## 4 Discussion
### VR Data Capture and Evaluation

Table 4: Designs of experiments for VR-based interventions. affective states [12; 19] and the SUS (System Usability Scale) to measure the ease of use of VR systems [16; 20]. Additionally, some studies captured behavioral and physiological data, including heart rate monitoring and physical exertion, to assess the demands of physical tasks [12; 23]. Metrics like gait tracking were employed in gait and balance training [24; 25]. However, due to the variety of subjective and objective measures across studies, this diversity, while enriching the research scope, may reduce the comparability of results between studies. Furthermore, some studies did not systematically analyze the physiological data collected, potentially limiting the comprehensiveness and reliability of the findings. Regarding measurement metrics, research extensively utilized cognitive, physical, and psychological assessment tools. Cognitive indicators included the MoCA, Stroop test, and Trail-Making Test, primarily assessing participants' cognitive status [26]. Physical indicators like the TUG (Timed Up-and-Go Test) and BESS (Balance Error Scoring System) measured balance and motor function [20; 25]. Additionally, some studies tracked mood and self-confidence changes using scales such as the FES (Falls Efficacy Scale) [19; 25]. However, the lack of consistency in measurement tools across studies, especially in cognitive and emotional assessments, may make it difficult to directly compare intervention outcomes. Future research could benefit from employing more standardized measurement tools for specific health goals to enhance the reliability and cross-study comparability of results. In terms of data analysis, most studies employed descriptive statistics to summarize participant characteristics and inferential statistics to test between- or within-group differences. For example, Brazil and Rys, [PERSON] et al. [16; 18] used \(t\)-tests or ANOVA to analyze group differences, while [PERSON] et al. and [PERSON] et al. [25; 26] analyzed pre- and post-test effects using paired \(t\)-tests. Some studies utilized multivariate analyses to explore the joint effects of multitasking interventions on multiple indicators [26]. Although these analytical methods ensure the scientific validity of the data and the reliability of statistical results, the lack of consistency in statistical methods and variable selection across studies may impact the comparability of results. Future research should consider adopting more standardized statistical methods and analysis frameworks to improve the comparability and integration of results across studies. To ensure data reliability and validity, several studies adopted rigorous data processing and validation methods. [PERSON] et al. [22] used [PERSON]'s alpha to validate the internal consistency of questionnaires. Most experimental procedures were standardized to ensure all participants performed tasks under the same conditions [18; 25]. For data processing tools, studies primarily relied on Excel and SPSS for collation and descriptive analysis [23], while more complex analyses employed MATLAB or Python [12; 16]. Although these measures enhance data validity and analytical accuracy, the diversity of data processing tools and validation methods may affect the reproducibility and comparability of findings. Future research should consider adopting standardized data processing and analysis tools to strengthen result robustness and cross-study comparability.