## 2 Materials and Methods
### Statistical Analysis  
Data were analyzed using an intention-to-treat (ITT) approach, a recommended strategy to minimize bias by analyzing all participants according to their original assignment [39]. This analysis was conducted by an investigator who was blinded to group allocation. The multiple imputation technique was applied using the predictive mean matching method to handle missing data and ensure the inclusion of all participants in the analysis [40]. Subsequently, data were described using normal distribution statistics, verified using the Shapiro-Wilk test, which is sensitive to deviations in small sample sizes, allowing confirmation of whether parametric tests were appropriate [41].  
Figure 2: Participant using AVG (**A**). Image of the visual feedback from the game (**B**).  
Homoscedasticity between groups was verified using [PERSON]'s test; thus, for WOMAC, a two-way repeated measures analysis of variance (ANOVA) was performed, followed by [PERSON]'s post hoc test to analyze intragroup differences (post-tests 1, 2, 3, and follow-up versus pre-test) and intergroup differences at each time point. For GRoC, an independent sample Student's _t_-test was applied. The ES was calculated using Cohen's d, which provides a standardized measure of the magnitude of differences, categorized as <0.2 (negligible), 0.2-0.49 (small), 0.5-0.79 (moderate), and \(\geq\)0.8 (large) [42].  
The MCID threshold for each WOMAC score (total and subscales of pain, stiffness, and function) was calculated using a standardized ES multiplied by the standard deviation, as proposed by [PERSON] et al. (2024) [43]. This approach helps assess inter-individual variability by identifying responders whose improvements exceed the MCID, indicating significant clinical benefits. Participants were then classified based on the change (\(\Delta\)) in scores (post-test 3 vs. pre-test) as responders (Rs), non-responders (NRs), and adverse responders (ARs) [44]. Because a lower WOMAC score indicates improvement, the upper MCID threshold (favorable) was expressed as a negative value, and the lower MCID threshold (unfavorable) is expressed as a positive value. The classification was defined as follows: Rs if \(\Delta\) score \(\geq-\)MCID, NRs if \(\Delta\) score \(<-\)MCID and \(<+\)MCID, and ARs if \(\Delta\) score \(\geq+\)MCID. Associations between groups and response categories (Rs, NRs, ARs) for each WOMAC score were analyzed using the chi-square test (\(\chi^{2}\)) due to its ability to assess independence between categorical variables [45]. Next, the two-proportion z-test was applied to perform specific comparisons within response categories, providing detailed insight into intergroup differences in response proportions [46]. The MCID thresholds were illustrated using GraphPad Prism 9.4.1 (GraphPad Software Inc.; San Diego, CA, USA).  
Finally, the sample was stratified based on the 25 th, 50 th, and 75 th percentiles of the baseline total WOMAC score, allowing observation of participant distribution across quartiles from pre-test to post-test 3. This stratification approach, which is equivalent to that used by [PERSON] et al. (2015), allows for precise categorization of patients by disability level, facilitating outcome assessment in patients with knee and hip OA [47]. Statistical analyses were performed using SPSS v.25 (IBM Corp., Armonk, NY, USA) and JASP v.0.18.3 ([[https://jasp-stats.org/](https://jasp-stats.org/)]([https://jasp-stats.org/](https://jasp-stats.org/)); accessed on 3 June 2024) with an alpha level of 0.05.