The Evaluation of Artificial Intelligence in Lifestyle Management of Diabetic Patients in Community
Study Details
Study Description
Brief 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.
| Condition or Disease | Intervention/Treatment | Phase |
|---|---|---|
|
N/A |
Detailed 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.
Study Design
Arms and Interventions
| Arm | Intervention/Treatment |
|---|---|
| Experimental: Intervention group giving personalized lifestyle intervention suggestions through AI |
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.
|
| No Intervention: Control group giving routine lifestyle suggestions |
Outcome Measures
Primary Outcome Measures
- Change of HbA1c [one year]
Change of HbA1c from baseline to endpoint (1 year follow-up)
Secondary Outcome Measures
- Change of systolic blood pressure [one year]
Change of systolic blood pressure from baseline to endpoint
- Change of diastolic blood pressure [one year]
Reduction of diastolic blood pressure from baseline to endpoint
- Change of LDL-c [one year]
Change of LDL-c from baseline to endpoint
Eligibility Criteria
Criteria
Inclusion Criteria:
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Well informed of the procedures of this trial and informed consent is obtained
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18-80 years old, gender is not limited
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Diagnosed diabetes (according to WHO1999 diagnostic criteria)
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Well compliance
Exclusion Criteria:
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Pregnant or lactating
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Poor blood glucose control (HbA1c>11%)
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A history of malignant tumor
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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]
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Poor blood pressure control [systolic blood pressure (SBP)>180mmHg, or diastolic blood pressure (DBP)>110mmHg
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With severe heart disease, cardiac function worse than grade II, anemia (Hb<9.0g/d1)
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Blood routine test indicates that the white blood cell count (WBC) <3*109/L
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Body Mass Index (BMI)<18.5 or ≥35kg/m2
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Drug or alcohol abuse
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Accompanying mental disorder who can't collaborate
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Abnormal digestion and absorption function
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Other endocrine diseases
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Other chronic diseases needed long-term glucocorticoid treatment
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With severe infection, immune dysfunction
Contacts and Locations
Locations
| Site | City | State | Country | Postal Code | |
|---|---|---|---|---|---|
| 1 | Jinbo Hu | Chongqing | Chongqing | China | 400016 |
Sponsors and Collaborators
- Chongqing Medical University
Investigators
- Study Chair: Jinbo Hu, PhD, First Affiliated Hospital of Chongqing Medical University
Study Documents (Full-Text)
None provided.More Information
Publications
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- BDT-CDR