Artificial Intelligence-Supported Mobile Application For Diabetes Self-Management

NCT06650098 | Not Yet Recruiting

• 1 other identifier: 2024-12/16
• Study Type: interventional
• Target: 156
• Locations: 1 country
• Sites: 1

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.

Conditions

Diabetes Mellitus; Artificial Intelligence (AI); Self-management

Keywords

diabetes mellitus; artificial intelligence; self-management; level of knowledge; compliance with Treatment

Outcome Measures

Primary Outcomes (1)

• Diabetes Self-Management Scale (DSMS)

  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

  6 months

Secondary Outcomes (2)

• Adult diabetes knowledge scale (ADSL)

  6 months

• Morisky Medication Adherence Scale (MMAS-8)

  6 months

Study Arms (3)

WEB based application

EXPERIMENTAL

The content plan for the web-based mobile application group will be prepared with technical support as specified. Patients will be able to log in to the mobile application with a username and password that will be defined specifically for them. Patients will be informed about how the website is used during the first meeting. They will be able to access all the information they need about diabetes with the web-based mobile application. Statistical data such as the frequency of individuals visiting the site, which sections they use more often and how much time they spend will be calculated.

Other: WEB based application

artificial intelligence-supported mobile application

EXPERIMENTAL

It is aimed that an artificial intelligence-based mobile application that includes information, nutrition, exercise programs, complications and medication tracking, personalized suggestions, alarms and reminders, which will enable diabetic individuals to follow their glucose targets, support patients in their diabetes education, awareness and disease management to continue more actively. In addition, it is aimed that patients can easily access information, prevent acute and chronic complications, present physical activity and nutrition suggestions in accordance with the person\'s lifestyle, follow up on medications with alarms and reminders, prevent the negative results of complications in advance, and improve individuals\' diabetes-specific knowledge levels, compliance with treatment, self-management and care with information and guidance about foot care to reduce the risk of diabetic feet, which is particularly risky for diabetic patients.

Other: artificial intelligence-supported mobile application

control group

NO INTERVENTION

No intervention will be applied to the control group, and they will receive routine clinical and outpatient training.

Interventions

artificial intelligence-supported mobile application OTHER

It is aimed that an artificial intelligence-based mobile application that includes information, nutrition, exercise programs, complications and medication tracking, personalized suggestions, alarms and reminders, which will enable diabetic individuals to follow their glucose targets, support patients in their diabetes education, awareness and disease management to continue more actively. In addition, it is aimed that patients can easily access information, prevent acute and chronic complications, present physical activity and nutrition suggestions in accordance with the person\'s lifestyle, follow up on medications with alarms and reminders, prevent the negative results of complications in advance, and improve individuals\' diabetes-specific knowledge levels, compliance with treatment, self-management and care with information and guidance about foot care to reduce the risk of diabetic feet, which is particularly risky for diabetic patients.

artificial intelligence-supported mobile application

WEB based application OTHER

The content plan for the web-based mobile application group will be prepared with technical support as specified. Patients will be able to log in to the mobile application with a username and password that will be defined specifically for them. Patients will be informed about how the website is used during the first meeting. They will be able to access all the information they need about diabetes with the web-based mobile application. Statistical data such as the frequency of individuals visiting the site, which sections they use more often and how much time they spend will be calculated.

WEB based application

Eligibility Criteria

• Sex: all
• Healthy Volunteers: No
• Age Groups: Child (0-17), Adult (18-64), Older Adult (65+)

You may qualify if:

• 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

You may not qualify if:

• 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

Sponsors & Collaborators

• Uludag University: lead

Study Sites (1)

Istanbul Basaksehir Cam and Sakura City Hospital

Istanbul, Başakşehir, 34480, Turkey (Türkiye)

Location

MeSH Terms

Conditions

Diabetes Mellitus; Patient Compliance

Condition Hierarchy (Ancestors)

Glucose Metabolism Disorders; Metabolic Diseases; Nutritional and Metabolic Diseases; Endocrine System Diseases; Patient Acceptance of Health Care; Treatment Adherence and Compliance; Health Behavior; Behavior

Central Study Contacts

Nilhan NŞ Töyer Şahin, PhD Student

CONTACT

+905333752295; nilhantyr@gmail.com

Seda SP PEHLİVAN, Associate Professor

CONTACT

pehlivans@uludag.edu.tr

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: RANDOMIZED
• Masking: SINGLE
• Who Masked: PARTICIPANT
• Purpose: SUPPORTIVE CARE
• Intervention Model: PARALLEL
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Phd Student

Study Record Dates

• First Submitted: October 18, 2024
• First Posted: October 21, 2024
• Study Start: April 1, 2025
• Primary Completion: January 1, 2026
• Study Completion: June 1, 2026
• Last Updated: March 25, 2025

Record last verified: 2025-03

Data Sharing

• IPD Sharing: Will not share

Locations

TU (1)