Automated Structured Education Based on an App and AI in Chinese Patients With Type 1 Diabetes

Central South University

Status: Unknown

Conditions: Type 1 Diabetes

Treatments: Behavioral: Automated structured education intervention based on an app and artificial intelligence

Study type: Interventional

Funder types: Other

Identifiers

NCT04016987

AI App-EC T1D 2019

Details and patient eligibility

About

In recent years, more and more attention has been paid to diabetes self-management. Glycemic control and self-management skills of patients with type 1 diabetes (T1DM) in China are poor. Artificial intelligence (AI) and the Internet offer a new way to improve the self-management skills of patients with chronic diseases. Few studies have combined AI technology with structured education intervention of type 1 diabetes. This study is innovative in that it compares the effectiveness of smartphone app between usual care, as well as automatic and individualized app education and standardized app education to explore whether the individualized treatment advocated by the latest guideline will bring any additional benefit to T1DM patients. The ultimate goal is to provide an effective and convenient approach for glycemic control of type 1 diabetes and reduce related disease burden in China.

Full description

This is a single-blinded, 1:1 paralleled group cluster randomized controlled trial (RCT). The intervention will last for 24 weeks. The laboratory staff who test the HbA1c level, the outcome assessor who collects the blood glucose data, and the statisticians will be blinded to the treatment allocation.

Sample size estimation: We propose to enroll 138 patients with type 1 diabetes (T1DM) by considering withdrawals, 69 in the smartphone app groups and 69 in the routine care group. Sample size estimation is based on hypothesized changes in the primary outcome HbA1c.

In order to ensure high quality data, two staff are responsible for the input of original data into the database to check and confirm the accuracy. When the data entered by two staff independently, the auxiliary staff decides which data to use.

Data analysis will be conducted under the intention-to-treat principle by including all the randomized patients in the data analysis. Missing data will be filled in with multiple imputation method. Any substantial difference in baseline characteristics will be adjusted with mixed-model regression analysis. Sensitivity analysis will be conducted by using per-protocol data by excluding those patients who drop out of the RCT.

Enrollment: 138 estimated patients

Sex: All

Ages: 18 to 50 years old

Volunteers: No Healthy Volunteers

Inclusion criteria

• Individuals diagnosed with Type 1 Diabetes according to the 1999 World Health Organization report
• Insulin dependence from disease onset
• Aged 18-50 years
• With a disease duration over 6 months
• With a HbA1c level over 7%
• Treated T1DM with multiple daily injections or insulin pump
• Individuals who own smartphone and are capable of using wechat or apps

Exclusion criteria

• Age below 18 years or above 50 years
• Being pregnant
• With mental disorders
• Have any other condition or disease that may hamper from compliance with the protocol or complication of the trial
• Already using a smartphone app for managing diabetes
• Having chronic complications including diabetic retinopathy, diabetic nephropathy or diabetic foot, diabetic neuropathy

Trial design

Primary purpose: Treatment

Allocation: Randomized

Interventional model: Parallel Assignment

Masking: Single Blind

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138 participants in 2 patient groups

Automated, Individualized Education

Experimental group

Description:

Subjects will be given instructions to install the patient-end App, which includes the following functions: diabetes education, patient-doctor communication, diabetes diary, peer support, reminder for blood sugar test and related abnormal results. They receive push notifications that provides recommended education materials which meet the needs of the patient by considering his/her baseline diabetes-related knowledge.

Treatment:

Behavioral: Automated structured education intervention based on an app and artificial intelligence

Routine care

No Intervention group

Description:

Subjects only receive the education provided by health-care professionals in the outpatient department