## 4 Discussion
### Limitations  
Several limitations are noted in the study. Racial and ethnic categories were limited to the choices available in the application. There was no designed control group in this study comparing digital to traditional self-management strategies. However, we used a comparable group from our real-world data (REM vs. WP). Both groups were characterized by no substantial difference in demographics. As in all studies involving retrospective real-world data, groups were not randomly assigned and treatment protocols were not prescribed. Both factors create challenges for indicating causal effects. It is certainly possible that users who chose to purchase the device and to be engaged were the most motivated to change. However, the users in all groups analyzed in the study were persons with T2 DM with hyperglycemia levels (all the users started with an average blood glucose of above 180 mg/dL in the first month of activity since registration). That may indicate that these users were not highly engaged in their treatment prior to joining \(\text{Dario}^{\text{TM}}\) despite their complex condition. An implication of racial and ethnic groups that were coded in the application may not capture results from groups that are not included in the app, and it is possible that people who chose to report their race/ethnicity were those who were the most motivated to change. Our study was designed to ensure that both groups analyzed showed evidence of being motivated about their diabetes care. Additionally, self-reported data are vulnerable to response bias. Specifically, users' racial/ethnic identities were collected via self-report, which is subject to response bias.  
In contrast to a substantial portion of health studies that require follow-up assessment from the experimenter party, these assessments were performed exclusively remotely. This reduced the study bias and enhanced the advantages of real-world data.  
Another limitation regarding the study array is assessing the average blood glucose levels only at two time points (1 st month and 12 th month). Following frequent fluctuations in blood glucose levels is important in diabetes management. We used a longitudinal collection of data of blood glucose measurements and statistical analysis (GLM) to allow robust estimation of the digital health effect over the evaluation period. Additionally, we applied in our models the frequency of measurements by month (the coefficient; F.m) represented in the month's duration engagement of the users. Blood glucose measurements and frequency of measurements across ethnicities were examined as predicting variables for reduction in blood glucose levels over time, with the limitation that there may be other predicting variables contributing to reduction in blood glucose, such as users' diet and eating habits or other unknown or unreported medical or mental conditions. However, these variables were found based on the model as significant predictors. Finally, we observed BMI outcomes with the limitation of absent information regarding users' eating behaviors or exercise regimes. However, the purpose of observing BMI outcomes is to add a related biochemical marker to blood glucose levels and to demonstrate a broader range of metabolic variables. In addition, the country of residence for the participants of this study was skewed towards the USA; therefore, it is not clear if the results are equally applicable to less economically developed countries. Hopefully, our results will encourage further research among less economically developed and more racially/ethnically diverse populations outside of the USA.