## 4 Discussion
Nonetheless, the use of realistic VR environments is common, suggesting that realism is an important aspect to consider when developing VR training applications. This is the case unless the target audience requires an abstract environment due to other conditions they may have [45]. Further, making the VR environment more engaging is also important for the training experience to be more enjoyable [48]. Regarding the tasks to complete, some studies laid out specific tasks for the users to complete, normally ranging in complexity. These studies proposed various parameters to assess the user's performance improvements and therefore to test the success of a system. The hardware used in the different studies also varied significantly. However, most studies used a joystick [28; 29; 30; 32; 33; 34; 35; 36; 37; 38; 39; 40; 41; 42; 43; 44; 45; 46; 47; 48] or similar, as a controller for the VR navigation, given that VR training systems are predominantly developed for powered WUs. Some studies developed more accessible controller hardware such as BCI [29], eye-trackers [28], sensors on the wheels [27], to find the requirements of their users. Although all studies aim to use VR as a system to teach driving skills which can be transferred to real-life scenarios, only fewer studies directly tested this by having the user navigate in a specific real environment before and after the VR training [32; 33; 34; 35; 36; 39; 42]. It is our advice that any future research should implement real-life tests comparing pre- and post-training performances to provide evidence of transferable skills. Additionally, each study used its own measures and analyses on which to base its conclusions on the usefulness of its system. The use of different data analyses makes it challenging to compare the outcomes of different systems and, therefore, to design better ones in the future. Standardised assessments would be of great interest and can be achieved by identifying a list of parameters that can be measured by any VR system. Task completion time and number of collisions are good candidates, as they are already commonly used; further, number of collisions are also used in the WST to assess whether skills have been acquired. In terms of statistical approaches to analyse the data, most studies used some sort of statistical analysis test, most commonly the ANOVA test; all types of analysis used by the studies are outlined in Table 3. Quantitative data analysis, such as the ANOVA test, could be useful to compare the performance of a new wheelchair users before and after VR training to determine whether acquisition of skills was successful in a standardisation protocol. A standardisation in which different studies acquire the same kind of data and perform the same statistical tests, as those we suggested, would allow for an easier comparison between the effectiveness of the system of different studies, and thus it would be easier to identify the areas in which different systems are effective. Furthermore, to successfully use VR, its limitations must be considered. A major limitation found by the different studies is cybersickness, a common side effect of VR due to the disorientation to the user's sense of motion [56]. This effect is mainly observed when using a HMD for VR, as [PERSON] et al. [42] found when comparing to monitor screens, and they found that HMD provoke much more symptoms of it. The assessment of cybersickness and the implementation of mitigating approaches should be incorporated into future VR training systems. At the moment, cybersickness is mainly assessed using self-reported questionnaires for SoP and users. However, some studies have shown that these aspects can be measured using implicit performance metrics such as users' HR during training [34; 35; 46; 47]. In fact, [PERSON] et al. [47] found a positive relationship between simulator sickness scores and HR measures, thus showing that the perceived level of cybersickness corresponds to an increased HR.