Clinical trial NCT04016987: Automated Structured Education Based on an App and AI in Chinese Patients With Type 1 Diabetes
Official title: Automated Structured Education Intervention Based on an App and Artificial Intelligence in Chinese Patients With Type 1 Diabetes
Study type: INTERVENTIONAL
Overall status: UNKNOWN
Phase: NA
Conditions: Type 1 Diabetes
Lead sponsor: Second Xiangya Hospital of Central South University
Sponsor class: OTHER
First posted: 2019-07-12
Enrollment: 138
Sex: ALL
Minimum age (years): 18
Maximum age (years): 50
Standard ages: ADULT
Accepts healthy volunteers: no
Allocation: RANDOMIZED
Intervention model: PARALLEL
Primary purpose: TREATMENT

Brief summary: 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.

Detailed 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.

Eligibility criteria: 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

Primary outcomes:
measure: changes in serum hemoglobin A1c level
timeFrame: from baseline to week 12, 24
Secondary outcomes:
measure: changes in Time in range (TIR)
timeFrame: from baseline to week 12, 24
measure: Chinese version of Diabetes Quality of Life scale
timeFrame: from baseline to week 12, 24
measure: Diabetes Self-Management Scale
timeFrame: from baseline to week 12, 24
measure: Chinese version of Diabetes Self-Care Activities
timeFrame: from baseline to week 12, 24
measure: Diabetes Empowerment Scale-Short Form
timeFrame: from baseline to week 12, 24
measure: State-Trait Anxiety Inventory (STAI)
timeFrame: from baseline to week 12, 24
measure: Beck's Depression Inventory (BDI)
timeFrame: from baseline to week 12, 24
measure: Fasting blood glucose
timeFrame: from baseline to week 12, 24
measure: Systolic blood pressure
timeFrame: from baseline to week 12, 24
measure: Diastolic blood pressure
timeFrame: from baseline to week 12, 24
measure: Total cholesterol
timeFrame: from baseline to week 12, 24
measure: High-density lipoprotein (HDL) cholesterol
timeFrame: from baseline to week 12, 24
measure: Low-density lipoprotein (LDL) cholesterol
timeFrame: from baseline to week 12, 24
measure: Triglycerides
timeFrame: from baseline to week 12, 24
measure: Height in meters
timeFrame: from baseline to week 12, 24
measure: Weight in kilograms
timeFrame: from baseline to week 12, 24
measure: Patients engagement with the app
timeFrame: automatically collected by the app from baseline to week 24
measure: Adverse events
timeFrame: every 4 weeks from baseline to week 24
Other outcomes:
Locations:
city: Changsha
country: China
facility: Institute of Metabolism and Endocrinology, Second Xiangya Hospital, Central South University
Study officials:
affiliation: Central South University
name: Xia Li, MD/PHD
role: PRINCIPAL_INVESTIGATOR
References:
citation: Huang F, Wu X, Xie Y, Liu F, Li J, Li X, Zhou Z. An automated structured education intervention based on a smartphone app in Chinese patients with type 1 diabetes: a protocol for a single-blinded randomized controlled trial. Trials. 2020 Nov 23;21(1):944. doi: 10.1186/s13063-020-04835-9.