# Considering the Consequences of Cybersickness in
## 2 Materials and Methods
### Data Analysis  
To conduct our investigation, which mostly relied on meta-analysis, we employed StataSE 14 [16] and Comprehensive Meta-Analysis (CMA) Version 3 [17] for computation. Specifically, three meta-analysis methods were adopted: SNK-test (Q-test), SMD (forest plot), and pooled mean.  
Firstly, each subscale was calculated together with the \(Q\)-test variable analysis, also known as the conventional test for heterogeneity. \(W_{i}\) is the weighting factor of the \(i\)th, and \(Y_{i}\) is the effect size of the \(i\)th. \(M\) is the average effect size of the number of studies \(k\). In addition, the \(Q\)-test in this formula is called the weighted sum of squares (WSS):  
\[Q=\sum_{i=1}^{k}W_{i}(Y_{i}-M)^{2} \tag{1}\]  
At the same time, \(p\)-values were utilized to compare the \(Q\)-value with a chi-squared distribution. This probability, called the \(p\)-value, was employed to represent significant differences between the attributes in the study and the SSQ scores, when the value was lower than 0.05.  
Secondly, we studied the STD mean difference (SMD). This is a measure of the effect size used in statistics to quantify the difference between the means of two groups or subgroups. When considering subgroups in this study, the SMDs were calculated in the same way by comparing the means of the three subgroups and dividing by the pooled standard deviation. It was important to ensure that the subgroups were well-defined and that any differences between them were appropriately accounted for. The outcomes with differences are presented in forest plots.  
Lastly, the pooled mean was calculated by weighting the mean of each study about cybersickness by the sample size and dividing the sum of the weighted means by the sum of the sample sizes across all collected papers. For each of the overall and subscale SSQ scores, the pooled means of various qualities were calculated, which determined the importance of cybersickness. Calculations were carried out for the pooled impact evaluation, with 95% confidence intervals (CI 95%).  
The tools and methods mentioned above were utilized to analyze the following factors. (1) Demographic factors. The age range of the patients was divided into three levels: youths under 30, adults between 30 and 60, and seniors above 60. This form of division was made taking into account the cognitive and motor skills of the user. The amount of data regarding gender differences was insufficient. In addition, there were several different types of patient symptoms related to virtual rehabilitation including physical inactivity, moderate cognitive impairment (MCI), acquired brain injuries. Cybersickness-inducing conditions were also included in the majority of article findings. (2) VR software. We mainly considered the exposure time of the VR device and the content of the VR application as software factors. (3) VR hardware. We identified 4 distinct forms of locomotion, 3 different types of control modes, and 2 types of display modes according to the applications in the collected papers. In the next steps, we calculated all of the elements related to cybersickness. Some of the publications had dropout participants, who experienced uncomfortable visual effects.