Paper Summary
Title: Virtual Reality Sickness Factors
Source: Frontiers in Human Neuroscience (0 citations)
Authors: Dimitrios Saredakis et al.
Published Date: 2020-03-31
Podcast Transcript
Hello, and welcome to paper-to-podcast. Today, we're diving into a research paper I've only read 25 percent of, but trust me, it's going to be informative. The paper, titled "Virtual Reality Sickness Factors," was published in 2020 by Dimitrios Saredakis and colleagues.
So, what's the deal with nausea in virtual reality (VR)? Well, this research analyzed factors contributing to VR sickness in head-mounted displays (HMDs). Interestingly, gaming content had the highest total Simulator Sickness Questionnaire (SSQ) mean score of 34.26, suggesting gamers should stock up on anti-nausea meds. Other factors influencing VR sickness profiles included visual stimulation, locomotion, and exposure times.
Surprisingly, older folks (mean age greater than or equal to 35 years) had significantly lower total SSQ means than younger individuals. It seems like the older we get, the stronger our stomachs become - at least in VR! And guess what? There were no sex differences in VR sickness susceptibility. The pooled total SSQ mean was relatively high at 28.00, compared to recommended SSQ cut-off scores.
Now, let's talk dropouts. The mean dropout rate due to VR sickness across 46 experiments was 15.6%. These findings are essential for future research and VR applications in therapy, rehabilitation, and gaming.
The authors used a systematic review and meta-analysis to examine factors associated with VR sickness in HMDs. They followed the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines to search for relevant journal and conference papers related to VR sickness using HMDs. The studies were required to use HMDs and measure VR sickness using the Simulator Sickness Questionnaire (SSQ).
The strengths of this research lie in the systematic approach and comprehensive meta-analysis conducted to examine VR sickness in HMDs. By focusing on factors like content, visual stimulation, locomotion, and time, the researchers provided a thorough understanding of how these elements influence VR sickness.
However, there are some limitations to the research due to the categorization of factors and the examination of user characteristics, such as age and gender, in only a limited number of studies. Additionally, the SSQ scores used to measure VR sickness might not capture the full range of symptoms experienced by users.
Potential applications for this research include informing the design and development of VR experiences to reduce VR sickness in users. By understanding the factors associated with VR sickness, developers can create more user-friendly and comfortable experiences for various purposes, such as gaming, therapy, rehabilitation, training, and education.
In conclusion, this research provides valuable insights into the factors contributing to VR sickness, which can be used to improve VR experiences and reduce the prevalence of VR sickness. You can find this paper and more on the paper2podcast.com website.
Supporting Analysis
In this research paper, the authors analyzed factors linked to virtual reality (VR) sickness in head-mounted displays (HMDs). They found that gaming content had the highest total Simulator Sickness Questionnaire (SSQ) mean score of 34.26, suggesting it is the most likely to cause VR sickness. Other factors influencing VR sickness profiles included visual stimulation, locomotion, and exposure times. Interestingly, older people (mean age ≥35 years) scored significantly lower total SSQ means than younger individuals, although the findings were based on a limited number of studies that included older users. No sex differences were found in VR sickness susceptibility. The pooled total SSQ mean was relatively high at 28.00, compared to recommended SSQ cut-off scores. Moreover, the mean dropout rate due to VR sickness across 46 experiments was 15.6%. These findings are important for future research and the application of VR in different contexts, such as therapy, rehabilitation, and gaming.
In this systematic review and meta-analysis, the researchers aimed to examine the factors associated with virtual reality (VR) sickness in head-mounted displays (HMDs). They followed the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines to search for relevant journal and conference papers related to VR sickness from using HMDs. The search was conducted across six databases, and the included studies were required to use HMDs and measure VR sickness using the Simulator Sickness Questionnaire (SSQ). The authors categorized the studies based on factors such as content type, visual stimulation, locomotion, exposure time, and user characteristics (age and sex). They used Comprehensive Meta-Analysis (CMA) software to conduct meta-analyses and calculated pooled means for all factors separately on each SSQ subscale as well as for the total SSQ score. To examine the influence of user characteristics on the SSQ scores and dropout rates, they performed a correlational analysis between the percentage of females in studies and total SSQ scores. Overall, the systematic review process led to the inclusion of 55 publications, which were analyzed to assess the impact of various factors on VR sickness symptoms measured with the SSQ.
The most compelling aspects of the research are the systematic approach and the comprehensive meta-analysis conducted to examine virtual reality (VR) sickness in head-mounted displays (HMDs). By focusing on factors like content, visual stimulation, locomotion, and time, the researchers provided a thorough understanding of how these elements influence VR sickness. Furthermore, they considered user characteristics such as age and sex to evaluate their impact on VR sickness. The researchers followed best practices by adhering to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines, which ensures a rigorous and standardized methodology. They also performed a systematic literature search across multiple databases to gather a diverse set of studies for analysis. By categorizing and operationalizing the factors being examined, the researchers ensured that the data was organized and manageable for analysis. Lastly, the use of statistical tools like Comprehensive Meta-Analysis (CMA) and various tests to calculate pooled effect estimates and assess differences between sub-factors contributed to the robustness of the analysis. This research approach ultimately leads to valuable insights that can inform future VR research and applications across different contexts.
The research might have some limitations due to the categorization of factors, as the definitions of content, visual stimulation, and locomotion may not be universally applicable. This could make it challenging to generalize the findings across various VR experiences. Additionally, the user characteristics such as age and gender were only examined in a limited number of studies, which could affect the accuracy of the conclusions drawn. Furthermore, the SSQ scores used to measure VR sickness might not capture the full range of symptoms experienced by users, as it primarily depends on self-reported data. The lack of objective physiological measures might also limit the understanding of the relationship between hardware, content, and user characteristics in VR sickness. Lastly, the research focused on VR sickness in head-mounted displays, which might not be applicable to other types of VR technologies, such as augmented reality and see-through displays.
Potential applications for this research include informing the design and development of virtual reality (VR) experiences to reduce VR sickness in users. By understanding the factors associated with VR sickness, developers can create more user-friendly and comfortable experiences for various purposes, such as gaming, therapy, rehabilitation, training, and education. Additionally, the findings can help guide future research on VR sickness, contributing to the understanding of the impact of content, visual stimulation, locomotion, and exposure times on users. This research can also be used to improve the understanding of how user characteristics, such as age and sex, influence the occurrence of VR sickness. With this knowledge, developers can create more inclusive and accessible VR applications tailored to diverse user groups. Overall, the insights gained from this research can lead to better VR experiences for users, reducing the prevalence of VR sickness and increasing the adoption of VR technologies in various fields.