The Evaluation of Artificial Intelligence in Lifestyle Management of Diabetic Patients in Community

Diabetes Clinical Trial

Official title:

Application and Effectiveness Evaluation of Artificial Intelligence in Lifestyle Management of Diabetic Patients of Community

Clinical Trial Details — Status: Not yet recruiting

Administrative data

• NCT number: NCT06118671
• Other study ID #: BDT-CDR
• Status: Not yet recruiting
• Phase: N/A
• Start date: November 7, 2023
• Est. completion date: November 7, 2024

Study information

• Verified date: November 2023
• Source: Chongqing Medical University
• Contact: Jinbo Hu, PhD
• Phone: +86 023-89011552
• Email: hujinbo_568[@]163.com
• Is FDA regulated: No
• Study type: Interventional

Clinical Trial Summary

The goal of this clinical trial is to learn about the application and effectiveness evaluation of artificial intelligence (AI) in lifestyle management of diabetic patients in community.

Description:

To determine the effectiveness of lifestyle intervention through AI on diabetic patients, whether it can improve blood sugar/ blood pressure/ body weight/ blood lipid control in people with type 1 or type 2 diabetes? Then find related risk factors. Participants will complete the basic information, diet structure, use of dietary supplements, living habits, and exercise according to the AI scale (AI scale can be entered through we-chat mini program search), and provided personalized lifestyle intervention plan according to the survey result, while the control group received routine lifestyle guidance. All included patients would fill in the patient's medication information according to the information content of the machine reading card, and follow up whether the medication is adjusted. The patient was included in the diabetes management we-chat group, followed up for 3 months /6 months according to the visit plan. The experimental group filled in the lifestyle assessment scale again, and personalized lifestyle intervention programs were provided according to the survey results, while the control group received routine follow-up and lifestyle guidance. Researchers will compare the two groups to see if the control of blood glucose, blood pressure, blood lipids and BMI of the experimental group after 6 months of AI-based lifestyle intervention was better than the control group.

Recruitment information / eligibility

• Status: Not yet recruiting
• Enrollment: 460
• Est. completion date: November 7, 2024
• Est. primary completion date: November 7, 2024
• Accepts healthy volunteers: No
• Gender: All
• Age group: 18 Years to 80 Years

Eligibility

Inclusion Criteria:

• Well informed of the procedures of this trial and informed consent is obtained

• 18-80 years old, gender is not limited

• Diagnosed diabetes (according to WHO1999 diagnostic criteria)

• Well compliance

Exclusion Criteria:

• Pregnant or lactating

• Poor blood glucose control (HbA1c>11%)

• A history of malignant tumor

• Abnormal liver or renal function [defined as alanine aminotransferase (ALT)>2.5 times higher than normal range, or eGFR<30 mL/min per 1.73 m2]

• Poor blood pressure control [systolic blood pressure (SBP)>180mmHg, or diastolic blood pressure (DBP)>110mmHg

• With severe heart disease, cardiac function worse than grade II, anemia (Hb<9.0g/d1)

• Blood routine test indicates that the white blood cell count (WBC) <3*109/L

• Body Mass Index (BMI)<18.5 or =35kg/m2

• Drug or alcohol abuse

• Accompanying mental disorder who can't collaborate

• Abnormal digestion and absorption function

• Other endocrine diseases

• Other chronic diseases needed long-term glucocorticoid treatment

• With severe infection, immune dysfunction

Related Conditions & MeSH terms

• Diabetes

Intervention

Behavioral:
• personalized lifestyle intervention by AI
The experimental group completed the basic information, diet structure, use of dietary supplements, living habits, and exercise according to the AI scale (AI scale can be entered through we-chat mini program search), and provided personalized lifestyle intervention plan according to the survey result.

Locations

Country	Name	City	State
China	Jinbo Hu	Chongqing	Chongqing

Sponsors (1)

Lead Sponsor	Collaborator
Chongqing Medical University

Country where clinical trial is conducted

China, 

References & Publications (9)

Arambepola C, Ricci-Cabello I, Manikavasagam P, Roberts N, French DP, Farmer A. The Impact of Automated Brief Messages Promoting Lifestyle Changes Delivered Via Mobile Devices to People with Type 2 Diabetes: A Systematic Literature Review and Meta-Analysis of Controlled Trials. J Med Internet Res. 2016 Apr 19;18(4):e86. doi: 10.2196/jmir.5425. — View Citation

Chiavaroli L, Lee D, Ahmed A, Cheung A, Khan TA, Blanco S, Mejia, Mirrahimi A, Jenkins DJA, Livesey G, Wolever TMS, Rahelic D, Kahleova H, Salas-Salvado J, Kendall CWC, Sievenpiper JL. Effect of low glycaemic index or load dietary patterns on glycaemic control and cardiometabolic risk factors in diabetes: systematic review and meta-analysis of randomised controlled trials. BMJ. 2021 Aug 4;374:n1651. doi: 10.1136/bmj.n1651. Erratum In: BMJ. 2021 Aug 26;374:n2114. — View Citation

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. — View Citation

Nundy S, Dick JJ, Chou CH, Nocon RS, Chin MH, Peek ME. Mobile phone diabetes project led to improved glycemic control and net savings for Chicago plan participants. Health Aff (Millwood). 2014 Feb;33(2):265-72. doi: 10.1377/hlthaff.2013.0589. — View Citation

Sarkar U, Karter AJ, Liu JY, Adler NE, Nguyen R, Lopez A, Schillinger D. The literacy divide: health literacy and the use of an internet-based patient portal in an integrated health system-results from the diabetes study of northern California (DISTANCE). J Health Commun. 2010;15 Suppl 2(Suppl 2):183-96. doi: 10.1080/10810730.2010.499988. — View Citation

Toi PL, Anothaisintawee T, Chaikledkaew U, Briones JR, Reutrakul S, Thakkinstian A. Preventive Role of Diet Interventions and Dietary Factors in Type 2 Diabetes Mellitus: An Umbrella Review. Nutrients. 2020 Sep 6;12(9):2722. doi: 10.3390/nu12092722. — View Citation

Viguiliouk E, Kendall CW, Kahleova H, Rahelic D, Salas-Salvado J, Choo VL, Mejia SB, Stewart SE, Leiter LA, Jenkins DJ, Sievenpiper JL. Effect of vegetarian dietary patterns on cardiometabolic risk factors in diabetes: A systematic review and meta-analysis of randomized controlled trials. Clin Nutr. 2019 Jun;38(3):1133-1145. doi: 10.1016/j.clnu.2018.05.032. Epub 2018 Jun 13. — View Citation

Wagner EH, Sandhu N, Newton KM, McCulloch DK, Ramsey SD, Grothaus LC. Effect of improved glycemic control on health care costs and utilization. JAMA. 2001 Jan 10;285(2):182-9. doi: 10.1001/jama.285.2.182. — View Citation

Wu Y, Min H, Li M, Shi Y, Ma A, Han Y, Gan Y, Guo X, Sun X. Effect of Artificial Intelligence-based Health Education Accurately Linking System (AI-HEALS) for Type 2 diabetes self-management: protocol for a mixed-methods study. BMC Public Health. 2023 Jul 11;23(1):1325. doi: 10.1186/s12889-023-16066-z. — View Citation

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	Change of HbA1c	Change of HbA1c from baseline to endpoint (1 year follow-up)	one year
Secondary	Change of systolic blood pressure	Change of systolic blood pressure from baseline to endpoint	one year
Secondary	Change of diastolic blood pressure	Reduction of diastolic blood pressure from baseline to endpoint	one year
Secondary	Change of LDL-c	Change of LDL-c from baseline to endpoint	one year