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

2019-07-17 10:04:59 | BioPortfolio


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.


This is a single-blinded, 1:1:1 parallel-group cluster randomized controlled trial (RCT). The intervention will last for 24 weeks. The laboratory staff who tests 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 183 patients with type 1 diabetes (T1DM) by considering withdrawals, 122 in the smartphone app groups (61 in the Automatic and individualized education through app group and 61 in the standardized education app group) and 61 in the usual 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 people are inconsistent, 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.

Study Design


Type 1 Diabetes


Automated structured education intervention based on an app and artificial intelligence


Not yet recruiting


Second Xiangya Hospital of Central South University

Results (where available)

View Results


Published on BioPortfolio: 2019-07-17T10:04:59-0400

Clinical Trials [4285 Associated Clinical Trials listed on BioPortfolio]

Quality Improvement Intervention in Colonoscopy Using Artificial Intelligence

Quality measures in colonoscopy are important guides for improving the quality of patient care. But quality improvement intervention is not taking place, primarily because of the inconveni...

Validation of an Artificial Intelligence-based Algorithm for Skeletal Age Assessment

The purpose of this study is to understand the effects of using a Artificial Intelligence algorithm for skeletal age estimation as a computer-aided diagnosis (CADx) system. In this prospec...

Programming Cochlear Implant With Artificial Intelligence

This thesis project proposes to investigate the "state of the art" of the programming of the cochlear implant. In the center of audiophonologie Brussels, the classic 'manual programming' h...

Development of Artificial Intelligence System for Detection and Diagnosis of Breast Lesion Using Mammography

This project aims to establish a comprehensive artificial intelligence system for detecting and qualitative diagnosing breast lesions. Mammary images will be used to construct a diagnosis ...

Multi-modal Imaging and Artificial Intelligence Diagnostic System for Multi-level Clinical Application

This study is to build an multi-modal artificial intelligence ophthalmological imaging diagnostic system covering multi-level medical institutions. We are going to evaluate this system in ...

PubMed Articles [28798 Associated PubMed Articles listed on BioPortfolio]

Study Protocol for the Effects of Artificial Intelligence (AI)-Supported Automated Nutritional Intervention on Glycemic Control in Patients with Type 2 Diabetes Mellitus.

Nutritional intervention is effective in improving glycemic control in patients with type 2 diabetes but requires large inputs of manpower. Recent improvements in photo analysis technology facilitated...

Artificial intelligence for precision education in radiology.

In the era of personalized medicine, the emphasis of health care is shifting from populations to individuals. Artificial intelligence (AI) is capable of learning without explicit instruction and has e...

Artificial Intelligence in Dermatology-Where We Are and the Way to the Future: A Review.

Although artificial intelligence has been available for some time, it has garnered significant interest recently and has been popularized by major companies with its applications in image identificati...

Development of a group structured education programme to support safe exercise in people with Type 1 diabetes: the EXTOD education programme.

To develop a structured education programme for individuals with Type 1 diabetes who are engaging in regular exercise.

Evaluation of Artificial Intelligence-Based Grading of Diabetic Retinopathy in Primary Care.

There has been wide interest in using artificial intelligence (AI)-based grading of retinal images to identify diabetic retinopathy, but such a system has never been deployed and evaluated in clinical...

Medical and Biotech [MESH] Definitions

The study and implementation of techniques and methods for designing computer systems to perform functions normally associated with human intelligence, such as understanding language, learning, reasoning, problem solving, etc.

A type of ARTIFICIAL INTELLIGENCE that enable COMPUTERS to independently initiate and execute LEARNING when exposed to new data.

A method of differentiating individuals based on the analysis of qualitative or quantitative biological traits or patterns. This process which has applications in forensics and identity theft prevention includes DNA profiles or DNA fingerprints, hand fingerprints, automated facial recognition, iris scan, hand geometry, retinal scan, vascular patterns, automated voice pattern recognition, and ultrasound of fingers.

The ability to understand and manage emotions and to use emotional knowledge to enhance thought and deal effectively with tasks. Components of emotional intelligence include empathy, self-motivation, self-awareness, self-regulation, and social skill. Emotional intelligence is a measurement of one's ability to socialize or relate to others.

Work consisting of a structured file of information or a set of logically related data stored and retrieved using computer-based means.

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

Quick Search

Relevant Topic

Diabetes is a lifelong condition that causes a person's blood sugar level to become too high. The two main types of diabetes are: type 1 diabetes type 2 diabetes In the UK, diabetes affects approximately 2.9 million people. There are a...

Searches Linking to this Trial