An observational study in Type 2 Diabetes Mellitus, sponsored by Alexandria University. Not yet recruiting at 1 site in Egypt. Open to participants aged 18 Years and older, including healthy volunteers. Per ClinicalTrials.gov, last updated 2026-11-04.
Sponsored by Alexandria University · Observational
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.
9,608 studies on the registry are indexed under Diabetes Mellitus, Type 2; 1,336 are open to participants now.
This study's planned enrollment of 200 is below the median of 300 across 1,625 observational studies indexed under Diabetes Mellitus, Type 2.
Browse Diabetes Mellitus, Type 2 studies →Alexandria University is the lead sponsor of 596 studies on the registry; 134 are open to participants now.
Of its 13 completed or terminated interventional studies of FDA-regulated products, 0 (0%) have results posted.
Counted across the registry records on this site, refreshed daily.
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.
Go through the criteria one by one — yes, no or not sure — and take the list to your doctor or the study team. Kept in this browser only.
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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.
Other: AI-Based Diabetes Risk Prediction Model
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
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.
Time frame: 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.
Time frame: Upon completion of data collection and model development (up to 6 months after study start)
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.
Time frame: 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.
Time frame: Immediately after the student uses the AI model (single assessment)
From the registry record's own update history. This site started tracking changes on Sep 25, 2026; for anything earlier, see the record history on ClinicalTrials.gov ↗
This study is not yet recruiting, as verified in Oct 2026. You cannot join it, but the record below documents what was studied.
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