Artificial Intelligence-Supported Mobile Application for Diabetes Self-Management

Not Applicable

Not yet recruiting

• Conditions: Diabetes Mellitus; Artificial Intelligence (AI); Self-management

• Registration Number: NCT06650098
• Lead Sponsor: Uludag University

Brief Summary

Patients in the AI-supported mobile application group will be able to log in with a username and password that will be defined specifically for them. Patients will be informed about how the application is used during their first interview. They will enter their personal and disease characteristics (age, gender, height, weight, HbA1C, HDL, LDL) into the application at the entrance. Other sections of the application will include exercise, nutrition, medication tracking, complication tracking and diabetic foot care sections. The person will be asked to enter relevant information in these fields according to their own life and condition (for example; how many times do you use insulin per day, what are your medication times, how do you spend your day in terms of exercise, how many meals do you eat, what is your diet, do you urinate frequently, are you extremely thirsty, are you hungry often, do you have numbness in your hands and feet, etc.). After the patient enters the necessary information, they will also be asked to enter their daily blood sugar measurement values into the system. Thus, the individual\'s hypo/hyperglycemia risk, risk analysis, nutrition recommendations, medication reminder system, exercise reminder and incentive warnings will be communicated to the individual thanks to the AI-based mobile application. The aim of this application is to reduce the risk of complications and improve the individual\'s quality of life by providing personalized recommendations for all the needs of the individual, including alarms and reminders, and to support patients to continue their diabetes education and disease management more actively.

Detailed Description

pre-test post-test control group design

Study Timeline

• Study First Submitted: 2024-10-18
• Study First Submitted QC: 2024-10-18
• Study First Posted: 2024-10-21
• Status Verified: 2025-03
• Last Update Submitted: 2025-03-24
• Last Update Posted: 2025-03-25
• Study Start: 2025-04-01
• Primary Completion: 2026-01-01
• Study Completion: 2026-06-01

Recruitment & Eligibility

• Status: NOT_YET_RECRUITING
• Sex: All
• Target Recruitment: 156

Inclusion Criteria

• Having been diagnosed with diabetes for at least 1 year
• Being between the ages of 18-65
• Being open to verbal communication
• Being able to read and write and speak Turkish
• Having a smart android phone and being able to use mobile applications
• Being willing to participate in the study

Exclusion Criteria

• Having a perception disorder and psychiatric disorder that prevents the patient from communicating,
• Having a condition that prevents them from using a smart phone (advanced retinopathy and neuropathy, internet problems)
• Being on intensive insulin treatment
• Having a condition that prevents them from continuing the application phase of the study
• Wanting to leave the study

Study & Design

• Study Type: INTERVENTIONAL
• Study Design: PARALLEL

Primary Outcome Measures

Name	Time	Method
Diabetes Self-Management Scale (DSMS)	6 months	Diabetes Self-Management Scale (DSMS): This scale was used to measure the behavioral component of individuals in the IMB model. This scale was developed by Schmitt et al. (2013) to examine the relationship between diabetes self-management and glycemic control in diabetic patients (Schmitt et al., 2013). The validity and reliability study of the Turkish Diabetes Self-Management Scale (DSMS) was conducted by Eroğlu and Sabuncu (2018) (Eroğlu and Sabuncu, 2018). The scale consists of 16 items and 4 sub-dimensions and is a 4-point Likert-type. The scale is answered as 3. It suits me very much, 2. It suits me a lot, 1. It suits me a little, 0. It does not suit me at all. Glucose Management subdimension: Items 1, 4, 6, 10, 12 (Items 4 and 12 are about medication use, items 1, 6, and 10 are about blood sugar monitoring). Diet Control subdimension: Items 2, 5, 9, 13. Physical Activity subdimension: Items 8, 11, 15. Use of Health Services subdimension: Items 3, 7, and 14. Item 16 is not includ

Secondary Outcome Measures

Name	Time	Method
Adult diabetes knowledge scale (ADSL)	6 months	Adult diabetes knowledge scale (ADSL): This scale was developed by Erenel Yavuz and Erol (2022) to measure the knowledge levels of adult individuals with diabetes (Erenel Yavuz and Erol, 2022). The scale consists of 5 sub-dimensions and 28 items: General Information About Diabetes (6 items), Blood Sugar Measurements and Values (5 items), Diabetes Risk Factors (4 items), Diabetes Symptoms (8 items), Diabetes Complications (5 items). The scale consists of two sets of items as true and false. Those who answer correctly in the yes/no/I don't know answer type questions are given 1 point, and those who don't know and those who answer incorrectly are given 0 points. The maximum score that can be obtained from the scale is 28, and the minimum score is 0. The Kuder-Richardson-20 reliability coefficient for the entire scale was found to be 0.94. Alpha values in the sub-dimensions are; General Information About Diabetes was found as 0.78, Blood Sugar Measurements and Values as 0.85, Diabet
Morisky Medication Adherence Scale (MMAS-8)	6 months	Morisky Medication Adherence Scale (MMAS-8) The MMAS-8 scale consists of 8 items. Each of the first 7 items has 2 possible responses (yes/no), while the 8th item is answered with a 5-point Likert scale. The possible total medication adherence score ranges between 0 and 8, and the higher the score, the better the adherence level. A total score \< 6 is considered low adherence, while a total score of ≥ 6 but \< 8 indicates moderate adherence, and a score of 8 indicates high adherence.

Trial Locations

Locations (1)

Istanbul Basaksehir Cam and Sakura City Hospital; 🇹🇷 İstanbul, Başakşehir, Turkey