## 4 Discussion  
### Principle Results  
The study results demonstrated that among high-risk T2 DM patients who use Dario technology, blood glucose levels (average blood glucose and glucose variability) were improved in different racial/ethnic groups: WP and REM (Latinos, Blacks, and Asians) (Figure 1) and in different BMI groups based on the statistical tests applied (Kruskal-Wallis) (Figure 2). In addition, there were no differences in blood glucose levels between the groups over a year of app usage. The GLM models showed that the frequency of measurements by month (number of months per year (F:m)) and the number of measurements (Bgm) at months 1 and 12 are predictors of blood glucose levels. Our findings strengthen the observation that a higher level of user engagement, reflected by a monthly number of measurements and active months, is associated with a reduction in blood glucose levels in the tested timeframe. Moreover, the improvement in average blood glucose, as examined in Model 1, was not different for separate racial/ethnic groups (WP/REM), consistent with the statistical tests applied (Kruskal-Wallis and paired Wilcoxon). The significant effect of the engagement variables (Bgm and F:m) among WP and REM populations indicated that using the digital therapeutic platform has the potential to enhance diabetes management, contributing to their improved glycemia over a full year. Multiple factors may contribute to healthcare disparities, such as immigrant status, living in rural regions, socioeconomic status, numeracy, educational level, health literacy, and access to health insurance [58; 59; 60]. Furthermore, environmental changes, food, and physical insecurity may influence the risk of obesity and diabetes as they are associated with genetics and epigenetic differences between varied races/ethnicities [61; 62]. Healthcare providers' bias may also impair health outcomes amongst racial/ethnic minority \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Coefficients** & **Estimate (\(\beta\))** & **Std. Error** & \(t\)**Value** & \(p\)**Value** & **Model Fit** \\ \hline \multicolumn{6}{c}{Model 1: all users} \\ \hline Intercept & 221.69 & 3.81 & 58.15 & \textless{}0.001 & AIC & 22,077.88 \\ Bgm & \(-0.20\) & 0.10 & \(-2.00\) & 0.045 & BIC & 22,105.88 \\ Dichotomic & \(-4.84\) & 4.43 & \(-1.09\) & 0.274 & \(\chi^{2}\) (3) & 42,776.44 \\ Bgm * & & & & & & \\ Dichotomic & 0.08 & 0.11 & 0.72 & 0.471 & \(p\)-value & 0.01 \\ ethnicity & & & & & & \(\text{R}^{2}\) & 0.01 \\  & & & & & observation & 2000 \\ \hline \multicolumn{6}{c}{Model 2: REM} \\ \hline Intercept & 244.63 & 17.88 & 13.68 & \textless{}0.001 & AIC & 2970.42 \\ F.m & \(-4.38\) & 1.67 & \(-2.62\) & 0.009 & BIC & 2981.15 \\  & & & & & \(\chi^{2}\) (1) & 30,593.58 \\  & & & & & \(p\)-value & 0.01 \\  & & & & & \(\text{R}^{2}\) & 0.03 \\  & & & & & Observation & 264 \\ \hline \multicolumn{6}{c}{Model 3: WP} \\ \hline Intercept & 236.44 & 11.07 & 21.37 & \textless{}0.001 & AIC & 8213.62 \\ F.m & \(-3.77\) & 1.02 & \(-3.69\) & \textless{}0.001 & BIC & 8227.43 \\  & & & & & \(\chi^{2}\) (1) & 55,724.84 \\  & & & & & \(\text{R}^{2}\) & 0.02 \\  & & & & & \(p\)-value & \textless{}0.001 \\  & & & & & Observation & 736 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: General linear model results. Avg.bg presents the predictable variable—the average blood glucose levels; Bgm presents the number of measurements; WP, White persons; REM presents people from racial and ethnic minority groups; dichotomic ethnicity presents WP/REM. F.m presents frequency of measurements by month. populations; such biases may take the form of disparities in treatment recommendations and expectations of treatment adherence [63]. In contrast to traditionally observed racial/ethnic disparities in diabetes treatment participation and outcomes, our study demonstrated the potential of digital therapeutic interventions to support and assist in diabetes management.