A Randomized Controlled Trial to Evaluate the Effect of an Artificial Intelligence-Supported Mobile Education Application on Self-Care Behaviors, Arteriovenous Fistula Care, and Biochemical Parameters in Hemodialysis Patients
Trial ID: NCT07300761
Status: NOT_YET_RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2026-01-02
Completion date: 2026-08-02
Sponsor: Ataturk University
Conditions: Hemodialysis Patient; Patient Education; Self Care
Interventions: AI-Supported Mobile Education
Enrollment: 76
Age range: 18 Years - 65 Years
Sex: ALL

Summary:
This randomized controlled trial aims to evaluate the effectiveness of an Artificial Intelligence-supported mobile education application designed to enhance self-care behaviors, arteriovenous fistula (AVF) care practices, and key biochemical parameters among adult hemodialysis (HD) patients. Chronic Kidney Disease (CKD) and its most common renal replacement therapy, hemodialysis, impose a substantial physical, psychological, and socioeconomic burden on patients. HD patients frequently experience fatigue, pain, cramping, sleep disturbances, thirst and fluid restriction challenges, dietary limitations, AVF-related complications, and emotional distress. These difficulties highlight the importance of strengthening patients' self-care abilities and promoting active involvement in disease management.

Despite the prevalence of mobile health (mHealth) technologies in chronic disease management, existing applications for HD patients remain limited, and none have integrated personalized artificial intelligence-based educational support. The absence of AI-driven patient education represents a significant gap in nursing science and digital health innovation. This project addresses that gap by developing and testing a structured, evidence-based mobile education program supported by artificial intelligence, designed specifically for HD patients.

The study will enroll 76 eligible hemodialysis patients from Bitlis State Hospital and Bitlis Tatvan State Hospital. Participants will be randomly assigned to either the intervention group or the control group using simple randomization. The intervention group will receive access to the AI-supported mobile application for six weeks, which includes modules on kidney function, CKD and treatment options, symptom management, dietary adherence, fluid management, treatment adherence, and AVF care. Each module incorporates written content, videos, visuals, voice-supported reading features, and an integrated "Ask a Question" function allowing patients to communicate directly with the research team. The control group will receive routine clinical care without additional intervention.

The artificial intelligence component will assist with content personalization, monitoring of patient engagement, data storage, automated reminders for non-active users, and supportive feedback based on learning progress and biochemical trends. The development of the mobile application will be guided by expert opinions from nephrology specialists, dialysis nurses, academicians, and dietitians. Readability of educational materials will be assessed using the Ateşman Readability Formula. A pilot study will be conducted prior to the trial to evaluate usability using the Web Analysis and Measurement Inventory (WAMMI).

Data collection will include a Patient Identification Form, the Hemodialysis Arteriovenous Fistula Self-Care Behavior Scale, the Hemodialysis Self-Management Scale, and a Biochemical Parameters Tracking Form. Pre-test data will be collected before the intervention; post-test data will be collected at the end of the six-week intervention period. Biochemical parameters will include BUN, creatinine, albumin, potassium, phosphorus, hemoglobin, uric acid levels, Kt/V, and dry weight, obtained from routine clinical records without additional blood sampling.

The primary outcomes will assess changes in self-care and self-management behaviors based on validated scales. Secondary outcomes will examine changes in biochemical parameters between the intervention and control groups. Data analysis will be performed using SPSS, employing descriptive statistics, normality testing, and appropriate statistical comparison tests, with significance set at p \< 0.05.

Ethical approval will be obtained from the appropriate institutional ethics committee, and written informed consent will be secured from all participants. Data confidentiality will be ensured using encrypted login systems and secure storage processes.

This trial is expected to contribute significantly to the scientific literature by being the first AI-supported mobile education intervention tailored for hemodialysis patients. Anticipated benefits include improved self-care behaviors, increased patient autonomy, reduced AVF complications, better adherence to dietary and fluid restrictions, and improved biochemical outcomes. Broader impacts of the project include the potential reduction of hospitalization rates, decreased healthcare costs, increased quality of life for HD patients, and the establishment of a digital model that can be adapted for other chronic disease populations.

Ultimately, this study aims to demonstrate that integrating artificial intelligence with mobile health education can create a transformative approach to patient empowerment, clinical care, and chronic disease management within the field of nephrology and nursing.

Eligibility criteria:
Inclusion Criteria:

Must be 18 years of age or older.

Must have an Arteriovenous Fistula (AVF).

Must not have a communication-hindering problem.

Must be receiving outpatient Hemodialysis (HD) treatment.

Must have been receiving HD treatment for longer than 6 months.

Must own a smartphone and have internet access.

Exclusion Criteria:

Patients who do not agree to participate in the research.

Patients who have been diagnosed with advanced cerebrovascular and peripheral vascular insufficiency.

Patients who do not complete the mobile education application

Outcomes:
Scale for Assessing Self-Care Behaviors Related to Arteriovenous Fistula in Hemodialysis Patients