Type 2 Diabetes MellitusAI-T2DM-NSNCT07916428
AI-Based Model for Predicting Type 2 Diabetes Risk Using Lifestyle Factors Among Senior Nursing Students
An observational study of Type 2 Diabetes Mellitus, run by Alexandria University at 1 site in Egypt.
Trial summary
This study will develop and test an artificial intelligence (AI) model that estimates the risk of type 2 diabetes among senior (third- and fourth-year) nursing students at the Faculty of Nursing, Alexandria University, Egypt. Type 2 diabetes is common in Egypt, and many cases are not diagnosed. Read more about this study
- Who is it for?
- Ages 18 years and olderHealthy volunteers can take part
- Conditions the study is for
- Type 2 Diabetes Mellitus
- What is being tested?
- AI-Based Diabetes Risk Prediction Model
- How many people
- 200 participants
- Where
- 1 siteEgypt
- Study dates
- Nov 2026to Dec 2026
- Phase
- Observational
- 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 18 years and older
- All sexes
- Healthy volunteers accepted
- Adult, Older adult
Study population: Third- and fourth-year (senior) undergraduate nursing students, male and female, enrolled at the Faculty of Nursing, Alexandria University, Egypt, who do not have diabetes or endocrine disorders affecting glucose metabolism and are not pregnant.
Who may be able to join
- Regularly enrolled third- or fourth-year (senior) undergraduate nursing students at the Faculty of Nursing, Alexandria University.
- Willing to provide written informed consent and participate voluntarily.
- Have access to an internet-enabled electronic device (smartphone, tablet, or computer) and basic digital literacy.
Who cannot join
- Previously diagnosed with type 1 or type 2 diabetes mellitus.
- Chronic endocrine disorders known to affect glucose metabolism.
- Pregnancy.
- Incomplete or invalid questionnaire responses.
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
About this study
Official title: Developing an Artificial Intelligence-Based Model for Predicting Type 2 Diabetes Risk Using Lifestyle Factors Among Senior Nursing Students at Alexandria University
This study will develop and test an artificial intelligence (AI) model that estimates the risk of type 2 diabetes among senior (third- and fourth-year) nursing students at the Faculty of Nursing, Alexandria University, Egypt. Type 2 diabetes is common in Egypt, and many cases are not diagnosed. Daily habits such as diet, physical activity, and body weight play a major role in who develops it. Nursing students will become health care providers who teach patients about healthy living, so it is important to find those who may be at risk early.
About 200 students will take part. Each student will complete a questionnaire about personal information and the Health-Promoting Lifestyle Profile II, which measures health habits such as nutrition, physical activity, stress management, and health responsibility. The researcher will measure height, weight, waist size, and blood pressure. Each student's diabetes risk will be scored using the Arabic version of the Australian Type 2 Diabetes Risk Assessment Tool (AUSDRISK), which classifies risk as low, moderate, or high.
Read more
These data will be used to build a machine learning model that predicts each student's diabetes risk level from lifestyle and body measurement factors, and shows which factors influenced the prediction most. Students will then try the model and rate how useful and easy to use it is.
No treatment or intervention will be given. The study aims to support early identification of students at risk and to provide a model that nursing education programs and community health nurses could use in preventive health efforts.
- Conditions
- Type 2 Diabetes Mellitus
- Being tested
- AI-Based Diabetes Risk Prediction Model
- Keywords
- Artificial Intelligence
- Medical subject headings
- Diabetes Mellitus, Type 2
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
AI-Based Diabetes Risk Prediction ModelOther
A machine learning model developed from students' lifestyle (HPLP-II), anthropometric, and blood pressure data to predict type 2 diabetes risk level (low, moderate, high) and identify the most influential factors. Students will use the model once and rate its usefulness and ease of use. No treatment or lifestyle intervention is delivered
| Study group | Role | What they receive |
|---|---|---|
| Senior Nursing Students | Description: Stratified random sample of third- and fourth-year (senior) nursing students at the Faculty of Nursing, Alexandria University, who complete the questionnaires and anthropometric measurements, contribute data for developing the AI model, and then try the model and rate its usefulness and ease of use. Receives: Other: AI-Based Diabetes Risk Prediction Model |
What is being measured
The primary outcome is the main question the study is designed to answer.
Type 2 Diabetes Risk Level According to AUSDRISK
Proportion of senior nursing students classified as low risk (total score 4 or less), moderate risk (5 to 10), or high risk (11 or more) for developing type 2 diabetes, using the Arabic version of the Australian Type 2 Diabetes Risk Assessment Tool (AUSDRISK). Total score ranges from 0 to 34; higher scores indicate higher risk.
Measured: aseline (single assessment at data collection, up to 1 day per participant)
Performance of the AI-Based Diabetes Risk Prediction Model
Accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) of the machine learning model in classifying students into low, moderate, or high AUSDRISK risk categories, assessed on a held-out test set.
Measured: Upon completion of data collection and model development (up to 6 months after study start)
Secondary outcomes (2)
Health-Promoting Lifestyle Profile II (HPLP-II) Score
Total and subscale scores (health responsibility, physical activity, nutrition, spiritual growth, interpersonal relations, stress management) of the 52-item Arabic HPLP-II, rated on a 4-point scale (1 = never to 4 = routinely). Higher scores indicate more frequent health-promoting behaviors.
Measured: Baseline (single assessment at data collection)
Perceived Usefulness and Ease of Use of the AI Model
Scores on a 12-item questionnaire (6 items on perceived usefulness, 6 on perceived ease of use) rated on a 7-point scale (1 = strongly disagree to 7 = strongly agree). Higher scores indicate better perceived usefulness and ease of use.
Measured: Immediately after the student uses the AI model (single assessment)
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted4 Nov 2026
- Study startNov 2026 (estimated)
- Primary completionDec 2026 (estimated)
- Study completionDec 2026 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Type 2 Diabetes MellitusStudies of the same condition, open and under way.
- More trials run by Alexandria UniversityThe sponsor's other registered studies.
- 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
- Observational
- Observational model
- Case-control
- Time perspective
- Cross-sectional
- Enrolment
- 200 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Alexandria University
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Mohamed Fakhry Ahmed Salem, Alexandria University
- Investigators
- Dr. Mohamed Fakhry Ahmed Salem, Principal investigator, Alexandria University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07916428
- Sponsor's protocol ID
- AU-20-6-242
- Acronym
- AI-T2DM-NS
- Status last verified
- Oct 2026
- First submitted
- 30 Oct 2026
- Last updated
- 4 Nov 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
Study at a glance
From the registry record, last updated by the sponsor on 4 Nov 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Developing an Artificial Intelligence-Based Model for Predicting Type 2 Diabetes Risk Using Lifestyle Factors Among Senior Nursing Students at Alexandria University
- ClinicalTrials.gov ID
- NCT07916428
- Acronym / sponsor ID
- AI-T2DM-NS / AU-20-6-242
- Conditions
- Type 2 Diabetes Mellitus
- Keywords
- Artificial Intelligence
- Study type
- Observational
- Recruitment status
- Not yet recruiting
- Enrolment
- 200 participants (estimated)
- Lead sponsor
- Alexandria UniversityUniversity, hospital or other
- Study start
- Nov 2026 (estimated)
- Primary completion
- Dec 2026 (estimated)
- Study completion
- Dec 2026 (estimated)
- First posted
- 4 Nov 2026 (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.
This study will develop and test an artificial intelligence (AI) model that estimates the risk of type 2 diabetes among senior (third- and fourth-year) nursing students at the Faculty of Nursing, Alexandria University, Egypt. Type 2 diabetes is common in Egypt, and many cases are not diagnosed. Daily habits such as diet, physical activity, and body weight play a major role in who develops it. Nursing students will become health care providers who teach patients about healthy living, so it is important to find those who may be at risk early.
About 200 students will take part. Each student will complete a questionnaire about personal information and the Health-Promoting Lifestyle Profile II, which measures health habits such as nutrition, physical activity, stress management, and health responsibility. The researcher will measure height, weight, waist size, and blood pressure. Each student's diabetes risk will be scored using the Arabic version of the Australian Type 2 Diabetes Risk Assessment Tool (AUSDRISK), which classifies risk as low, moderate, or high.
These data will be used to build a machine learning model that predicts each student's diabetes risk level from lifestyle and body measurement factors, and shows which factors influenced the prediction most. Students will then try the model and rate how useful and easy to use it is.
No treatment or intervention will be given. The study aims to support early identification of students at risk and to provide a model that nursing education programs and community health nurses could use in preventive health efforts.
Study design
- Observational model
- Case-control
- Time perspective
- Cross-sectional
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Senior Nursing Students | – | Other: AI-Based Diabetes Risk Prediction ModelDescription: Stratified random sample of third- and fourth-year (senior) nursing students at the Faculty of Nursing, Alexandria University, who complete the questionnaires and anthropometric measurements, contribute data for developing the AI model, and then try the model and rate its usefulness and ease of use. |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
- Study population
- Third- and fourth-year (senior) undergraduate nursing students, male and female, enrolled at the Faculty of Nursing, Alexandria University, Egypt, who do not have diabetes or endocrine disorders affecting glucose metabolism and are not pregnant.
Key inclusion criteria (3 listed)
- Regularly enrolled third- or fourth-year (senior) undergraduate nursing students at the Faculty of Nursing, Alexandria University.
- Willing to provide written informed consent and participate voluntarily.
- Have access to an internet-enabled electronic device (smartphone, tablet, or computer) and basic digital literacy.
Key exclusion criteria (4 listed)
- Previously diagnosed with type 1 or type 2 diabetes mellitus.
- Chronic endocrine disorders known to affect glucose metabolism.
- Pregnancy.
- Incomplete or invalid questionnaire responses.
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 |
|---|---|---|
| AI-Based Diabetes Risk Prediction ModelA machine learning model developed from students' lifestyle (HPLP-II), anthropometric, and blood pressure data to predict type 2 diabetes risk level (low, moderate, high) and identify the most influential factors. Students will use the model once and rate its usefulness and ease of use. No treatment or lifestyle intervention is delivered | Other | Senior Nursing Students |
Primary outcome
Type 2 Diabetes Risk Level According to AUSDRISK
Time frame: aseline (single assessment at data collection, up to 1 day per participant)
Proportion of senior nursing students classified as low risk (total score 4 or less), moderate risk (5 to 10), or high risk (11 or more) for developing type 2 diabetes, using the Arabic version of the Australian Type 2 Diabetes Risk Assessment Tool (AUSDRISK). Total score ranges from 0 to 34; higher scores indicate higher risk.
Performance of the AI-Based Diabetes Risk Prediction Model
Time frame: Upon completion of data collection and model development (up to 6 months after study start)
Accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) of the machine learning model in classifying students into low, moderate, or high AUSDRISK risk categories, assessed on a held-out test set.
2 secondary outcomes
Health-Promoting Lifestyle Profile II (HPLP-II) Score
Time frame: Baseline (single assessment at data collection)
Perceived Usefulness and Ease of Use of the AI Model
Time frame: Immediately after the student uses the AI model (single assessment)
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 4 Nov 2026.
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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.
Dr. Mohamed Fakhry Ahmed Salem principal investigator
Alexandria University
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Type 2 Diabetes Mellitus, run by Alexandria University at 1 site in Egypt.
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 3 inclusion and 4 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 4 Nov 2026. 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 Egypt. 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 Dr. Mohamed Fakhry Ahmed Salem, PhD (01286038014, Mohamed.Fakhry@alexu.edu.eg), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Type 2 Diabetes Risk Level According to AUSDRISK (time frame: aseline (single assessment at data collection, up to 1 day per participant)). The study lists 2 primary outcomes in all. 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 4 Nov 2026.