomology

Cybersickness and visual fatigue overlap as lists of symptoms for the first include the second. Cybersickness describes several oculomotor symptoms. Questionnaires, like the virtual reality sickness questionnaire (VRSQ) designed for cybersickness (Kim et al. 2018 ; Sevinc and Berkman 2020 ; Cid et al. 2021 ), or the simulator sickness questionnaire (SSQ) by Kennedy et al. ( 1993 ), list similar symptoms to those for visual fatigue like the computer vision syndrome questionnaire (CVS-Q) (Seguí et al. 2015 ; Sheppard and Wolffsohn 2018 ) or questionnaires developed for discomfort during stereoscopic viewing (Lambooij et al. 2007 , 2009; Zeri and Livi 2015 ). Namely, “headache” and “blurred vision” are common symptoms reported for both states. Questionnaires about visual fatigue in VR usually have not been designed to assess HMD-viewing context directly. In summary, we can see that cybersickness and visual fatigue intersect on at least two symptoms.

3.2.3.3 Visual fatigue and cybersickness influencing factors intersect

Common possible factors can influence both cybersickness and visual fatigue (see Tables 1 and 2 ): age, stereoscopic visual ability, optical misalignment (inter-pupillary distance in Table 1 ), global visual flow (motion parallax in Table 2 ), and color. These advocates for both VRISE intersecting based on individual, hardware, and software characteristics.

3.2.3.4 Visual fatigue is not a sub-symptom of cybersickness

Wang et al. ( 2019 ) show that users can present visual fatigue without reporting visually induced motion sickness. Bando et al. ( 2012 ) remind that static stereoscopic images drive visual fatigue and that moving images increase fatigue. Conversely, binocular cues influence perceived motion in VR and can impact vection (Luu et al. 2021b ). Active viewing induces higher vection compared to passive viewing. Stereoscopy seems to increase vection by changing optical flow proprieties (Palmisano et al. 2020b ). Therefore, visually induced motion and vergence-accommodation conflict play a role in both VRISE. But visual fatigue can occur in VR without visually induced motion sickness. Visual fatigue is not a sub-symptom of cybersickness but an intersecting VRISE.

The following section concentrates on works tackling visual fatigue when working in VR.

3.2.4 Visual fatigue and working in VR

Visual fatigue is already an issue in everyday work, with various screen uses putting a large population at risk. At least 50% is potentially at risk (Sheppard and Wolffsohn 2018 ). Near work on computer screens is an issue regarding dry eye, ametropia, and accommodation or vergence mechanisms. Therefore, adding HMDs would increase screen use at the workplace. HMD use seems to drive higher visual fatigue than PC screen, tablet, or smartphone uses (Han et al. 2017 ; Yu et al. 2018 ; Souchet et al. 2018 ; Zhang et al. 2020 ). Here, we focus on visual fatigue while using VR and right after. Examples of video game use show that VR impacts accommodation and vergence compared to a baseline, whether duration use is 10 or 50 min (Szpak et al. 2020 ). The Szpak et al. ( 2020 ) study took 40 min after VR use for those measures to go back to baseline. However, their study shows that starting after 10 min exposure user’s oculomotor functions similarly changed. Several works present the comparable results about how accommodation and vergence are negatively impacted after playing video games (Yoon et al. 2020 ; Alhassan et al. 2021 ). However, studies sometimes find contradictory results. There was no decrease in accommodative and vergence functions after 25 min of playing in Munsamy et al. ( 2020 ). In other studies, there was an improvement in the amplitude of accommodation after 10 min of use two times a day for two weeks (Long et al. 2020 ) which could be due to changes to the ciliary muscle. Similar findings by are presented by Mohamed Elias et al. ( 2019 ). Interestingly, during video game play for 20 min, blinks seem similarly impacted with HMD and PC, but lipid layer thickness increased more in VR (Marshev et al. 2021 ).

However, video games in VR findings might not apply to typical tasks of office workers in a VR environment. Eleven studies, including interaction types related to what office work in VR would require from users, detect visual fatigue (see Table 3 ).

In summary, despite the few works directly tackling VR-induced visual fatigue at work, existing experiments point out that visual fatigue arises with similar interactions and content to what working in VR would require. Volume visualization does not always require stereoscopic images (Laha et al. 2012 ). By extension, not all work tasks would require stereoscopy. Therefore, stereoscopy use must be used with discretion. Only a few contributions directly investigate visual fatigue in the context of office-like tasks in VR. Dedicated works should better measure, detect, and evaluate visual fatigue induced by HMDs and the consequences on human performance while working in VR. This section shows that visual fatigue is already a concern for general screen uses. VR would generate an extra load on workers’ visual systems and, therefore, their well-being. Furthermore, the possible influence of visual fatigue on available memory workload could directly influence work performance in VR (Park et al. 2015 ; Eckstein et al. 2017 ; Daniel and Kapoula 2019 ; Alhusuny et al. 2020 ; Bernhardt and Poltavski 2021 ; Souchet et al. 2021b ).

3.3 Muscle fatigue and musculoskeletal discomfort

3.3.1 muscle fatigue and musculoskeletal discomfort overview.

According to Gandevia ( 2001 ), muscle fatigue is defined as an “ exercise-induced reduction in the ability of a muscle or muscle group to generate maximal force or power. ” This leads to difficulty performing a voluntary task (Gruet et al. 2013 ; Taylor et al. 2016 ). Muscle fatigue mainly refers to intense exercises like sports or physically demanding work (Wan et al. 2017 ) (e.g., prolonged standing Halim et al. 2012 ; Coenen et al. 2018 )) but also screen work (Coenen et al. 2019 )). Repeated issues regarding muscle load can lead to musculoskeletal disorders and are the most common (almost 24% of EU workers) work-related problem in Europe (European Agency for Safety and Health at Work 2007 ). For office workers, neck, shoulder, forearm/hands pain, upper and low back pain are the primary disorders associated with office work (Eltayeb et al. 2009 ; Calik et al. 2020 ; Heidarimoghadam et al. 2020 ; Frutiger and Borotkanics 2021 ). Despite some short-term physical discomfort, musculoskeletal disorders appear temporarily. After a few minutes of rest, users recover from muscle fatigue (Sesboüé and Guincestre 2006 ). On the other hand, symptoms associated with prolonged use of computers and the internet are headache, neck and wrist pain, and backache (Borhany et al. 2018 ). Such symptoms are likely to arise in VR as hands are not the only interaction modality in VR. The head is widely exploited (Monteiro et al. 2021 ). Similar to visual fatigue, computer, and office work already raised the issue of musculoskeletal discomfort, and VR could add to physical load (Reenen et al. 2008 ; Waongenngarm et al. 2020 ).

3.3.2 Muscle fatigue and musculoskeletal discomfort occurrence

In VR, users are interacting with a computer-generated virtual environment. The stimuli, inputs from users, and feedback depend primarily on HMDs. Then, depending on the interaction modalities, a user can use controllers, their hands, their head, their eyes, and other body movements to induce changes in this virtual environment (Rogers et al. 2019 ; Kim et al. 2020 ; Monteiro et al. 2021 ; Vergari et al. 2021 ). Ultimately, the entirety of the body could be interfaced. Therefore, users need to wear different hardware and perform repeated gestures that are not always in their habit and can lead to muscle fatigue.

Since physical load varies heavily depending on the work context, we directly focus on VR-related factors. In Table 4 , we summarized factors identified in 11 contributions regarding muscle fatigue and musculoskeletal discomfort while using VR (Chihara and Seo 2018 ; Kim and Shin 2018 ; Lee and Han 2018 ; Dube and Arif 2019 ; Song et al. 2019 ; Yan et al. 2019 ; Bourdin et al. 2019 ; Kartick et al. 2020 ; Li et al. 2020b , a ; Penumudi et al. 2020 ).

Other possible factors might influence muscle fatigue and musculoskeletal discomfort, but that have yet to be further tested in the VR context (therefore, we did not list them in Table 4 ), including the following:

Cognitive exertion has a negative effect on subsequent physical performance (Brown et al. 2020 ).

Depending on environmental illumination and screen brightness (PC), workers might compensate with postural changes, influencing muscle fatigue (Merbah et al. 2020 ).

Stress could promote muscle fatigue (Dehdashti et al. 2017 ).

Contributions we used to define factors influencing muscle fatigue, and musculoskeletal discomfort are presented in the following section as they apply to possible tasks while working in VR.

3.3.3 Muscle fatigue, musculoskeletal discomfort, and working in VR

During the late 1990s, Nichols ( 1999 ) had already identified muscle fatigue or musculoskeletal discomfort issues. Sixteen articles met inclusion/exclusion criteria.

E. Kim and Shin ( 2018 ) compared keyboard and mouse document editing tasks on a computer with an HTC Vive. The authors show that HMD has higher physical stress because of weight and lower resolution (reading text). VR text-entry requires more contributions causing muscle fatigue (Dube and Arif 2019 ). The weight of HMDs themselves could be a source of discomfort (Yan et al. 2019 ) as users’ neck joint torque is affected and the optimal center of mass position of HMDs is varying depending on users’ postures (Chihara and Seo 2018 ; Ito et al. 2019 ; Sun et al. 2019 ). HMD weight can be perceived as higher by users the lower the number of belts (Song et al. 2019 ). The physical tension on the neck can change with an increased number of belts distributing the weight. According to Penumudi et al. ( 2020 ), shoulder flexion angle, neck flexion moment, and muscle activities of the neck and shoulder are excessive with vertical target locations when interacting with targets at several angles in the 3D environment. Interaction gestures play a role depending on their amplitude. This can lead to musculoskeletal discomfort, so some contributions develop microgestures (Li et al. 2020a ). However, depending on the tasks in the virtual environment, involving more of the body can be necessary (Kartick et al. 2020 ). When comparing the same real gestures versus VR gestures (CAVE), Ahmed et al. ( 2017 ) showed that physical fatigue is higher in VR. Bourdin et al. ( 2019 ) showed that modifying postural/gesture feedbacks of users’ avatar in VR drive unconscious motor and muscular adjustments. Time seems a factor to consider when watching VR videos as it provokes erector spinae and upper trapezius muscles fatigue (Lee and Han 2018 ). Conversely, watching 360° videos, despite more neck movements, seems to lead to less fatigue than traditional video (ibid.). As little as 15 min in VR for laparoscopic tasks drive users to declare slight physical discomfort (Li et al. 2020b ). The arm fatigue issue is inerasably tackled during the design of virtual environments (Evangelista Belo et al. 2021 ; Iqbal et al. 2021 ). It indicates that the issue of muscle fatigue is increasingly acknowledged by peers.

In summary, few contributions have considered possible muscle fatigue provoked by state-of-the-art virtual environments and HMDs. Based on such a few previous scientific works, it is difficult to identify the magnitude of possible risks regarding this issue. But like any human–computer interaction situation, VR could ultimately lead to repetitive strain injury (van Tulder et al. 2007 ). Therefore, peers and application creators must acknowledge that muscle fatigue could influence use and users' discomfort. However, since VR requires interactions different from computer work, it could also be a way to induce task variation at the job level, which might help alleviate general musculoskeletal discomfort and, ultimately, disorders (Luger et al. 2014 ).

3.4 Acute stress

3.4.1 stress overview.

Stress is a concept whose definition is not unified in a collective theory (Epel et al. 2018 ). Revisiting the stress definition based on theories of the neurobiology of a “Bayesian and Selfish Brain,” Peters et al. ( 2017 ) define stress as the individual state of uncertainty about what needs to be done to safeguard physical, mental, or social well-being . This definition relies on the human strategy to reallocate energy to reach homeostasis or allostasis in reaction to stress induction, which defines adaptation to maintain equilibrium in the human’s systems (Ganzel et al. 2010 ; Dewe et al. 2012 ; Asarian et al. 2012 ; Ramsay and Woods 2014 ; Boucher and Plusquellec 2019 ). The transactional theory of stress (Lazarus and Folkman 1984 ; Biggs et al. 2017 ) predicts that stress as a process is transactional. The path from a stressful situation to the outcome is individualized, situationally specific, and inseparable from the cognition of the experience process. To disambiguate our interpretation of stress (Bienertova‐Vasku et al . 2020 ), we consider stress as a negatively perceived factor or situation (psychology). Three components define stress (Kim and Diamond 2002 ; Fink 2016 ): arousal or excitability (Cohen 2011 ), perceived aversiveness (Kim and Diamond 2002 ), and uncontrollability (Breier et al. 1987 ). Stress defines a wide range of human interactions with its environment (Schneiderman et al. 2004 ). We focus on acute stress provoked by VR at work in our context. Therefore, we describe the acute stress response occurrence hereinafter.

3.4.2 Acute stress occurrence

Acute stress is defined as a sudden or short time stressor (trauma, perceived threat, death of a loved one, job loss, etc.) as opposed to chronic stress—long time stressor (Fink, 2007 , p. 192‑193). Acute stress with animal models is usually divided into physical (shock, cold, loud noises, etc.) and psychological (novelty, social conflict, unfamiliarity with environment, etc.) (Monroe and Cummins 2015 ; Monroe and Slavich 2016 ). With animal models, Li et al. ( 2019 ) indicate that the effects of physical stress appear early but are relatively moderate. In contrast, the effects of psychological stress appear late but are more severe. However, with humans, physical and psychological stress could interact and accumulate (Abdelall et al. 2020 ). Acute stress responses occur within seconds to several hours (Godoy et al., 2018 ; Shields et al., 2017 ). There are individual differences in how people respond and cope to stressors (Dewe 2017 ; Stephenson and DeLongis 2020 ).

Stress at the workplace covers various experiences one can face (Colligan MSW and Higgins 2006 ). We focus on the acute stress that may be compounded by VR or a new source in office tasks. Acute stress, in general, can impair executive functions (Shields et al. 2016 ). According to LeBlanc ( 2009 ), stress reduces selective attention (Lee and Choo 2013 ; Bater and Jordan 2020 ), impairs working memory, enhances memory consolidation (Roesler and McGaugh 2019 ), and impairs memory recall/retrieval (Staresina and Wimber 2019 ; Klier et al. 2020 ). Therefore, we can infer that stress could impair work performance when fulfilling tasks in VR depending on task typologies.

A summary of factors favoring acute stress in the office-like tasks in VR context is proposed in Table 5 . Hereafter it is described how acute psychological and physical stress can be induced at the workplace when using VR. As little literature about time pressure and task difficulty in VR regarding stress has been found, those factors are presented in the last section about mental overload as they also seem to influence it.

3.4.3 Acute stress and working in VR

One study assessing stress in VR office linked to the apparatus and three public speaking induced-stress (Trier Social Stress Test corresponding to presenting during a meeting) articles met inclusion/exclusion criteria.

3.4.3.1 Techno-stress provoked by VR

Growing information and communication technology (ICT) use at the workplace induces a specific type of stress factor: techno-stress (Brivio et al. 2018 ; La Torre et al. 2019 ). Techno-stress refers to an IT user’s experience of stress when using technologies (Ragu-Nathan et al. 2008 ) . It has been observed with the introduction of many ICTs in the workplace (Tarafdar et al. 2015 ; Wang et al. 2020 ; Karimikia et al. 2020 ). Techno-stress can lie on the Transactional Theory of Stress (Zhao et al. 2020 ) presented above. La Torre et al. ( 2019 ) list five factors contributing to techno-stress. We specifically concentrate on techno-complexity. Techno-complexity defines the inherent quality of an ICT, which drives employees to feel that their computer skills are inadequate. Symptoms include poor concentration, irritability, memory disturbances, and exhaustion. Since VR is new for most workers, it is reasonable to presume it could lead to techno-complexity stress. Workers will have to constantly learn how to use this ICT (Tarafdar et al. 2019 ). VR might replace a part of existing ICTs. However, it might add to and result in techno-overload, which is simultaneous, different streams of information that increase the pace and volume of work (Atanasoff and Venable 2017 ).

Inside this techno-overload, the “information overload” dimension (Nisafani et al. 2020 ) could apply in data analyses in VR, for instance. Since VR is new for most workers and implies side effects, we can predict high psychological and physiological demands (Atanasoff and Venable 2017 ; Zhao et al. 2020 ). However, VR is not considered in overviews about techno-stress (Bondanini et al. 2020 ; Karimikia et al. 2020 ), but coping with VR-induced techno-complexity could result in a stress response similar to other apparatuses (Weinert et al. 2020 ; Dragano and Lunau 2020 ; Tarafdar et al. 2020 ). The dynamic to have workers in a virtual office can facilitate such techno-stress (Stich 2020 ). Ultimately, techno-stress could negatively impact general and task performance (Tams et al. 2018 ; Nisafani et al. 2020 ).

In summary, techno-complexity is critical as it could make VR perceived as non-efficient to fulfill tasks. It could impact task performance itself, and VR could be an additional stress source that negatively impacts workers' well-being.

3.4.3.2 Public speaking-induced stress in VR meetings

Meetings in VR are one popular use case at work. During those meetings, workers need to speak in public. Depending on the worker, one can suffer from public speaking anxiety, common in the general population (Ebrahimi et al. 2019 ; Marcel 2019 ; Gallego et al. 2021 ). Public speaking is well known to induce acute stress, even in healthy adults without public speaking anxiety. This is why the Trier social stress test (TSST) is used to study stress in-lab (Allen et al. 2017 ; Labuschagne et al. 2019 ; Narvaez Linares et al. 2020 ). Immersive virtual environments replicating the TSST showed a higher cortisol reactivity than non-immersive (Zimmer et al. 2019 ; Helminen et al. 2019 ). Audience feedback in VR seems to impact stress (Barreda-Ángeles et al. 2020 ). Hence, it could mean that VR induces higher stress during meetings requiring workers to do presentations.

In summary, public speaking could induce higher stress in VR compared to PC. Therefore, it should be considered a stressor that can affect workers even in VR.

3.5 Mental overload

3.5.1 mental workload overview.

Cognitive load and mental workload are often used as synonyms in the literature (Van Acker et al. 2018 ). The cognitive load concept is used in the learning field, while the mental workload is used in ergonomics / human factors (Orru and Longo 2019 ). Vanneste et al. ( 2020 ) mention that despite differing definitions, the two concepts share a common ground: the amount of working memory resources used for a given task (Baddeley 2012 ; Leppink 2017 ). These working memory resources are limited (Camina and Güell 2017 ; Chai et al. 2018 ; Adams et al. 2018 ). According to Eriksson et al. ( 2015 ), working memory maintains information in an easily accessible state over brief periods of time (several seconds to minutes) for use in an ongoing task. Working memory resources are a limited set of resources (pool of energy) available for mental processes (operations, from sensory-level processing to meaning-level processing) that are allocated across different tasks, modalities, and processing (Basil 2012 ).

Van Acker et al. ( 2018 ) indicate that mental workload is a subjectively experienced physiological processing state, revealing the interplay between one’s limited and multidimensional cognitive resources and the cognitive work demands. Numerous theories of mental workload compete and ad hoc definitions and frameworks are proposed in the literature (Lim et al. 2013 ; Dehais et al. 2020 ; Vanneste et al. 2020 ). Synthetizing 82 previous works, Van Acker et al. ( 2018 ) propose an explanatory framework of mental workload. This framework by Van Acker et al. ( 2018 ) gathered common predictive components of mental workload in work-related tasks at an occupational level. We concentrate on how working memory can be overloaded, impacting task performances, quality, and completion times (work-related in Van Acker et al. ( 2018 ) framework). Mental workload depends on cognitive work demands (Causse et al. 2017 ) and resource consumption. Two demands are often referenced: time pressure and task difficulty/complexity (Galy et al. 2012 ). However, time pressure is not listed in Acker et al.'s ( 2018 ) framework.

3.5.2 Mental overload occurrence

Depending on task characteristics, workers can face suboptimal levels of mental workload, both underload, and overload. M. S. Young et al. ( 2015 ) indicate that overload occurs when the operator is faced with more stimuli than (s)he is able to handle while maintaining their own standards of performance. Conversely, M. S. Young et al. ( 2015 ) describe that too little stimulation can lead to underload, as resources are either allocated elsewhere or otherwise shrink through underuse. The most commonly accepted hypothesis describes the relationship between mental workload and performance through an ‘‘inverted U-shape,” which is disputed (Babiloni 2019 ). M. S. Young et al. ( 2015 ) propose an updated representation of relationships between performance, task demands, and resource supply. High task demand, thus increasing resource demand, does not constantly negatively impact performance. Peers rarely concentrate on mental underload as the concept is difficult to define and explain correctly (Young et al. 2015 ; Sharples 2019 ). Mental workload is also dependent on attention (Curtin and Ayaz 2019 ; Sepp et al. 2019 ) or engagement (Dehais et al. 2020 ).

Zimmerman ( 2017 ) defines task load as a measurement of human performance that broadly refers to the levels of difficulty individual encounters when executing a task . Multitasking can negatively impact task performance (Modi et al. 2020 ). Stress and task difficulty impact cognition (Kim et al. 2017 ). Depending on the level of mental workload (dependent time pressure and task difficulty (Galy et al. 2012 )), stress, and despite a link between difficulty and repetition rate, difficulty may either enhance task performance or decrease it (Song et al. 2011 ; Main et al. 2017 ). De Dreu et al. ( 2019 ) showed that high task difficulty leads to lower performance and higher response times. Using VR rather than classical paper-and-pencil or computerized measures to perform neuropsychological assessments revealed increased complexity and difficulty, suggesting that VR requires additional cognitive resources (Neguţ et al. 2016 ). Interestingly, Neguţ et al . observed that the most substantial effect is measured with healthy participants (compared to clinical participants).

Denovan and Dagnall ( 2019 ) define time pressure as insufficient time to complete necessary tasks. This insufficient time available is an individual perception of the amount of time necessary to fulfill a task (Ordóñez et al. 2015 ). It is a challenging stressor that can be coped via extra efforts, leading to strain and exhaustion (Prem et al. 2018 ). Caviola et al. ( 2017 ) show that solving complex math problems under time pressure fosters strategies that can be applied rapidly but negatively impact task performance. Time pressure can be a stressor that impairs performances, but less so with procedural tasks (McCoy et al. 2014 ; Prasad et al. 2020 ). The more time for a task, the less stress (Heikoop et al. 2017 ). The surgery literature informs us that time pressure negatively impacts performances (Arora et al. 2010 ) and decision-making (Modi et al. 2020 ).

In summary, mental overload can occur when performing a given task, leading to non-optimal working memory resource allocation, depending on task demand (mismatch between demands and capabilities), which reduces performance. Mental overload occurs depending on intrinsic task load, users’ characteristics, feedback, and coping strategies depending on the task, which ultimately impacts performance.

3.5.3 Mental overload and working in VR

Scientific knowledge regarding a VR office, especially the possible mental workload consequence, seems rare. Other contexts, close to typical tasks performed in such virtual environments, are presented hereafter. Table 6 summarizes twenty-one studies that analyzed mental workload in VR with office-like tasks. Four of the following studies compare PC to VR (Zhang et al. 2017 ; Broucke and Deligiannis 2019 ; Makransky et al. 2019 ; Tian et al. 2021 ), which is relevant in our use-case scenarios as we focus on replacing current tasks completed on a PC by VR. Contradictory results regarding mental workload are observed. Filho et al. ( 2018 ) directly investigate office-like task issues by creating “VirtualDesk,” consisting of data visualization and analytics. Mental workload appears similar in VR to PC. However, VR presents a lower mental workload for geo-visualization and trajectory data exploration than PC (Filho et al. 2018 , 2019 , 2020 ). But, Shen et al. ( 2019a ) report that tasks in a virtual environment versus a real office drive higher mental fatigue. Five studies experiment with various HCI aspects, which show that independent of the task goal, the interface and interactions already impact mental workload (Geiger et al. 2018 ; Speicher et al. 2018b ; Zielasko et al. 2019 ; Biener et al. 2020 ; Gao et al. 2021 ; Wu et al. 2021 ). It appears that assistance within the interface helps to reduce mental workload and promote higher performance in VR (Geiger et al. 2018 ; Gupta et al. 2020 ; Gao et al. 2021 ). The physical efforts a task requires may also impact mental workload, e.g., text input posture and required movement (Knierim et al. 2018 ; Speicher et al. 2018a ).

The literature that assesses mental workload in VR is still scarce. Therefore, it would be inappropriate to generalize the present results to all workplace tasks context. However, the presented studies give an insight into the effects of VR on mental workload. Taking advantage of spatialization possibilities within VR seems to reduce mental workload if tasks require such cognitively related resources (Filho et al. 2018 , 2020 ; Wismer et al. 2018 ; Broucke and Deligiannis 2019 ; Armougum et al. 2019 ). On the other side, VR seems to lead to mental overload with tasks not requiring such spatialization cues or interactions when those interactions are too far from what users are accustomed to as well (Wismer et al. 2018 ; Bernard et al. 2019 ; Baceviciute et al. 2021 ). Those results seem moderated by expertise within VR and the task demands (Aksoy et al. 2019 ; Luong et al. 2019 ; Armougum et al. 2019 ). For instance, outside of VR, when time and load on the resources are high, humans hit the maximum resource allocation capacity (McGregor et al. 2021 ).

4 Discussion and limitations

4.1 cybersickness and working in vr.

Most paradigms to study cybersickness (visually induced motion sickness) are games, driving tasks, or videos inducing a lot of movements to ensure that symptoms will occur (rollercoaster, multiple head movements, walking in VR, etc.). However, those paradigms represent little of the office work experience. We summarized ten previous works tackling cybersickness with work tasks (Zielasko et al. 2017 , 2019 ; Lages and Bowman 2018 ; Andersen et al. 2019 ; Guo et al. 2019a ; Coburn et al. 2020 ; Boges et al. 2020 ; Filho et al. 2018 , 2019 , 2020 ). Locomotion type heavily influences cybersickness in those experiments. It appears that sitting and avoiding too many movements in the virtual environment would reduce the chances for workers to present cybersickness symptoms. Ultimately, generalizing VR use when part of the population is at risk of side effects could, in the future, become a form of discrimination for potential workers (Stanney et al. 2020b ). Fifty different factors could induce cybersickness (Rebenitsch and Owen 2021 ). During experiments with VR, more than 15% of participants are susceptible to dropout because of VR side effects (Saredakis et al. 2020 ). This implies that part of the workers might not even maintain application use. Thus, cybersickness can negatively impact VR adoption at the workplace.

4.2 Visual fatigue and working in VR

Few contributions regarding visual fatigue and the vergence-accommodation conflict in VR are available to date in the work context. Visual fatigue is already an issue in everyday work with various screen uses as at least 50% of the population is at risk (Sheppard and Wolffsohn 2018 ). Adding HMDs could increase screen use at work, and it seems that HMDs drive toward higher visual fatigue than PC, tablet, or smartphone uses (Han et al. 2017 ; Yu et al. 2018 ; Souchet et al. 2018 ; Zhang et al. 2020 ). The vergence-accommodation issue arises when displaying stereoscopy. Therefore, not displaying stereoscopy unless it is beneficial to task completion should be considered. However, the optical proprieties of HMDs and other variables of virtual environments themselves could influence visual fatigue. Updating Bando et al. ( 2012 ) list, we identify fifteen possible factors that could induce visual fatigue in VR. Furthermore, a possible influence of visual fatigue on available memory workload could directly influence work performance in VR (Park et al. 2015 ; Eckstein et al. 2017 ; Daniel and Kapoula 2019 ; Alhusuny et al. 2020 ; Bernhardt and Poltavski 2021 ). We reviewed eleven experiments with stimuli or tasks that could apply to the work (Souchet et al. 2018 , 2019 , 2021a ; Shen et al. 2019b ; Hirota et al. 2019 ; Jacobs et al. 2019 ; Iskander et al. 2019 ; Wang et al. 2019 ; Thai et al. 2020 ; Yoon et al. 2020 ; Chen and Hou 2021 ). Those studies mostly show the impacts of VR on accommodation and vergence during and after use. The mean duration of immersion in the ten reviewed studies was 26.22 min. Therefore, immersion of about 26 min or more is likely to induce visual fatigue.

4.3 Muscle fatigue, musculoskeletal discomfort, and working in VR

Like any human–computer interaction situation, VR could lead to repetitive strain injury (van Tulder et al. 2007 ). Therefore, peers and application creators must acknowledge how muscle fatigue could influence use and users' discomfort. Conversely, VR could also be a way to induce task variation at the job level, which might help alleviate general musculoskeletal discomfort (Luger et al. 2014 ). Therefore, it is unclear how significant muscle fatigue can be with VR use. Previous studies show negative impacts on.

Users’ neck joint torque

Stress on the neck and shoulders

Flexion angle, neck flexion moment, muscle activities changes

Excessive vertical target locations

Arm fatigue

More experiments are needed to encompass VR risks regarding muscle fatigue.

4.4 Acute stress and working in VR

Stressful work tasks are particular to individuals and situations. Introducing VR as a new ICT tool can lead to additional stress. Encompassing every factor is too complex. We chose to concentrate on techno-stress (with techno-complexity and techno-overload, which directly related to the user experience of the hardware and software), public speaking, task difficulty, and time pressure. Four articles met our criteria. In the short term, these stressors could negatively influence work performances and use performances in VR since stress impacts cognitive resources. However, it is unclear how those stress factors arise in VR compared to PC.

4.5 Mental overload and working in VR

Introducing virtual reality as a new ICT tool implies changing interactions and interfaces. Therefore, expertise with VR and new ways of fulfilling tasks could impact mental workload. The interaction and interface could lead to mental overload if they require higher working memory resources. It appears that typical tasks transposed to VR do require more working memory resources, and this includes reading and writing with a keyboard. However, VR allows information spatialization. Despite requiring higher working memory resources, such spatialization seems to promote high performance when tasks take advantage of spatial information. Typically, data visualization and analytics seem to take advantage of VR because of these spatial information possibilities. VR and its effects on mental workload lack contributions directly assessing work tasks. Looking at assimilable tasks, VR impacts on mental workload are mixed and sometimes contradictory. Consistent findings are that mental workload in VR seems higher than other apparatuses. However, this does not always negatively impact task performance.

Furthermore, workers' expertise in both VR and tasks influence performance and objective and subjective mental workload. Poor or inadequate interaction metaphors and interfaces could lead to mental overload and decreased task performance. Furthermore, workers could put VR aside when high time pressures and task loads require high performance if VR provokes mental overload. However, at the time of this narrative review, little scientific data can generalize those predictions. Possible working memory resource saturation is provoked by cybersickness, visual fatigue (Mittelstaedt et al. 2019 ; Mittelstaedt 2020 ; Park et al. 2021 ), and acute stress (Epps 2018 ; Collins et al. 2019 ; Borghini et al. 2020 ; Wulvik et al. 2020 ) should also be considered. This could impact the total amount of available mental resources and reduce the user’s ability to allocate sufficient resources to tasks in VR.

4.6 Limitations of the present review

This narrative review is more summative and less detailed than the results of a formal methodology review (Pautasso 2013 ; Stratton 2016 ) with more narrowed keywords, publication date range, and inclusion and exclusion criteria. The motivation was to gather information usually scattered in various articles within multiple fields. This allowed for a review of five different VRISE. We concentrated on typical office-like tasks. Due to the primary uses of VR for video games (entertainment in general) and training, part of the presented articles may not directly relate to virtual environments for work. However, our contribution is one of the first to address VR ergonomic risks when introduced in the workplace. Hence, part of described factors and VRISE at work are speculative based on previous works.

4.7 General discussion

We extend the Chen et al. ( 2021 ) review despite the abovementioned limitations. Our work gives a unique insight into the current and possible future issues in introducing VR at work following Stanney et al. ( 2020b ). Stanney et al . focused on cybersickness. Here, we differentiate cybersickness from visual fatigue to better represent VRISE and VR ergonomic risks in general. Furthermore, we orientated our review to directly summarize findings from experiments using stimuli close to tasks an office worker could fulfill to be more focused. By treating five VRISE risks, we also show that the current contributions’ focus on cybersickness is necessary but should not consume all peers' efforts. Indeed, other concerns regarding visual fatigue, muscle fatigue, acute stress, and mental overload require further study.

Furthermore, cybersickness is a portmanteau word that often leads to cloudy VRISE and other VR side-effect explanations. Cybersickness should not be used to encompass all VRISE. Peers abundantly tackled cybersickness within the past three years (Gallagher and Ferrè 2018 ; Nesbitt and Nalivaiko 2018 ; Weech et al. 2018 ; Descheneaux et al. 2020 ; Chang et al. 2020 ; Saredakis et al. 2020 ; Stanney et al. 2020b ; Grassini and Laumann 2021 ; Howard and Van Zandt 2021 ; Rebenitsch and Owen 2021 ). The uncertainty around this concept is partly due to theoretical challenges and the widely varying VR environments. Immersing a human being in a computer-generated environment induces various modifications compared to their real environment. Although a general concept of VR side effects is tempting, this may imply too many variables to consider. Human perception, cognition, and action are large research fields, independently of VR. Therefore, the cybersickness concept should be used when talking about visually induced motion sickness but not encompass all symptoms that occur in VR. Hence, despite the shortcomings of a narrative review, this allowed us to go over the concept of “cybersickness” to clarify the “VRISE” one and add other side effects that are task-related (acute stress and mental overload).

By cross comparing literature, we showed part of the variety of VRISE risks. Furthermore, to the best of our knowledge, the present review is the first to formally treat sensorimotor mismatch and psychological risks of VR in the same paper. We reproduced Rebenitsch and Owen’s ( 2021 ) list of factors that could induce cybersickness and extended the logic to visual and muscle fatigue. We relied on existing models listing factors not developed directly with VR for acute stress and mental overload. Thanks to this method, we can see that various factors could influence cybersickness, visual fatigue, muscle fatigue, acute stress, and mental overload in VR. Sometimes those possible factors are listed for each side effect, leading one to consider possible interactions between those states. When using cybersickness list of factors and comparing it to the other four VRISE (see supplementary materials), we identify that 31 out of 50 factors are similar among all VRISE although not always using the same terminology. The duration factor is present in all five VRISE, age and scene content or scene complexity in four VRISE, position tracking error, the ratio of virtual to real world in and body mass index in three VRISE.

Moreover, it allows identifying possible interactions between those five side effects. Interactions are not directly treated in this paper, but there are growing advocates for them (Park et al. 2015 ; Iskander et al. 2018 ; Alsuraykh et al. 2019 ; Mittelstaedt et al. 2019 ; Parent et al. 2019 ; Alhusuny et al. 2020 ). Those interactions are issues implying difficulties when characterizing what is measured when assessing VR side effects, particularly with complex stimuli.

4.8 Proposal of a research agenda regarding VRISE risks at work

Immersing humans in VR mobilizes several sensorimotor stimulations. In current HMDs, this mainly constitutes visual, vestibular, and proprioceptive systems. The existing scientific literature draws guidelines to identify and reduce VR ergonomic risks. However, it should be clear to potential VR users and creators that, to date, no existing method can fully alleviate VR side effects. Therefore, scientific and industrial contributions are still needed to better consider VR ergonomic risks and human factors. The EU-OSHA already identified these issues (EU-OSHA 2019 ). Therefore, we can reasonably imagine that regulation and legislation regarding VR use at work shall emanate from the EU and other governmental agencies. In their present form, HMDs and virtual environments have issues complying with workers' safety and health, fulfilling office-like tasks. Stanney et al. ( 2020b ) provide an R&D agenda to resolve cybersickness. Most actions they list are estimated to happen within one to five years when their work has been published. This is an optimistic agenda. Even if only focused on cybersickness, the phenomenon's complexity could take longer to check most of the listed actions. Like we have seen with cybersickness and visual fatigue, conceptual issues are at stake. VRISE is a broad term encompassing many factors and the possible relationship between side effects should be considered. Based on our review of VR side effects, it is clear that robust methods to monitor cybersickness, visual fatigue, and muscle fatigue require more scientific contributions. Furthermore, no theory predicting VR side effects makes consensus, and peers require more experimental work. This can apply to visual fatigue, muscle fatigue, acute stress, and mental overload in VR. Therefore, Table 7 lists a research agenda.

We should be parsimonious with the introduction of VR at work. Increasing scientific works are prompt to point out the benefits of HMDs. However, they often do not mention risks. Our narrative review concentrated on ergonomic risks, which could directly impact workers' safety and health. We do not have enough data on the introduction of VR for a large part of office workers. VR has been used in specific industries like automotive, design, or aviation, for pilot training purposes. In those cases, benefits (economic or task risk reduction) seem to surpass ergonomic risks. However, similar benefits still need to be determined for office-like tasks. Workers’ performance, health, and safety are at stake.

French Agency for Food, Environmental and Occupational Health & Safety.

Abdelall ES, Eagle Z, Finseth T et al (2020) The interaction between physical and psychosocial stressors. Front Behav Neurosci. https://doi.org/10.3389/fnbeh.2020.00063

Article   Google Scholar  

Adams F (2010) Embodied cognition. phenom. Cogn Sci 9:619–628. https://doi.org/10.1007/s11097-010-9175-x

Adams EJ, Nguyen AT, Cowan N (2018) Theories of working memory: differences in definition, degree of modularity, role of attention, and purpose. Lang Speech Hear Serv Sch 49:340–355. https://doi.org/10.1044/2018_LSHSS-17-0114

Ahmed SF, McDermott KC, Burge WK et al (2018) Visual function, digital behavior and the vision performance index. OPTH 12:2553–2561. https://doi.org/10.2147/OPTH.S187131

Ahmed S, Leroy L, Bouaniche A (2017) Questioning the use of virtual reality in the assessment of the physical impacts of real-task gestures and tasks. In: 2017 23rd International conference on virtual system multimedia (VSMM). pp 1–10

Aksoy E, Izzetoglu K, Baysoy E et al (2019) Performance Monitoring via Functional Near Infrared Spectroscopy for Virtual Reality Based Basic Life Support Training. Front Neurosci. https://doi.org/10.3389/fnins.2019.01336

Alais D, Burr D (2019) Cue combination within a bayesian framework. In: Lee AKC, Wallace MT, Coffin AB et al (eds) Multisensory processes: the auditory perspective. Springer, Cham, pp 9–31

Google Scholar  

Alhassan M, Alhamad F, Bokhary K, Almustanyir A (2021) Effects of virtual reality head-mounted displays on oculomotor functions. Int J Ophthalmol Vis Sci. https://doi.org/10.11648/j.ijovs.20210601.12

Alhusuny A, Cook M, Khalil A et al (2020) Impact of accommodation, convergence and stereoacuity on perceived symptoms and surgical performance among surgeons. Surg Endosc. https://doi.org/10.1007/s00464-020-08167-2

Allen AP, Kennedy PJ, Dockray S et al (2017) The trier social stress test: principles and practice. Neurobiol Stress 6:113–126. https://doi.org/10.1016/j.ynstr.2016.11.001

Almeida A, Rebelo F, Noriega P, Vilar E (2018) Virtual reality self induced cybersickness: an exploratory study. In: Rebelo F, Soares M (eds) Advances in ergonomics in design. Springer, Cham, pp 26–33

Alsuraykh NH, Wilson ML, Tennent P, Sharples S (2019) How stress and mental workload are connected. In: Proceedings of the 13th EAI international conference on pervasive computing technologies for healthcare. association for computing machinery, New York, USA. pp 371–376

Altena E, Daviaux Y, Sanz-Arigita E et al (2019) How sleep problems contribute to simulator sickness: preliminary results from a realistic driving scenario. J Sleep Res 28:e12677. https://doi.org/10.1111/jsr.12677

Andersen BJH, Davis ATA, Weber G, Wünsche BC (2019) Immersion or diversion: does virtual reality make data visualisation more effective? In: 2019 International conference on electronics, information, and communication (ICEIC). pp 1–7

Anses (2014) Effets sanitaires potentiels des technologies audiovisuelles en 3D stéréoscopique. Agence nationale de sécurité sanitaire de l’alimentation, de l’environnement et du travail, Maisons-Alfort, France

Anses (2019) AVIS et RAPPORT de l’Anses relatif aux effets sur la santé humaine et sur l’environnement (faune et flore) des systèmes utilisant des diodes électroluninescentes (LED). Agence nationale de sécurité sanitaire de l’alimentation, de l’environnement et du travail, France

Anses (2021) AVIS et RAPPORT de l’Anses relatifs aux effets sanitaires potentiels liés à l’exposition aux technologies utilisant la réalité augmentée et la réalité virtuelle. Agence nationale de sécurité sanitaire de l’alimentation, de l’environnement et du travail, Maisons-Alfort, France

Arafat IM, Shahnewaz Ferdous SM, Quarles J (2018) Cybersickness-provoking virtual reality alters brain signals of persons with multiple sclerosis. In: 2018 IEEE Conference on virtual reality and 3D User interfaces (VR). pp 1–120

Armougum A, Orriols E, Gaston-Bellegarde A et al (2019) Virtual reality: a new method to investigate cognitive load during navigation. J Environ Psychol 65:101338. https://doi.org/10.1016/j.jenvp.2019.101338

Arora S, Sevdalis N, Nestel D et al (2010) The impact of stress on surgical performance: a systematic review of the literature. Surgery 147:318-330.e6. https://doi.org/10.1016/j.surg.2009.10.007

Asarian L, Gloy V, Geary N (2012) Homeostasis. In: Ramachandran VS (ed) Encyclopedia of human behavior, 2nd edn. Academic Press, San Diego, pp 324–333

Atanasoff L, Venable MA (2017) Technostress: Implications for adults in the workforce. Career Dev Q 65:326–338. https://doi.org/10.1002/cdq.12111

Babiloni F (2019) Mental workload monitoring: new perspectives from neuroscience. In: Longo L, Leva MC (eds) Human mental workload: models and applications. Springer, Cham, pp 3–19

Baceviciute S, Terkildsen T, Makransky G (2021) Remediating learning from non-immersive to immersive media: using EEG to investigate the effects of environmental embeddedness on reading in virtual reality. Comput Educ 164:104122. https://doi.org/10.1016/j.compedu.2020.104122

Baddeley A (2012) Working memory: theories, models, and controversies. Annu Rev Psychol 63:1–29. https://doi.org/10.1146/annurev-psych-120710-100422

Bando T, Iijima A, Yano S (2012) Visual fatigue caused by stereoscopic images and the search for the requirement to prevent them: a review. Display 33:76–83. https://doi.org/10.1016/j.displa.2011.09.001

Banks MS, Read JCA, Allison RS, Watt SJ (2012) Stereoscopy and the human visual system. SMPTE Motion Imaging J 121:24–43. https://doi.org/10.5594/j18173

Banks MS, Kim J, Shibata T (2013) Insight into vergence/accommodation mismatch. In: Head- and helmet-mounted displays XVIII: design and applications. International society for optics and photonics, 8735

Barreda-Ángeles M, Aleix-Guillaume S, Pereda-Baños A (2020) Users’ psychophysiological, vocal, and self-reported responses to the apparent attitude of a virtual audience in stereoscopic 360°-video. Virtual Real 24:289–302. https://doi.org/10.1007/s10055-019-00400-1

Basil MD (2012) Multiple resource theory. In: Seel NM (ed) Encyclopedia of the sciences of learning. Springer, US, Boston, MA, pp 2384–2385

Bater LR, Jordan SS (2020) Selective attention. In: Zeigler-Hill V, Shackelford TK (eds) Encyclopedia of personality and individual differences. Springer, Cham, pp 4624–4628

Bernard F, Zare M, Sagot J-C, Paquin R (2019) virtual reality simulation and ergonomics assessment in aviation maintainability. In: Bagnara S, Tartaglia R, Albolino S, et al. (eds) Proceedings of the 20th congress of the international ergonomics association (IEA 2018). Springer International Publishing, Cham, pp 141–154

Bernhardt KA, Poltavski D (2021) Symptoms of convergence and accommodative insufficiency predict engagement and cognitive fatigue during complex task performance with and without automation. Appl Ergon 90:103152. https://doi.org/10.1016/j.apergo.2020.103152

Best S (1996) Perceptual and oculomotor implications of interpupillary distance settings on a head-mounted virtual display. In: Proceedings of the IEEE 1996 national aerospace and electronics conference NAECON 1996. pp 429–434 vol.1

Biener V, Schneider D, Gesslein T et al (2020) Breaking the screen: interaction across touchscreen boundaries in virtual reality for mobile knowledge workers. IEEE Trans Visual Comput Graph 26:3490–3502. https://doi.org/10.1109/TVCG.2020.3023567

Bienertova-Vasku J, Lenart P, Scheringer M (2020) Eustress and distress: neither good nor bad, but rather the same? BioEssays 42:1900238. https://doi.org/10.1002/bies.201900238

Biggs A, Brough P, Drummond S (2017) Lazarus and folkman’s psychological stress and coping theory. The handbook of stress and health. Wiley, New Jersey, pp 349–364

Biggs AT, Geyer DJ, Schroeder VM, et al (2018) Adapting virtual reality and augmented reality systems for naval aviation training. Naval medical research unit dayton wright-patterson AFB United States

Biocca F (1992) Will Simulation Sickness Slow Down the Diffusion of Virtual Environment Technology. Presence Teleoperators Virtual Environ 1:334–343. https://doi.org/10.1162/pres.1992.1.3.334

Bishop JM, Martin AO (2014) Contemporary sensorimotor theory: a brief introduction. In: Bishop JM, Martin AO (eds) Contemporary sensorimotor theory. Springer, Cham, pp 1–22

Blake R, Wilson HR (2011) Binocular vision. Vision Res 51:754–770. https://doi.org/10.1016/j.visres.2010.10.009

Bockelman P, Lingum D (2017) Factors of cybersickness. In: Stephanidis C (ed) HCI international 2017 – posters’ extended abstracts. Springer, Cham, pp 3–8

Boges D, Agus M, Sicat R et al (2020) Virtual reality framework for editing and exploring medial axis representations of nanometric scale neural structures. Comput Graph 91:12–24. https://doi.org/10.1016/j.cag.2020.05.024

Bondanini G, Giorgi G, Ariza-Montes A et al (2020) Technostress dark side of technology in the workplace: a scientometric analysis. Int J Environ Res Public Health 17:8013. https://doi.org/10.3390/ijerph17218013

Borghini G, Di Flumeri G, Aricò P et al (2020) A multimodal and signals fusion approach for assessing the impact of stressful events on air traffic controllers. Sci Rep 10:8600. https://doi.org/10.1038/s41598-020-65610-z

Borhany T, Shahid E, Siddique WA, Ali H (2018) Musculoskeletal problems in frequent computer and internet users. J Fam Med Prim Care 7:337–339. https://doi.org/10.4103/jfmpc.jfmpc_326_17

Bosten JM, Goodbourn PT, Lawrance-Owen AJ et al (2015) A population study of binocular function. Vision Res 110:34–50. https://doi.org/10.1016/j.visres.2015.02.017

Boucher P, Plusquellec P (2019) Acute stress assessment from excess cortisol secretion: fundamentals and perspectives. Front Endocrinol. https://doi.org/10.3389/fendo.2019.00749

Bourdin P, Martini M, Sanchez-Vives MV (2019) Altered visual feedback from an embodied avatar unconsciously influences movement amplitude and muscle activity. Sci Rep 9:19747. https://doi.org/10.1038/s41598-019-56034-5

Bracq M-S, Michinov E, Arnaldi B et al (2019) Learning procedural skills with a virtual reality simulator: an acceptability study. Nurse Educ Today 79:153–160. https://doi.org/10.1016/j.nedt.2019.05.026

Breier A, Albus M, Pickar D et al (1987) Controllable and uncontrollable stress in humans: alterations in mood and neuroendocrine and psychophysiological function. AJP 144:1419–1425. https://doi.org/10.1176/ajp.144.11.1419

Brivio E, Gaudioso F, Vergine I et al (2018) Preventing technostress through positive technology. Front Psychol. https://doi.org/10.3389/fpsyg.2018.02569

Broucke SV, Deligiannis N (2019) Visualization of real-time heterogeneous smart city data using virtual reality. In: 2019 IEEE International smart cities conference (ISC2). pp 685–690

Brown DMY, Graham JD, Innes KI et al (2020) Effects of prior cognitive exertion on physical performance: a systematic review and meta-analysis. Sports Med 50:497–529. https://doi.org/10.1007/s40279-019-01204-8

Buhrmann T, Di Paolo EA, Barandiaran X (2013) A dynamical systems account of sensorimotor contingencies. Front Psychol. https://doi.org/10.3389/fpsyg.2013.00285

Calik BB, Yagci N, Oztop M, Caglar D (2020) Effects of risk factors related to computer use on musculoskeletal pain in office workers. Int J Occup Saf Ergon. https://doi.org/10.1080/10803548.2020.1765112

Camina E, Güell F (2017) The neuroanatomical, neurophysiological and psychological basis of memory: current models and their origins. Front Pharmacol. https://doi.org/10.3389/fphar.2017.00438

Caserman P, Garcia-Agundez A, Gámez Zerban A, Göbel S (2021) Cybersickness in current-generation virtual reality head-mounted displays: systematic review and outlook. Virtual Reality. https://doi.org/10.1007/s10055-021-00513-6

Castelo-Branco R, Brás C, Leitão AM (2021) Inside the matrix: immersive live coding for architectural design. Int J Archit Comput 19:174–189. https://doi.org/10.1177/1478077120958164

Causse M, Chua Z, Peysakhovich V et al (2017) Mental workload and neural efficiency quantified in the prefrontal cortex using fNIRS. Sci Rep 7:5222. https://doi.org/10.1038/s41598-017-05378-x

Caviola S, Carey E, Mammarella IC, Szucs D (2017) Stress, time pressure, strategy selection and math anxiety in mathematics: a review of the literature. Front Psychol. https://doi.org/10.3389/fpsyg.2017.01488

Chai WJ, Abd Hamid AI, Abdullah JM (2018) Working memory from the psychological and neurosciences perspectives: a review. Front Psychol. https://doi.org/10.3389/fpsyg.2018.00401

Chang E, Kim HT, Yoo B (2020) Virtual reality sickness: a review of causes and measurements. Int J Hum Comput Interact 36:1658–1682. https://doi.org/10.1080/10447318.2020.1778351

Chen Y, Wang X, Xu H (2021) Human factors/ergonomics evaluation for virtual reality headsets: a review. CCF Trans Pervasive Comp Interact. https://doi.org/10.1007/s42486-021-00062-6

Chen X-L, Hou W-J (2021) Visual fatigue assessment model based on eye-related data in virtual reality. In: 2021 IEEE 7th International conference on virtual reality (ICVR). pp 262–268

Chihara T, Seo A (2018) Evaluation of physical workload affected by mass and center of mass of head-mounted display. Appl Ergon 68:204–212. https://doi.org/10.1016/j.apergo.2017.11.016

Cho T-H, Chen C-Y, Wu P-J et al (2017) The comparison of accommodative response and ocular movements in viewing 3D and 2D displays. Displays 49:59–64. https://doi.org/10.1016/j.displa.2017.07.002

Cobb SVG, Nichols S, Ramsey A, Wilson JR (1999) Virtual reality-induced symptoms and effects (VRISE). Presence Teleoperators Virtual Environ 8:169–186. https://doi.org/10.1162/105474699566152

Coburn J, Salmon J, Freeman I (2020) The effects of transition style for collaborative view sharing in immersive virtual reality. Comput Graph 92:44–54. https://doi.org/10.1016/j.cag.2020.08.003

Coenen P, Willenberg L, Parry S et al (2018) Associations of occupational standing with musculoskeletal symptoms: a systematic review with meta-analysis. Br J Sports Med 52:176–183. https://doi.org/10.1136/bjsports-2016-096795

Coenen P, van der Molen HF, Burdorf A et al (2019) Associations of screen work with neck and upper extremity symptoms: a systematic review with meta-analysis. Occup Environ Med 76:502–509. https://doi.org/10.1136/oemed-2018-105553

Cohen RA (2011) Arousal. In: Kreutzer JS, DeLuca J, Caplan B (eds) Encyclopedia of clinical neuropsychology. Springer, New York, pp 247–249

Coles-Brennan C, Sulley A, Young G (2019) Management of digital eye strain. Clin Exp Optom 102:18–29. https://doi.org/10.1111/cxo.12798

Colligan MSWTW, Higgins EM (2006) Workplace stress. J Work Behav Health 21:89–97. https://doi.org/10.1300/J490v21n02_07

Collins J, Regenbrecht H, Langlotz T, et al (2019) Measuring cognitive load and insight: a methodology exemplified in a virtual reality learning context. In: 2019 IEEE International symposium on mixed and augmented reality (ISMAR). pp 351–362

Colman AM (2009) Sensory conflict theory In: a dictionary of psychology. Oxford University Press

Çöltekin A, Lochhead I, Madden M et al (2020) Extended reality in spatial sciences: a review of research challenges and future directions. ISPRS Int J Geo Inf 9:439. https://doi.org/10.3390/ijgi9070439

Curtin A, Ayaz H (2019) Chapter 22 - neural efficiency metrics in neuroergonomics: theory and applications. In: Ayaz H, Dehais F (eds) Neuroergonomics. Academic Press, pp 133–140

Daniel F, Kapoula Z (2019) Induced vergence-accommodation conflict reduces cognitive performance in the stroop test. Sci Rep. https://doi.org/10.1038/s41598-018-37778-y

Davis S, Nesbitt K, Nalivaiko E (2014) A Systematic review of cybersickness. In: Proceedings of the 2014 conference on interactive entertainment. Association for Computing Machinery, New York, USA, pp 1–9

de Dreu MJ, Schouwenaars IT, Rutten G-JM et al (2019) Brain activity associated with expected task difficulty. Front Hum Neurosci. https://doi.org/10.3389/fnhum.2019.00286

Dehais F, Lafont A, Roy R, Fairclough S (2020) A Neuroergonomics Approach to Mental Workload Engagement and human performance. Front Neurosci. https://doi.org/10.3389/fnins.2020.00268

Dehdashti A, Mehralizadeh S, Mahjoubi Z (2017) Workplace stresses and musculoskeletal disorders among nurses: a cross-sectional study. Middle East J Rehabil Health Stud. https://doi.org/10.5812/mejrh.57480

Dehghani M, Acikgoz F, Mashatan A, Lee SH (2021) A holistic analysis towards understanding consumer perceptions of virtual reality devices in the post-adoption phase. Behav Infor Technol. https://doi.org/10.1080/0144929X.2021.1876767

del Cid DA, Larranaga D, Leitao M et al (2021) Exploratory factor analysis and validity of the virtual reality symptom questionnaire and computer use survey. Ergonomics 64:69–77. https://doi.org/10.1080/00140139.2020.1820083

del Seguí M, M, Cabrero-García J, Crespo A, et al (2015) A reliable and valid questionnaire was developed to measure computer vision syndrome at the workplace. J Clin Epidemiol 68:662–673. https://doi.org/10.1016/j.jclinepi.2015.01.015

Dell’Anna A, Paternoster A (2013) Phenomenal consciousness and the sensorimotor approach. a critical account. Open J Philos 3:435–442. https://doi.org/10.4236/ojpp.2013.34064

Dennison MS, D’Zmura M (2017) Cybersickness without the wobble: experimental results speak against postural instability theory. Appl Ergon 58:215–223. https://doi.org/10.1016/j.apergo.2016.06.014

Dennison MS, Wisti AZ, D’Zmura M (2016) Use of physiological signals to predict cybersickness. Displays 44:42–52. https://doi.org/10.1016/j.displa.2016.07.002

Denovan A, Dagnall N (2019) Development and evaluation of the chronic time pressure inventory. Front Psychol. https://doi.org/10.3389/fpsyg.2019.02717

Descheneaux CR, Reinerman-Jones L, Moss J et al (2020) Negative effects associated with HMDs in augmented and virtual reality. In: Chen JYC, Fragomeni G (eds) Virtual, augmented and mixed reality design and interaction. Springer, Cham, pp 410–428

Dewe P (2017) Demand, resources, and their relationship with coping. The handbook of stress and health. Wiley, New Jersey, pp 427–442

Dewe PJ, O’Driscoll MP, Cooper CL (2012) Theories of psychological stress at work. In: Gatchel RJ, Schultz IZ (eds) Handbook of occupational health and wellness. Springer, US, Boston, MA, pp 23–38

Diaz G, Cooper J, Rothkopf C, Hayhoe M (2013) Saccades to future ball location reveal memory-based prediction in a virtual-reality interception task. J vis 13:20–20. https://doi.org/10.1167/13.1.20

Dodgson NA (2004) Variation and extrema of human interpupillary distance. In: Stereoscopic displays and virtual reality systems XI. international society for optics and photonics, pp 36–46

Doherty K, Doherty G (2018) The construal of experience in HCI: understanding self-reports. Int J Hum Comput Stud 110:63–74. https://doi.org/10.1016/j.ijhcs.2017.10.006

Dragano N, Lunau T (2020) Technostress at work and mental health: concepts and research results. Curr Opin Psychiatry 33:407–413. https://doi.org/10.1097/YCO.0000000000000613

Dube TJ, Arif AS (2019) Text entry in virtual reality: a comprehensive review of the literature. In: Kurosu M (ed) Human-computer interaction recognition and interaction technologies. Springer, Cham, pp 419–437

Dużmańska N, Strojny P, Strojny A (2018) can simulator sickness be avoided? a review on temporal aspects of simulator sickness. Front Psychol. https://doi.org/10.3389/fpsyg.2018.02132

Ebrahimi OV, Pallesen S, Kenter RMF, Nordgreen T (2019) Psychological interventions for the fear of public speaking: a meta-analysis. Front Psychol. https://doi.org/10.3389/fpsyg.2019.00488

Eckstein MK, Guerra-Carrillo B, Singley ATM, Bunge SA (2017) Beyond eye gaze: what else can eyetracking reveal about cognition and cognitive development? Dev Cogn Neurosci 25:69–91. https://doi.org/10.1016/j.dcn.2016.11.001

Ehrenbrusthoff K, Ryan CG, Grüneberg C, Martin DJ (2018) A systematic review and meta-analysis of the reliability and validity of sensorimotor measurement instruments in people with chronic low back pain. Musculoskelet Sci Pract 35:73–83. https://doi.org/10.1016/j.msksp.2018.02.007

Eltayeb S, Staal JB, Hassan A, de Bie RA (2009) Work related risk factors for neck, shoulder and arms complaints: a cohort study among dutch computer office workers. J Occup Rehabil 19:315. https://doi.org/10.1007/s10926-009-9196-x

Emoto M, Niida T, Okano F (2005) Repeated vergence adaptation causes the decline of visual functions in watching stereoscopic television. J Disp Technol 1:328–340. https://doi.org/10.1109/jdt.2005.858938

Ens B, Bach B, Cordeil M, et al (2021) Grand challenges in immersive analytics. In: Proceedings of the 2021 CHI conference on human factors in computing systems. ACM, Yokohama Japan, pp 1–17

Epel ES, Crosswell AD, Mayer SE et al (2018) More than a feeling: a unified view of stress measurement for population science. Front Neuroendocrinol 49:146–169. https://doi.org/10.1016/j.yfrne.2018.03.001

Epps J (2018) Task load and stress. The wiley handbook of human computer interaction. Wiley, New Jersey, pp 207–223

Erickson A, Kim K, Bruder G, Welch GF (2020) Effects of dark mode graphics on visual acuity and fatigue with virtual reality head-mounted displays. In: 2020 IEEE Conference on virtual reality and 3D user interfaces (VR). pp 434–442

Eriksson J, Vogel EK, Lansner A et al (2015) Neurocognitive architecture of working memory. Neuron 88:33–46. https://doi.org/10.1016/j.neuron.2015.09.020

EU-OSHA, (2019) Digitalisation and occupational safety and health. European Agency for Safety and Health at Work, Bilbao Spain, p 45

Evangelista Belo JM, Feit AM, Feuchtner T, Grønbæk K (2021) XRgonomics: facilitating the creation of ergonomic 3D interfaces. In: Proceedings of the 2021 CHI conference on human factors in computing systems. Association for Computing Machinery, New York, USA, pp 1–11

Evans BJ (2007) Chapter 2 - detecting binocular vision anomalies in primary eyecare practice. In: Evans BJ (ed) Pickwell’s binocular vision anomalies, 5th edn. Butterworth-Heinemann, Edinburgh, pp 12–38

Evans JStBT, Stanovich KE (2013) Dual-process theories of higher cognition: advancing the debate. Perspect Psychol Sci 8:223–241. https://doi.org/10.1177/1745691612460685

Filho JAW, Freitas CMDS, Nedel L (2018) VirtualDesk: a comfortable and efficient immersive information visualization approach. Comput Graph Forum 37:415–426. https://doi.org/10.1111/cgf.13430

Filho JAW, Freitas CMDS, Nedel L (2019) Comfortable immersive analytics with the virtualdesk metaphor. IEEE Comput Graph Appl 39:41–53. https://doi.org/10.1109/MCG.2019.2898856

Filho JAW, Stuerzlinger W, Nedel L (2020) Evaluating an immersive space-time cube geovisualization for intuitive trajectory data exploration. IEEE Trans Visual Comput Graph 26:514–524. https://doi.org/10.1109/TVCG.2019.2934415

Fink G (2007) Encyclopedia of stress four-volume set. Elsevier, San Diego, p 98

Fink G (2016) Chapter 1 - stress, definitions, mechanisms, and effects outlined: lessons from anxiety. In: Fink G (ed) Stress: Concepts, cognition, emotion, and behavior. Academic Press, San Diego, pp 3–11

Flament-Fultot M (2016) Counterfactuals versus constraints: towards an implementation theory of sensorimotor mastery. J Conscious Stud 23:153–176

Fortuin MF, Lambooij MT, IJsselsteijn WA et al (2010) An exploration of the initial effects of stereoscopic displays on optometric parameters. Ophthalmic Physiol Opt 31:33–44. https://doi.org/10.1111/j.1475-1313.2010.00804.x

Frutiger M, Borotkanics R (2021) Systematic review and meta-analysis suggest strength training and workplace modifications may reduce neck pain in office workers. Pain Pract 21:100–131. https://doi.org/10.1111/papr.12940

Fuchs P (2017) Virtual reality headsets - a theoretical and pragmatic approach, 1st edn. CRC Press, London, UK

Fuchs P (2018) The challenges and risks of democratization of VR-AR. In: Arnaldi B, Guitton P, Moreau G (eds) Virtual reality and augmented reality. Wiley, New Jersey, pp 289–301

Furnham A, Boo HC (2011) A literature review of the anchoring effect. J Socio Econ 40:35–42. https://doi.org/10.1016/j.socec.2010.10.008

Gallagher M, Ferrè ER (2018) Cybersickness: a multisensory integration perspective. Multisens Res 31:645–674. https://doi.org/10.1163/22134808-20181293

Gallego A, McHugh L, Penttonen M, Lappalainen R (2021) Measuring public speaking anxiety: self-report, behavioral, and physiological. Behav Modif. https://doi.org/10.1177/0145445521994308

Galy E, Cariou M, Mélan C (2012) What is the relationship between mental workload factors and cognitive load types? Int J Psychophysiol 83:269–275. https://doi.org/10.1016/j.ijpsycho.2011.09.023

Gandevia SC (2001) Spinal and supraspinal factors in human muscle fatigue. Physiol Rev 81:1725–1789. https://doi.org/10.1152/physrev.2001.81.4.1725

Ganzel BL, Morris PA, Wethington E (2010) Allostasis and the human brain: integrating models of stress from the social and life sciences. Psychol Rev 117:134–174. https://doi.org/10.1037/a0017773

Gao B, Chen Z, Chen X et al (2021) The effects of audiovisual landmarks on spatial learning and recalling for image browsing interface in virtual environments. J Syst Architect. https://doi.org/10.1016/j.sysarc.2021.102096

Geiger A, Bewersdorf I, Brandenburg E, Stark R (2018) Visual feedback for grasping in virtual reality environments for an interface to instruct digital human models. In: Ahram T, Falcão C (eds) Advances in usability and user experience. Springer, Cham, pp 228–239

Gesslein T, Biener V, Gagel P, et al (2020) Pen-based interaction with spreadsheets in mobile virtual reality. In: 2020 IEEE International symposium on mixed and augmented reality (ISMAR). pp 361–373

Godoy LD, Rossignoli MT, Delfino-Pereira P et al (2018) A comprehensive overview on stress neurobiology: basic concepts and clinical implications. Front Behav Neurosci. https://doi.org/10.3389/fnbeh.2018.00127

Goldinger SD, Papesh MH, Barnhart AS et al (2016) The poverty of embodied cognition. Psychon Bull Rev 23:959–978. https://doi.org/10.3758/s13423-015-0860-1

Grassini S, Laumann K (2021) Immersive visual technologies and human health. In: European conference on cognitive ergonomics 2021. association for computing machinery, New York, USA, pp 1–6

Grassini S, Laumann K (2020) Are modern head-mounted displays sexist? a systematic review on gender differences in HMD-mediated virtual reality. Front Psychol. https://doi.org/10.3389/fpsyg.2020.01604

Grubert J, Ofek E, Pahud M, Kristensson PO (2018) The office of the future: virtual, portable, and global. IEEE Comput Graph Appl 38:125–133. https://doi.org/10.1109/MCG.2018.2875609

Gruet M, Temesi J, Rupp T et al (2013) Stimulation of the motor cortex and corticospinal tract to assess human muscle fatigue. Neuroscience 231:384–399. https://doi.org/10.1016/j.neuroscience.2012.10.058

Guo J, Weng D, Been-Lirn Duh H et al (2017) Effects of using HMDs on visual fatigue in virtual environments. 2017 IEEE Virtual Reality (VR) IEEE. Los Angeles, CA, USA, pp 249–250

Guo J, Weng D, Zhang Z et al (2019b) Subjective and objective evaluation of visual fatigue caused by continuous and discontinuous use of HMDs. J Soc Infor Disp 27:108–119. https://doi.org/10.1002/jsid.750

Guo J, Weng D, Zhang Z, et al (2019a) Evaluation of maslows hierarchy of needs on long-term use of HMDs – a case study of office environment. In: 2019a IEEE Conference on virtual reality and 3D user interfaces (VR). pp 948–949

Guo J, Weng D, Fang H, et al (2020) Exploring the differences of visual discomfort caused by long-term immersion between virtual environments and physical environments. In: 2020 IEEE Conference on virtual reality and 3D user interfaces (VR). pp 443–452

Gupta K, Hajika R, Pai YS, et al (2020) Measuring human trust in a virtual assistant using physiological sensing in virtual reality. In: 2020 IEEE Conference on virtual reality and 3D user interfaces (VR). pp 756–765

Halim I, Omar AR, Saman AM, Othman I (2012) Assessment of muscle fatigue associated with prolonged standing in the workplace. Saf Health Work 3:31–42. https://doi.org/10.5491/SHAW.2012.3.1.31

Han J, Bae SH, Suk H-J (2017) Comparison of visual discomfort and visual fatigue between head-mounted display and smartphone. Electron Imaging 2017:212–217. https://doi.org/10.2352/issn.2470-1173.2017.14.hvei-146

Heidarimoghadam R, Mohammadfam I, Babamiri M et al (2020) Study protocol and baseline results for a quasi-randomized control trial: an investigation on the effects of ergonomic interventions on work-related musculoskeletal disorders, quality of work-life and productivity in knowledge-based companies. Int J Ind Ergon 80:103030. https://doi.org/10.1016/j.ergon.2020.103030

Heikoop DD, de Winter JCF, van Arem B, Stanton NA (2017) Effects of platooning on signal-detection performance, workload, and stress: a driving simulator study. Appl Ergon 60:116–127. https://doi.org/10.1016/j.apergo.2016.10.016

Helminen EC, Morton ML, Wang Q, Felver JC (2019) A meta-analysis of cortisol reactivity to the trier social stress test in virtual environments. Psychoneuroendocrinology 110:104437. https://doi.org/10.1016/j.psyneuen.2019.104437

Heo J-Y, Kim K, Fava M et al (2017) Effects of smartphone use with and without blue light at night in healthy adults: a randomized, double-blind, cross-over, placebo-controlled comparison. J Psychiatr Res 87:61–70. https://doi.org/10.1016/j.jpsychires.2016.12.010

Hess RF, To L, Zhou J et al (2015) Stereo vision: the haves and have-nots. I Perception 6:2041669515593028. https://doi.org/10.1177/2041669515593028

Hibbard PB, van Dam LCJ, Scarfe P (2020) The implications of interpupillary distance variability for virtual reality. In: 2020 International conference on 3D immersion (IC3D). pp 1–7

Hirota M, Kanda H, Endo T et al (2019) Comparison of visual fatigue caused by head-mounted display for virtual reality and two-dimensional display using objective and subjective evaluation. Ergonomics 62:759–766. https://doi.org/10.1080/00140139.2019.1582805

Hodges LF, Davis ET (1993) Geometric considerations for stereoscopic virtual environments. Presence Teleoperators Virtual Environ 2:34–43. https://doi.org/10.1162/pres.1993.2.1.34

Hoffman DM, Girshick AR, Akeley K, Banks MS (2008) Vergence–accommodation conflicts hinder visual performance and cause visual fatigue. J vis 8:1–30. https://doi.org/10.1167/8.3.33

Holliman NS, Dodgson NA, Favalora GE, Pockett L (2011) Three-dimensional displays: a review and applications analysis. IEEE Trans Broadcast 57:362–371. https://doi.org/10.1109/tbc.2011.2130930

Howard MC, Van Zandt EC (2021) A meta-analysis of the virtual reality problem: Unequal effects of virtual reality sickness across individual differences. Virtual Reality. https://doi.org/10.1007/s10055-021-00524-3

Howarth PA, Hodder SG (2008) Characteristics of habituation to motion in a virtual environment. Displays 29:117–123. https://doi.org/10.1016/j.displa.2007.09.009

European Agency for Safety and Health at Work (2007) Introduction to work-related musculoskeletal disorders - Safety and health at work. In: www.osha.europa.eu . https://osha.europa.eu/en/publications/factsheet-71-introduction-work-related-musculoskeletal-disorders/view . Accessed 22 Mar 2021

Iqbal H, Latif S, Yan Y et al (2021) Reducing arm fatigue in virtual reality by introducing 3D-spatial offset. IEEE Access 9:64085–64104. https://doi.org/10.1109/ACCESS.2021.3075769

Iskander J, Hossny M (2021) Measuring the likelihood of VR visual fatigue through ocular biomechanics. Displays 70:102105. https://doi.org/10.1016/j.displa.2021.102105

Iskander J, Hossny M, Nahavandi S (2018) a review on ocular biomechanic models for assessing visual fatigue in virtual reality. IEEE Access 6:19345–19361. https://doi.org/10.1109/ACCESS.2018.2815663

Iskander J, Hossny M, Nahavandi S (2019) Using biomechanics to investigate the effect of VR on eye vergence system. Appl Ergon 81:102883. https://doi.org/10.1016/j.apergo.2019.102883

Iskenderova A, Weidner F, Broll W (2017) Drunk virtual reality gaming: exploring the influence of alcohol on cybersickness. In: Proceedings of the annual symposium on computer-human interaction in play. ACM, Amsterdam The Netherlands, pp 561–572

Ito K, Tada M, Ujike H, Hyodo K (2019) Effects of weight and balance of head mounted display on physical load. In: Chen JYC, Fragomeni G (eds) Virtual, augmented and mixed reality multimodal interaction. Springer, Cham, pp 450–460

Jacobs J, Wang X, Alexa M (2019) Keep it simple: depth-based dynamic adjustment of rendering for head-mounted displays decreases visual comfort. ACM Trans Appl Percept 16:1–16. https://doi.org/10.1145/3353902

Jiang B, Hung GK, Ciuffreda KJ (2002) Models of vergence and accommodation-vergence interactions. In: Hung GK, Ciuffreda KJ (eds) Models of the visual system. Springer, US, Boston, MA, pp 341–384

Karajeh H, Maqableh M, Masadeh R (2014) A review on stereoscopic 3D home entertainment for the twenty first century. 3D Research. https://doi.org/10.1007/s13319-014-0026-3

Karimikia H, Singh H, Joseph D (2020) Negative outcomes of ICT use at work: meta-analytic evidence and the role of job autonomy. INTR 31:159–190. https://doi.org/10.1108/INTR-09-2019-0385

Kartick P, Quevedo AJU, Gualdron DR (2020) Design of Virtual reality reach and grasp modes factoring upper limb ergonomics. In: 2020 IEEE Conference on virtual reality and 3D user interfaces abstracts and workshops (VRW). pp 798–799

Keller K, Colucci D (1998) Perception in HMDs: what is it in head-mounted displays (HMDs) that really make them all so terrible? pp 46–53

Kemeny A, Chardonnet JR, Colombet F (2020) Getting rid of cybersickness: in virtual reality augmented reality, and simulators. Springer, Cham

Kennedy RS, Lane NE, Berbaum KS, Lilienthal MG (1993) Simulator sickness questionnaire: an enhanced method for quantifying simulator sickness. Int J Aviat Psychol 3:203–220. https://doi.org/10.1207/s15327108ijap0303_3

Khakurel J, Melkas H, Porras J (2018) Tapping into the wearable device revolution in the work environment: a systematic review. Inf Technol People 31:791–818. https://doi.org/10.1108/ITP-03-2017-0076

Kim JJ, Diamond DM (2002) The stressed hippocampus, synaptic plasticity and lost memories. Nat Rev Neurosci 3:453–462. https://doi.org/10.1038/nrn849

Kim E, Shin G (2018) Head rotation and muscle activity when conducting document editing tasks with a head-mounted display. Proc Hum Factor Ergon Soc Ann Meet 62:952–955. https://doi.org/10.1177/1541931218621219

Kim YY, Kim HJ, Kim EN et al (2005) Characteristic changes in the physiological components of cybersickness. Psychophysiology 42:616–625. https://doi.org/10.1111/j.1469-8986.2005.00349.x

Kim J, Kane D, Banks MS (2014) The rate of change of vergence–accommodation conflict affects visual discomfort. Vision Res 105:159–165. https://doi.org/10.1016/j.visres.2014.10.021

Kim J-Y, Kim S-H, So G-J (2016) The modeling of color fatigue in 3-dimensional stereoscopic video. IJCTE 8:229–234. https://doi.org/10.7763/IJCTE.2016.V8.1049

Kim Y, Woo J, Woo M (2017) Effects of stress and task difficulty on working memory and cortical networking. Percept Mot Skills 124:1194–1210. https://doi.org/10.1177/0031512517732851

Kim HK, Park J, Choi Y, Choe M (2018) Virtual reality sickness questionnaire (VRSQ): motion sickness measurement index in a virtual reality environment. Appl Ergon 69:66–73. https://doi.org/10.1016/j.apergo.2017.12.016

Kim YM, Rhiu I, Yun MH (2020) A Systematic review of a virtual reality system from the perspective of user experience. Int J Hum Comput Interact 36:893–910. https://doi.org/10.1080/10447318.2019.1699746

Kim H, Kim DJ, Chung WH et al (2021) Clinical predictors of cybersickness in virtual reality (VR) among highly stressed people. Sci Rep 11:12139. https://doi.org/10.1038/s41598-021-91573-w

Kim D, Jung YJ, Kim E, et al (2011) Human brain response to visual fatigue caused by stereoscopic depth perception. In: 2011 17th International conference on digital signal processing (DSP). pp 1–5

Kirschner PA (2017) Stop propagating the learning styles myth. Comput Educ 106:166–171. https://doi.org/10.1016/j.compedu.2016.12.006

Klier C, Buratto LG, Klier C, Buratto LG (2020) Stress and long-term memory retrieval: a systematic review. Trends Psychiatry Psychother 42:284–291. https://doi.org/10.1590/2237-6089-2019-0077

Knierim P, Schwind V, Feit AM, et al (2018) Physical keyboards in virtual reality: analysis of typing performance and effects of avatar hands. In: Proceedings of the 2018 CHI conference on human factors in computing systems. Association for Computing Machinery, New York, USA, pp 1–9

Koohestani A, Nahavandi D, Asadi H et al (2019) A knowledge discovery in motion sickness: a comprehensive literature review. IEEE Access 7:85755–85770. https://doi.org/10.1109/ACCESS.2019.2922993

Kuze J, Ukai K (2008) Subjective evaluation of visual fatigue caused by motion images. Displays 29:159–166. https://doi.org/10.1016/j.displa.2007.09.007

Kweon SH, Kweon HJ, Kim S, et al (2018) A brain wave research on VR (Virtual Reality) usage: comparison between VR and 2D video in EEG measurement. advances in human factors and systems interaction AHFE 2017 advances in intelligent systems and computing 592:194–203. https://doi.org/10.1007/978-3-319-60366-7_19

La Torre G, Esposito A, Sciarra I, Chiappetta M (2019) Definition, symptoms and risk of techno-stress: a systematic review. Int Arch Occup Environ Health 92:13–35. https://doi.org/10.1007/s00420-018-1352-1

Labuschagne I, Grace C, Rendell P et al (2019) An introductory guide to conducting the trier social stress test. Neurosci Biobehav Rev 107:686–695. https://doi.org/10.1016/j.neubiorev.2019.09.032

Lackner JR (2014) Motion sickness: more than nausea and vomiting. Exp Brain Res 232:2493–2510. https://doi.org/10.1007/s00221-014-4008-8

Lages WS, Bowman DA (2018) Move the object or move myself? walking vs. manipulation for the examination of 3D scientific data. Front ICT. https://doi.org/10.3389/fict.2018.00015

Laha B, Sensharma K, Schiffbauer JD, Bowman DA (2012) Effects of immersion on visual analysis of volume data. IEEE Trans Visual Comput Graph 18:597–606. https://doi.org/10.1109/TVCG.2012.42

Lambooij MTM, IJsselsteijn WA, Heynderickx I (2007) Visual discomfort in stereoscopic displays: a review. Stereosc Disp Virtual Real Syst XIV Int Soc Opt Photonics. https://doi.org/10.1117/12.705527

Lambooij M, IJsselsteijn W, Fortuin M, Heynderickx I (2009) Visual discomfort and visual fatigue of stereoscopic displays: a review. J Imaging Sci Technol 53:1–14. https://doi.org/10.2352/j.imagingsci.technol.2009.53.3.030201

LaViola JJ (2000) A discussion of cybersickness in virtual environments. ACM SIGCHI Bull 32:47–56. https://doi.org/10.1145/333329.333344

LaViola JJ, Kruijff E, McMahan RP, et al (2017) 3D user interfaces: theory and practice, second edition. addison-wesley, boston columbus indianapolis new york san francisco amsterdam cape town dubai london madrid milan munich paris montreal toronto delhi mexico city são paulo sidney hong kong seoul singapore taipei tokyo

Lavoie R, Main K, King C, King D (2020) Virtual experience, real consequences: the potential negative emotional consequences of virtual reality gameplay. Virtual Real. https://doi.org/10.1007/s10055-020-00440-y

Lawrenson JG, Hull CC, Downie LE (2017) The effect of blue-light blocking spectacle lenses on visual performance, macular health and the sleep-wake cycle: a systematic review of the literature. Ophthalmic Physiol Opt 37:644–654. https://doi.org/10.1111/opo.12406

Lawson BD (2014) Motion sickness symptomatology and origins. In: Hale KS, Stanney KM (eds) Handbook of virtual environments design, implementation, and applications, second. CRC Press, Boca Raton, FL, USA

Lazarus RS, Folkman S (1984) Stress, appraisal, and coping, 1st edn. Springer, New York

LeBlanc VR (2009) The effects of acute stress on performance: implications for health professions education. Acad Med 84:S25. https://doi.org/10.1097/ACM.0b013e3181b37b8f

Lee K, Choo H (2013) A critical review of selective attention: an interdisciplinary perspective. Artif Intell Rev 40:27–50. https://doi.org/10.1007/s10462-011-9278-y

Lee DH, Han SK (2018) Effects of watching virtual reality and 360° videos on erector spinae and upper trapezius muscle fatigue and cervical flexion-extension angle. KSPE 35:1107–1114. https://doi.org/10.7736/KSPE.2018.35.11.1107

Leppink J (2017) Cognitive load theory: practical implications and an important challenge. J Taibah Univ Med Sci 12:385–391. https://doi.org/10.1016/j.jtumed.2017.05.003

Leroy L (2016) Eyestrain reduction in stereoscopy. Wiley, New Jersey

Li Y, Qin J, Yan J et al (2019) Differences of physical vs. psychological stress: evidences from glucocorticoid receptor expression, hippocampal subfields injury, and behavioral abnormalities. Brain Imaging Behav 13:1780–1788. https://doi.org/10.1007/s11682-018-9956-3

Li G, Rempel D, Liu Y, Harris-Adamson C (2020a) The design and assignment of microgestures to commands for virtual and augmented reality tasks. Proc Hum Factors Ergon Soc Ann Meet 64:2061–2063. https://doi.org/10.1177/1071181320641498

Li M, Ganni S, Ponten J, et al (2020b) Analysing usability and presence of a virtual reality operating room (VOR) simulator during laparoscopic surgery training. In: 2020b IEEE Conference on virtual reality and 3D user interfaces (VR). pp 566–572

Li Y, Sarcar S, Zheng Y, Ren X (2021) Exploring text revision with backspace and caret in virtual reality. In: Proceedings of the 2021 CHI conference on human factors in computing systems. Association for Computing Machinery, New York, USA, pp 1–12

Lim H-K, Kim H, Jang T, Lee Y (2013) Research trends of international guides for human error prevention in nuclear power plants. J Ergon Soc Korea. https://doi.org/10.5143/JESK.2013.32.1.125

Long Y, Shen Y, Guo D et al (2020) The effects of consumer-grade virtual reality headsets on adult visual function. Semin Ophthalmol 35:170–173. https://doi.org/10.1080/08820538.2020.1776342

Lopes P, Tian N, Boulic R (2020) Exploring blink-rate behaviors for cybersickness detection in VR. In: 2020 IEEE Conference on virtual reality and 3D user interfaces abstracts and workshops (VRW). pp 794–795

Luger T, Bosch T, Veeger D, de Looze M (2014) The influence of task variation on manifestation of fatigue is ambiguous – a literature review. Ergonomics 57:162–174. https://doi.org/10.1080/00140139.2014.885088

Luong T, Martin N, Argelaguet F, Lécuyer A (2019) Studying the mental effort in virtual versus real environments. In: 2019 IEEE Conference on virtual reality and 3D user interfaces (VR). pp 809–816

Luu W, Zangerl B, Kalloniatis M et al (2021a) Vision impairment provides new insight into self-motion perception. Invest Ophthalmol vis Sci 62:4–4. https://doi.org/10.1167/iovs.62.2.4

Luu W, Zangerl B, Kalloniatis M, Kim J (2021b) Effects of stereopsis on vection, presence and cybersickness in head-mounted display (HMD) virtual reality. Sci Rep 11:12373. https://doi.org/10.1038/s41598-021-89751-x

MacArthur C, Grinberg A, Harley D, Hancock M (2021) You’re making me sick: a systematic review of how virtual reality research considers gender & cybersickness. In: Proceedings of the 2021 CHI conference on human factors in computing systems. Association for Computing Machinery, New York, USA, pp 1–15

Mahani M-AN, Sheybani S, Bausenhart KM et al (2017) Multisensory perception of contradictory information in an environment of varying reliability: evidence for conscious perception and optimal causal inference. Sci Rep 7:3167. https://doi.org/10.1038/s41598-017-03521-2

Main LC, Wolkow A, Chambers TP (2017) Quantifying the physiological stress response to simulated maritime pilotage tasks: the influence of task complexity and pilot experience. J Occup Environ Med 59:1078–1083. https://doi.org/10.1097/JOM.0000000000001161

Makransky G, Terkildsen TS, Mayer RE (2019) Adding immersive virtual reality to a science lab simulation causes more presence but less learning. Learn Instr 60:225–236. https://doi.org/10.1016/j.learninstruc.2017.12.007

Marcel M (2019) Communication apprehension across the career span. Int J Bus Commun. https://doi.org/10.1177/2329488419856803

Marshev V, Bolloch J, Pallamin N et al (2021) Impact of virtual reality headset use on eye blinking and lipid layer thickness. J Fr Ophtalmol. https://doi.org/10.1016/j.jfo.2020.09.032

Marvan T, Havlík M (2021) Is predictive processing a theory of perceptual consciousness? New Ideas Psychol 61:100837. https://doi.org/10.1016/j.newideapsych.2020.100837

Matsuura Y (2019) Aftereffect of stereoscopic viewing on human body II. In: Takada H, Miyao M, Fateh S (eds) Stereopsis and hygiene. Springer, Singapore, pp 89–99

Mays L (2009) Accommodation-vergence interactions. In: Binder MD, Hirokawa N, Windhorst U (eds) Encyclopedia of neuroscience. Springer, Berlin, Heidelberg, pp 10–10

McCoy SK, Hutchinson S, Hawthorne L et al (2014) Is pressure stressful? the impact of pressure on the stress response and category learning. Cogn Affect Behav Neurosci 14:769–781. https://doi.org/10.3758/s13415-013-0215-1

McGregor M, Azzopardi L, Halvey M (2021) Untangling cost, effort, and load in information seeking and retrieval. In: Proceedings of the 2021 conference on human information interaction and retrieval. Association for Computing Machinery, New York, USA, pp 151–161

Mehra D, Galor A (2020) Digital screen use and dry eye: a review. Asia Pac J Ophthalmol 9:491–497. https://doi.org/10.1097/APO.0000000000000328

Melzer J, Brozoski F, Letowski T et al (2009) Guidelines for HMD design. Sensat Percept Cognit Issues Helmet Mounted Disp. https://doi.org/10.1117/12.848931

Merbah J, Gorce P, Jacquier-Bret J (2020) Effects of environmental illumination and screen brightness settings on upper limb and axial skeleton parameters: how do users adapt postures? Ergonomics 63:1561–1570. https://doi.org/10.1080/00140139.2020.1808248

Merhi, O., Faugloire, E., Flanagan, M., & Stoffregen, T. A. (2007). Motion Sickness, Console Video Games, and Head-Mounted Displays. Human Factors: The Journal of the Human Factors and Ergonomics Society, 49(5), 920‑934. https://doi.org/10.1518/001872007x230262

Miglio F, Naroo S, Zeri F et al (2021) The effect of active smoking, passive smoking, and e-cigarettes on the tear film: an updated comprehensive review. Exp Eye Res 210:108691. https://doi.org/10.1016/j.exer.2021.108691

Mittelstaedt JM (2020) Individual predictors of the susceptibility for motion-related sickness: a systematic review. J Vestib Res 30:165–193. https://doi.org/10.3233/VES-200702

Mittelstaedt JM, Wacker J, Stelling D (2019) VR aftereffect and the relation of cybersickness and cognitive performance. Virtual Real 23:143–154. https://doi.org/10.1007/s10055-018-0370-3

Modi HN, Singh H, Darzi A, Leff DR (2020) Multitasking and time pressure in the operating room: impact on surgeons’ brain function. Ann Surg 272:648–657. https://doi.org/10.1097/SLA.0000000000004208

Mohamed Elias Z, Batumalai UM, Azmi ANH (2019) Virtual reality games on accommodation and convergence. Appl Ergon 81:102879. https://doi.org/10.1016/j.apergo.2019.102879

Monroe SM, Cummins LF (2015) Stress: psychological perspectives. In: Wright JD (ed) International encyclopedia of the social & behavioral sciences, 2nd edn. Elsevier, Oxford, pp 583–587

Monroe SM, Slavich GM (2016) Chapter 13 - psychological stressors: overview. In: Fink G (ed) Stress: concepts, cognition, emotion, and behavior. Academic Press, San Diego, pp 109–115

Monteiro P, Gonçalves G, Coelho H et al (2021) Hands-free interaction in immersive virtual reality: a systematic review. IEEE Trans Visual Comput Graph 27:2702–2713. https://doi.org/10.1109/TVCG.2021.3067687

Munafo J, Diedrick M, Stoffregen TA (2017) The virtual reality head-mounted display oculus rift induces motion sickness and is sexist in its effects. Exp Brain Res 235:889–901. https://doi.org/10.1007/s00221-016-4846-7

Munsamy AJ, Paruk H, Gopichunder B et al (2020) The effect of gaming on accommodative and vergence facilities after exposure to virtual reality head-mounted display. J Optom 13:163–170. https://doi.org/10.1016/j.optom.2020.02.004

Narvaez Linares NF, Charron V, Ouimet AJ et al (2020) A systematic review of the trier social stress test methodology: issues in promoting study comparison and replicable research. Neurobiol Stress 13:100235. https://doi.org/10.1016/j.ynstr.2020.100235

Neguţ A, Matu S-A, Sava FA, David D (2016) Task difficulty of virtual reality-based assessment tools compared to classical paper-and-pencil or computerized measures. Comput Hum Behav 54:414–424. https://doi.org/10.1016/j.chb.2015.08.029

Nemeth J, Tapaszto B, Aclimandos WA et al (2021) Update and guidance on management of myopia european society of ophthalmology in cooperation with international myopia institute. Eur J Ophthalmol. https://doi.org/10.1177/1120672121998960

Nesbitt K, Nalivaiko E (2018) Cybersickness. In: Lee N (ed) Encyclopedia of computer graphics and games. Springer, Cham, pp 1–6

Neveu P, Roumes C, Philippe M et al (2016) Stereoscopic viewing can induce changes in the CA/C ratio. Investig Opthalmol Visual Sci 57:4321–4326. https://doi.org/10.1167/iovs.15-18854

Nichols S (1999) Physical ergonomics of virtual environment use. Appl Ergon 30:79–90. https://doi.org/10.1016/s0003-6870(98)00045-3

Nichols S, Patel H (2002) Health and safety implications of virtual reality: a review of empirical evidence. Appl Ergon 33:251–271. https://doi.org/10.1016/s0003-6870(02)00020-0

Nisafani AS, Kiely G, Mahony C (2020) Workers’ technostress: a review of its causes, strains, inhibitors, and impacts. J Decis Syst. https://doi.org/10.1080/12460125.2020.1796286

Niwano Y, Iwasawa A, Tsubota K et al (2019) Protective effects of blue light-blocking shades on phototoxicity in human ocular surface cells. BMJ Open Ophthalmol 4:e000217. https://doi.org/10.1136/bmjophth-2018-000217

O’Regan JK, Noë A (2001) A sensorimotor account of vision and visual consciousness. Behav Brain Sci 24:939–973. https://doi.org/10.1017/s0140525x01000115

Ofek E, Grubert J, Pahud M, et al (2020) Towards a practical virtual office for mobile knowledge workers

Olson BV, McGuire C, Crawford A (2020) Improving the quality of work life: an interdisciplinary lens into the worker experience. In: Dhiman S (ed) The palgrave handbook of workplace well-being. Springer, Cham, pp 1–32

Ordóñez LD, Benson L, Pittarello A (2015) Time-pressure perception and decision making. In: Keren G, Wu G (eds) The wiley blackwell handbook of judgment and decision making. Wiley, UK, pp 517–542

Orru G, Longo L (2019) The evolution of cognitive load theory and the measurement of its intrinsic, extraneous and germane loads: a review. In: Longo L, Leva MC (eds) Human mental workload: models and applications. Springer, Cham, pp 23–48

Palmisano S, Allison RS, Kim J (2020a) Cybersickness in head-mounted displays is caused by differences in the user’s virtual and physical head Pose. Front Virtual Real. https://doi.org/10.3389/frvir.2020.587698

Palmisano S, Nakamura S, Allison RS, Riecke BE (2020b) The stereoscopic advantage for vection persists despite reversed disparity. Atten Percept Psychophys 82:2098–2118. https://doi.org/10.3758/s13414-019-01886-2

Panke K, Pladere T, Velina M, et al (2019) Ocular performance evaluation: how prolonged near work with virtual and real 3D image modifies our visual system. In: Proceedings of the 2nd international conference on applications of intelligent systems. Association for Computing Machinery, New York, USA, pp 1–5

Parent M, Peysakhovich V, Mandrick K et al (2019) The diagnosticity of psychophysiological signatures: can we disentangle mental workload from acute stress with ECG and fNIRS? Int J Psychophysiol 146:139–147. https://doi.org/10.1016/j.ijpsycho.2019.09.005

Park S, Won MJ, Lee EC et al (2015) Evaluation of 3D cognitive fatigue using heart–brain synchronization. Int J Psychophysiol 97:120–130. https://doi.org/10.1016/j.ijpsycho.2015.04.006

Park S, Kim L, Kwon J et al (2021) Evaluation of visual-induced motion sickness from head-mounted display using heartbeat evoked potential: a cognitive load-focused approach. Virtual Real. https://doi.org/10.1007/s10055-021-00600-8

Parker L (1983) The history of stereoscopy in art, science and entertainment. Proc SPIE Opt Entertain. https://doi.org/10.1117/12.935066

Parker AJ (2016) Vision in our three-dimensional world. Philos Trans R Soc B Biol Sci 371:20150251. https://doi.org/10.1098/rstb.2015.0251

Patterson R (2009) Review paper: human factors of stereo displays: an update. J Soc Infor Disp 17:987–996. https://doi.org/10.1889/jsid17.12.987

Patterson RE (2015) Basics of human binocular vision. In: Robert PP, Earl, (eds) Human factors of stereoscopic 3D displays. Springer, London, pp 9–21

Patterson R, Silzars A (2009) Immersive stereo displays, intuitive reasoning, and cognitive engineering. J Soc Infor Disp 17:443–448. https://doi.org/10.1889/JSID17.5.443

Patterson R, Winterbottom MD, Pierce BJ (2006) Perceptual issues in the use of head-mounted visual displays. Hum Factors J Hum Factors Ergon Soc 48:555–573. https://doi.org/10.1518/001872006778606877

Pautasso M (2013) Ten simple rules for writing a literature review. PLoS Comput Biol 9:e1003149. https://doi.org/10.1371/journal.pcbi.1003149

Penumudi SA, Kuppam VA, Kim JH, Hwang J (2020) The effects of target location on musculoskeletal load, task performance, and subjective discomfort during virtual reality interactions. Appl Ergon 84:103010. https://doi.org/10.1016/j.apergo.2019.103010

Peters A, McEwen BS, Friston K (2017) Uncertainty and stress: why it causes diseases and how it is mastered by the brain. Prog Neurobiol 156:164–188. https://doi.org/10.1016/j.pneurobio.2017.05.004

Pouget A, Beck JM, Ma WJ, Latham PE (2013) Probabilistic brains: knowns and unknowns. Nat Neurosci 16:1170–1178. https://doi.org/10.1038/nn.3495

Prasad K, Poplau S, Brown R et al (2020) Time pressure during primary care office visits: a prospective evaluation of data from the healthy work place study. J Gen Intern Med 35:465–472. https://doi.org/10.1007/s11606-019-05343-6

Prem R, Paškvan M, Kubicek B, Korunka C (2018) Exploring the ambivalence of time pressure in daily working life. Int J Stress Manag 25:35–43. https://doi.org/10.1037/str0000044

Priya DB, Subramaniyam M (2020) A systematic review on visual fatigue induced by tiny screens (smartphones). IOP Conf Ser Mater Sci Eng 912:062009. https://doi.org/10.1088/1757-899X/912/6/062009

Ragu-Nathan TS, Tarafdar M, Ragu-Nathan BS, Tu Q (2008) The consequences of technostress for end users in organizations: conceptual development and empirical validation. Inf Syst Res 19:417–433. https://doi.org/10.1287/isre.1070.0165

Ramadan MZ, Alhaag MH (2018) Evaluating the user physical stresses associated with watching 3d and 2d displays over extended time using heart rate variability, galvanic skin resistance, and performance measure. J Sens 2018:e2632157. https://doi.org/10.1155/2018/2632157

Ramsay DS, Woods SC (2014) Clarifying the roles of homeostasis and allostasis in physiological regulation. Psychol Rev 121:225–247. https://doi.org/10.1037/a0035942

Reason JT (1978) Motion sickness adaptation: a neural mismatch model. J R Soc Med 71:819–829

Rebenitsch L, Owen C (2016) Review on cybersickness in applications and visual displays. Virtual Real 20:101–125. https://doi.org/10.1007/s10055-016-0285-9

Rebenitsch L, Owen C (2017) Evaluating factors affecting virtual reality display. In: Lackey S, Chen J (eds) Virtual, augmented and mixed reality. Springer, Cham, pp 544–555

Rebenitsch L, Owen C (2021) Estimating cybersickness from virtual reality applications. Virtual Real 25:165–174. https://doi.org/10.1007/s10055-020-00446-6

Reenen HHH, van der Beek AJ, Blatter BM et al (2008) Does musculoskeletal discomfort at work predict future musculoskeletal pain? Ergonomics 51:637–648. https://doi.org/10.1080/00140130701743433

Reichelt S, Häussler R, Fütterer G, Leister N (2010) Depth cues in human visual perception and their realization in 3D displays. Three-dimensional imaging, visualization, and display 2010 and display technologies and applications for defense, security, and avionics IV. SPIE, Orlando, Florida, USA, pp 92–103

Roesler R, McGaugh JL (2019) Memory consolidation. In: Reference module in neuroscience and biobehavioral psychology. Elsevier

Rogers K, Funke J, Frommel J, et al (2019) Exploring interaction fidelity in virtual reality: object manipulation and whole-body movements. In: Proceedings of the 2019 CHI conference on human factors in computing systems. Association for Computing Machinery, New York, USA, pp 1–14

Rößing C (2016) Human Visual Perception. In: Terzis A (ed) Handbook of camera monitor systems: the automotive mirror-replacement technology based on ISO 16505. Springer, Cham, pp 279–312

Rotter P (2017) Why did the 3D revolution fail?: the present and future of stereoscopy [commentary]. IEEE Technol Soc Mag 36:81–85. https://doi.org/10.1109/MTS.2017.2654294

Rzayev R, Ugnivenko P, Graf S, et al (2021) Reading in VR: The effect of text presentation type and location. In: Proceedings of the 2021 chi conference on human factors in computing systems. Association for Computing Machinery, New York, USA, pp 1–10

Saghafian M, Sitompul TA, Laumann K et al (2021) Application of human factors in the development process of immersive visual technologies: challenges and future improvements. Front Psychol. https://doi.org/10.3389/fpsyg.2021.634352

Saredakis D, Szpak A, Birckhead B et al (2020) Factors associated with virtual reality sickness in head-mounted displays: a systematic review and meta-analysis. Front Hum Neurosci. https://doi.org/10.3389/fnhum.2020.00096

Sasaki K, Yoshizawa M, Sugita N, Abe M (2015) Evaluation of visual fatigue while watching artificial three-dimensional image with vertical parallax. In: 2015 IEEE 4th Global conference on consumer electronics (GCCE). IEEE, Osaka, Japan, pp 666–667

Schleußinger M (2021) Information retrieval interfaces in virtual reality—a scoping review focused on current generation technology. PLoS One 16:e0246398. https://doi.org/10.1371/journal.pone.0246398

Schmidt M, Newbutt N, Schmidt C, Glaser N (2021) A process-model for minimizing adverse effects when using head mounted display-based virtual reality for individuals with autism. Front Virtual Real. https://doi.org/10.3389/frvir.2021.611740

Schneiderman N, Ironson G, Siegel SD (2004) Stress and health: psychological, behavioral, and biological determinants. Annu Rev Clin Psychol 1:607–628. https://doi.org/10.1146/annurev.clinpsy.1.102803.144141

Schor CM (1992) A dynamic model of cross-coupling between accommodation and convergence: simulations of step and frequency responses. Optom Vis Sci 69:258–269. https://doi.org/10.1097/00006324-199204000-00002

Schor CM, Kotulak JC (1986) Dynamic interactions between accommodation and convergence are velocity sensitive. Vis Res 26:927–942. https://doi.org/10.1016/0042-6989(86)90151-3

Schor CM, Tsuetaki TK (1987) Fatigue of accommodation and vergence modifies their mutual interactions. Investig Opthalmol Vis Sci 28:1250–1259

Schubert RS, Hartwig J, Müller M, et al (2016) Are age differences missing in relative and absolute distance perception of stereoscopically presented virtual objects? In: Proceedings of the 22nd ACM conference on virtual reality software and technology. Association for Computing Machinery, New York, USA, pp 307–308

Sepp S, Howard SJ, Tindall-Ford S et al (2019) Cognitive load theory and human movement: towards an integrated model of working memory. Educ Psychol Rev 31:293–317. https://doi.org/10.1007/s10648-019-09461-9

Sesboüé B, Guincestre J-Y (2006) Muscular fatigue. Ann Readapt Med Phys 49:348–354. https://doi.org/10.1016/j.annrmp.2006.04.020

Sevinc V, Berkman MI (2020) Psychometric evaluation of simulator sickness questionnaire and its variants as a measure of cybersickness in consumer virtual environments. Appl Ergon 82:102958. https://doi.org/10.1016/j.apergo.2019.102958

Sharples S, Cobb S, Moody A, Wilson JR (2008) Virtual reality induced symptoms and effects (VRISE): comparison of head mounted display (HMD), desktop and projection display systems. Displays 29:58–69. https://doi.org/10.1016/j.displa.2007.09.005

Sharples S (2019) Workload II: A future paradigm for analysis and measurement. In: Bagnara S, Tartaglia R, Albolino S, et al. (eds) Proceedings of the 20th congress of the international ergonomics association (IEA 2018). Springer International Publishing, Cham, pp 489–498

Shen R, Weng D, Guo J et al (2019b) Effects of dynamic disparity on visual fatigue caused by watching 2D videos in HMDs. In: Wang Y, Huang Q, Peng Y (eds) Image and graphics technologies and applications. Springer, Singapore, pp 310–321

Shen R, Weng D, Chen S, et al (2019a) Mental fatigue of long-term office tasks in virtual environment. In: 2019a IEEE International symposium on mixed and augmented reality adjunct (ISMAR-Adjunct). pp 124–127

Sheppard AL, Wolffsohn JS (2018) Digital eye strain: prevalence, measurement and amelioration. BMJ Open Ophthalmol 3:e000146. https://doi.org/10.1136/bmjophth-2018-000146

Shibata T, Kim J, Hoffman DM, Banks MS (2011) The zone of comfort: predicting visual discomfort with stereo displays. J Vis 11:1–29. https://doi.org/10.1167/11.8.11

Shields GS, Sazma MA, Yonelinas AP (2016) The effects of acute stress on core executive functions: a meta-analysis and comparison with cortisol. Neurosci Biobehav Rev 68:651–668. https://doi.org/10.1016/j.neubiorev.2016.06.038

Shields GS, Sazma MA, McCullough AM, Yonelinas AP (2017) The effects of acute stress on episodic memory: a meta-analysis and integrative review. Psychol Bull 143:636–675. https://doi.org/10.1037/bul0000100

Somrak A, Humar I, Hossain MS et al (2019) Estimating VR sickness and user experience using different HMD technologies: an evaluation study. Futur Gener Comput Syst 94:302–316. https://doi.org/10.1016/j.future.2018.11.041

Song J, Chung T, Kang J, Nam K (2011) The changes in performance during stress-inducing cognitive task: focusing on processing difficulty. In: Park JJ, Yang LT, Lee C (eds) Future information technology. Springer, Berlin, Heidelberg, pp 345–347

Song Y, Liu Y, Yan Y (2019) The effects of center of mass on comfort of soft belts virtual reality devices. In: Rebelo F, Soares MM (eds) Advances in ergonomics in design. Springer, Cham, pp 312–321

Souchet AD, Philippe S, Lévêque A et al (2021a) Short- and long-term learning of job interview with a serious game in virtual reality: influence of eyestrain, stereoscopy, and apparatus. Virtual Real. https://doi.org/10.1007/s10055-021-00548-9

Souchet AD, Philippe S, Lourdeaux D, Leroy L (2021b) Measuring visual fatigue and cognitive load via eye tracking while learning with virtual reality head-mounted displays: a review. Int J Hum Comput Interact. https://doi.org/10.1080/10447318.2021.1976509

Souchet AD, Philippe S, Zobel D, et al (2018) Eyestrain impacts on learning job interview with a serious game in virtual reality: a randomized double-blinded study. In: Proceedings of the 24th ACM symposium on virtual reality software and technology. ACM, Tokyo Japan, pp 1–12

Souchet AD, Philippe S, Ober F, et al (2019) Investigating cyclical stereoscopy effects over visual discomfort and fatigue in virtual reality while learning. In: 2019 IEEE International symposium on mixed and augmented reality (ISMAR). pp 328–338

Souchet AD (2020) Visual fatigue impacts on learning via serious game in virtual reality. PhD Thesis, Paris 8 University

Speicher M, Feit AM, Ziegler P, Krüger A (2018a) Selection-based text entry in virtual reality. In: Proceedings of the 2018a CHI conference on human factors in computing systems. association for computing machinery, New York, NY, USA, pp 1–13

Speicher M, Hell P, Daiber F, et al (2018b) A virtual reality shopping experience using the apartment metaphor. In: Proceedings of the 2018b International conference on advanced visual interfaces. association for computing machinery, New York, NY, USA, pp 1–9

Stanney K, Fidopiastis C, Foster L (2020a) Virtual reality is sexist: but it does not have to Be. Front Robot AI. https://doi.org/10.3389/frobt.2020.00004

Stanney K, Lawson BD, Rokers B et al (2020b) Identifying causes of and solutions for cybersickness in immersive technology: reformulation of a research and development agenda. Int J Hum Comput Interact 36:1783–1803. https://doi.org/10.1080/10447318.2020.1828535

Staresina BP, Wimber M (2019) A neural chronometry of memory recall. Trends Cogn Sci 23:1071–1085. https://doi.org/10.1016/j.tics.2019.09.011

Stauffert J-P, Niebling F, Latoschik ME (2020) Latency and cybersickness: impact, causes, and measures. Rev Front Virtual Real. https://doi.org/10.3389/frvir.2020.582204

Stephenson E, DeLongis A (2020) Coping strategies. The wiley encyclopedia of health psychology. Wiley, New Jersey, pp 55–60

Stich J-F (2020) A review of workplace stress in the virtual office. Intell Build Int 12:208–220. https://doi.org/10.1080/17508975.2020.1759023

Stratton SJ (2016) Comprehensive reviews. Prehosp Disaster Med 31:347–348. https://doi.org/10.1017/S1049023X16000649

Sugita N, Yamaga T, Yoshizawa M, et al (2014) Visual fatigue induced by accommodation convergence mismatch while viewing three-dimensional television. In: 2014 IEEE 3rd Global conference on consumer electronics (GCCE). IEEE, Tokyo, Japan, pp 250–251

Sun Y, Kar G, Stevenson Won A, Hedge A (2019) Postural risks and user experience of 3d interface designs for virtual reality-based learning environments. Proc Hum Factors Ergon Soc Ann Meet 63:2313–2317. https://doi.org/10.1177/1071181319631023

Sweeney LE, Seidel D, Day M et al (2014) Adaptive virtual environments for neuropsychological assessment in serious games. Vis Res 105:121–129. https://doi.org/10.1016/j.visres.2014.10.007

Szopa A, Soares MM (2021) Handbook of standards and guidelines in human factors and ergonomics: second edition, 2nd edn. CRC Press

Szpak A, Michalski SC, Saredakis D et al (2019) Beyond feeling sick: the visual and cognitive aftereffects of virtual reality. IEEE Access 7:130883–130892. https://doi.org/10.1109/ACCESS.2019.2940073

Szpak A, Michalski SC, Loetscher T (2020) Exergaming with beat saber: an investigation of virtual reality aftereffects. J Med Internet Res 22:e19840. https://doi.org/10.2196/19840

Tams S, Thatcher J, Grover V (2018) Concentration, competence, confidence, and capture: an experimental study of age, interruption-based technostress, and task performance. J Assoc Infor Syst 19:2

Tarafdar M, Pullins EB, Ragu-Nathan TS (2015) Technostress: negative effect on performance and possible mitigations. Inf Syst J 25:103–132. https://doi.org/10.1111/isj.12042

Tarafdar M, Cooper CL, Stich J (2019) The technostress trifecta - techno eustress, techno distress and design: theoretical directions and an agenda for research. Info Syst J 29:6–42. https://doi.org/10.1111/isj.12169

Tarafdar M, Pirkkalainen H, Salo M, Makkonen M (2020) Taking on the “dark side”––coping with technostress. IT Prof 22:82–89. https://doi.org/10.1109/MITP.2020.2977343

Taylor JL, Amann M, Duchateau J et al (2016) Neural contributions to muscle fatigue: from the brain to the muscle and back again. Med Sci Sports Exerc 48:2294–2306. https://doi.org/10.1249/MSS.0000000000000923

Terzic K, Hansard ME (2017) Causes of discomfort in stereoscopic content: a review. https://doi.org/10.48550/arXiv.1703.04574

Thai KTP, Jung S, Lindeman RW (2020) On the use of ”active breaks” to perform eye exercises for more comfortable VR experiences. In: 2020 IEEE Conference on virtual reality and 3D user interfaces abstracts and workshops (VRW). pp 468–476

Tian F, Zhang Y, Li Y (2021) From 2D to VR film: a research on the load of different cutting rates based on EEG data processing. Information 12:130. https://doi.org/10.3390/info12030130

Treisman M (1977) Motion sickness: an evolutionary hypothesis. Science 197:493–495. https://doi.org/10.1126/science.301659

Tu Y, Shi Y, Wang L et al (2021) 17.2: Invited paper: influence of blue light from smartphone on visual fatigue. SID Symp Dig Techn Pap 52:108–111. https://doi.org/10.1002/sdtp.14396

Turnbull PRK, Phillips JR (2017) Ocular effects of virtual reality headset wear in young adults. Sci Rep 7:16172. https://doi.org/10.1038/s41598-017-16320-6

Ukai K, Howarth PA (2008) Visual fatigue caused by viewing stereoscopic motion images: Background, theories, and observations. Displays 29:106–116. https://doi.org/10.1016/j.displa.2007.09.004

Urey H, Chellappan KV, Erden E, Surman P (2011) State of the art in stereoscopic and autostereoscopic displays. Proc IEEE. https://doi.org/10.1109/JPROC.2010.2098351

Valori I, McKenna-Plumley PE, Bayramova R et al (2020) Proprioceptive accuracy in immersive virtual reality: a developmental perspective. PLoS ONE 15:e0222253. https://doi.org/10.1371/journal.pone.0222253

Van Acker BB, Parmentier DD, Vlerick P, Saldien J (2018) Understanding mental workload: from a clarifying concept analysis toward an implementable framework. Cogn Tech Work 20:351–365. https://doi.org/10.1007/s10111-018-0481-3

Van den Berg MMHE, Maas J, Muller R et al (2015) Autonomic nervous system responses to viewing green and built settings: differentiating between sympathetic and parasympathetic activity. Int J Environ Res Public Health 12:15860–15874. https://doi.org/10.3390/ijerph121215026

van Tulder M, Malmivaara A, Koes B (2007) Repetitive strain injury. Lancet 369:1815–1822. https://doi.org/10.1016/S0140-6736(07)60820-4

Vanneste P, Raes A, Morton J et al (2020) Towards measuring cognitive load through multimodal physiological data. Cogn Tech Work. https://doi.org/10.1007/s10111-020-00641-0

Varmaghani S, Abbasi Z, Weech S, Rasti J (2021) Spatial and attentional aftereffects of virtual reality and relations to cybersickness. Virtual Real. https://doi.org/10.1007/s10055-021-00535-0

Vergari M, Kojić T, Vona F, et al (2021) Influence of interactivity and social environments on user experience and social acceptability in virtual reality. In: 2021 IEEE virtual reality and 3D user interfaces (VR). pp 695–704

Vernazzani A (2019) The structure of sensorimotor explanation. Synthese 196:4527–4553. https://doi.org/10.1007/s11229-017-1664-9

Wahl S, Engelhardt M, Schaupp P et al (2019) The inner clock—Blue light sets the human rhythm. J Biophotonics 12:e201900102. https://doi.org/10.1002/jbio.201900102

Walsh KS, McGovern DP, Clark A, O’Connell RG (2020) Evaluating the neurophysiological evidence for predictive processing as a model of perception. Ann N Y Acad Sci 1464:242–268. https://doi.org/10.1111/nyas.14321

Wan J, Qin Z, Wang P et al (2017) Muscle fatigue: general understanding and treatment. Exp Mol Med 49:e384–e384. https://doi.org/10.1038/emm.2017.194

Wang Y, Zhai G, Chen S et al (2019) Assessment of eye fatigue caused by head-mounted displays using eye-tracking. Biomed Eng Online 18:111. https://doi.org/10.1186/s12938-019-0731-5

Wang B, Liu Y, Parker SK (2020) How does the use of information communication technology affect individuals? a work design perspective. Annals 14:695–725. https://doi.org/10.5465/annals.2018.0127

Wang A, Kuo H, Huang S (2010) Effects of polarity and ambient illuminance on the searching performance and visual fatigue for various aged users. In: The 40th International conference on computers indutrial engineering. pp 1–3

Waongenngarm P, van der Beek AJ, Akkarakittichoke N, Janwantanakul P (2020) Perceived musculoskeletal discomfort and its association with postural shifts during 4-h prolonged sitting in office workers. Appl Ergon 89:103225. https://doi.org/10.1016/j.apergo.2020.103225

Weech S, Varghese JP, Barnett-Cowan M (2018) Estimating the sensorimotor components of cybersickness. J Neurophysiol 120:2201–2217. https://doi.org/10.1152/jn.00477.2018

Weinert C, Pflügner K, Maier C (2020) Do users respond to challenging and hindering techno-stressors differently? a laboratory experiment. In: Davis FD, Riedl R, vom Brocke J et al (eds) Information systems and neuroscience. Springer, Cham, pp 79–89

Weingarten E, Chen Q, McAdams M et al (2016) From primed concepts to action: a meta-analysis of the behavioral effects of incidentally presented words. Psychol Bull 142:472–497. https://doi.org/10.1037/bul0000030

Widyanti A, Hafizhah HN (2021) The influence of personality, sound, and content difficulty on virtual reality sickness. Virtual Real. https://doi.org/10.1007/s10055-021-00525-2

Williams D (2020) Predictive coding and thought. Synthese 197:1749–1775. https://doi.org/10.1007/s11229-018-1768-x

Willingham DT, Hughes EM, Dobolyi DG (2015) The scientific status of learning styles theories. Teach Psychol 42:266–271. https://doi.org/10.1177/0098628315589505

Wismer A, Reinerman-Jones L, Teo G et al (2018) A workload comparison during anatomical training with a physical or virtual model. In: Schmorrow DD, Fidopiastis CM (eds) Augmented cognition: users and contexts. Springer, Cham, pp 240–252

Wu H, Deng Y, Pan J et al (2021) User capabilities in eyes-free spatial target acquisition in immersive virtual reality environments. Appl Ergon 94:103400. https://doi.org/10.1016/j.apergo.2021.103400

Wulvik AS, Dybvik H, Steinert M (2020) Investigating the relationship between mental state (workload and affect) and physiology in a control room setting (ship bridge simulator). Cogn Tech Work 22:95–108. https://doi.org/10.1007/s10111-019-00553-8

Yan Y, Chen K, Xie Y et al (2019) The effects of weight on comfort of virtual reality devices. In: Rebelo F, Soares MM (eds) Advances in ergonomics in design. Springer, Cham, pp 239–248

Yildirim C (2020) Don’t make me sick: investigating the incidence of cybersickness in commercial virtual reality headsets. Virtual Real 24:231–239. https://doi.org/10.1007/s10055-019-00401-0

Yoon HJ, Kim J, Park SW, Heo H (2020) Influence of virtual reality on visual parameters: immersive versus non-immersive mode. BMC Ophthalmol 20:200. https://doi.org/10.1186/s12886-020-01471-4

Young MS, Brookhuis KA, Wickens CD, Hancock PA (2015) State of science: mental workload in ergonomics. Ergonomics 58:1–17. https://doi.org/10.1080/00140139.2014.956151

Youssef PN, Sheibani N, Albert DM (2011) Retinal Light Toxicity. Eye 25:1–14. https://doi.org/10.1038/eye.2010.149

Yu X, Weng D, Guo J, et al (2018) Effect of using HMDs for one hour on preteens visual fatigue. In: 2018 IEEE International symposium on mixed and augmented reality adjunct (ISMAR-Adjunct). IEEE, Munich, Germany, pp 93–96

Yuan J, Mansouri B, Pettey JH et al (2018) The Visual Effects Associated with Head-Mounted Displays. Int J Ophthalmol Clin Res. https://doi.org/10.23937/2378-346x/1410085

Yue K, Wang D, Chiu SC, Liu Y (2020) Investigate the 3D visual fatigue using modified depth-related visual evoked potential paradigm. IEEE Trans Neural Syst Rehabil Eng 28:2794–2804. https://doi.org/10.1109/TNSRE.2021.3049566

Zeri F, Livi S (2015) Visual discomfort while watching stereoscopic three-dimensional movies at the cinema. Ophthalmic Physiol Opt 35:271–282. https://doi.org/10.1111/opo.12194

Zhang Y, Yang Y, Feng S et al (2020) The evaluation on visual fatigue and comfort between the VR HMD and the iPad. In: Karwowski W, Goonetilleke RS, Xiong S et al (eds) Advances in physical, social & occupational ergonomics. Springer, Cham, pp 213–219

Zhang S, Zhang Y, Sun Y, et al (2017) Graph theoretical analysis of EEG functional network during multi-workload flight simulation experiment in virtual reality environment. In: 2017 39th Annual international conference of the IEEE engineering in medicine and biology society (EMBC). pp 3957–3960

Zhao X, Xia Q, Huang W (2020) Impact of technostress on productivity from the theoretical perspective of appraisal and coping processes. Infor Manag 57:103265. https://doi.org/10.1016/j.im.2020.103265

Zielasko D, Weyers B, Bellgardt M, et al (2017) Remain seated: towards fully-immersive desktop VR. In: 2017 IEEE 3rd workshop on everyday virtual reality (WEVR). pp 1–6

Zielasko D, Weyers B, Kuhlen TW (2019) A non-stationary office desk substitution for desk-based and HMD-projected virtual reality. In: 2019 IEEE Conference on virtual reality and 3D user interfaces (VR). pp 1884–1889

Zielasko D, Riecke BE (2021) To sit or not to sit in VR: analyzing influences and (dis)advantages of posture and embodied interaction. Computers 10:73. https://doi.org/10.3390/computers10060073

Zimmer P, Buttlar B, Halbeisen G et al (2019) Virtually stressed? a refined virtual reality adaptation of the trier social stress test (TSST) induces robust endocrine responses. Psychoneuroendocrinology 101:186–192. https://doi.org/10.1016/j.psyneuen.2018.11.010

Zimmerman ME (2017) Task load. In: Kreutzer J, DeLuca J, Caplan B (eds) Encyclopedia of clinical neuropsychology. Springer, Cham, pp 1–1

Download references

This study was funded by European Union’s Horizon 2020 research and innovation program under grant agreement No 883293 (INFINITY project). H2020 European Research Council, 883293, Domitile Lourdeaux

Author information

Authors and affiliations.

Alliance Sorbonne Université, Université de Technologie de Compiègne, CNRS, Heudiasyc UMR 7253, Compiègne, France

Alexis D. Souchet & Domitile Lourdeaux

Neuroscience Department, IRBA, Brétigny-sur-Orge, France

Alexis D. Souchet

DFKI, Kaiserslautern, Germany

Alain Pagani

South Dakota Mines, Rapid City, SD, USA

Lisa Rebenitsch

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to Alexis D. Souchet .

Ethics declarations

Conflict of interest.

The authors declare that they have no conflict of interest.

Additional information

Publisher's note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file1 (XLSX 13 kb)

Supplementary file2 (png 131 kb), rights and permissions.

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ .

Reprints and permissions

About this article

Souchet, A.D., Lourdeaux, D., Pagani, A. et al. A narrative review of immersive virtual reality’s ergonomics and risks at the workplace: cybersickness, visual fatigue, muscular fatigue, acute stress, and mental overload. Virtual Reality 27 , 19–50 (2023). https://doi.org/10.1007/s10055-022-00672-0

Download citation

Received : 17 January 2022

Accepted : 21 June 2022

Published : 16 July 2022

Issue Date : March 2023

DOI : https://doi.org/10.1007/s10055-022-00672-0

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Virtual reality
• Cybersickness
• Visual fatigue
• Muscle fatigue
• Mental overload
• Find a journal
• Publish with us
• Track your research

The Utility of Virtual Reality in Interventions for Autism Spectrum Disorder: A Systematic Review

--> Brattan, Victoria Caroline (2019) The Utility of Virtual Reality in Interventions for Autism Spectrum Disorder: A Systematic Review. D.Clin.Psychol thesis, University of Leeds.

Autism spectrum disorder (ASD) is a neurodevelopmental condition characterised by impairments in social communication and interaction. Prevalence rates of anxiety are higher in this population compared to the general population. Anxiety and autistic traits can seriously impede an individual’s capacity to function in the social world. Current psychosocial interventions for ASD individuals are aimed to develop skills in interaction and communication or to address anxiety. The development and use of virtual reality for clinical interventions is on the rise, and its potential benefits for ASD individuals are numerous. As yet, little is known about the utility of VR-based interventions for ASD. We conducted a systematic review of randomised and non-randomised studies that employ VR for intervention in the core deficits of ASD and/or anxiety, and which report pre- post intervention data or change over time. Twenty-four studies met the inclusion criteria for the review. A quality assessment of included studies was conducted to evaluate their risk of bias. The review yielded five randomised controlled studies and 19 non-randomised or case studies. Studies employed VR in its variety of forms, including head mounted displays, desktop VR, and cave environments. A variety of interventions were employed, including CBT, scaffolded hierarchical learning, and social cognition training. Findings from the review suggest that VR-based interventions for ASD individuals are feasible and demonstrate effectiveness in the development of affect recognition and emotion regulation skills, as well as for job interviewing skills. Additionally, studies demonstrate its promise for development of communication and conversational skills. Further research is required of higher quality to determine the efficacy and effectiveness of studies in this and other areas. In particular, it is important that studies progress from exploratory use of VR toward more theory and evidence informed intervention protocols for ASD individuals. Additionally, follow-up research studies of the impact of intervention on individuals’ daily lives is also necessary to determine the generalisability of skills developed in VR, and real-life impact.

--> Final eThesis - complete (pdf) -->

Filename: VCB_DClinPsychol_ResearchThesis_forHardBound.pdf

Embargo Date:

You do not need to contact us to get a copy of this thesis. Please use the 'Download' link(s) above to get a copy. You can contact us about this thesis . If you need to make a general enquiry, please see the Contact us page.

• Open access
• Published: 01 May 2024

The effectiveness of virtual reality training on knowledge, skills and attitudes of health care professionals and students in assessing and treating mental health disorders: a systematic review

• Cathrine W. Steen 1 , 2 ,
• Kerstin Söderström 1 , 2 ,
• Bjørn Stensrud 3 ,
• Inger Beate Nylund 2 &
• Johan Siqveland 4 , 5  

BMC Medical Education volume  24 , Article number:  480 ( 2024 ) Cite this article

474 Accesses

1 Altmetric

Metrics details

Virtual reality (VR) training can enhance health professionals’ learning. However, there are ambiguous findings on the effectiveness of VR as an educational tool in mental health. We therefore reviewed the existing literature on the effectiveness of VR training on health professionals’ knowledge, skills, and attitudes in assessing and treating patients with mental health disorders.

We searched MEDLINE, PsycINFO (via Ovid), the Cochrane Library, ERIC, CINAHL (on EBSCOhost), Web of Science Core Collection, and the Scopus database for studies published from January 1985 to July 2023. We included all studies evaluating the effect of VR training interventions on attitudes, knowledge, and skills pertinent to the assessment and treatment of mental health disorders and published in English or Scandinavian languages. The quality of the evidence in randomized controlled trials was assessed with the Cochrane Risk of Bias Tool 2.0. For non-randomized studies, we assessed the quality of the studies with the ROBINS-I tool.

Of 4170 unique records identified, eight studies were eligible. The four randomized controlled trials were assessed as having some concern or a high risk of overall bias. The four non-randomized studies were assessed as having a moderate to serious overall risk of bias. Of the eight included studies, four used a virtual standardized patient design to simulate training situations, two studies used interactive patient scenario training designs, while two studies used a virtual patient game design. The results suggest that VR training interventions can promote knowledge and skills acquisition.

Conclusions

The findings indicate that VR interventions can effectively train health care personnel to acquire knowledge and skills in the assessment and treatment of mental health disorders. However, study heterogeneity, prevalence of small sample sizes, and many studies with a high or serious risk of bias suggest an uncertain evidence base. Future research on the effectiveness of VR training should include assessment of immersive VR training designs and a focus on more robust studies with larger sample sizes.

Trial registration

This review was pre-registered in the Open Science Framework register with the ID-number Z8EDK.

Peer Review reports

A robustly trained health care workforce is pivotal to forging a resilient health care system [ 1 ], and there is an urgent need to develop innovative methods and emerging technologies for health care workforce education [ 2 ]. Virtual reality technology designs for clinical training have emerged as a promising avenue for increasing the competence of health care professionals, reflecting their potential to provide effective training [ 3 ].

Virtual reality (VR) is a dynamic and diverse field, and can be described as a computer-generated environment that simulates sensory experiences, where user interactions play a role in shaping the course of events within that environment [ 4 ]. When optimally designed, VR gives users the feeling that they are physically within this simulated space, unlocking its potential as a dynamic and immersive learning tool [ 5 ]. The cornerstone of the allure of VR is its capacity for creating artificial settings via sensory deceptions, encapsulated by the term ‘immersion’. Immersion conveys the sensation of being deeply engrossed or enveloped in an alternate world, akin to absorption in a video game. Some VR systems will be more immersive than others, based on the technology used to influence the senses. However, the degree of immersion does not necessarily determine the user’s level of engagement with the application [ 6 ].

A common approach to categorizing VR systems is based on the design of the technology used, allowing them to be classified into: 1) non-immersive desktop systems, where users experience virtual environments through a computer screen, 2) immersive CAVE systems with large projected images and motion trackers to adjust the image to the user, and 3) fully immersive head-mounted display systems that involve users wearing a headset that fully covers their eyes and ears, thus entirely immersing them in the virtual environment [ 7 ]. Advances in VR technology have enabled a wide range of VR experiences. The possibility for health care professionals to repeatedly practice clinical skills with virtual patients in a risk-free environment offers an invaluable learning platform for health care education.

The impact of VR training on health care professionals’ learning has predominantly been researched in terms of the enhancement of technical surgical abilities. This includes refining procedural planning, familiarizing oneself with medical instruments, and practicing psychomotor skills such as dexterity, accuracy, and speed [ 8 , 9 ]. In contrast, the exploration of VR training in fostering non-technical or ‘soft’ skills, such as communication and teamwork, appears to be less prevalent [ 10 ]. A recent systematic review evaluates the outcomes of VR training in non-technical skills across various medical specialties [ 11 ], focusing on vital cognitive abilities (e.g., situation awareness, decision-making) and interprofessional social competencies (e.g., teamwork, conflict resolution, leadership). These skills are pivotal in promoting collaboration among colleagues and ensuring a safe health care environment. At the same time, they are not sufficiently comprehensive for encounters with patients with mental health disorders.

For health care professionals providing care to patients with mental health disorders, acquiring specific skills, knowledge, and empathic attitudes is of utmost importance. Many individuals experiencing mental health challenges may find it difficult to communicate their thoughts and feelings, and it is therefore essential for health care providers to cultivate an environment where patients feel safe and encouraged to share feelings and thoughts. Beyond fostering trust, health care professionals must also possess in-depth knowledge about the nature and treatment of various mental health disorders. Moreover, they must actively practice and internalize the skills necessary to translate their knowledge into clinical practice. While the conventional approach to training mental health clinical skills has been through simulation or role-playing with peers under expert supervision and practicing with real patients, the emergence of VR applications presents a compelling alternative. This technology promises a potentially transformative way to train mental health professionals. Our review identifies specific outcomes in knowledge, skills, and attitudes, covering areas from theoretical understanding to practical application and patient interaction. By focusing on these measurable concepts, which are in line with current healthcare education guidelines [ 12 ], we aim to contribute to the knowledge base and provide a detailed analysis of the complexities in mental health care training. This approach is designed to highlight the VR training’s practical relevance alongside its contribution to academic discourse.

A recent systematic review evaluated the effects of virtual patient (VP) interventions on knowledge, skills, and attitudes in undergraduate psychiatry education [ 13 ]. This review’s scope is limited to assessing VP interventions and does not cover other types of VR training interventions. Furthermore, it adopts a classification of VP different from our review, rendering their findings and conclusions not directly comparable to ours.

To the best of our knowledge, no systematic review has assessed and summarized the effectiveness of VR training interventions for health professionals in the assessment and treatment of mental health disorders. This systematic review addresses the gap by exploring the effectiveness of virtual reality in the training of knowledge, skills, and attitudes health professionals need to master in the assessment and treatment of mental health disorders.

This systematic review follows the guidelines of Preferred Reporting Items for Systematic Reviews and Meta-Analysis [ 14 ]. The protocol of the systematic review was registered in the Open Science Framework register with the registration ID Z8EDK.

We included randomized controlled trials, cohort studies, and pretest–posttest studies, which met the following criteria: a) a population of health care professionals or health care professional students, b) assessed the effectiveness of a VR application in assessing and treating mental health disorders, and c) reported changes in knowledge, skills, or attitudes. We excluded studies evaluating VR interventions not designed for training in assessing and treating mental health disorders (e.g., training of surgical skills), studies evaluating VR training from the first-person perspective, studies that used VR interventions for non-educational purposes and studies where VR interventions trained patients with mental health problems (e.g., social skills training). We also excluded studies not published in English or Scandinavian languages.

Search strategy

The literature search reporting was guided by relevant items in PRISMA-S [ 15 ]. In collaboration with a senior academic librarian (IBN), we developed the search strategy for the systematic review. Inspired by the ‘pearl harvesting’ information retrieval approach [ 16 ], we anticipated a broad spectrum of terms related to our interdisciplinary query. Recognizing that various terminologies could encapsulate our central ideas, we harvested an array of terms for each of the four elements ‘health care professionals and health care students’, ‘VR’, ‘training’, and ‘mental health’. The pearl harvesting framework [ 16 ] consists of four steps which we followed with some minor adaptions. Step 1: We searched for and sampled a set of relevant research articles, a book chapter, and literature reviews. Step 2: The librarian scrutinized titles, abstracts, and author keywords, as well as subject headings used in databases, and collected relevant terms. Step 3: The librarian refined the lists of terms. Step 4: The review group, in collaboration with a VR consultant from KildeGruppen AS (a Norwegian media company), validated the refined lists of terms to ensure they included all relevant VR search terms. This process for the element VR resulted in the inclusion of search terms such as ‘3D simulated environment’, ‘second life simulation’, ‘virtual patient’, and ‘virtual world’. We were given a peer review of the search strategy by an academic librarian at Inland Norway University of Applied Sciences.

In June and July 2021, we performed comprehensive searches for publications dating from January 1985 to the present. This period for the inclusion of studies was chosen since VR systems designed for training in health care first emerged in the early 1990s. The searches were carried out in seven databases: MEDLINE and PsycInfo (on Ovid), ERIC and CINAHL (on EBSCOhost), the Cochrane Library, Web of Science Core Collection, and Scopus. Detailed search strategies from each database are available for public access at DataverseNO [ 17 ]. On July 2, 2021, a search in CINAHL yielded 993 hits. However, when attempting to transfer these records to EndNote using the ‘Folder View’—a feature designed for organizing and managing selected records before export—only 982 records were successfully transferred. This discrepancy indicates that 11 records could not be transferred through Folder View, for reasons not specified. The process was repeated twice, consistently yielding the same discrepancy. The missing 11 records pose a risk of failing to capture relevant studies in the initial search. In July 2023, to make sure that we included the latest publications, we updated our initial searches, focusing on entries since January 1, 2021. This ensured that we did not miss any new references recently added to these databases. Due to a lack of access to the Cochrane Library in July 2023, we used EBMR (Evidence Based Medicine Reviews) on the Ovid platform instead, including the databases Cochrane Central Register of Controlled Trials, Cochrane Database of Systematic Reviews, and Cochrane Clinical Answers. All references were exported to Endnote and duplicates were removed. The number of records from each database can be observed in the PRISMA diagram [ 14 ], Fig.  1 .

PRISMA flow chart of the records and study selection process

Study selection and data collection

Two reviewers (JS, CWS) independently assessed the titles and abstracts of studies retrieved from the literature search based on the eligibility criteria. We employed the Rayyan website for the screening process [ 18 ]. The same reviewers (JS, CWS) assessed the full-text articles selected after the initial screening. Articles meeting the eligibility criteria were incorporated into the review. Any disagreements were resolved through discussion.

Data extracted from the studies by the first author (CWS) and cross-checked by another reviewer (JS) included: authors of the study, publication year, country, study design, participant details (education, setting), interventions (VR system, class label), comparison types, outcomes, and main findings. This data is summarized in Table  1 and Additional file 1 . In the process of reviewing the VR interventions utilized within the included studies, we sought expertise from advisers associated with VRINN, a Norwegian immersive learning cluster, and SIMInnlandet, a center dedicated to simulation in mental health care at Innlandet Hospital Trust. This collaboration ensured a thorough examination and accurate categorization of the VR technologies applied. Furthermore, the classification of the learning designs employed in the VP interventions was conducted under the guidance of an experienced VP scholar at Paracelcus Medical University in Salzburg.

Data analysis

We initially intended to perform a meta-analysis with knowledge, skills, and attitudes as primary outcomes, planning separate analyses for each. However, due to significant heterogeneity observed among the included studies, it was not feasible to carry out a meta-analysis. Consequently, we opted for a narrative synthesis based on these pre-determined outcomes of knowledge, skills, and attitudes. This approach allowed for an analysis of the relationships both within and between the studies. The effect sizes were calculated using a web-based effect size calculator [ 27 ]. We have interpreted effect sizes based on commonly used descriptions for Cohen’s d: small = 0.2, moderate = 0.5, and large = 0.8, and for Cramer’s V: small = 0.10, medium = 0.30, and large = 0.50.

Risk of bias assessment

JS and CWS independently evaluated the risk of bias for all studies using two distinct assessment tools. We used the Cochrane risk of bias tool RoB 2 [ 28 ] to assess the risk of bias in the RCTs. With the RoB 2 tool, the bias was assessed as high, some concerns or low for five domains: randomization process, deviations from the intended interventions, missing outcome data, measurement of the outcome, and selection of the reported result [ 28 ].

We used the Risk Of Bias In Non-randomized Studies of Interventions (ROBINS-I) tool [ 29 ] to assess the risk of bias in the cohort and single-group studies. By using ROBINS-I for the non-randomized trials, the risk of bias was assessed using the categories low, moderate, serious, critical or no information for seven domains: confounding, selection of participants, classification of interventions, deviations from intended interventions, missing data, measurement of outcomes, and selection of the reported result [ 29 ].

We included eight studies in the review (Fig.  1 ). An overview of the included studies is presented in detail in Table  1 .

Four studies were RCTs [ 19 , 20 , 21 , 22 ], two were single group pretest–posttest studies [ 23 , 26 ], one was a controlled before and after study [ 25 ], and one was a cohort study [ 24 ]. The studies included health professionals from diverse educational backgrounds, including some from mental health and medical services, as well as students in medicine, social work, and nursing. All studies, published from 2009 to 2021, utilized non-immersive VR desktop system interventions featuring various forms of VP designs. Based on an updated classification of VP interventions by Kononowicz et al. [ 30 ] developed from a model proposed by Talbot et al. [ 31 ], we have described the characteristics of the interventions in Table  1 . Four of the studies utilized a virtual standardized patient (VSP) intervention [ 20 , 21 , 22 , 23 ], a conversational agent that simulates clinical presentations for training purposes. Two studies employed an interactive patient scenario (IPS) design [ 25 , 26 ], an approach that primarily uses text-based multimedia, enhanced with images and case histories through text or voice narratives, to simulate clinical scenarios. Lastly, two studies used a virtual patient game (VP game) intervention [ 19 , 24 ]. These interventions feature training scenarios using 3D avatars, specifically designed to improve clinical reasoning and team training skills. It should be noted that the interventions classified as VSPs in this review, being a few years old, do not encompass artificial intelligence (AI) as we interpret it today. However, since the interventions include some kind of algorithm that provides answers to questions, we consider them as conversational agents, and therefore as VSPs. As the eight included studies varied significantly in terms of design, interventions, and outcome measures, we could not incorporate them into a meta-analysis.

The overall risk of bias for the four RCTs was high [ 19 , 20 , 22 ] or of some concern [ 21 ] (Fig.  2 ). They were all assessed as low or of some concern in the domains of randomization. Three studies were assessed with a high risk of bias in one [ 19 , 20 ] or two domains [ 22 ]; one study had a high risk of bias in the domain of selection of the reported result [ 19 ], one in the domain of measurement of outcome [ 20 ], and one in the domains of deviation from the intended interventions and missing outcome data [ 22 ]. One study was not assessed as having a high risk of bias in any domain [ 21 ].

Risk of bias summary: review authors assessments of each risk of bias item in the included RCT studies

For the four non-randomized studies, the overall risk of bias was judged to be moderate [ 26 ] or serious [ 23 , 24 , 25 ] (Fig.  3 ). One study had a serious risk of bias in two domains: confounding and measurement of outcomes [ 23 ]. Two studies had a serious risk of bias in one domain, namely confounding [ 24 , 25 ], while one study was judged not to have a serious risk of bias in any domain [ 26 ].

Risk of bias summary: review authors assessments of each risk of bias item in the included non-randomized studies

Three studies investigated the impact of virtual reality training on mental health knowledge [ 24 , 25 , 26 ]. One study with 32 resident psychiatrists in a single group pretest–posttest design assessed the effect of a VR training intervention on knowledge of posttraumatic stress disorder (PTSD) symptomatology, clinical management, and communication skills [ 26 ]. The intervention consisted of an IPS. The assessment of the outcome was conducted using a knowledge test with 11 multiple-choice questions and was administered before and after the intervention. This study reported a significant improvement on the knowledge test after the VR training intervention.

The second study examined the effect of a VR training intervention on knowledge of dementia [ 25 ], employing a controlled before and after design. Seventy-nine medical students in clinical training were divided into two groups, following a traditional learning program. The experimental group received an IPS intervention. The outcome was evaluated with a knowledge test administered before and after the intervention with significantly higher posttest scores in the experimental group than in the control group, with a moderate effects size observed between the groups.

A third study evaluated the effect of a VR training intervention on 299 undergraduate nursing students’ diagnostic recognition of depression and schizophrenia (classified as knowledge) [ 24 ]. In a prospective cohort design, the VR intervention was the only difference in the mental health related educational content provided to the two cohorts, and consisted of a VP game design, developed to simulate training situations with virtual patient case scenarios, including depression and schizophrenia. The outcome was assessed by determining the accuracy of diagnoses made after reviewing case vignettes of depression and schizophrenia. The study found no statistically significant effect of VR training on diagnostic accuracy between the simulation and the non-simulation cohort.

Summary: All three studies assessing the effect of a VR intervention on knowledge were non-randomized studies with different study designs using different outcome measures. Two studies used an IPS design, while one study used a VP game design. Two of the studies found a significant effect of VR training on knowledge. Of these, one study had a moderate overall risk of bias [ 26 ], while the other was assessed as having a serious overall risk of bias [ 25 ]. The third study, which did not find any effect of the virtual reality intervention on knowledge, was assessed to have a serious risk of bias [ 24 ].

Three RCTs assessed the effectiveness of VR training on skills [ 20 , 21 , 22 ]. One of them evaluated the effect of VR training on clinical skills in alcohol screening and intervention [ 20 ]. In this study, 102 health care professionals were randomly allocated to either a group receiving no training or a group receiving a VSP intervention. To evaluate the outcome, three standardized patients rated each participant using a checklist based on clinical criteria. The VSP intervention group demonstrated significantly improved posttest skills in alcohol screening and brief intervention compared to the control group, with moderate and small effect sizes, respectively.

Another RCT, including 67 medical college students, evaluated the effect of VR training on clinical skills by comparing the frequency of questions asked about suicide in a VSP intervention group and a video module group [ 21 ]. The assessment of the outcome was a psychiatric interview with a standardized patient. The primary outcome was the frequency with which the students asked the standardized patient five questions about suicide risk. Minimal to small effect sizes were noted in favor of the VSP intervention, though they did not achieve statistical significance for any outcomes.

One posttest only RCT evaluated the effect of three training programs on skills in detecting and diagnosing major depressive disorder and posttraumatic stress disorder (PTSD) [ 22 ]. The study included 30 family physicians, and featured interventions that consisted of two different VSPs designed to simulate training situations, and one text-based program. A diagnostic form filled in by the participants after the intervention was used to assess the outcome. The results revealed a significant effect on diagnostic accuracy for major depressive disorder for both groups receiving VR training, compared to the text-based program, with large effect sizes observed. For PTSD, the intervention using a fixed avatar significantly improved diagnostic accuracy with a large effect size, whereas the intervention with a choice avatar demonstrated a moderate to large effect size compared to the text-based program.

Summary: Three RCTs assessed the effectiveness of VR training on clinical skills [ 20 , 21 , 22 ], all of which used a VSP design. To evaluate the effect of training, two of the studies utilized standardized patients with checklists. The third study measured the effect on skills using a diagnostic form completed by the participants. Two of the studies found a significant effect on skills [ 20 , 22 ], both were assessed to have a high risk of bias. The third study, which did not find any effect of VR training on skills, had some concern for risk of bias [ 21 ].

Knowledge and skills

One RCT study with 227 health care professionals assessed knowledge and skills as a combined outcome compared to a waitlist control group, using a self-report survey before and after the VR training [ 19 ]. The training intervention was a VP game designed to practice knowledge and skills related to mental health and substance abuse disorders. To assess effect of the training, participants completed a self-report scale measuring perceived knowledge and skills. Changes between presimulation and postsimulation scores were reported only for the within treatment group ( n  = 117), where the composite postsimulation score was significantly higher than the presimulation score, with a large effect size observed. The study was judged to have a high risk of bias in the domain of selection of the reported result.

One single group pretest–posttest study with 100 social work and nursing students assessed the effect of VSP training on attitudes towards individuals with substance abuse disorders [ 23 ]. To assess the effect of the training, participants completed an online pretest and posttest survey including questions from a substance abuse attitudes survey. This study found no significant effect of VR training on attitudes and was assessed as having a serious risk of bias.

Perceived competence

The same single group pretest–posttest study also assessed the effect of a VSP training intervention on perceived competence in screening, brief intervention, and referral to treatment in encounters with patients with substance abuse disorders [ 23 ]. A commonly accepted definition of competence is that it comprises integrated components of knowledge, skills, and attitudes that enable the successful execution of a professional task [ 32 ]. To assess the effect of the training, participants completed an online pretest and posttest survey including questions on perceived competence. The study findings demonstrated a significant increase in perceived competence following the VSP intervention. The risk of bias in this study was judged as serious.

This systematic review aimed to investigate the effectiveness of VR training on knowledge, skills, and attitudes that health professionals need to master in the assessment and treatment of mental health disorders. A narrative synthesis of eight included studies identified VR training interventions that varied in design and educational content. Although mixed results emerged, most studies reported improvements in knowledge and skills after VR training.

We found that all interventions utilized some type of VP design, predominantly VSP interventions. Although our review includes a limited number of studies, it is noteworthy that the distribution of interventions contrasts with a literature review on the use of ‘virtual patient’ in health care education from 2015 [ 30 ], which identified IPS as the most frequent intervention. This variation may stem from our review’s focus on the mental health field, suggesting a different intervention need and distribution than that observed in general medical education. A fundamental aspect of mental health education involves training skills needed for interpersonal communication, clinical interviews, and symptom assessment, which makes VSPs particularly appropriate. While VP games may be suitable for clinical reasoning in medical fields, offering the opportunity to perform technical medical procedures in a virtual environment, these designs may present some limitations for skills training in mental health education. Notably, avatars in a VP game do not comprehend natural language and are incapable of engaging in conversations. Therefore, the continued advancement of conversational agents like VSPs is particularly compelling and considered by scholars to hold the greatest potential for clinical skills training in mental health education [ 3 ]. VSPs, equipped with AI dialogue capabilities, are particularly valuable for repetitive practice in key skills such as interviewing and counseling [ 31 ], which are crucial in the assessment and treatment of mental health disorders. VSPs could also be a valuable tool for the implementation of training methods in mental health education, such as deliberate practice, a method that has gained attention in psychotherapy training in recent years [ 33 ] for its effectiveness in refining specific performance areas through consistent repetition [ 34 ]. Within this evolving landscape, AI system-based large language models (LLMs) like ChatGPT stand out as a promising innovation. Developed from extensive datasets that include billions of words from a variety of sources, these models possess the ability to generate and understand text in a manner akin to human interaction [ 35 ]. The integration of LLMs into educational contexts shows promise, yet careful consideration and thorough evaluation of their limitations are essential [ 36 ]. One concern regarding LLMs is the possibility of generating inaccurate information, which represents a challenge in healthcare education where precision is crucial [ 37 ]. Furthermore, the use of generative AI raises ethical questions, notably because of potential biases in the training datasets, including content from books and the internet that may not have been verified, thereby risking the perpetuation of these biases [ 38 ]. Developing strategies to mitigate these challenges is imperative, ensuring LLMs are utilized safely in healthcare education.

All interventions in our review were based on non-immersive desktop VR systems, which is somewhat surprising considering the growing body of literature highlighting the impact of immersive VR technology in education, as exemplified by reviews such as that of Radianti et al. [ 39 ]. Furthermore, given the recent accessibility of affordable, high-quality head-mounted displays, this observation is noteworthy. Research has indicated that immersive learning based on head-mounted displays generally yields better learning outcomes than non-immersive approaches [ 40 ], making it an interesting research area in mental health care training and education. Studies using immersive interventions were excluded in the present review because of methodological concerns, paralleling findings described in a systematic review on immersive VR in education [ 41 ], suggesting the potential early stage of research within this field. Moreover, the integration of immersive VR technology into mental health care education may encounter challenges associated with complex ethical and regulatory frameworks, including data privacy concerns exemplified by the Oculus VR headset-Facebook integration, which could restrict the implementation of this technology in healthcare setting. Prioritizing specific training methodologies for enhancing skills may also affect the utilization of immersive VR in mental health education. For example, integrating interactive VSPs into a fully immersive VR environment remains a costly endeavor, potentially limiting the widespread adoption of immersive VR in mental health care. Meanwhile, the use of 360-degree videos in immersive VR environments for training purposes [ 42 ] can be realized with a significantly lower budget. Immersive VR offers promising opportunities for innovative training, but realizing its full potential in mental health care education requires broader research validation and the resolution of existing obstacles.

This review bears some resemblance to the systematic review by Jensen et al. on virtual patients in undergraduate psychiatry education [ 13 ] from 2024, which found that virtual patients improved learning outcomes compared to traditional methods. However, these authors’ expansion of the commonly used definition of virtual patient makes their results difficult to compare with the findings in the present review. A recognized challenge in understanding VR application in health care training arises from the literature on VR training for health care personnel, where ‘virtual patient’ is a term broadly used to describe a diverse range of VR interventions, which vary significantly in technology and educational design [ 3 , 30 ]. For instance, reviews might group different interventions using various VR systems and designs under a single label (virtual patient), or primary studies may use misleading or inadequately defined classifications for the virtual patient interventions evaluated. Clarifying the similarities and differences among these interventions is vital to inform development and enhance communication and understanding in educational contexts [ 43 ].

Strengths and limitations

To the best of our knowledge, this is the first systematic review to evaluate the effectiveness of VR training on knowledge, skills, and attitudes in health care professionals and students in assessing and treating mental health disorders. This review therefore provides valuable insights into the use of VR technology in training and education for mental health care. Another strength of this review is the comprehensive search strategy developed by a senior academic librarian at Inland Norway University of Applied Sciences (HINN) and the authors in collaboration with an adviser from KildeGruppen AS (a Norwegian media company). The search strategy was peer-reviewed by an academic librarian at HINN. Advisers from VRINN (an immersive learning cluster in Norway) and SIMInnlandet (a center for simulation in mental health care at Innlandet Hospital Trust) provided assistance in reviewing the VR systems of the studies, while the classification of the learning designs was conducted under the guidance of a VP scholar. This systematic review relies on an established and recognized classification of VR interventions for training health care personnel and may enhance understanding of the effectiveness of VR interventions designed for the training of mental health care personnel.

This review has some limitations. As we aimed to measure the effect of the VR intervention alone and not the effect of a blended training design, the selection of included studies was limited. Studies not covered in this review might have offered different insights. Given the understanding that blended learning designs, where technology is combined with other forms of learning, have significant positive effects on learning outcomes [ 44 ], we were unable to evaluate interventions that may be more effective in clinical settings. Further, by limiting the outcomes to knowledge, skills, and attitudes, we might have missed insights into other outcomes that are pivotal to competence acquisition.

Limitations in many of the included studies necessitate cautious interpretation of the review’s findings. Small sample sizes and weak designs in several studies, coupled with the use of non-validated outcome measures in some studies, diminish the robustness of the findings. Furthermore, the risk of bias assessment in this review indicates a predominantly high or serious risk of bias across most of the studies, regardless of their design. In addition, the heterogeneity of the studies in terms of study design, interventions, and outcome measures prevented us from conducting a meta-analysis.

Further research

Future research on the effectiveness of VR training for specific learning outcomes in assessing and treating mental health disorders should encompass more rigorous experimental studies with larger sample sizes. These studies should include verifiable descriptions of the VR interventions and employ validated tools to measure outcomes. Moreover, considering that much professional learning involves interactive and reflective practice, research on VR training would probably be enhanced by developing more in-depth study designs that evaluate not only the immediate learning outcomes of VR training but also the broader learning processes associated with it. Future research should also concentrate on utilizing immersive VR training applications, while additionally exploring the integration of large language models to augment interactive learning in mental health care. Finally, this review underscores the necessity in health education research involving VR to communicate research findings using agreed terms and classifications, with the aim of providing a clearer and more comprehensive understanding of the research.

This systematic review investigated the effect of VR training interventions on knowledge, skills, and attitudes in the assessment and treatment of mental health disorders. The results suggest that VR training interventions can promote knowledge and skills acquisition. Further studies are needed to evaluate VR training interventions as a learning tool for mental health care providers. This review emphasizes the necessity to improve future study designs. Additionally, intervention studies of immersive VR applications are lacking in current research and should be a future area of focus.

Availability of data and materials

Detailed search strategies from each database is available in the DataverseNO repository, https://doi.org/10.18710/TI1E0O .

Abbreviations

Virtual Reality

Cave Automatic Virtual Environment

Randomized Controlled Trial

Non-Randomized study

Virtual Standardized Patient

Interactive Patient Scenario

Virtual Patient

Post Traumatic Stress Disorder

Standardized Patient

Artificial intelligence

Inland Norway University of Applied Sciences

Doctor of Philosophy

Frenk J, Chen L, Bhutta ZA, Cohen J, Crisp N, Evans T, et al. Health professionals for a new century: transforming education to strengthen health systems in an interdependent world. Lancet. 2010;376(9756):1923–58.

Article   Google Scholar  

World Health Organization. eLearning for undergraduate health professional education: a systematic review informing a radical transformation of health workforce development. Geneva: World Health Organization; 2015.

Google Scholar  

Talbot T, Rizzo AS. Virtual human standardized patients for clinical training. In: Rizzo AS, Bouchard S, editors. Virtual reality for psychological and neurocognitive interventions. New York: Springer; 2019. p. 387–405.

Chapter   Google Scholar  

Merriam-Webster dictionary. Springfield: Merriam-Webster Incorporated; c2024. Virtual reality. Available from: https://www.merriam-webster.com/dictionary/virtual%20reality . [cited 2024 Mar 24].

Winn W. A conceptual basis for educational applications of virtual reality. Technical Publication R-93–9. Seattle: Human Interface Technology Laboratory, University of Washington; 1993.

Bouchard S, Rizzo AS. Applications of virtual reality in clinical psychology and clinical cognitive neuroscience–an introduction. In: Rizzo AS, Bouchard S, editors. Virtual reality for psychological and neurocognitive interventions. New York: Springer; 2019. p. 1–13.

Waller D, Hodgson E. Sensory contributions to spatial knowledge of real and virtual environments. In: Steinicke F, Visell Y, Campos J, Lécuyer A, editors. Human walking in virtual environments: perception, technology, and applications. New York: Springer New York; 2013. p. 3–26. https://doi.org/10.1007/978-1-4419-8432-6_1 .

Choudhury N, Gélinas-Phaneuf N, Delorme S, Del Maestro R. Fundamentals of neurosurgery: virtual reality tasks for training and evaluation of technical skills. World Neurosurg. 2013;80(5):e9–19. https://doi.org/10.1016/j.wneu.2012.08.022 .

Gallagher AG, Ritter EM, Champion H, Higgins G, Fried MP, Moses G, et al. Virtual reality simulation for the operating room: proficiency-based training as a paradigm shift in surgical skills training. Ann Surg. 2005;241(2):364–72. https://doi.org/10.1097/01.sla.0000151982.85062.80 .

Kyaw BM, Saxena N, Posadzki P, Vseteckova J, Nikolaou CK, George PP, et al. Virtual reality for health professions education: systematic review and meta-analysis by the Digital Health Education Collaboration. J Med Internet Res. 2019;21(1):e12959. https://doi.org/10.2196/12959 .

Bracq M-S, Michinov E, Jannin P. Virtual reality simulation in nontechnical skills training for healthcare professionals: a systematic review. Simul Healthc. 2019;14(3):188–94. https://doi.org/10.1097/sih.0000000000000347 .

World Health Organization. Transforming and scaling up health professionals’ education and training: World Health Organization guidelines 2013. Geneva: World Health Organization; 2013. Available from: https://www.who.int/publications/i/item/transforming-and-scaling-up-health-professionals%E2%80%99-education-and-training . Accessed 15 Jan 2024.

Jensen RAA, Musaeus P, Pedersen K. Virtual patients in undergraduate psychiatry education: a systematic review and synthesis. Adv Health Sci Educ. 2024;29(1):329–47. https://doi.org/10.1007/s10459-023-10247-6 .

Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. Syst Rev. 2021;10(1):89. https://doi.org/10.1186/s13643-021-01626-4 .

Rethlefsen ML, Kirtley S, Waffenschmidt S, Ayala AP, Moher D, Page MJ, et al. PRISMA-S: an extension to the PRISMA statement for reporting literature searches in systematic reviews. Syst Rev. 2021;10(1):39. https://doi.org/10.1186/s13643-020-01542-z .

Sandieson RW, Kirkpatrick LC, Sandieson RM, Zimmerman W. Harnessing the power of education research databases with the pearl-harvesting methodological framework for information retrieval. J Spec Educ. 2010;44(3):161–75. https://doi.org/10.1177/0022466909349144 .

Steen CW, Söderström K, Stensrud B, Nylund IB, Siqveland J. Replication data for: the effectiveness of virtual reality training on knowledge, skills and attitudes of health care professionals and students in assessing and treating mental health disorders: a systematic review. In: Inland Norway University of Applied S, editor. V1 ed: DataverseNO; 2024. https://doi.org/10.18710/TI1E0O .

Ouzzani M, Hammady H, Fedorowicz Z, Elmagarmid A. Rayyan—a web and mobile app for systematic reviews. Syst Rev. 2016;5(1):210. https://doi.org/10.1186/s13643-016-0384-4 .

Albright G, Bryan C, Adam C, McMillan J, Shockley K. Using virtual patient simulations to prepare primary health care professionals to conduct substance use and mental health screening and brief intervention. J Am Psych Nurses Assoc. 2018;24(3):247–59. https://doi.org/10.1177/1078390317719321 .

Fleming M, Olsen D, Stathes H, Boteler L, Grossberg P, Pfeifer J, et al. Virtual reality skills training for health care professionals in alcohol screening and brief intervention. J Am Board Fam Med. 2009;22(4):387–98. https://doi.org/10.3122/jabfm.2009.04.080208 .

Foster A, Chaudhary N, Murphy J, Lok B, Waller J, Buckley PF. The use of simulation to teach suicide risk assessment to health profession trainees—rationale, methodology, and a proof of concept demonstration with a virtual patient. Acad Psych. 2015;39:620–9. https://doi.org/10.1007/s40596-014-0185-9 .

Satter R. Diagnosing mental health disorders in primary care: evaluation of a new training tool [dissertation]. Tempe (AZ): Arizona State University; 2012.

Hitchcock LI, King DM, Johnson K, Cohen H, McPherson TL. Learning outcomes for adolescent SBIRT simulation training in social work and nursing education. J Soc Work Pract Addict. 2019;19(1/2):47–56. https://doi.org/10.1080/1533256X.2019.1591781 .

Liu W. Virtual simulation in undergraduate nursing education: effects on students’ correct recognition of and causative beliefs about mental disorders. Comput Inform Nurs. 2021;39(11):616–26. https://doi.org/10.1097/CIN.0000000000000745 .

Matsumura Y, Shinno H, Mori T, Nakamura Y. Simulating clinical psychiatry for medical students: a comprehensive clinic simulator with virtual patients and an electronic medical record system. Acad Psych. 2018;42(5):613–21. https://doi.org/10.1007/s40596-017-0860-8 .

Pantziaras I, Fors U, Ekblad S. Training with virtual patients in transcultural psychiatry: Do the learners actually learn? J Med Internet Res. 2015;17(2):e46. https://doi.org/10.2196/jmir.3497 .

Wilson DB. Practical meta-analysis effect size calculator [Online calculator]. n.d. https://campbellcollaboration.org/research-resources/effect-size-calculator.html . Accessed 08 March 2024.

Sterne JA, Savović J, Page MJ, Elbers RG, Blencowe NS, Boutron I, et al. RoB 2: a revised tool for assessing risk of bias in randomised trials. Br Med J. 2019;366:l4898.

Sterne JA, Hernán MA, Reeves BC, Savović J, Berkman ND, Viswanathan M, et al. ROBINS-I: a tool for assessing risk of bias in non-randomised studies of interventions. Br Med J. 2016;355:i4919. https://doi.org/10.1136/bmj.i4919 .

Kononowicz AA, Zary N, Edelbring S, Corral J, Hege I. Virtual patients - what are we talking about? A framework to classify the meanings of the term in healthcare education. BMC Med Educ. 2015;15(1):11. https://doi.org/10.1186/s12909-015-0296-3 .

Talbot TB, Sagae K, John B, Rizzo AA. Sorting out the virtual patient: how to exploit artificial intelligence, game technology and sound educational practices to create engaging role-playing simulations. Int J Gaming Comput-Mediat Simul. 2012;4(3):1–19.

Baartman LKJ, de Bruijn E. Integrating knowledge, skills and attitudes: conceptualising learning processes towards vocational competence. Educ Res Rev. 2011;6(2):125–34. https://doi.org/10.1016/j.edurev.2011.03.001 .

Mahon D. A scoping review of deliberate practice in the acquisition of therapeutic skills and practices. Couns Psychother Res. 2023;23(4):965–81. https://doi.org/10.1002/capr.12601 .

Ericsson KA, Lehmann AC. Expert and exceptional performance: evidence of maximal adaptation to task constraints. Annu Rev Psychol. 1996;47(1):273–305.

Roumeliotis KI, Tselikas ND. ChatGPT and Open-AI models: a preliminary review. Future Internet. 2023;15(6):192. https://doi.org/10.3390/fi15060192 .

Kasneci E, Sessler K, Küchemann S, Bannert M, Dementieva D, Fischer F, et al. ChatGPT for good? On opportunities and challenges of large language models for education. Learn Individ Differ. 2023;103:102274. https://doi.org/10.1016/j.lindif.2023.102274 .

Thirunavukarasu AJ, Ting DSJ, Elangovan K, Gutierrez L, Tan TF, Ting DSW. Large language models in medicine. Nat Med. 2023;29(8):1930–40. https://doi.org/10.1038/s41591-023-02448-8 .

Touvron H, Lavril T, Gautier I, Martinet X, Marie-Anne L, Lacroix T, et al. LLaMA: open and efficient foundation language models. arXivorg. 2023;2302.13971. https://doi.org/10.48550/arxiv.2302.13971 .

Radianti J, Majchrzak TA, Fromm J, Wohlgenannt I. A systematic review of immersive virtual reality applications for higher education: design elements, lessons learned, and research agenda. Comput Educ. 2020;147:103778. https://doi.org/10.1016/j.compedu.2019.103778 .

Wu B, Yu X, Gu X. Effectiveness of immersive virtual reality using head-mounted displays on learning performance: a meta-analysis. Br J Educ Technol. 2020;51(6):1991–2005. https://doi.org/10.1111/bjet.13023 .

Di Natale AF, Repetto C, Riva G, Villani D. Immersive virtual reality in K-12 and higher education: a 10-year systematic review of empirical research. Br J Educ Technol. 2020;51(6):2006–33. https://doi.org/10.1111/bjet.13030 .

Haugan S, Kværnø E, Sandaker J, Hustad JL, Thordarson GO. Playful learning with VR-SIMI model: the use of 360-video as a learning tool for nursing students in a psychiatric simulation setting. In: Akselbo I, Aune I, editors. How can we use simulation to improve competencies in nursing? Cham: Springer International Publishing; 2023. p. 103–16. https://doi.org/10.1007/978-3-031-10399-5_9 .

Huwendiek S, De leng BA, Zary N, Fischer MR, Ruiz JG, Ellaway R. Towards a typology of virtual patients. Med Teach. 2009;31(8):743–8. https://doi.org/10.1080/01421590903124708 .

Ødegaard NB, Myrhaug HT, Dahl-Michelsen T, Røe Y. Digital learning designs in physiotherapy education: a systematic review and meta-analysis. BMC Med Educ. 2021;21(1):48. https://doi.org/10.1186/s12909-020-02483-w .

Download references

Acknowledgements

The authors thank Mole Meyer, adviser at SIMInnlandet, Innlandet Hospital Trust, and Keith Mellingen, manager at VRINN, for their assistance with the categorization and classification of VR interventions, and Associate Professor Inga Hege at the Paracelcus Medical University in Salzburg for valuable contributions to the final classification of the interventions. The authors would also like to thank Håvard Røste from the media company KildeGruppen AS, for assistance with the search strategy; Academic Librarian Elin Opheim at the Inland Norway University of Applied Sciences for valuable peer review of the search strategy; and the Library at the Inland Norway University of Applied Sciences for their support. Additionally, we acknowledge the assistance provided by OpenAI’s ChatGPT for support with translations and language refinement.

Open access funding provided by Inland Norway University Of Applied Sciences The study forms a part of a collaborative PhD project funded by South-Eastern Norway Regional Health Authority through Innlandet Hospital Trust and the Inland University of Applied Sciences.

Author information

Authors and affiliations.

Mental Health Department, Innlandet Hospital Trust, P.B 104, Brumunddal, 2381, Norway

Cathrine W. Steen & Kerstin Söderström

Inland Norway University of Applied Sciences, P.B. 400, Elverum, 2418, Norway

Cathrine W. Steen, Kerstin Söderström & Inger Beate Nylund

Norwegian National Advisory Unit On Concurrent Substance Abuse and Mental Health Disorders, Innlandet Hospital Trust, P.B 104, Brumunddal, 2381, Norway

Bjørn Stensrud

Akershus University Hospital, P.B 1000, Lørenskog, 1478, Norway

Johan Siqveland

National Centre for Suicide Research and Prevention, Oslo, 0372, Norway

You can also search for this author in PubMed   Google Scholar

Contributions

CWS, KS, BS, and JS collaboratively designed the study. CWS and JS collected and analysed the data and were primarily responsible for writing the manuscript text. All authors contributed to the development of the search strategy. IBN conducted the literature searches and authored the chapter on the search strategy in the manuscript. All authors reviewed, gave feedback, and granted their final approval of the manuscript.

Corresponding author

Correspondence to Cathrine W. Steen .

Ethics declarations

Ethics approval and consent to participate.

Not applicable.

Consent for publication

Not applicable .

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Additional file 1: table 2..

Effects of VR training in the included studies: Randomized controlled trials (RCTs) and non-randomized studies (NRSs).

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ . The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/ ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Cite this article.

Steen, C.W., Söderström, K., Stensrud, B. et al. The effectiveness of virtual reality training on knowledge, skills and attitudes of health care professionals and students in assessing and treating mental health disorders: a systematic review. BMC Med Educ 24 , 480 (2024). https://doi.org/10.1186/s12909-024-05423-0

Download citation

Received : 19 January 2024

Accepted : 12 April 2024

Published : 01 May 2024

DOI : https://doi.org/10.1186/s12909-024-05423-0

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Health care professionals
• Health care students
• Virtual reality
• Mental health
• Clinical skills
• Systematic review

BMC Medical Education

ISSN: 1472-6920

• Submission enquiries: [email protected]
• General enquiries: [email protected]

• Publications
• Completed Theses
• Hardware & Infrastructure
• Help our Research

Completed Bachelor and Master Theses

Here, we show an excerpt of bachelor and master theses that have been completed since 2017.

Master Thesis: Be the teacher! - Viewport Sharing in Collaborative Virtual Environments [in progress]

Have you ever tried to show somebody a star in the night sky by pointing towards it? Conveying viewport relative information to the people surrounding us is always challenging, which often leads to difficulties and misunderstandings when trying to teach content to others. This issue also occurs in collaborative virtual environments, especially when used in an educational setting. However, virtual reality allows us to manipulate the otherwise constrained space to perceive the viewports of collaborators in a more direct manner. The goal of this thesis is to develop, analyze, implement and evaluate techniques on how students in a collaborative virtual environment can better perceive the instructor’s viewport. Considerations such as cybersickness, personal space, and performance need to be weighed and compared. The thesis should be implemented in Unity (Unreal can be discussed), and you should be interested in working with networked collaborative environments. Further details will be discussed in a meeting, but experience with Unity or more general Netcode would be very helpful.

Master Thesis: Impact of Framerate in Virtual Reality [in progress]

When talking about rendering in virtual reality high framerates and low latency are said to be crucial. While there is a lot of research regarding the impact of latency on the user this master thesis aims to focus on the impact of the framerate. The goal of the thesis is to design and evaluate a VR application that measures the influence of the framerate on the user. The solution should be evaluated in an expert study. Further details will be discussed in a meeting. Contact: Marcel Krüger, M.Sc. Simon Oehrl, M. Sc.

Bachelor Thesis: Interaction with Biological Neural Networks in the Context of Brain Simulation [in progress]

The ability to explore and analyze data generated by brain simulations can give various new insights about the inner workings of neural networks. One of the biggest challenges is to find interaction techniques and user interfaces that allow scientists to easily explore these types of data. Immersive technology can aid in this task and support the user in finding relevant information in large-scale neural networks simulations. The goal of this thesis is to explore techniques to view and interact with biological neural networks from a brain simulation in Unreal Engine 4. The solution should provide easy-to-use and intuitive abilities to explore the network and access neuron-specific properties/data. A strong background in C++ is needed, experience with UE4 would be very helpful. Since this thesis focuses on the visualization of such networks, an interest in immersive visualization is needed, knowledge about neural networks or brain simulation is not necessary. Contact: Marcel Krüger, M.Sc.

Bachelor/Master Thesis: Exploring Immersive Visualization of Artificial Neural Networks with the ANNtoNIA Framework [in progress]

Bachelor Thesis: Group Navigation with Virtual Agents [in progress]

Bachelor Thesis: Walking and Talking Side-by-Side with a Virtual Agent [in progress]

• Common head-mounted displays (HMDs) only provide a small field of view , thus limiting the peripheral view of the user. To this end, seeing an agent in a side-by-side alignment is either hampered or not possible at all without constantly turning one’s head. For room-mounted displays such as CAVEs with at least three projection screens, the alignment itself is possible.
• Which interaction partner aligns with the other? Influencing aspects here are, e.g., is the goal of the joint locomotion known by both walkers, or just by one?
• For the fine-grained alignment , the agent’s animation or the user’s navigation strategy needs to allow many nuances, trajectory- and speed-wise.

Master Thesis: Exploring a Virtual City with an Accompanying Guide [in progress]

Master Thesis: Unaided Scene Exploration while being Guided by Pedestrians-as-Cues [in progress]

Bachelor Thesis: Teacher Training System to Experience how own Behavior Influences Student Behavior [in progress]

Bachelor/Master Thesis: Immersive Node Link Visualization of Invertible Neural Networks

Neural networks have the ability to approximate arbitrary functions. For example, neural networks can model manufacturing processes, i.e., given the machine parameters, a neural network can predict the properties of the resulting work piece. In practice, however, we are more interested in the inverse problem, i.e., given the desired work piece properties, generate the optimal machine parameters. Invertible neural networks (INNs) have shown to be well suited to address this challenge. However, like almost all kinds of neural networks, they are an opaque model. This means that humans cannot easily interpret the inner workings of INNs. To gain insights into the underlying process and the reasons for the model’s decisions, an immersive visualization should be developed in this thesis. The visualization should make use of the ANNtoNIA framework (developed at VCI), which is based on Python and Unreal Engine 4. Requirements are a basic understanding of Machine Learning and Neural Networks as well as good Python programming skills. Understanding of C++ and Unreal Engine 4 is a bonus but not necessary. Contact: Martin Bellgardt, M. Sc.

Master Thesis: Automatic Gazing Behavior for Virtual Agents Based on the Visible Scene Content [in progress]

Master Thesis: Active Bezel Correction to Reduce the Transparency Illusion of Visible Bezels Behind Opaque Virtual Objects

Bachelor/Master Thesis: Augmented Reality for Process Documentation in Textile Engineering [in progress]

Bachelor Thesis: Fast Body Avatar Calibration Based on Limited Sensor Input

Bachelor Thesis: Benchmarking Interactive Crowd Simulations for Virtual Environments in HMD and CAVE Settings

Bachelor Thesis: Investigating the effect of incorrect lighting on the user

Master Thesis: Frame extrapolation to enhance rendering framerate

Bachelor Thesis: The grid processing library

Scalar, vector, tensor and higher-order fields are commonly used to represent scientific data in various disciplines including geology, physics and medicine. Although standards for storage of such data exists (e.g. HDF5), almost every application has its custom in-memory format. The core idea of this engineering-oriented work is to develop a library to standardize in-memory representation of such fields, and providing functionality for (parallel) per-cell and per-region operations on them (e.g. computation of gradients/Jacobian/Hessian). Contact: Ali Can Demiralp, M. Sc.

Bachelor Thesis: Scalar and vector field compression for GPUs based on ASTC texture compression [in progress]

Scalar and vector fields are N-dimensional, potentially non-regular, grids commonly used to store scientific data. Adaptive Scalable Texture Compression (ASTC) is a lossy block-based texture compression method, which covers the features of all texture compression approaches to date and more. The limited memory space of GPUs pose a challenge to interactive compute and visualization on large datasets. The core idea of this work is to explore the potential uses of ASTC for compression of large 2D/3D scalar and vector fields, attempting the minimize and bound the errors introduced by lossiness. Contact: Ali Can Demiralp, M. Sc.

Master Thesis: The multi-device ray tracing library

There are various solutions for ray tracing on CPUs and GPUs today: Intel Embree for shared parallelism on the CPU, Intel Ospray for distributed parallelism on the CPU, NVIDIA OptiX for shared and distributed parallelism on the GPU. Each of these libraries have their pros and cons. Intel Ospray scales to distributed settings for large data visualization, however is bound by the performance of the CPU which is subpar to the GPU for the embarassingly-parallel problem of ray tracing. NVIDIA OptiX provides a powerful programmable pipeline similar to OpenGL but is bound by the memory limitations of the GPU. The core idea of this engineering-oriented work is to develop a library (a) enabling development of ray tracing algorithms without explicit knowledge of the device the algorithm will run on, (b) bringing ease-of-use of Intel Ospray and functional programming concepts of NVIDIA OptiX together. Contact: Ali Can Demiralp, M. Sc.

Master Thesis: Numerical relativity library

Numerical relativity is one of the branches of general relativity that uses numerical methods to analyze problems. The primary goal of numerical relativity is to study spacetimes whose exact form is not known. Within this context the geodesic equation generalizes the notion of a straight line to curved spacetime. The core idea of this work is to develop a library for solving the geodesic equation, which in turn enables 4-dimensional spacetime ray tracing. The implementation should at least provide the Schwarzschild and Kerr solutions to the Einstein Field Equations, providing visualizations of non-rotating and rotating uncharged black holes. Contact: Ali Can Demiralp, M. Sc.

Master Thesis: Mean curvature flow for truncated spherical harmonics expansions

Curvature flows produce successively smoother approximations of a given piece of geometry, by reducing a fairing energy. Within this context, mean curvature flow is a curvature flow defined for hypersurfaces in a Riemannian manifold (e.g. smooth 3D surfaces in Euclidean space), which emphasizes regions of higher frequency and converges to a sphere. Truncated spherical harmonics expansions are commonly used to represent scientific data as well as arbitrary geometric shapes. The core idea of this work is to establish the mathematical concept of mean curvature flow within the spherical harmonics basis, which is empirically done through interpolation of the harmonic coefficients to the coefficient 0,0. Contact: Ali Can Demiralp, M. Sc.

Master Thesis: Orientation distribution function topology

Topological data analysis methods have been applied extensively to scalar and vector fields for revealing features such as critical and saddle points. There is recent effort on generalizing these approaches to tensor fields, although limited to 2D. Orientation distribution functions, which are the spherical analogue to a tensor, are often represented using truncated spherical harmonics expansions and are commonly used in visualization of medical and chemistry datasets. The core idea of this work is to establish the mathematical framework for extraction of topological skeletons from an orientation distribution function field. Contact: Ali Can Demiralp, M. Sc.

Master Thesis: Variational inference tractography

Tractography is a method for estimation of nerve tracts from discrete brain data, often obtained through Magnetic Resonance Imaging. The family of Markov Chain Monte Carlo (MCMC) methods form the current standard to (global) tractography, and have been extensively researched to date. Yet, Variational Inference (VI) methods originating in Machine Learning provide a quicker alternative to statistical inference. Stein Variational Gradient Descent (SVGD) is one such method which not only extracts minima/maxima but is able to estimate the complete distribution. The core idea of this work is to apply SVGD to tractography, working with both Magnetic Resonance and 3D-Polarized Light Imaging data. Contact: Ali Can Demiralp, M. Sc.

Master Thesis: Block connectivity matrices

Connectivity matrices are square matrices for describing structural and functional connections between distinct brain regions. Traditionally, connectivity matrices are computed for segmented brain data, describing the connectivity e.g. among Brodmann areas in order to provide context to the neuroscientist. The core idea in this work is to take an alternative approach, dividing the data into a regular grid and computing the connectivity between each block, in a hierarchical manner. The presentation of such data as a matrix is non-trivial, since the blocks are in 3D and the matrix is bound to 2D, hence it is necessary to (a) reorder the data using space filling curves so that the spatial relationship between the blocks are preserved (b) seek alternative visualization techniques to replace the matrix (e.g. volume rendering). Contact: Ali Can Demiralp, M. Sc.

Bachelor Thesis: Lip Sync in Unreal Engine 4 [in progress]

Computer-controlled, embodied, intelligent virtual agents are increasingly often embedded in various applications to enliven the virtual sceneries. Thereby, conversational virtual agents are of prime importance. To this end, adequate mimics and lip sync is required to show realistic and plausible talking characters. The goal of this bachelor thesis is to enable an effective however easy-to-integrate lip sync in our Unreal projects for text-to-speech input as well as recorded speech. Contact: Jonathan Ehret, M.Sc.

Master Thesis: Meaningful and Self-Reliant Spare Time Activities of Virtual Agents

Bachelor Thesis: Joining Social Groups of Conversational Virtual Agents

Bachelor Thesis: Integrating Human Users into Crowd Simulations

Bachelor Thesis: Supporting Scene Exploration in the Realm of Social Virtual Reality

Master Thesis: Efficient Terrain Rendering for Virtual Reality Applications with Level of Detail via Progressive Meshes

Terrain rendering is a major and widely researched field. It has a variety of applications, from software that allows the user to interactively explore a surface, like NASA World Wind or Google Earth, over flight simulators to computer games. It is not surprising that terrain rendering is also interesting for virtual reality applications as virtual reality can also be a tool to support the solution of difficult problems by providing natural ways of interaction. In combination virtual reality and terrain rendering can combine their solution promoting potential. In this work the terrain representing LOD structure will be a Progressive Mesh (PM) constructed from an unconnected point cloud. A preprocessing step first connects the point cloud so that it forms a triangle mesh which approximates the underlying surface. Then the mesh can be decimated so that the data volume of the PM can be reduced if needed. When the mesh has the desired complexity the actual Progressive Mesh structure is build. Afterwards, real-time rendering just needs to read the PM structure from a file and can perform a selective refinement on it to match the current viewer position and direction. Contact: Prof. Dr. Tom Vierjahn

Master Thesis: Voxel-based Transparency: Comparing Rasterization and Volume Ray Casting

This thesis deals with a performance visualization scheme, which represents the load factor of single threads on a high performance computer through colored voxels. The voxels are arranged in a three-dimensional grid so only the shell of the grid is initially visible. The aim of this thesis is to introduce transparent voxels to the visualization in order to let the user look also into the inside of the grid and thereby display more information at once. First, an intuitive user interface for assigning transparencies to each voxel is presented. For the actual rendering of transparent voxels, two different approaches are then examined: rasterization and volume ray casting. Efficient implementations of both transparency rendering techniques are realized by exploiting the special structure of the voxel grid. The resulting algorithms are able to render even very large grids of transparent voxels in real-time. A more detailed comparison of both approaches eventually points out the better suited of the two methods and shows to what extent the transparency rendering enhances the performance visualization. Contact: Prof. Dr. Tom Vierjahn

Bachelor Thesis: Designing and Implementing Data Structures and Graphical Tools for Data Flow Networks Controlling Virtual Environments

In this bachelor thesis a software tool for editing graphs is designed and implemented. There exist some interactive tools but this new tool is specifically aimed at creating, editing and working with graphs representing data flow networks like they appear in virtual environments. This thesis compares existing tools and documents the implementation process of VistaViz (the application). In the current stage of development, VistaViz is able to create new graphs, load existing ones and make them editable interactively. Contact: Prof. Dr. Tom Vierjahn

Master Thesis: Raycasting of Hybrid Scenes for Interactive Virtual Environments

Scientific virtual reality applications often make use of both geometry and volume data. For example in medical applications, a three dimensional scan of the patient such as a CT scan results in a volume dataset. Ray casting could make the algorithms needed to handle these hybrid scenes significantly simpler than the more traditional rasterizing algorithms. It is a very flexible and powerful way of generating images of virtual environments. Also there are many effects that can be easily realized using ray-based algorithms such as shadows and ambient occlusion. This thesis describes a ray casting renderer that was implemented in order to measure how well a ray casting based renderer performs and if it is feasible to use it to visualize interactive virtual environments. Having a performance baseline for an implementation of a modern ray caster has multiple advantages. The renderer itself could be used to measure how different techniques could improve the performance of the ray casting. Also with such a renderer it is possible to test hardware. This helps to estimate how much the available hardware would have to improve in order to make ray casting a sensible choice for rendering virtual environments. Contact: Prof. Dr. Tom Vierjahn

Master Thesis: CPU Ray-Tracing in ViSTA

Scientific data visualization is an inherent tool in modern science. Virtual reality (VR) is one of the areas where data visualization constitutes an indispensable part. Current advances in VR as well as the growing ubiquitousness of the VR tools bring the necessity to visualize large data volumes in real time to the forefront. However, it also presents new challenges to the visualization software used in high performance computer clusters. CPU-based real-time rendering algorithms can be used in such visualization tasks. However, they only recently started to achieve real-time performance, mostly due to the progress in hardware development. Currently ray tracing is one of the most promising algorithms for CPU-based real-time rendering. This work aims at studying the possibility to use CPU-based ray tracing in VR scenarios. In particular, we consider the CPU-based rendering algorithm implemented in the Intel OSPRay framework. For VR tasks, the ViSTA Virtual Reality Toolkit, developed at the Virtual Reality and Immersive Visualization Group at RWTH Aachen University is used. Contact: Prof. Dr. Tom Vierjahn

Master Thesis: Streaming Interactive 3D-Applications to Web Environments

This thesis develops a framework for streaming interactive 3D-applications to web environments. The framework uses a classical client-server architecture where the client is implemented as a web application. The framework aims at providing a flexible and scalable solution for streaming an application inside a local network as well as remotely over the internet. It supports streaming to multiple clients simultaneously and provides solutions for handling the input of multiple users as well as streaming at multiple resolutions. Its main focus lies on reducing the latency perceived by the user. The thesis evaluates the image-based compression standards JPEG and ETC as well as the video-based compression standards H.264 and H.265 for use in the framework. The communication between the client and the server was implemented using standardized web technologies such as WebSockets and WebRTC. The framework was integrated into a real-world application and was able to stream it locally and remotely achieving satisfying latencies. Contact: Prof. Dr. Tom Vierjahn

Master Thesis: Benchmarking interactive Rendering Frameworks for virtual Environments

In recent years virtual reality applications that utilize head mounted displays have become more popular due to the release of head mounted displays such as the Oculus Rift and the HTC Vive. Applications that utilize head mounted displays, however, require very fast rendering algorithms. Traditionally, the most common way to achieve real time rendering is triangle rasterization; another approach is ray tracing. In order to provide insight into the performance behavior of rasterization and raytracing, in this Master thesis a toolkit for benchmarking the performance of different rendering frameworks under different conditions was implemented. It was used to benchmark the performance of CPU-ray-tracing-based, GPU-ray-tracing-based and rasterization-based rendering in order to identify the influence of different factors on the rendering time for different rendering schemes. Contact: Prof. Dr. Tom Vierjahn

Master Thesis: Generating co-verbal Gestures for a Virtual Human using Recurrent Neural Networks [in progress]

Master Thesis: Extraction and Interactive Rendering of Dissipation Element Geometry – A ParaView Plugin

After approximate Dissipation Elements (DE) were introduced by Vierjahn et al., the goal of this work is to make their results available for users in the field of fluid mechanics in form of a ParaView plugin, a standard software in this field. It allows to convert DE trajectories into the approximated, tube- like, form and render it via ray tracing and classic OpenGL rendering. The results are suggesting it is ready to be tested by engineers working with ParaView, as interactive frame rates and fast loading times are achieved. By using approximated DEs instead of DE trajectories, significant amounts of data storage can be saved. Contact: Prof. Dr. Tom Vierjahn

Bachelor Thesis: Comparison and Evaluation of two Frameworks for in situ Visualization

On the road to exa-scale computing, the widening gap between I/O-capabilities and compute power of today’s compute clusters encourages the use of in situ methods. This thesis evaluates two frameworks designed to simplify in situ coupling, namely SENSEI and Conduit, and compares them in terms of runtime overhead, memory footprint, and implementation complexity. The frameworks were used to implement in situ pipelines between a proxy simulation and an analysis based on the OSPRay ray tracing framework. The frameworks facilitate a low-complexity integration of in situ analysis methods, providing considerable speedups with an acceptable memory footprint. The use of general-purpose in situ coupling frameworks allows for an easy integration of simulations with analysis methods, providing the advantages of in situ methods with little effort. Contact: Prof. Dr. Tom Vierjahn

Master Thesis: An Intelligent Recommendation System for an Efficient and Effective Control of Virtual Agents in a Wizard-of-Oz paradigm.

In this work, techniques were studied to control virtual agents embedded as interaction partners in immersive, virtual environments. He implemented a graphical user interface (GUI) for a Wizard-of-Oz paradigm, allowing to select and control individual virtual agents manually. The key component of the GUI is an intelligent recommendation system predicting which virtual agents are very likely to be the next interaction partners based on the user’s actions in order to allow an efficient and effective control. Published as poster at VRST 2017. Contact: Andrea Bönsch, M. Sc.

Master Thesis: Automatic Virtual Tour Generation for Immersive Virtual Environments based on Viewpoint Quality

The exploration of a virtual environment is often the first and one of the most important actions a user performs when experiencing it for the first time, as knowledge of the scene and a cognitive map of the environment are prerequisites for many other tasks. However, as the user does not know the environment, their exploration path is likely to be flawed, taking longer than necessary, missing important parts of the scene and visiting other parts multiple times by accident. This can be remedied by virtual tours that provide an efficient path through the environment that visits all important places. However, for most virtual environments, manually created virtual tours are not available. Furthermore, most scenes are not provided with the information of where the most important locations are, such that automatic generation of good virtual tours is challenging. However, the informativeness of a position in a virtual environment can be computed automatically using viewpoint quality estimation techniques. In addition to providing interesting places as waypoints, this concept also allows the evaluation of the quality of the tour between waypoints. Therefore, in this thesis, an automatic method to compute efficient and informative virtual tours through a virtual scenery is designed and developed, based on an evolutionary approach that aims at maximizing the quality of the viewpoints encountered during the tour. Contact: Dr. Sebastian Freitag

Master Thesis: Fluid Sketching - 3D Sketching Based on Fluid Flow in Immersive Virtual Environments

Bachelor Thesis: Conformal Mapping of the Cortical Surface

The cerebral cortex holds most of the cerebrum’s functional processing ability. To visualize functional areas on the cortical surface, the cortical surface is usually mapped to a representation which makes the convoluted areas of the brain visible. This work focuses on mapping the surface into the 2D domain. For this purpose, two parameterization algorithms have been implemented: Linear Angle Based Parameterization (Zayer et al., 2007) and Least Squares Conformal Maps (Lévy et al., 2002). The results of the two algorithms are then compared to the iterative flattening approach by Fischl et al. regarding computational time and introduced distortions. Contact: Dr. Claudia Hänel

EDITORIAL article

This article is part of the research topic.

Virtual Agents in Virtual Reality: Design and Implications for VR Users

Editorial: Virtual Agents in Virtual Reality: Design and Implications for VR Users Provisionally Accepted

• 1 École Nationale d'Ingénieurs de Saint-Etienne, France
• 2 Ecole Centrale de Lyon, France
• 3 Origami team, UMR5205 Laboratoire d'Informatique en Image et Systèmes d'Information (LIRIS), France
• 4 CESI Ecole d'Ingénieurs, France
• 5 Vienna University of Technology, Austria
• 6 Sorbonne Université(CNRS), France
• 7 UMR7222 Institut des Systèmes Intelligents et Robotiques (ISIR), France

The final, formatted version of the article will be published soon.

The following research topic has emerged in a context of expansion of virtual communication and virtual social links in our society. At the same time, the enhancement of artificial intelligence technologies and autonomous agents designs has facilitating for populating Virtual Environments (VE) with not only real users, but also with more and more believable Virtual Human Agents (VHA) Demeure et al. (2011).This research topic focuses on Virtual Reality (VR), virtual agents design and its effect on VR users. Previous research has shown that the content provided to users in VR do have an effect on them, mainly due to the immersion and sense of presence that VEs can induce Slater et al. (1996). Realism and believability of the environments designed for being used in VR technologies is felt by the users on usually at least their visual perception and interaction capabilities, sometimes more. Indeed, other multisensory perceptions have been explored in the literature, such as touch Hoppe et al. (2020), sound Griol et al. (2019) or even smell Javerliat et al. (2022), which are constantly improving. This empowers VR with the ability to enable the replication of real-world human behaviours, as an ecological environment where real situations can be mimicked and elicit realistic reactions on users. These reactions can be evaluated either through self-reported assessments or physiological measures during the experiment. Relying on the social aspect of human life, our topic aimed to gather research studies that proposed VR experiments with social situations between users and virtual agents Von der Pütten et al. (2009). We wondered here which aspects of virtual agents design could affect VR users behaviours, in which manner, and how VHAs are perceived. Four articles have been published, all relying on user experiments: two propose an evaluation of the effect of specific agents behaviours on VR users actions or reactions, and two design a new dataset of virtual agents and rate their perception by users.Bönsch et al, 2024 propose an innovative approach of guidance towards points of interests in a virtual environment, through social behaviours of a virtual crowd of humans. In this paper, two modalities are explored, compared to a control condition with a virtual crowd with no guidance actions: i) the crowd of agents actively supports the user towards the points of interests, and ii) the crowd implicitly induces users towards the goals, as a more passive flow such as walking in a specific direction. Results show that the active support modality is found as more effective, nonetheless the authors note that the implicit flow of agents could be promising for specific conditions (large streets, "leader" user personality etc.). Authors' results support for the use of agents as guides in VR, through visual but also audio stimuli, reinforcing the interest of multisensory for the design of VR experiments with virtual agents.Ban et al, 2024's article originality relies on the kind of measurement they used: a physiological measure on the saliva, to qualify the autonomic nervous system reaction of VR users facing stressful situations. In their experiment, in a customer service training, VR users are sequentially exposed to virtual agents with high-intensity stressor and high-intensity one. The authors succeed to show similar results compared to previous studies in both real-life and VR, conducted before with a different user task than here -the trier social stress test. This shows the ecological validity of their VR environment to elicit physiological reactions due to a quantity of induced stress, and the correctness of their evaluation through the objective measurements they use. Future work would be to evaluate which factors of VHAs' behaviours (gestures, voice tone) have a greater influence on users' stress responses.Siehl et al, 2024 create a new publicly available dataset of animated virtual humanoid male characters, reusing and adapting a generator of 2D faces ; they evaluate their emotional effect on users in terms of trustworthiness, valence and arousal. Their results show that manipulations on the animations that were expected to accordingly influence the trustworthiness have indeed modified this emotional perception, verified here through a first user study relying on video stimuli. Moreover, the authors have conducted another study with additional audio stimuli to accentuate the increase or decrease of trustworthiness towards users, which have produced the same results, even with a stronger effect. Future improvements mainly remain on adapting the social context of such virtual characters animations when displayed to users, for a more in-context evaluation of this VHA dataset. Do et al, 2023's article offers multiple contributions: i) the creation of a new rich and diverse dataset of 210 fully rigged avatars, with high racial diversity and inclusion; ii) a precise and complete methodology for the creation of such kind of dataset (modelling process, recruitment strategy of VHA designers and community/race representatives, iterations, user evaluations); iii) knowledge about the perception of the dataset itself and more generally of virtual agents of different races by a very diverse panel of users. In this study, the identification of VHAs' race by the users that have declared themselves as pertaining to the same race as the one of the VHA displayed is very accurate. Moreover, Asian, Black, and White VHA's are recognized by all participants, whereas Hispanic and MENA VHAs are only validated as such by users who have identified themselves as belonging to these groups. Future work could also be done on variety inside groups, e.g. to represent the diversity of Hispanic profiles across Latin America, or MENA or NHPI cultural diversity, which can be directly reflected on their style, e.g., hair cuts.Through these articles, this research topic contributes to the diffusion of public datasets of virtual human agents, to the explanation of design methodology and evaluation approach on virtual agents, and to the understanding of human-agent perception and interactions in VR. Future studies could contribute to extend the development of multisensory aspects in the design of virtual agents behaviours, in line with the audio features explored here, and on the in-context user evaluations of social VHAs, through subjective and objective measurements such as physiological data.

Keywords: virtual reality, Virtual agents, user-agent interactions, virtual humans design, social behaviours

Received: 26 Apr 2024; Accepted: 06 May 2024.

Copyright: © 2024 Raimbaud, Biancardi, Podkosova and Pelachaud. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Mx. Pierre Raimbaud, École Nationale d'Ingénieurs de Saint-Etienne, Saint-Etienne, France

People also looked at

109 Virtual Reality Topics & Essay Examples

When writing a virtual reality essay, it is hard to find just one area to focus on. Our experts have outlined 104 titles for you to choose from.

🏆 Best Virtual Reality Topics & Essay Examples

🕶️ good virtual reality research topics, 🤖 interesting virtual reality research paper topics, ❓ research questions about virtual reality.

Humanity has made amazing leaps in technology over the past several years. We have reached frontiers previously thought impossible, like the recreation of virtual environments using computers. These three-dimensional worlds can be accessed and explored by people. This is made possible with VR headsets, such as Oculus Rift or HTC Vive. If you’re eager to find out more, peek at our collection of VR research topics below!

• Virtual Reality Versus Augmented Reality In fact, this amounts to one of the merits of a virtual reality environment. A case example of this type of virtual reality is the Virtual Reality games.
• Virtual Reality Technology The third negative impact of virtual reality is that it causes human beings to start living in the world of fantasy.
• Virtual Reality Tourism Technology In the world of virtual tourism, we can be transported to any country and have the ability to interact and manipulate the elements within the world we are touring in a way that would not […]
• Virtual Reality’s Main Benefits The rapid development and the growing popularity of virtual reality raise a logical interest concerning the advantages and disadvantages that are related to the application of this new technology in various spheres of knowledge and […]
• Virtual Reality Technology for Wide Target Audience Due to the numerous applications in both leisure and industry, as well as massive popularity with audiences of different ages, there is a chance that, in several years, evaluating the target audiences of Virtual Reality […]
• Virtual Reality’s Benefits and Usages in Concurrent Engineering Figure 1: Phases of concurrent engineering Source As shown in the figure above, the initial stage of concurrent engineering is the identification of the components of the design system.
• A Growth Trajectory of the Virtual Reality Drilling Rig Training During the final three months of development, the VR training program will be refined and tested for usability and effectiveness. Collecting feedback from users is essential for the success of the VR drilling rig training […]
• “The Role of Virtual Reality in Criminal Justice Pedagogy” by Smith The journal is titled “The role of virtual reality in criminal justice pedagogy: An examination of mental illness occurring in corrections”.
• Virtual Reality and Cybersecurity As a result, it is the mandate of the framework entities to establish solutions to the inherent barriers to the implementation of the business plan.
• A Stand-Up Comedy Virtual Reality Platform for Qatar Tourism Choosing the right number of avatars, customization of the product, and pricing the product were the three major challenges that were faced in this project. The second challenge that emerged in the development stage was […]
• Entrepreneurial Opportunities in Virtual Reality In terms of the practical context, the research will focus on the organizations and sectors which are the primary beneficiaries of virtual reality and remote work during the pandemic.
• Virtual Reality Space Product Project Challenges During the project, several challenges came up, which included providing leadership to the team, identifying the customer segment for the product, and understanding the “pains” of the customer segment.
• Reflection on Aspects of Virtual Reality Videos For instance, the video Wolves in the Walls has good graphics and gives the independence to look at every section of the set-up separately.
• Augmented and Virtual Reality for Modern Firms The business environment is not an exception, as firms seek to maximize their value through the implementation of high-tech solutions. AR is another major component of contemporary professional training, as it contributes to the better […]
• The Rules of the Virtual Reality Online environment has been providing the platform for casual interactions as well as economic activities for quite a while.
• How Virtual Reality Is Changing the World of Interior Design In order to become competitive in the sphere of luxury interior design, “More” must make its projects look modern and trendy.
• Rusnak’s “The Thirteenth Floor” and The Concept of Virtual Reality In such consideration, this paper conducts a comparative analysis of The Thirteenth Floor and how the concept of virtual reality was developed and is applied in today’s films.
• Top Companies in the Virtual Reality Industry Currently, Google is the leading search engine company, and there are signs that the company might emerge as one of the heavyweights in the virtual reality industry.
• Screen Culture: Immersion and Virtual Reality If paralleling with the world of video games, the protagonist in that projected art work is the most close to the vision that the user could be associated with.
• Virtual Reality: A Powerful New Technology for Filming The creation of VR highlights a new perception of space because, through technology, people can be transmitted to a different environment.
• Internet, Virtual Reality, and World Wide Web Defining the concept of the Internet is a challenging task, mostly because of the changes that it has undergone over the course of its development.
• Virtual Reality Technology and Soccer Training Moreover, the level of interactivity needs to be significant, and the most attention should be devoted to the modeling of situations that are viewed as the most problematic.
• Char Davies’ Osmose as Virtual Reality Environment On the following position, the installment suggests the invitees a chance to trail the discrete interactor’s voyage of imageries from end to end of this counterpart of natural surroundings.
• Virtual Reality in Healthcare Training The objective data will be gathered to inform the exploration of the first question, and it will focus on such performance measures as time, volume, and efficiency of task completion; the number of errors pre- […]
• Scholar VR: Virtual Reality Planning Service Studio To ensure that the small and mid-sized companies in the United Kingdom understand the leverage they can get by using VR technology.
• IOS and Browser Applications and Virtual Reality From the consumer’s point of view, any mobile application is good if it is of interest to the public and covers a large target audience.
• Virtual Reality’ Sports Training System Working Steps The efficiency of the given technology is evidenced by the fact that it is used by various coaches and teams to provide training for their players. For this reason, it is possible to predict the […]
• Virtual Reality Technology in Soccer Training Therefore, it is imperative to invest in this area to protect the safety of our technology and ensure that we have a viable product.
• Virtual Reality Technology in Referee Training Referees need to experience the practical nature of the profession during the training process, and the VR technology will eliminate the underlying challenges to the development of experience in the profession.
• Surgeon Students’ Virtual Reality Learning Programs In order for the students to feel like they are operating on living patients instead of waving instruments in the air, it is necessary to provide the environment that would compensate for the shortcomings of […]
• Virtual Reality and Solitary Confinement Nowadays, the majority of the representatives of the general public all over the world are familiar with the concept of virtual reality, and many of them have already experienced it.
• Samsung Gear Virtual Reality Product Launch The paper at hand is devoted to the analysis of the launch of Samsung Gear VR from different perspectives: the product development model, the business analysis, its technical implementation, etc.
• Virtual Reality in Military Health Care The purpose of the research is to identify the capabilities of VR and its applications in military health care. This study will explore the current uses of VR, its different functionalities, applications in the field […]
• Virtual Reality Ride Experience at Disneyland Florida The basic concept of the proposed ride is to utilize the current advances in VR technology to create a simulated experience for park-goers that is safe, widely usable, and sufficiently immersive that there is a […]
• Imagineering Myths About Virtual Reality Walt Disney Imagineering team, which encompassed a wide range of professionals responsible for various entertainments offered by theme parks, resorts, and other venues, is currently devoting a lot of time and effort to unlock the […]
• Virtual Reality Industry Analysis While it is true that the production and sale of virtual reality headsets could be in the millions in the future as the technology develops and becomes more acceptable, it cannot be stated at the […]
• Virtual Reality in Construction Originally, the use of virtual reality in construction within the past decade has been limited to 3D object design wherein separate 3D representations of the exterior and interior of the buildings are designed utilizing 3D […]
• Virtual Reality in Soccer Training The following work will focus on the analysis of the use of Virtual Reality in the training of soccer players with the evaluation of the practices adopted by particular soccer teams.
• Abstract on Architecture and the Role of Virtual Reality
• Advantages and Disadvantages of Escapism and Virtual Reality
• Strategic Analysis of the Creation of a New Rating System in Virtual Reality Gaming
• Study on Real/Virtual Relationships Through a Mobile Augmented Reality Application
• Benefits and Dangers of Virtual Reality
• Can Virtual Reality Kill?
• Cognitive Psychology & Virtual Reality Systems
• Computer Science and Virtual Reality
• Development of Virtual Reality Technology in the Aspect of Educational Applications
• Difference Between Augmented Reality and Virtual Reality
• Role of Virtual Reality in Education
• Humanity Versus Virtual Reality
• Simulation and Virtual Reality in a Sport Management Curriculum Setting
• Smart VR: A Virtual Reality Environment for Mathematics
• Sports Management Curriculum, Virtual Reality, and Traditional Simulation
• SWOT Analysis: The Lego Product and the ‘Virtual Reality’
• The Augmented Reality and Virtual Reality Market Forecast and Opportunities in U.S.
• Tracking Strategy in Increased Reality and Virtual Reality
• Using the Virtual Reality to Develop Educational Games for Middle School Science Classrooms
• What Is Virtual Reality?
• What Are the Advantages and Disadvantages of Virtual Reality?
• What Do Consumers Prefer for the Attributes of Virtual Reality Head-Mount Displays?
• Virtual Reality and Its Potential to Become the Greatest Technological Advancement
• Lucid Dreams as the First Virtual Reality
• Development of Virtual Reality
• Introduction to Virtual Reality Technology and Society
• Issue “Virtual Reality in Marketing”: Definition, Theory and Practice
• Applying Virtual Reality in Tourism
• Application of Virtual Reality in Military
• Augmented Reality & Virtual Reality Industry Forecast and Analysis to 2013 – 2018
• Breakthrough Virtual Reality Sex Machine
• Components Driving Virtual Reality Today and Beyond
• Data Correlation-Aware Resource Management in Wireless Virtual Reality (VR): An Echo State Transfer Learning Approach
• Gaming to Health Care: Using Virtual Reality in Physical Rehabilitation
• Smart Phones and Virtual Reality in 10 Years
• Evolution of Art in Virtual Reality
• Use of Virtual Reality in Molecular Docking Science Experiments
• Use of Virtual Reality for Concussion Diagnosis
• Virtual Reality as Analgesia: An Alternative Approach for Managing Chronic Pain
• Virtual Reality: The Real Life Implications of Raising a Virtual Child
• When Virtual Reality Meets Realpolitik: Social Media Shaping the Arab Government-Citizen Relationship
• Can Virtual Reality Ever Be Implemented in Routine Clinical Settings?
• What Is More Attractive, Virtual Reality or Augmented Reality?
• What Is Virtual Reality and How It Works?
• What Are the Benefits of Virtual Reality?
• Is Virtual Reality Dangerous?
• How Is Virtual Reality Used in Everyday Life?
• What Are the Risks of Virtual Reality?
• What Is the Future of Virtual Reality in Education?
• How Do You Think Virtual Reality Devices Will Change Our World?
• What Are Three Disadvantages of Virtual Reality?
• What’s the Point of Virtual Reality?
• How Can Virtual Reality Optimize Education?
• How Did Virtual Reality Affect Our Lives?
• Will Virtual Reality Eventually Replace Our Real Reality?
• What Are Some Cool Virtual Reality Ideas?
• When Will We Have Full-Sensory Virtual Reality?
• What Do I Need to Develop Virtual Reality Games?
• Why Did Virtual Reality Never Take Off so Far?
• What Are Medical Applications of Virtual Reality?
• How Virtual Reality Can Help in Treatment of Posttraumatic Stress Disorder?
• What Are the Biggest Problems Virtual Reality Can Solve?
• What Unsolved Problems Could Virtual Reality Be a Solution For?
• How Would a Fully Immersive Virtual Reality Work?
• When Will Virtual Reality Become Popular?
• What’s the Best Way to Experience Virtual Reality Technology?
• How Will Virtual Reality Change Advertising?
• Which Are the Best Virtual Reality Companies in India?
• What Are the Pros and Cons of Virtual Reality?
• What Are the Coding Languages Required for Virtual Reality?
• Chicago (A-D)
• Chicago (N-B)

IvyPanda. (2024, March 2). 109 Virtual Reality Topics & Essay Examples. https://ivypanda.com/essays/topic/virtual-reality-essay-topics/

"109 Virtual Reality Topics & Essay Examples." IvyPanda , 2 Mar. 2024, ivypanda.com/essays/topic/virtual-reality-essay-topics/.

IvyPanda . (2024) '109 Virtual Reality Topics & Essay Examples'. 2 March.

IvyPanda . 2024. "109 Virtual Reality Topics & Essay Examples." March 2, 2024. https://ivypanda.com/essays/topic/virtual-reality-essay-topics/.

1. IvyPanda . "109 Virtual Reality Topics & Essay Examples." March 2, 2024. https://ivypanda.com/essays/topic/virtual-reality-essay-topics/.

Bibliography

IvyPanda . "109 Virtual Reality Topics & Essay Examples." March 2, 2024. https://ivypanda.com/essays/topic/virtual-reality-essay-topics/.

• Innovation Titles
• Mobile Technology Paper Topics
• Integrity Questions
• Software Engineering Topics
• Web Technology Essay Topics
• Online Community Essay Topics
• Virtual Team Ideas
• Internet of Things Topics

Case Studies

The technology used by the budding engineers at nyu tandon school of engineering.

• Customer: NYU Tandon School of Engineering
• Location: New York City, USA
• Watch the testimonial video here

The engine of innovation for Brooklyn and New York City

Within the bustling metropolis of New York City, the NYU Tandon School of Engineering stands out when it comes to cutting-edge research, transformative education, and impactful technological advancements. Founded in 1854, NYU Tandon fosters an environment where aspiring engineers can thrive and redefine the boundaries of what is possible. Located in Brooklyn at the center of technology and entrepreneurship, NYU Tandon provides its students invaluable opportunities for internships and other real-world opportunities. Hence, NYU Tandon is considered “the engine of innovation for Brooklyn and New York City.” The Integrated Design & Media (IDM) program empowers creative practice, design research, and multidisciplinary experimentation with emerging media technologies. IDM actively encourages and supports diversity in technology. Students in the IDM course work on projects ranging from using motion capture technology to developing novel uses of virtual/augmented reality. This vibrant range of ideas fuels a dynamic learning environment where creativity, critical thinking, and problem-solving thrive.

The complexity of the digital landscape

In the realm of digital media, there are several significant challenges that both creators and users face. These challenges stem from rapid advancements in technology, evolving consumer behaviors, and the complex dynamics of the digital landscape. To stay innovative, NYU Tandon needs to equip its students with the latest technology to keep ahead of the game. “The students are very eager to know what's the next thing that's coming out. They want to make sure that they have that on their portfolio and they want to make sure that they know how to do it well,” says Todd Bryant, Director of Production for the Integrated Design and Media Program.

Harnessing their own creativity

Portability is a necessity for students’ work. After using the ASUS ProArt Studiobook 16 OLED , the master students in the IDM course changed their thesis projects approach. Now they can take their work anywhere they want and still keep the quality consistent. Master's student Christopher Strawley mentions how having both the processing power and graphical power in a portable package is “unparalleled.” Todd Bryant points out that students find the ASUS Dial very intuitive when working on non-linear editing. “They didn't know what true color really was [before], and they were working on subpar machinery that wasn't going to prepare them for what computing can do in the real world,” he explains. After using the ProArt Studiobook 16 OLED, students can remove any limitations and challenge themselves and their own creativity.

Powering virtual reality

NYU Tandon has installed Puget System's Ryzen X670E ATX with ASUS ProArt Motherboard as part of its workflow. This custom-made workstation comes with a personalized manual packed with information about all its functionalities. The workstation features many inputs and outputs, and NYU Tandon uses all its USB ports for USB interfaces that run up to 64 channels of audio. To finish their projects, students can easily connect up to four monitors to the workstation if needed. This has enabled them to run really large projects in real-time off external drives via USB connection.

To empower its virtual reality, NYU Tandon uses the ASUS ProArt Station PD5 in its XR lab. By having processing power, students can now compile shaders without any downtime. This enables them to see the mistakes they may have made and fix them as quickly as possible. Virtual production requires a lot of technology; having multiple USB ports at the front of the PD5 station makes it very easy for students to connect external devices needed for their workflows.

Different monitors for different set-ups

NYU Tandon has connected the ASUS ProArt Display PA348CGV with the Puget Systems computer. Using the ultra-wide display, the students are able to see their creations from a cinematic perspective. The wide monitor allows them to display any of their software anywhere they want on the screen and run it with a 120Hz refresh rate.

To ensure students see the correct color representation, they have also connected the ASUS ProArt Display PA328CGV to one of their workstations. As well as viewing virtual worlds at 165Hz, this Calman factory-certified calibrated screen makes them stand out when it comes to color. “The blacks are pure black, and we don't lose everything in the white," says Bryant. The ProArt display offers Delta E 2 color accuracy, making it an ideal option for color-critical tasks. With a 100% sRGB and Rec 709 color gamut, this display is suitable for students who need accurate color representation for their projects.

In its volumetric studio, the university uses the ASUS ProArt Display OLED PA32DC . The OLED monitor comes with a built-in colorimeter that ensures the monitor is calibrated for color-consistent results. The display has a true 10-bit color depth and a million-to-one contrast ratio that is essential for the volumetric footage to ensure true color accuracy across all deliverables. All the ASUS monitors have the flicker-free, low-blue light technology, which is a lifesaver for the students' eyes when they work late at night.

Portable workflow solutions

NYU Tandon also uses two ASUS ProArt portable monitors in its workflow. The ASUS ProArt Display PA147CDV is used as a sequencer, where all animation is made. This 14-inch form factor is ideal for non-linear editing.

The ASUS ProArt PA148CTV portable monitor is used as a second display in students' thesis projects. “I've been using the ASUS ProArt Display PA148CTV as a second monitor for my thesis project in VR, which allows me to cast from a camera in the engine from a third-person perspective. So I'm able to display both what the person in VR is seeing and a third-person camera,” explains IDM master student Chris Crawley.

The ‘eyes light up’ moment

After using the ASUS ProArt monitors and laptops, students realized what color is supposed to look like. “It was an awakening for them; we call it the eyes light up moment," explains Bryant. The inspiration these colors give them means they are not limited to hardware. Students can now experiment with a variety of colors, giving them the freedom to create art in ways they could not before. “The products have just facilitated them to be able to make their wildest creative dreams come true,” he concludes.

Product installed

ProArt Display OLED PA32DC

• -31.5” 4K UHD
• -99% DCI-P3
• -Built-in Motorized Colorimeter

Learn more about ProArt Display OLED PA32DC

ProArt Display PA348CGV

• -34” 3440x 1440
• -98% DCI-P3, 120Hz
• -USB-C with 90W Power Delivery

Learn more about ProArt Display PA348CGV

ProArt Display PA328CGV

• -32” QHD (2560 x 1440 )
• -165Hz, 95% DCI-P3

Learn more about ProArt Display PA328CGV

ProArt X670E-Creator WIFI

• -DDR5 support
• -Dual USB4® ports
• -10 Gb and 2.5 Gb Ethernet, WiFi 6E

Learn more about ProArt X670E-Creator WIFI