## 1 Introduction  
Among people living with diabetes, out-of-range glycemia is associated with long-term diabetes complications that lead to hospitalizations and a high burden of health costs, loss of productivity, disability, and mortality [1, 2, 3, 4]. According to the Centers for Disease Control and Prevention (CDC), 37.3 million people have diabetes (11.3% of the US population) [5], and 8.25 million hospital discharges were reported with diabetes, including 226,000 cases of hyperglycic crises [3]. Furthermore, diabetes was the seventh leading cause of death in the United States. The diabetes economic burden is also substantial, with an estimated cost of USD 327 billion in the United States' total population, including racial and ethnic populations [6]. Despite these extreme consequences, most patients fail to achieve optimal glycemia [7, 8]. Some demographic and health factors, e.g., age (mainly 60-69), level of education, weight, smoking status, and marital status, are associated with poor adherence to treatment, nonengagement in diabetes self-management, and out-of-range glycemia [9, 10, 11]. Furthermore,the estimated prevalence of diagnosed and undiagnosed T2 DM among USA adults above 20 years of age is higher in ethnic minority groups in comparison with White persons: White 11.1%; Black 16.7%; Mexican American 17.7%; Asian 16% [12]. Being overweight is currently recognized as one of the most important variables in prediabetes and T2 DM management [13; 14; 15], markedly with increasing BMI over 30 kg/m\({}^{2}\)[15; 16; 17]. Previous studies have examined the association between objectively measured BMI and T2 DM, showing that increased BMI is positively associated with T2 DM [18; 19]. Target glycemia is generally assessed by periodic glycated hemoglobin (A1C) and routine repeated measurements of blood glucose; target A1C is considered as 7.0% [20]. According to the American Diabetes Association (ADA) guidelines, adults with diabetes (nonpregnant) and postprandial capillary plasma glucose above 180 mg/dL are considered nontarget glycemic outcomes. Multiple risk factors are involved in the pathogenesis of T2 DM with complex underlying gene-environment interactions, including age, ethnicity, family history, low socioeconomic status, obesity, metabolic syndrome, and certain unhealthy lifestyle behaviors [21; 22; 23; 24; 25; 26; 27; 28]. Healthcare disparities associated with significant racial/ethnic groups have been observed in T2 DM and obesity, including impairments in diagnosis, out-of-range glycemia, and development of diabetes-related complications due to low socioeconomic status, living in rural communities, reduced access to care, and a lack of health education [12; 29; 30; 31]. According to the National Diabetes Report 2020 in the United States, the incidence of diagnosed diabetes for 2017-2018 among people aged 18 years or older was higher among people from racial and ethnic minority (REM) populations than White people (WP) populations [32]. In addition, REM (e.g., Black, Latino, and Asian) populations significantly present with cardiovascular and cerebrovascular diseases [33], nephropathy, and lower extremity amputations compared to WP populations [34; 35]. Lower rates of health insurance, delivery of medical care, and social policies with limited access to healthy foods and places for physical activity are some of the possible reasons that REM populations do not achieve glycemic goals [36; 37; 38]. Some social and cultural healthcare disparities might be mitigated through digital therapeutics approaches [36]. Digital health implementations have grown markedly and are now frequently used in health education promotion, disease management, and surveillance of many chronic diseases [39; 40]. The rapid adoption of mobile devices and Internet services has also reached rural communities and people at increased/higher risk for a chronic condition [41; 42; 43; 44; 45]. Digital tools can also help to facilitate automation and health service delivery [46].