NCT06118671

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.

Trial Health

43
At Risk

Trial Health Score

Automated assessment based on enrollment pace, timeline, and geographic reach

Trial has exceeded expected completion date
Enrollment
460

participants targeted

Target at P75+ for not_applicable diabetes

Timeline
Completed

Started Nov 2023

Shorter than P25 for not_applicable diabetes

Geographic Reach
1 country

1 active site

Status
unknown

Health score is calculated from publicly available data and should be used for screening purposes only.

Trial Relationships

Click on a node to explore related trials.

Study Timeline

Key milestones and dates

First Submitted

Initial submission to the registry

October 23, 2023

Completed
15 days until next milestone

First Posted

Study publicly available on registry

November 7, 2023

Completed
Same day until next milestone

Study Start

First participant enrolled

November 7, 2023

Completed
1 year until next milestone

Primary Completion

Last participant's last visit for primary outcome

November 7, 2024

Completed
Same day until next milestone

Study Completion

Last participant's last visit for all outcomes

November 7, 2024

Completed
Last Updated

November 7, 2023

Status Verified

November 1, 2023

Enrollment Period

1 year

First QC Date

October 23, 2023

Last Update Submit

November 1, 2023

Conditions

Diabetes

Keywords

lifestyle interventionartificial intelligence

Outcome Measures

Primary Outcomes (1)

  • Change of HbA1c

    Change of HbA1c from baseline to endpoint (1 year follow-up)

    one year

Secondary Outcomes (3)

  • Change of systolic blood pressure

    one year

  • Change of diastolic blood pressure

    one year

  • Change of LDL-c

    one year

Study Arms (2)

Intervention group

EXPERIMENTAL

giving personalized lifestyle intervention suggestions through AI

Behavioral: personalized lifestyle intervention by AI

Control group

NO INTERVENTION

giving routine lifestyle suggestions

Interventions

personalized lifestyle intervention by AIBEHAVIORAL

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.

Intervention group

Eligibility Criteria

Age18 Years - 80 Years
Sexall
Healthy VolunteersNo
Age GroupsAdult (18-64), Older Adult (65+)

You may qualify if:

  • Well informed of the procedures of this trial and informed consent is obtained
  • years old, gender is not limited
  • Diagnosed diabetes (according to WHO1999 diagnostic criteria)
  • Well compliance

You may not qualify if:

  • 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

Contact the study team to confirm eligibility.

Sponsors & Collaborators

Study Sites (1)

Jinbo Hu

Chongqing, Chongqing Municipality, 400016, China

Location

Related Publications (9)

  • 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: 29773539BACKGROUND

  • 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.

    PMID: 37434126BACKGROUND

  • 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.

    PMID: 32899917BACKGROUND

  • 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.

    PMID: 29960809BACKGROUND

  • 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.

    PMID: 24493770BACKGROUND

  • 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.

    PMID: 27095386BACKGROUND

  • 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.

    PMID: 20845203BACKGROUND

  • 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.

    PMID: 34348965BACKGROUND

  • 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.

    PMID: 11176811BACKGROUND

MeSH Terms

Conditions

Diabetes Mellitus

Condition Hierarchy (Ancestors)

Glucose Metabolism DisordersMetabolic DiseasesNutritional and Metabolic DiseasesEndocrine System Diseases

Study Officials

  • Jinbo Hu, PhD

    First Affiliated Hospital of Chongqing Medical University

    STUDY CHAIR

Central Study Contacts

Jinbo Hu, PhD

CONTACT

+86 023-89011552hujinbo_568@163.com

Bing Kang, PhD

CONTACT

+86 023-89011552kangbing@hospital.cqmu.edu.cn

Study Design

Study Type
interventional
Phase
not applicable
Allocation
RANDOMIZED
Masking
SINGLE
Who Masked
PARTICIPANT
Purpose
OTHER
Intervention Model
PARALLEL
Sponsor Type
OTHER
Responsible Party
PRINCIPAL INVESTIGATOR
PI Title
Professor.

Study Record Dates

First Submitted

October 23, 2023

First Posted

November 7, 2023

Study Start

November 7, 2023

Primary Completion

November 7, 2024

Study Completion

November 7, 2024

Last Updated

November 7, 2023

Record last verified: 2023-11

Data Sharing

IPD Sharing
Will not share

Locations

CN(1)