Blended Resources for Integrated Diabetes Guidance and Empowerment
ConditionType 2 Diabetes Mellitus
Trial statusNot yet recruiting
Trial phaseNot applicable
Trial typeInterventional
Biological sexAll
Age18-70
SponsorPeking University
About this trial
The goal of this cluster randomized clinical trial is to learn if an AI-enabled blended care model (the BRIDGE program) works to treat type 2 diabetes in adults. It will also learn about the cost-effectiveness and implementation feasibility of this model in primary care settings. The main questions it aims to answer are:
Does the AI-driven intervention lower HbA1c levels (blood sugar) compared to standard care?
Does this model improve participants' quality of life, self-management behaviors, and digital literacy?
Researchers will compare the "Diet-Medicine Companion" (Shi Yi Ban Lv) mini-program combined with family doctor support to standard community care to see if the blended care model works to manage diabetes.
Participants will:
Use the "Diet-Medicine Companion" mini-program to upload diet photos daily and receive AI feedback for 6 months
Receive periodic guidance and phone reminders from case administrators (family doctors)
Complete questionnaires and blood tests (HbA1c) at baseline, 3 months, and 6 months
Eligibility criteria
This trial does not accept healthy volunteersQualifiers
Aged 18 to 70 years (inclusive).
Glycosylated hemoglobin (HbA1c) level ≥ 6.5%.
Resided in the local area for at least 6 months.
Capable of using a smartphone to take photos and use the WeChat mini-program.
Disqualifiers
Diagnosed with Type 1 diabetes, gestational diabetes, or secondary diabetes.
Presence of severe diabetic complications.
Received radiotherapy or chemotherapy within the past 6 months.
Diagnosed with severe intellectual disabilities, Alzheimer's disease, or other serious psychiatric disorders.
Trial design
Design model
Parallel
Treatments tested in this trial
AI-driven Dietary Management and Human-in-the-loop Support
DeviceThe intervention consists of a 6-month AI-enabled blended care program using the "Diet-Medicine Companion" (Shi Yi Ban Lv) WeChat mini-program. Patient Component (AI Support): Participants are required to upload dietary photos and blood glucose records via the mini-program at least once daily. The system, powered by a Large Language Model (LLM), provides immediate, personalized dietary feedback and answers diabetes-related queries via an AI chatbot. Provider Component (Human Support): Case administrators (family doctors) monitor patient data through a provider dashboard. The protocol involves "human-in-the-loop" support, where doctors provide telephone reminders if participants are inactive (no uploads) for more than 7 days. Doctors also intervene to provide medical guidance or health education based on specific system alerts or patient needs.
Treatment groups
1,200 Participants
are divided into 2 treatment groupsGroup A: InterventionExperimental treatment 1 intervention
Group B: ControlNo intervention 0 interventions
Trial outcomes
Primary outcomes
1
Change in Glycosylated Hemoglobin (HbA1c) Levels
Glycosylated Hemoglobin (HbA1c) reflects the average plasma glucose concentration over the preceding 3 months. It serves as the gold standard biomarker for long-term glycemic control in diabetes management. Data will be obtained from venous blood samples collected at community health centers. A lower HbA1c percentage indicates better glycemic control.
Time frame
Baseline, Month 3, and Month 6Secondary outcomes
1
Change in Body Mass Index (BMI)
Calculated as weight in kilograms divided by the square of height in meters (kg/m\^2). BMI is used as an indicator of body fatness and weight management status. A lower BMI (within the healthy range) generally indicates better weight control outcomes.
Time frame
Baseline, Month 3, and Month 62
Change in Blood Pressure
Measured as Systolic and Diastolic Blood Pressure in mmHg using a standard sphygmomanometer. Lower blood pressure values (approaching the normal range) indicate better cardiovascular risk management.
Time frame
Baseline, Month 3, and Month 63
Incidence of Diabetes Complications
The occurrence of new or worsening diabetes-related complications, including but not limited to neuropathy, retinopathy, nephropathy, and cardiovascular events. Data will be collected through medical record reviews and patient self-reports. A lower incidence rate indicates better disease management
Time frame
Baseline, Month 3, and Month 64
Quality of Life (EQ-5D-5L)
Assessed using the EuroQol 5-Dimension 5-Level (EQ-5D-5L) questionnaire. It covers five dimensions: mobility, self-care, usual activities, pain/discomfort, and anxiety/depression. Responses are converted into a single utility index score ranging from less than 0 to 1. Higher scores indicate better health-related quality of life.
Time frame
Baseline, Month 3, and Month 6Other outcomes
1
Frequency of Dietary Uploads (Adherence)
Measured as the total count of dietary records (photos or text entries) uploaded by each participant via the "Diet-Medicine Companion" (Shi Yi Ban Lv) mini-program. Data is automatically logged by the system's backend server. This metric serves as a direct objective indicator of participant engagement and adherence to the intervention protocol (which requires daily uploads). Higher numbers indicate better adherence.
Time frame
Throughout the 6-month intervention periodLocations
Sponsors and contacts
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Source ClinicalTrials.gov
This trial is not recruiting at the moment. You can still explore other options: