Diabetes MellitusNCT06650098
Artificial Intelligence-Supported Mobile Application For Diabetes Self-Management
A study testing artificial intelligence-supported mobile application and WEB based application in people with Diabetes Mellitus and Artificial Intelligence (AI), run by Uludag University at 1 site in Turkey (Türkiye).
Trial 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. Read more about this study
- Who is it for?
- Any age
- Conditions the study is for
- Diabetes Mellitus, Artificial Intelligence (AI), Self-management
- What is being tested?
- artificial intelligence-supported mobile application and WEB based application
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 156 participants
- Where
- 1 siteTurkey (Türkiye)
- Study dates
- 1 Apr 2025to 1 Jun 2026
- Phase
- Interventional
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages Any age
- All sexes
- No healthy volunteers
- Child, Adult, Older adult
Who may be able to join
- 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
Who cannot join
- 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
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
Seda SP PEHLİVAN, Associate Professor contact
Turkey (Türkiye)1 site
Istanbul Basaksehir Cam and Sakura City Hospital
Istanbul, Başakşehir, 34480
Muhittin BALTA General Hospital Deputy Chief Physician, Doctor, +90 212 909 60 00, ist.camsakurash@saglik.gov.tr
Nilhan NS TÖYER ŞAHİN, PhD Student
About this study
Official title: The Effect of Web-Based and Artificial Intelligence-Assisted Personalized Applications on Knowledge Levels Compliance With Treatment and Self-Management Among Diabetic Individuals
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.
Read the detailed description
pre-test post-test control group design
- Being tested
- artificial intelligence-supported mobile application, WEB based application
- Keywords
- diabetes mellitus, artificial intelligence, self-management, level of knowledge, compliance with Treatment
- Medical subject headings
- Diabetes Mellitus, Patient Compliance
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
artificial intelligence-supported mobile applicationOther
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.
WEB based applicationOther
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.
| Study group | Role | What they receive |
|---|---|---|
| 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. Receives: 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. Receives: 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. |
What is being measured
The primary outcome is the main question the study is designed to answer.
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
Measured: 6 months
Secondary outcomes (2)
Adult diabetes knowledge scale (ADSL)
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
Measured: 6 months
Morisky Medication Adherence Scale (MMAS-8)
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.
Measured: 6 months
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted21 Oct 2024
- Study start1 Apr 2025 (estimated)
- Primary completion1 Jan 2026 (estimated)
- Study completion1 Jun 2026 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Diabetes MellitusStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Supportive care
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- Single blind
- Who is masked
- Participant
- Enrolment
- 156 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Uludag University
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Nilhan Toyer Sahin, Uludag University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06650098
- Sponsor's protocol ID
- 2024-12/16
- Status last verified
- Mar 2025
- First submitted
- 18 Oct 2024
- Last updated
- 25 Mar 2025
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Study at a glance
From the registry record, last updated by the sponsor on 25 Mar 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- The Effect of Web-Based and Artificial Intelligence-Assisted Personalized Applications on Knowledge Levels Compliance With Treatment and Self-Management Among Diabetic Individuals
- ClinicalTrials.gov ID
- NCT06650098
- Acronym / sponsor ID
- 2024-12/16
- Keywords
- diabetes mellitus, artificial intelligence, self-management, level of knowledge, compliance with Treatment
- Study type
- Interventional
- Recruitment status
- Not yet recruiting
- Enrolment
- 156 participants (estimated)
- Lead sponsor
- Uludag UniversityUniversity, hospital or other
- Study start
- 1 Apr 2025 (estimated)
- Primary completion
- 1 Jan 2026 (estimated)
- Study completion
- 1 Jun 2026 (estimated)
- First posted
- 21 Oct 2024 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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\&#39;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\&#39;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 design
- Primary purpose
- Supportive care
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- Single blind (participant)
- Number of arms
- 3
| Arm | Type | Receives |
|---|---|---|
| WEB based application | Experimental | Other: WEB based applicationThe 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. |
| artificial intelligence-supported mobile application | Experimental | Other: artificial intelligence-supported mobile applicationIt 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. |
| control group | No intervention | –No intervention will be applied to the control group, and they will receive routine clinical and outpatient training. |
Who may qualify
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Child, Adult, Older adult
Key inclusion criteria (6 listed)
- 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
Key exclusion criteria (5 listed)
- 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
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| artificial intelligence-supported mobile applicationIt 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 |
| WEB based applicationThe 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 |
Primary outcome
Diabetes Self-Management Scale (DSMS)
Time frame: 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
2 secondary outcomes
Adult diabetes knowledge scale (ADSL)
Time frame: 6 months
Morisky Medication Adherence Scale (MMAS-8)
Time frame: 6 months
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 25 Mar 2025.
- Individual participant dataSharing planned: No
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
Seda SP PEHLİVAN, Associate Professor contact
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing artificial intelligence-supported mobile application and WEB based application in people with Diabetes Mellitus and Artificial Intelligence (AI), run by Uludag University at 1 site in Turkey (Türkiye).
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 6 inclusion and 5 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: opening soon, as of the sponsor's last update on 25 Mar 2025. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Turkey (Türkiye). Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Nilhan NŞ Töyer Şahin, PhD Student (+905333752295, nilhantyr@gmail.com), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Diabetes Self-Management Scale (DSMS) (time frame: 6 months). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 25 Mar 2025.