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| ID | Type | Description | Link |
|---|---|---|---|
| #7-24-ICTST2DY-05 | Other Grant/Funding Number | American Diabetes Association's (ADA) |
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| Name | Class |
|---|---|
| Stanford University | OTHER |
| American Diabetes Association | OTHER |
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Currently, clinicians are unable to predict a patient's risk of long-term disease progression and development of a long-term complication based on the data that is available to them. The first aim of this is to develop and validate an Artificial Intelligence (AI) powered prediction model for Type 2 Diabetes (T2D) disease progression using existing data from previously collected studies and real-world electronic health medical data. Investigators will use clinical, pharmacologic, and genomic factors to develop the prediction model based on the most relevant clinical outcomes of change in Hemoglobin A1c (HbA1c) and the development of a microvascular complication.
Despite the availability of newer medication options, lifestyle intervention is not effective in most youth and current therapeutic options are ineffective at producing sustained glycemic control. Newer and innovative methods are needed to identify the youth at highest risk of progression in terms of increase in HbA1c and development of long-term complications and to motivate behavioral change in youth. The goal of this aim is to create an AI-powered digital twin model for 50 youth with T2D using their baseline clinical, genetic, pharmacologic and lifestyle data and utilize AI algorithms developed in Aim 1 to simulate disease progression and treatment response. Investigators will then evaluate the digital twin model in an randomized controlled trail and prospectively compare the generated digital twin data to observed values over one year. Investigators will also measure whether knowledge of the digital twin prediction with targeted healthcare recommendations influence medication and lifestyle change adherence in the digital twin arm (n= 25) compared to the control arm (n= 25).
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Digital twin arm | Experimental | Participants in the digital twin arm will receive information on their disease progression which will be based on projected change in HbA1C in alternative realities and specific recommendations on medication dosing and lifestyle changes based on this data. The digital twin information will be presented on an iPad in a game- like manner. The alternate realities will include scenarios of change in medication adherence, physical activity metrics, dietary changes etc. |
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| Control arm | Placebo Comparator | Participants in the control arm will receive standard of care which is medication change recommendations based on HbA1C and blood glucose values every 3 months and standard lifestyle education. |
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| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| phone application | Device | Participants in the digital twin arm will receive information on their disease progression which will be based on projected change in HbA1C in alternative realities and specific recommendations on medication dosing and lifestyle changes based on this data. The digital twin information will be presented on an iPad in a game- like manner. The alternate realities will include scenarios of change in medication adherence, physical activity metrics, dietary changes etc. |
| Measure | Description | Time Frame |
|---|---|---|
| Change in HbA1C | The primary outcome will be the ability of the digital twin model to accurately predict longitudinal disease progression measured as the digital twin predicted HbA1C versus measured HbA1C and the difference in HbA1C between the digital twin arm and control arms. | From enrollment to the close out visit at the 1-year mark |
| Measure | Description | Time Frame |
|---|---|---|
| Sleep Quality | Change in measures related to sleep quality. (Actigraph measured duration, Insomnia Severity Index (ISI)). Respondents rate each element of the questionnaire using Likert-type scales. Responses can range from 0 to 4, where higher scores indicate more acute symptoms of insomnia. Scores are tallied and can be compared both to scores obtained at a different phase of treatment and to the scores of other individuals. A total score of: 0-7 indicates "no clinically significant insomnia" 8-14 means "sub-threshold insomnia" 15-21 means "clinical insomnia (moderate severity)" 22-28 means "clinical insomnia (severe)" |
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Inclusion Criteria:
Exclusion Criteria:
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| Name | Role | Phone | Extension | |
|---|---|---|---|---|
| Avani A Narayan, MS | Contact | 415-530-8047 | avani.narayan@ucsf.edu | |
| Laura A Dapkus Humphries, NCPT | Contact | 628-224-8364 | laura.dapkus@ucsf.edu |
| Name | Affiliation | Role |
|---|---|---|
| Shylaja A Srinivasan, MD | University of California, San Francisco | Principal Investigator |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| UCSF Benioff Children's Hospital Oakland, Pediatric Diabetes Clinic | Oakland | California | 94609 | United States |
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| ID | Term |
|---|---|
| D003924 | Diabetes Mellitus, Type 2 |
| ID | Term |
|---|---|
| D003920 | Diabetes Mellitus |
| D044882 | Glucose Metabolism Disorders |
| D008659 | Metabolic Diseases |
| D009750 | Nutritional and Metabolic Diseases |
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| ID | Term |
|---|---|
| D059039 | Standard of Care |
| ID | Term |
|---|---|
| D019984 | Quality Indicators, Health Care |
| D011787 | Quality of Health Care |
| D006298 | Health Services Administration |
| D017530 | Health Care Quality, Access, and Evaluation |
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| Standard of Care (SOC) | Other | Participants in the control arm will receive standard of care which is medication change recommendations based on HbA1C and blood glucose values every 3 months and standard lifestyle education. |
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| From time of enrollment to the study close out visit at the 1-year mark |
| Psycho-Social Outcome | Change in measures related to psycho-social outcomes for Quality of Life. (PedsQL-Diabetes Module). The Pediatric Quality of Life (PedsQL) Diabetes Module scores quality of life in children and adolescents with diabetes using a 0-100 scale, where higher scores indicate better quality of life. The PedsQL Diabetes Module uses a 5-point response scale (0-4) for each item. It assesses various aspects of life affected by diabetes, including physical symptoms, treatment barriers, and emotional well-being. | From time of enrollment to the study close out visit at the 1-year mark |
| Sugar Intake | Change in measures related to sugar intake (g/day). | From time of enrollment to the study close out visit at the 1-year mark |
| Physical Activity | Change in measures related to physical activity (days/week) | From time of enrollment to the study close out visit at the 1-year mark |
| BMI | Change in BMI (BMI z-score). | From time of enrollment to the study close out visit at the 1-year mark |
| CGM Time In Range | Change in CGM based measures including time in the target glucose range of 70 to 180 mg/dL. | From time of enrollment to the study close out visit at the 1-year mark |
| CGM Time Above Range | Change in CGM based measures including time above the target glucose range of greater than 250 mg/dL for glucose and co-efficient of variation of glucose. | From time of enrollment to the study close out visit at the 1-year mark |
| Psycho-Social Outcome | Change in measures related to psycho-social outcomes for Diabetes Distress. (Diabetes Distress Scale). Diabetes Distress Scale (DDS) is used to assess the level of emotional distress related to managing diabetes.
| From time of enrollment to the study close out visit at the 1-year mark |
| Psycho-Social Outcome | Change in measures related to psycho-social outcomes for Overall QOL and Wellbeing using the Patient-Reported Outcomes Measurement Information System (PROMIS) Global Health questionnaire.
| From time of enrollment to the study close out visit at the 1-year mark |
| Psycho-Social Outcome | Change in measures related to psycho-social outcomes for Overall QOL and Wellbeing using the World Health Organization- Five (WHO-5) questionnaire.
| From time of enrollment to the study close out visit at the 1-year mark |
| UCSF Benioff Children's Hospital San Francisco, Madison Clinic for Pediatric Diabetes | San Francisco | California | 94158 | United States |
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| D004700 | Endocrine System Diseases |