Therapeutic Efficacy and Safety Evaluation of AI in the Management of Diabetes: A RCT Trial

NCT06957093 | Recruiting

• 1 other identifier: K2024283
• Study Type: interventional
• Target: 400
• Locations: 1 country
• Sites: 1

Brief Summary

Purpose: To evaluate the efficacy of artificial intelligence (AI)-based decision-making technology in managing glycated hemoglobin (HbA1c) and blood glucose levels compared to the control group. Methods: For the AI Intervention group, the patients will be trained to independently use the diabetes telemedicine platform application. Each patient will be equipped with a glucometer and exercise bracelet, and the data will be automatically transmitted to the medical server via Bluetooth. The healthcare platform will analyze the uploaded data and provide feedback suggestions on medication, diet, and exercise automatically. The platform will also monitor the medical and lifestyle data of the patients every two weeks, offer feedback based on the analyses, and remind the patient to adhere to the self-management protocol based on the platform. The platform is a digitally integrated healthcare platform that patients can use independently without the need for monitoring and assistance by healthcare professionals. The glucometer and pedometer bracelet will automatically connect to the platform through Bluetooth. The patient lab sheet identification and structured conversion system, AI for food picture identification and calorie calculation systems, and the AI decision-making system are on the cloud server. Patients upload image information, such as lab sheets and meal pictures, through the patient's diabetes mobile health system, and the cloud platform intelligently analyzes the patient's disease, medication, and daily life status to develop personalized solutions according to individual control goals. Free outpatient visits will be provided to both the intervention and control groups every twelve weeks. For the conventional treatment group, patients will receive a free blood glucometer and will have regular outpatient appointments. There is no limit to the number of outpatient visits; however, they are required to regularly monitor and record their blood glucose, diet, and exercise data to ensure that the medical team objectively conduct their diagnosis and treatment activities. The medical team will provide free outpatient visits every 12 weeks, along with advice on medication, diet, and exercise based on the individual's blood glucose level. Expected results: A significant difference in HbA1c change from baseline to 48 weeks and improved FPG and 2-hour postprandial blood glucose levels in the AI intervention group were observed.

Conditions

Diabetes Mellitus Type 2 (T2DM); Artificial Intelligence (AI)

Keywords

Diabetes mellitus type 2 (T2DM); Artificial Intelligence (AI)

Outcome Measures

Primary Outcomes (4)

• HbA1c

  Change From Baseline in HbA1c levels at 24 and 48 Weeks

  48weeks

• Fasting Blood Glucose (FBG)

  Change from baseline in mean fasting blood glucose at 48 weeks

  48 weeks

• 2-hour Postprandial Blood Glucose (2hPPG)

  Change from baseline in mean 2-hour postprandial blood glucose at 48 weeks

  48 weeks

• Hypoglycemic events

  Number of hypoglycemic events from baseline to 48 weeks

  48 weeks

Secondary Outcomes (7)

• Healthcare expenses

  48weeks

• Insulin and oral hypoglycemic agent dosing

  48 weeks

• Serum lipids

  48 weeks

• blood pressure

  48 weeks

• BMI

  48 weeks

Study Arms (2)

Artificial Intelligence Intervention Group

EXPERIMENTAL

The patients will be trained to independently use the diabetes telemedicine platform application. Each patient will be equipped with a glucometer and exercise bracelet, and the data will be automatically transmitted to the medical server via Bluetooth. The healthcare platform will analyze the uploaded data and provide feedback suggestions on medication, diet, and exercise automatically. The platform will also monitor the medical and lifestyle data of the patients every two weeks,offer feedback based on the analyses, and remind the patient to adhere to the self-management protocol based on the platform. Free outpatient visits will be provided to both the intervention and control groups every twelve weeks.

Other: artificial intelligence

Conventional Treatment Group

ACTIVE COMPARATOR

Patients in the control group will receive a free blood glucometer and will have regular outpatient appointments every 12 weeks..

Other: Routine diagnosis and treatment group for diabetes

Interventions

artificial intelligence OTHER

The platform will also monitor the medical and lifestyle data of the patients every two weeks,offer feedback based on the analyses, and remind the patient to adhere to the self-management protocol based on the platform.

Artificial Intelligence Intervention Group

Routine diagnosis and treatment group for diabetes OTHER

There is no limit to the number of outpatient visits for the control group; however, they are required to regularly monitor and record their blood glucose, diet, and exercise data to ensure that the medical team (endocrinologist and nutritionist) objectively conducttheir diagnosis and treatment activities. The medical team will provide free outpatient visits every 12 weeks, along with advice on medication, diet, and exercise based on the individual's blood glucose level.

Conventional Treatment Group

Eligibility Criteria

• Age: 18 Years - 75 Years
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)

You may qualify if:

• Age: ≥18 years，≤75 years;
• Diagnosed with type 2 diabetes for ≥1 year;
• % ≤HbA1c ≤11%;
• Body mass index ≥18.5 kg/m2;
• Proficient ability to use smart phones;
• Agreed to utilize a digital integrated healthcare platform for diabetes care and research;
• Informed consents are obtained from the participants.

You may not qualify if:

• Presence of other types of diabetes, such as type 1 diabetes and gestational diabetes;
• Severe diabetic complications;
• Medical history of chronic liver diseases, including hemochromatosis, hepatocellular carcinoma, autoimmune liver disease, cirrhosis, viral hepatitis (including hepatitis A, B, and C), or hepatolenticular degeneration;
• Kidney injury (serum creatinine ≥1.5 times the upper limit of the reference) ; Serum ALT and AST levels elevated \>2-fold;
• Medical history of mental disorders, such asschizophrenia, depression, or bipolar affective disorder;
• Excessive alcohol intake or drug abuse in the past 3 months;
• Use of medications affecting glucose metabolism, such as corticosteroids or ·consumption of immunosuppressive and anti-obesity medications in the past 3 months;
• Pregnancy, planning for pregnancy, or lactation; or any other conditions unsuitable for trial participation;
• Participatingor plan to participate in other clinical trials; and other cases that are inappropriate to participate.

Sponsors & Collaborators

• The First Hospital of Jilin University: lead

Study Sites (1)

The First Hospital of Jilin University

Changchun, Jilin, 130000, China

RECRUITING

Related Publications (9)

• Lee YB, Kim G, Jun JE, Park H, Lee WJ, Hwang YC, Kim JH. An Integrated Digital Health Care Platform for Diabetes Management With AI-Based Dietary Management: 48-Week Results From a Randomized Controlled Trial. Diabetes Care. 2023 May 1;46(5):959-966. doi: 10.2337/dc22-1929.

  PMID: 36821833 BACKGROUND

• Kim EK, Kwak SH, Jung HS, Koo BK, Moon MK, Lim S, Jang HC, Park KS, Cho YM. The Effect of a Smartphone-Based, Patient-Centered Diabetes Care System in Patients With Type 2 Diabetes: A Randomized, Controlled Trial for 24 Weeks. Diabetes Care. 2019 Jan;42(1):3-9. doi: 10.2337/dc17-2197. Epub 2018 Oct 30.

  PMID: 30377185 BACKGROUND

• Dobson R, Whittaker R, Jiang Y, Maddison R, Shepherd M, McNamara C, Cutfield R, Khanolkar M, Murphy R. Effectiveness of text message based, diabetes self management support programme (SMS4BG): two arm, parallel randomised controlled trial. BMJ. 2018 May 17;361:k1959. doi: 10.1136/bmj.k1959.

  PMID: 29773539 BACKGROUND

• Agarwal P, Mukerji G, Desveaux L, Ivers NM, Bhattacharyya O, Hensel JM, Shaw J, Bouck Z, Jamieson T, Onabajo N, Cooper M, Marani H, Jeffs L, Bhatia RS. Mobile App for Improved Self-Management of Type 2 Diabetes: Multicenter Pragmatic Randomized Controlled Trial. JMIR Mhealth Uhealth. 2019 Jan 10;7(1):e10321. doi: 10.2196/10321.

  PMID: 30632972 BACKGROUND

• Doupis J, Festas G, Tsilivigos C, Efthymiou V, Kokkinos A. Smartphone-Based Technology in Diabetes Management. Diabetes Ther. 2020 Mar;11(3):607-619. doi: 10.1007/s13300-020-00768-3. Epub 2020 Jan 25.

  PMID: 31983028 BACKGROUND

• Sun C, Sun L, Xi S, Zhang H, Wang H, Feng Y, Deng Y, Wang H, Xiao X, Wang G, Gao Y, Wang G. Mobile Phone-Based Telemedicine Practice in Older Chinese Patients with Type 2 Diabetes Mellitus: Randomized Controlled Trial. JMIR Mhealth Uhealth. 2019 Jan 4;7(1):e10664. doi: 10.2196/10664.

  PMID: 30609983 BACKGROUND

• Wang H, Yuan X, Wang J, Sun C, Wang G. Telemedicine maybe an effective solution for management of chronic disease during the COVID-19 epidemic. Prim Health Care Res Dev. 2021 Sep 29;22:e48. doi: 10.1017/S1463423621000517.

  PMID: 34583801 BACKGROUND

• Bulut C, Kato Y. Epidemiology of COVID-19. Turk J Med Sci. 2020 Apr 21;50(SI-1):563-570. doi: 10.3906/sag-2004-172.

  PMID: 32299206 BACKGROUND

• Mahajan V, Singh T, Azad C. Using Telemedicine During the COVID-19 Pandemic. Indian Pediatr. 2020 Jul 15;57(7):652-657. Epub 2020 May 14.

  PMID: 32412914 BACKGROUND

MeSH Terms

Conditions

Diabetes Mellitus, Type 2

Interventions

Artificial Intelligence

Condition Hierarchy (Ancestors)

Diabetes Mellitus; Glucose Metabolism Disorders; Metabolic Diseases; Nutritional and Metabolic Diseases; Endocrine System Diseases

Intervention Hierarchy (Ancestors)

Algorithms; Mathematical Concepts

Study Officials

• Chenglin Sun, Doctor

  The First Hospital of Jilin University

  PRINCIPAL INVESTIGATOR

Central Study Contacts

Chenglin Sun, Doctor

CONTACT

+86 13944855718; clsun213@163.com

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: RANDOMIZED
• Masking: NONE
• Purpose: TREATMENT
• Intervention Model: PARALLEL
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: April 17, 2025
• First Posted: May 4, 2025
• Study Start: June 15, 2025
• Primary Completion (Estimated): December 1, 2026
• Study Completion (Estimated): December 1, 2026
• Last Updated: December 9, 2025

Record last verified: 2025-05

Data Sharing

• IPD Sharing: Will not share

Locations

CN (1)