## 5 Discussion  
In this publication, we present the first MR application that uses both grayscale and color passthrough to assist TED and aid patients in learning how to count carbohydrates. Our MR application blends virtual food, specifically designed for patients in Ecuador, into a real environment. Diet management plays a vital role in maintaining overall health, and for individuals with diabetes, controlling food intake is essential [23]. The amount of insulin needed is directly affected by the quantity of carbohydrates ingested during meals. Thus, understanding the carbohydrate content in food enables patients to better manage their condition autonomously and to enhance their glycemic control. While our study did not assess the impact of the application on patients' glycemic control, this remains a key area for future research. Our application exploits MR technology to enhance the learning experience in a way that traditional methods cannot. While the task of associating a number with a food item to indicate the amount of carbohydrate choices may seem simple, the use of MR offers several key advantages that enhance the learning experience. The immersive nature of MR increases participant engagement and attention, which can lead to better retention \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **d.f.** & \(\mathbf{H}\) & \(\mathbf{p}\) \\ \hline Post-test Traditional & 12 & 14.992 & 0.242 \\ Post-test AR application & 15 & 9.703 & 0.838 \\ Post-test MR-Quest 2 & 14 & 9.236 & 0.816 \\ Post-test MR-Quest 3 & 15 & 15.286 & 0.431 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Kruskal-Wallis tests were applied to the post-knowledge scores considering age. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Variables** & **Know** & **SA#1** & **US#1** & **US#2** & **US\#3** & **SA#4** & **SA#5** & **SA#6** \\ \hline SA\#1 & \(-\)0.03 & & & & & & \\ US\#1 & **0.28** & **0.29** & & & & & \\ US\#2 & \(-\)0.10 & **0.46** & **0.39** & & & & \\ US\#3 & 0.17 & **0.34** & **0.44**\({}^{\text{q}}\) & **0.44**\({}^{\text{a}}\) & & & \\ SA\#4 & 0.12 & **0.29** & **0.36**\({}^{\text{q}}\) & **0.34** & **0.35**\({}^{\text{q}}\) & & & \\ SA\#5 & 0.18 & **0.25** & **0.27** & **0.33** & **0.47** & **0.46** & & \\ SA\#6 & \(-\)0.11 & 0.23 & 0.11 & **0.38** & 0.14 & **0.32** & **0.36** & \\ SA\#7 & \(-\)0.13 & **0.41** & \(-\)0.06 & 0.22 & 0.11 & **0.35** & **0.34** & **0.52**\({}^{\text{q}}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Correlation matrix among some of the variables using data from all of the patients who used the AR, MR with Quest 2, and MR with Quest 3 applications. and understanding of the knowledge. Additionally, MR facilitates more realistic and dynamic interactions with 3D models of food items, providing a lifelike experience that helps participants better contextualize and transfer their learning to real-world situations. This interaction strengthens the ecological validity of the learning process, ensuring that the skills learned are more readily applicable in real-life contexts. The use of passthrough technology further enhances the experience by seamlessly blending virtual elements with the real environment, something that traditional 2D images or simpler learning methods cannot achieve. Incorporating MR technology, the Meta Quest 3 introduces hardware advancements that further enhance user immersion. Meta Quest 3 has higher resolution and a larger field of view that enhances the immersive user experience. However, with respect to MR, a key improvement in the Meta Quest 3 is its color passthrough, which offers a significantly enhanced experience. This improvement is not only due to the addition of color but also the increased image stability. On the other hand, with the Meta Quest 2, image distortion begins below 0.6 m [17]. While distortion still occurs with Meta Quest 3, it only appears at much closer distances, such as when a hand is brought close to the face or the headset is near a real table. Meta Quest 3 remains stable at distances of over 40 cm.