Virtual Health Coaching With Artificial Intelligence for Glycemic Control in Type 2 Diabetes (DIACOACH-AI)

Effectiveness of Virtual Health Coaching Based on Artificial Intelligence to Improve Glycemic Control in Patients With Type 2 Diabetes Mellitus

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.

Study Overview

• Status: Not yet recruiting
• Conditions: Type 2 Diabetes Mellitus (T2DM)
• Intervention / Treatment: Behavioral: Virtual Health Coaching with Artificial Intelligence (DIACOACH); Behavioral: Standard Diabetes Education

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.

• Study Type: Interventional
• Enrollment (Estimated): 206
• Phase: Not Applicable

Contacts and Locations

• Study Contact: Name: Nuurhidayat Jafar, Dr; Phone Number: +6281355576066; Email: nuurhidayat@unhas.ac.id

Study Locations

• Indonesia
  • South Sulawesi
    • Makassar, South Sulawesi, Indonesia, 90245
      • Public Health Center of Bontomatene and Public Health Center of Binamu Kota
      • Contact: Akbar Akbar; Phone Number: +6285299176008; Email: akbar27021992@gmail.com

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Adult; Older Adult
• Accepts Healthy Volunteers: No

Description

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

Study Plan

How is the study designed?

Design Details

• Primary Purpose: Treatment
• Allocation: Non-Randomized
• Interventional Model: Parallel Assignment
• Masking: None (Open Label)

Number of Arms

2

Arms and Interventions

Participant Group / Arm	Intervention / Treatment
Experimental: 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.	Behavioral: 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.
Active Comparator: 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.	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.

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Fasting Blood Glucose (FBG)	Change in fasting blood glucose (mg/dL) from baseline to post-intervention, measured using standardized laboratory methods	Baseline and 12 weeks after intervention
Glycated Hemoglobin (HbA1c)	Change in HbA1c (%) from baseline to post-intervention, measured using standardized laboratory methods.	Baseline and 12 weeks after intervention.

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Self-care Adherence	Measured using the Summary of Diabetes Self-Care Activities (SDSCA) questionnaire. Higher scores indicate better adherence	Baseline and 12 weeks after intervention.
Dietary Behavior	Changes in dietary behavior assessed using validated dietary recall questionnaires.	Baseline and 12 weeks after intervention.
Physical Activity	Measured using the International Physical Activity Questionnaire (IPAQ), reported in MET-minutes/week.	Baseline and 12 weeks after intervention.

Collaborators and Investigators

• Sponsor: Hasanuddin University

Study record dates

Study Major Dates

• Study Start (Estimated): September 15, 2025
• Primary Completion (Estimated): December 31, 2025
• Study Completion (Estimated): June 15, 2026

Study Registration Dates

• First Submitted: September 3, 2025
• First Submitted That Met QC Criteria: September 3, 2025
• First Posted (Estimated): September 10, 2025

Study Record Updates

• Last Update Posted (Estimated): September 10, 2025
• Last Update Submitted That Met QC Criteria: September 3, 2025
• Last Verified: September 1, 2025

More Information

Terms related to this study

• Keywords: Glycemic Control; Artificial Intelligence (AI); Virtual Health Coaching
• Additional Relevant MeSH Terms: Endocrine System Diseases; Metabolic Diseases; Glucose Metabolism Disorders; Diabetes Mellitus; Nutritional and Metabolic Diseases; Diabetes Mellitus, Type 2; Algorithms; Mathematical Concepts; Artificial Intelligence
• Other Study ID Numbers: UNHAS-VHC-AI-2025; 02209/UN4.22/PT.01.03/2025 (Other Grant/Funding Number: Ministry of Education, Culture, Research, and Technology of the Republic of Indonesia)

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: UNDECIDED
• IPD Plan Description: At this stage, the plan to share individual participant data (IPD) has not yet been determined. The decision will depend on institutional policies, ethical approvals, and participant consent. Data may be shared in the future upon request, following anonymization and appropriate agreements.

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No