Assessing Adherence to Digital Health Technologies Among Hispanic/Latino Adults With or At Risk of Type 2 Diabetes:
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|ClinicalTrials.gov Identifier: NCT04820348|
Recruitment Status : Recruiting
First Posted : March 29, 2021
Last Update Posted : June 16, 2021
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.
|Condition or disease|
|Diabetes Mellitus, Type 2 Depression Anxiety Diet Habit|
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.
|Study Type :||Observational|
|Estimated Enrollment :||30 participants|
|Official Title:||Assessing Adherence to Digital Health Technologies Among Hispanic/Latino Adults With or At Risk of Type 2 Diabetes: A Feasibility Study|
|Actual Study Start Date :||April 9, 2021|
|Estimated Primary Completion Date :||September 30, 2021|
|Estimated Study Completion Date :||December 15, 2021|
- Adherence to wearing continuous glucose monitoring (CGM) devices to measure glucose levels. [ Time Frame: 2 weeks ]Unit of measure: Percent of time and percent of days CGM is active during the study period.
- Adherence to wearing ActiGraph to measure physical activity and sleep. [ Time Frame: 2 weeks ]Unit of measure: Percent of time and percent of days ActiGraph is active during the study period.
- Adherence to wearing Fitbit to measure physical activity and sleep. [ Time Frame: 2 weeks ]Unit of measure: Percent of time and percent of days Fitbit is active during the study period.
- Adherence to using the HealthSense app to capture food intake data. [ Time Frame: 2 weeks ]Unit of measure: Percent of days HealthSense app is active during the study period.
- Adherence to using the MyFitnessPal app to capture food intake data. [ Time Frame: 2 weeks ]Unit of measure: Percent of days MyFitnessPal app is active during the study period.
- Feasibility of measuring glycemic impact of meal quantity and composition using CGMs. [ Time Frame: 2 weeks ]Unit of measure: Percent of participants from whom data are captured on glucose levels AND food intake.
- Feasibility of measuring the behavioral impact of meal quantity and composition using food logs and ecological momentary assessment of hunger, satisfaction and satiety. [ Time Frame: 2 weeks ]Unit of measure: Percent of participants from whom data are captured on food intake AND hunger, satisfaction and satiety.
- Feasibility of assessing depression and anxiety symptoms objectively based on video-recording of the participants. [ Time Frame: 2 weeks ]Unit of measure: Percent of participants from whom data are captured via video-recording on psychological status (depression and anxiety).
- Feasibility of assessing the prevalence of depression and anxiety with a focus on culturally-specific presentations of distress. [ Time Frame: 2 weeks ]Unit of measure: Percent of participants from whom data are captured on psychological status (depression and anxiety) AND ethnicity.
- Feasibility of assessing the comorbidity of depressive/anxiety symptoms and type 2 diabetes and the impact of depression and anxiety on diet-medication adherence and lifestyle choices. [ Time Frame: 2 weeks ]Unit of measure: Percent of participants from whom data are captured on depressive/anxiety symptoms AND diabetes status AND diet-medication adherence AND lifestyle choices.
To learn more about this study, you or your doctor may contact the study research staff using the contact information provided by the sponsor.
Please refer to this study by its ClinicalTrials.gov identifier (NCT number): NCT04820348
|Contact: Rony Santiago||805 682 7640 ext email@example.com|
|Contact: Namino Glantz, PhD||805 682 7640 ext firstname.lastname@example.org|
|United States, California|
|Sansum Diabetes Research Institute||Recruiting|
|Santa Barbara, California, United States, 93105|
|Principal Investigator: David Kerr, MD|
|Principal Investigator:||David Kerr, MD||Sansum Diabetes Research Institute|