Virtual Health Coaching With Artificial Intelligence for Glycemic Control in Type 2 Diabetes

NCT ID: NCT07165730

Last Updated: 2025-09-10

Basic Information

• Recruitment Status: NOT_YET_RECRUITING
• Clinical Phase: NA
• Total Enrollment: 206 participants
• Study Classification: INTERVENTIONAL
• Study Start Date: 2025-09-15
• Study Completion Date: 2026-06-15

Brief Summary

This study aims to evaluate the effectiveness of a virtual health coaching program based on artificial intelligence (AI) in improving blood sugar control among people with type 2 diabetes in primary health care settings in Jeneponto and Bantaeng, Indonesia. The intervention will be delivered through the DIACOACH application, which provides personalized coaching, lifestyle guidance, and self-care support. Participants will be randomly assigned to either the intervention group, receiving virtual health coaching for 12 weeks, or the control group, receiving standard diabetes education. Main outcomes include fasting blood sugar and HbA1c levels. Other outcomes include self-care adherence, dietary behavior, and quality of life. This trial seeks to provide evidence on whether AI-based virtual coaching is a practical and effective digital health solution to support diabetes self-management in community health settings with limited resources.

Detailed Description

Type 2 diabetes mellitus (T2DM) is a chronic metabolic disorder characterized by elevated blood glucose levels due to impaired insulin production or insulin resistance. Globally, the prevalence of T2DM continues to rise, and in Indonesia the number of cases has surpassed 10 million, placing the country among the top seven worldwide for diabetes prevalence. Poor glycemic control leads to serious complications such as kidney disease, cardiovascular disorders, and neuropathy, which reduce patients' quality of life and increase health care costs. One of the main challenges in diabetes management is the low level of patient adherence to recommended lifestyle changes, including diet, physical activity, and regular monitoring. Patients often struggle to maintain long-term self-management because of limited access to continuous education, motivation, and personalized support in primary health care settings.

Digital health innovations, including virtual health coaching, have emerged as promising solutions to address these challenges. Virtual health coaching provides structured, ongoing support by using digital platforms to deliver education, reminders, and behavioral feedback. The integration of artificial intelligence (AI) into virtual coaching systems allows the intervention to be more adaptive and personalized, responding to the unique behaviors and needs of each participant. The DIACOACH application has been specifically developed to support diabetes self-management in Indonesia. It offers adaptive education modules, lifestyle modification reminders, diet and exercise monitoring, medication tracking, and a chatbot interface for personalized interaction. Early community service activities using DIACOACH have shown improvements in patient knowledge, motivation, and preliminary indicators of glycemic control such as fasting blood glucose and HbA1c levels. However, systematic research is required to evaluate its effectiveness in a controlled study, particularly in primary care contexts with limited resources.

This study is designed as a quasi-experimental trial with a non-equivalent control group design. Participants will be adults aged 30 to 56 years who have been diagnosed with T2DM for more than three months, are clinically stable, able to read, and have access to a smartphone or digital device. Individuals with type 1 diabetes, severe complications, ongoing steroid therapy, or those enrolled in other structured interventions will be excluded. Eligible participants will be recruited from primary health care centers (Puskesmas) in Jeneponto and Bantaeng, Indonesia. After baseline assessments, participants will be allocated to either the intervention group or the control group.

The intervention group will use the DIACOACH application for twelve weeks. Through this platform, participants will receive adaptive education tailored to their daily routines, reminders about diet and physical activity, and continuous encouragement to maintain self-care practices. The app integrates principles of Social Cognitive Theory and Self-Determination Theory to build self-efficacy and intrinsic motivation. The AI-based system provides feedback based on real-time data entered by participants, such as blood glucose values, diet logs, exercise activities, and medication adherence. The control group will continue with standard diabetes education typically offered at health centers, which usually consists of routine counseling sessions and printed health information.

Primary outcomes will include fasting blood glucose (FBG), HbA1c, and random blood glucose (RBG), measured at baseline and after the twelve-week intervention. Secondary outcomes will assess changes in dietary behavior, physical activity, adherence to self-care, satisfaction with the intervention, and overall quality of life. Data will be collected through laboratory tests, validated questionnaires, and digital records from the application. Quantitative data will be analyzed using appropriate statistical tests such as paired t-tests and analysis of variance to evaluate differences between groups and across time points. In addition, a qualitative component will be conducted by interviewing selected participants from the intervention group to explore their perceptions, experiences, barriers, and facilitators in using AI-based virtual coaching for diabetes self-management.

The study is expected to provide comprehensive evidence on the feasibility, acceptability, and effectiveness of AI-based virtual health coaching in primary health care. As a preliminary study, it will serve to validate the DIACOACH platform and assess its potential impact on clinical outcomes as well as patient engagement. If the intervention is found to be effective, it may offer a scalable and low-cost strategy to improve diabetes management in Indonesia, particularly in underserved and resource-constrained communities. Furthermore, this research could contribute to the integration of digital health innovations into the national primary health care system and support ongoing initiatives to reduce the burden of diabetes and its complications.

Conditions

Type 2 Diabetes Mellitus (T2DM)

Study Design

• Allocation Method: NON_RANDOMIZED
• Intervention Model: PARALLEL
• Primary Study Purpose: TREATMENT
• Blinding Strategy: NONE

Study Groups

Virtual Health Coaching (DIACOACH)

Participants in this group will receive a 12-week virtual health coaching program through the DIACOACH application. The app provides adaptive education, reminders, self-care monitoring, and AI-based personalized feedback to support glycemic control.

Group Type: EXPERIMENTAL

Virtual Health Coaching with Artificial Intelligence (DIACOACH)

Intervention Type: BEHAVIORAL

Participants in the intervention group will receive a 12-week virtual health coaching program using the DIACOACH application. The program includes adaptive digital education on diet, physical activity, and medication adherence, personalized reminders, daily self-monitoring, and feedback powered by artificial intelligence. The intervention is designed to improve self-care adherence and glycemic control (FBG, HbA1c, RBG) among adults with type 2 diabetes mellitus in primary care settings.

Standard Diabetes Education

Participants in this group will receive standard diabetes education provided by primary health care centers, including routine counseling sessions and printed health information.

Group Type: ACTIVE_COMPARATOR

Standard Diabetes Education

Intervention Type: BEHAVIORAL

Participants in the control group will receive standard diabetes education usually provided in primary health care centers. This includes routine counseling sessions, printed educational materials, and general advice on diet, physical activity, and medication adherence, without the use of the DIACOACH application.

Interventions

Virtual Health Coaching with Artificial Intelligence (DIACOACH)

Participants in the intervention group will receive a 12-week virtual health coaching program using the DIACOACH application. The program includes adaptive digital education on diet, physical activity, and medication adherence, personalized reminders, daily self-monitoring, and feedback powered by artificial intelligence. The intervention is designed to improve self-care adherence and glycemic control (FBG, HbA1c, RBG) among adults with type 2 diabetes mellitus in primary care settings.

Intervention Type: BEHAVIORAL

Standard Diabetes Education

Participants in the control group will receive standard diabetes education usually provided in primary health care centers. This includes routine counseling sessions, printed educational materials, and general advice on diet, physical activity, and medication adherence, without the use of the DIACOACH application.

Intervention Type: BEHAVIORAL

Eligibility Criteria

Inclusion Criteria

Diagnosed with Type 2 Diabetes Mellitus for more than 3 months

Age between 30 and 56 years

Able to read and understand instructions

Have access to a digital device (smartphone or similar)

In stable clinical condition

Exclusion Criteria

Diagnosed with Type 1 Diabetes Mellitus

Patients with severe complications (e.g., advanced nephropathy, retinopathy, cardiovascular disease)

Patients currently undergoing other structured interventions

Patients on long-term steroid therapy

Patients without access to a digital device

• Minimum Eligible Age: 18 Years
• Maximum Eligible Age: 65 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: No

Sponsors

Hasanuddin University

OTHER

Sponsor Role: lead

Responsible Party

Andina Setyawati

Doctor

Responsibility Role: PRINCIPAL_INVESTIGATOR

Locations

Public Health Center of Bontomatene and Public Health Center of Binamu Kota

Makassar, South Sulawesi, Indonesia

Countries

Indonesia

Central Contacts

Nuurhidayat Jafar, Dr

Role: CONTACT

nuurhidayat@unhas.ac.id

+6281355576066

Facility Contacts

Akbar Akbar

Role: primary

akbar27021992@gmail.com

+6285299176008

Other Identifiers

02209/UN4.22/PT.01.03/2025

Identifier Type: OTHER_GRANT

Identifier Source: secondary_id

UNHAS-VHC-AI-2025

Identifier Type: -

Identifier Source: org_study_id