Assessing Adherence to Digital Health Technologies Among Hispanic/Latino Adults With or At Risk of Type 2 Diabetes:

Assessing Adherence to Digital Health Technologies Among Hispanic/Latino Adults With or At Risk of Type 2 Diabetes: A Feasibility Study

In an effort to personalize medical care, novel approaches have been used to categorize sub-populations of patients with type 2 diabetes (T2D). These are based on biological and genetic variables, allowing identification of clusters with significantly different clinical characteristics and risks of complications that may be more amenable to targeted and precise therapeutic interventions. Increasingly, wearable and other digital health technologies have the potential to capture additional and objective information to support personalized medicine but at present underserved populations have largely been excluded from clinical trials incorporating digital health.

With this study, the Investigators aim to build on prior work using specially trained community health workers ("Community Scientists") to support engagement with an underserved population and to encourage adherence to using wearables and other digital health technologies. In the US, this is especially imperative for the Hispanic/Latino population, which is at high risk for T2D and associated complications.

As treatment choices for type 2 diabetes (T2D) evolve from a one-size-fits-all approach into a patient-centered precision medicine model, there is a need for a deeper understanding of the clinically meaningful differences between individuals to inform therapy choice.

Recently there have been new approaches to creating sub-groups of populations with T2D based on biological, psychosocial, and genetic variables which have identified clusters of patients with significantly different clinical characteristics and risk of associated complications. By incorporating personal, wearable digital health technologies, it will become possible to further refine such stratification through the inclusion of additional variables and advances in big data analytics and machine learning. The vision is that identifying sub-groups at high risk of complications early in the course of T2D will help clinicians to offer more effective personalized therapies.

In the US, the prevalence of both diagnosed and undiagnosed T2D is nearly twice as high among Mexican-origin Hispanic/Latino adults compared to non-Hispanic whites. Rates of diabetes-related complications are also higher among Hispanic/Latino adults. T2D is also associated with a high burden of depression. There are independent barriers to the treatment of depression in the Hispanic/Latino population, and a population with comorbid depression and T2D could represent a distinct endophenotype requiring modified treatment plans that address common pathophysiological pathways linking both diseases. Of particular interest is the common presence of anxiety symptoms that can worsen depression prognosis and muddle the diagnostic picture. For this purpose, and to elucidate better endophenotypes in our study, attention will be paid to anxious distress, a specifier of major depressive disorder that could potentially be very pertinent to this population, and bring about somatic complaints, insomnia, and irritability.

Although wearable technologies for self-monitoring such as continuous glucose monitors (CGM) are used in diabetes care, the overwhelming experience has been in type 1 diabetes and insulin-treated type 2 diabetes. There is much less use in individuals with non-insulin treated T2D or those at risk of diabetes. Across all forms of diabetes, minority use of CGM has been consistently and markedly less than in the general population with diabetes.

Diet plays a crucial role in the management of T2D. To design personalized dietary recommendations, it is vital to understand an individual's food behaviors. Mobile health platforms present the opportunity to collect detailed information regarding daily food choices. In this study, data collected through daily food logging and ecological momentary assessment (EMA) on hunger, satisfaction, and satiety will be used to quantify and understand the individual's dietary behaviors and glycemic outcomes.

To summarize the rationale behind this study, developments in precision medicine have allowed for the categorization of individuals with T2D into sub-groups that may be amenable to different therapeutic strategies. However, there is also a need to better understand the impact of behavioral and psychological factors on the risk of progression of T2D and responses to existing and new therapies, especially in the context of development of depressive symptomatology. These may be especially relevant for US minorities, such as Hispanic/Latino adults who have an excess burden of T2D and the associated complications compared to non-Hispanic whites. Digital health has the potential to be of enormous value provided it is acceptable and will be used by underserved communities.

Inclusion Criteria:

1. Adults ≥ 18 years of age at Visit 1.
2. Self-reported Hispanic/Latino heritage.
3. Self-reported diagnosis of type 2 diabetes OR self-reported as at risk for developing type 2 diabetes OR healthy individuals who are not at-risk or have diabetes (healthy controls).
4. Based on staff's judgment, the subject must have a good understanding, ability, and willingness to adhere to the protocol and to participate in the video-recording part of the study.
5. Exclusive and continuous use, for the 2 week participation period, of a study-compatible smartphone.

Exclusion Criteria:

1. Currently taking insulin as treatment for diabetes.
2. Pregnant.
3. Any active clinically significant disease or disorder, which in the investigator's opinion could interfere with the participation in the trial.
4. Mental incapacity, psychiatric disorder, unwillingness or language barriers precluding comprehension of study activities and informed consent.
5. Current participation in other trials involving medications or devices.
6. Meets criteria for substance use disorders as defined by DSM-5 (e.g., abuse of alcohol, narcotics, or illicit drugs).
7. Self-reported diagnosis of bipolar disorder, schizoaffective disorder, schizophrenia or antisocial personality disorder.
8. At high risk for suicide based on staff judgement.
9. Neuromuscular disease.