Ignite Creation Date:
2025-12-24 @ 12:11 PM
Ignite Modification Date:
2025-12-30 @ 2:48 AM
Study NCT ID:
NCT07300761
Status:
NOT_YET_RECRUITING
Last Update Posted:
2025-12-24
First Post:
2025-11-25
Is NOT Gene Therapy:
True
Has Adverse Events:
False
Brief Title:
AI-Integrated Mobile Education and Self-Management in Hemodialysis
Sponsor:
Organization:
Raw JSON
{'hasResults': False, 'derivedSection': {'miscInfoModule': {'versionHolder': '2025-12-24'}}, 'protocolSection': {'designModule': {'phases': ['NA'], 'studyType': 'INTERVENTIONAL', 'designInfo': {'allocation': 'RANDOMIZED', 'maskingInfo': {'masking': 'NONE', 'maskingDescription': 'The project document does not state that any parties are masked (blinded) in the clinical trial.\n\nGiven the nature of the intervention, the study is inherently open-label (unmasked):\n\nParticipants (Patients): Patients in the Intervention Group are fully aware that they are using a mobile education application, and those in the Control Group are aware they are only receiving routine care. Therefore, participant masking is not possible.\n\nPersonnel (Researchers): Researchers are responsible for introducing the application and conducting face-to-face interviews for data collection. They are aware of which group the patient belongs to.\n\nOutcome Assessment: While objective outcomes (such as biochemical parameters) are gathered from hospital records, the document does not specify a plan for an independent, blinded assessor or data analyst.\n\nIn summary, as the application form does not detail any masking procedures, it must be reported that no other parties are masked.'}, 'primaryPurpose': 'SUPPORTIVE_CARE', 'interventionModel': 'PARALLEL', 'interventionModelDescription': "This is a Randomized Controlled Interventional Thesis study aiming to evaluate the effect of an AI-supported mobile education application on Hemodialysis (HD) patients' self-care, AVF behaviors, and biochemical parameters. The study targets 76 HD patients (38 per group) at two hospitals in Bitlis, assigned via simple randomization. The intervention group uses the app for 6 weeks; the control group receives routine care. Data is collected via pre- and post-tests (6 weeks) using the Hemodialysis Self-Management Scale and AVF Self-Care Scale. Biochemical parameters (e.g., BUN, creatinine, albumin) are tracked using hospital records"}, 'enrollmentInfo': {'type': 'ESTIMATED', 'count': 76}}, 'statusModule': {'overallStatus': 'NOT_YET_RECRUITING', 'startDateStruct': {'date': '2026-01-02', 'type': 'ESTIMATED'}, 'expandedAccessInfo': {'hasExpandedAccess': False}, 'statusVerifiedDate': '2025-12', 'completionDateStruct': {'date': '2026-08-02', 'type': 'ESTIMATED'}, 'lastUpdateSubmitDate': '2025-12-18', 'studyFirstSubmitDate': '2025-11-25', 'studyFirstSubmitQcDate': '2025-12-18', 'lastUpdatePostDateStruct': {'date': '2025-12-24', 'type': 'ACTUAL'}, 'studyFirstPostDateStruct': {'date': '2025-12-24', 'type': 'ACTUAL'}, 'primaryCompletionDateStruct': {'date': '2026-02-02', 'type': 'ESTIMATED'}}, 'outcomesModule': {'primaryOutcomes': [{'measure': 'Scale for Assessing Self-Care Behaviors Related to Arteriovenous Fistula in Hemodialysis Patients', 'timeFrame': '6 weeks', 'description': "Scale for Assessing Self-Care Behaviors Related to Arteriovenous Fistula in Hemodialysis Patients\n\nThis scale was developed by Sousa et al. to assess patients' self-care behaviors related to arteriovenous fistula and to monitor educational processes. The scale was originally developed in Portuguese, and its validity and reliability for the Turkish population were established by İkiz and Yıldırım Usta.\n\nThe scale consists of 16 items rated on a 5-point Likert-type scale and includes two subscales:\n\nSymptom Management (items 1, 3, 6, 11, 13, and 16)\n\nComplication Prevention (items 2, 4, 5, 7, 8, 9, 10, 12, 14, and 15)\n\nThe total score ranges from 16 to 80, with higher scores indicating a higher level of self-care.\n\nThe Cronbach's alpha reliability coefficient of the scale is 0.91. The average administration time is approximately 2-3 minutes."}], 'secondaryOutcomes': [{'measure': 'Hemodialysis Self-Management Scale', 'timeFrame': '6 weeks.', 'description': 'This scale assesses self-management behaviors in patients undergoing hemodialysis. The scale consists of 11 items rated on a 4-point Likert-type scale (Never = 1, Sometimes = 2, Often = 3, Always = 4). Total scores range from 11 to 44, with higher scores indicating better self-management.\n\nThe scale includes four subscales: partnership (items 5 and 7), self-care (items 1, 4, and 8), problem solving (items 2, 3, and 6), and emotional management (items 9, 10, and 11).'}]}, 'oversightModule': {'oversightHasDmc': False, 'isFdaRegulatedDrug': False, 'isFdaRegulatedDevice': False}, 'conditionsModule': {'keywords': ['Artificial Intelligence', 'Patient Education', 'Mobile Application', 'Self-Care, Hemodialysis', 'Nursing Care'], 'conditions': ['Hemodialysis Patient', 'Patient Education', 'Self Care']}, 'descriptionModule': {'briefSummary': '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.\n\nDespite 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.\n\nThe 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.\n\nThe 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).\n\nData 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.\n\nThe 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.\n\nEthical 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.\n\nThis 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.\n\nUltimately, 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.', 'detailedDescription': "This project is a Randomized Controlled Interventional Thesis study designed to bridge a critical gap in chronic disease management literature: the lack of personalized, technology-supported education for Hemodialysis (HD) patients.Study Rationale and ObjectivesWhile HD treatment is life-saving, patients face multiple, complex issues, including AVF complications, fluid restriction difficulties, and emotional distress, all requiring high levels of patient self-care and self-management. Existing mobile health (mHealth) applications for HD patients are limited and typically lack personalized features.The core objective is to test the central hypothesis (H1) that an AI-supported mobile education program will have a statistically significant positive effect on patients' self-management scores, AVF self-care behavior scores, and objectively measured biochemical parameters (including creatinine, albumin, and KT/V) compared to routine care.Methodology and Unique Intervention FeaturesThe study follows a strict four-stage methodological plan:Content Design and Validation: The mobile application content is developed based on current literature and then rigorously validated by expert consensus (involving at least ten specialists, including nephrologists, dialysis nurses, and dietitians). Content readability is verified using the Ateşman formula to ensure patient comprehension. Pilot Study and Usability: A preliminary pilot study is conducted with a small group (excluded from the main study) to assess the application's usability and acceptability using the WAMMI (Website Analysis and Measurement Inventory) scale before the main intervention begins.Randomized Intervention: Seventy-six eligible patients are randomly assigned to either the Intervention Group (receiving the mobile education for 6 weeks) or the Control Group (receiving only routine care).Outcome Measurement and AnalysisPrimary outcomes focus on patient behavior and knowledge, measured by pre- and post-tests (after 6 weeks) using validated instruments: the Hemodialysis Self-Management Scale and the AVF Self-Care Behavior Assessment Scale. Secondary outcomes are clinical, measured by tracking biochemical parameters (BUN, creatinine, albumin, etc.) obtained directly from the patient's hospital records, thus avoiding additional invasive procedures. Data analysis will be conducted using SPSS, with a significance level set at p \\< 0.05.Expected Impact and Value AddedSuccessful completion is expected to demonstrate significant clinical and economic value by:Improving patient quality of life and autonomy.Reducing the incidence of complications (e.g., AVF failure) and subsequently lowering hospital readmission rates.Providing a cost-effective, sustainable model for chronic disease education that can reduce the financial burden on health institutions and social security.Contributing a novel, evidence-based technological solution to the field of nursing and mHealth literature."}, 'eligibilityModule': {'sex': 'ALL', 'stdAges': ['ADULT', 'OLDER_ADULT'], 'maximumAge': '65 Years', 'minimumAge': '18 Years', 'healthyVolunteers': False, 'eligibilityCriteria': 'Inclusion Criteria:\n\nMust be 18 years of age or older.\n\nMust have an Arteriovenous Fistula (AVF).\n\nMust not have a communication-hindering problem.\n\nMust be receiving outpatient Hemodialysis (HD) treatment.\n\nMust have been receiving HD treatment for longer than 6 months.\n\nMust own a smartphone and have internet access.\n\nExclusion Criteria:\n\nPatients who do not agree to participate in the research.\n\nPatients who have been diagnosed with advanced cerebrovascular and peripheral vascular insufficiency.\n\nPatients who do not complete the mobile education application'}, 'identificationModule': {'nctId': 'NCT07300761', 'acronym': 'AI-MOBI-HEMODI', 'briefTitle': 'AI-Integrated Mobile Education and Self-Management in Hemodialysis', 'organization': {'class': 'OTHER', 'fullName': 'Ataturk University'}, 'officialTitle': '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', 'orgStudyIdInfo': {'id': 'B.30.2.ATA.0.01.00/726'}}, 'armsInterventionsModule': {'armGroups': [{'type': 'EXPERIMENTAL', 'label': 'AI-Supported Mobile Education Group', 'description': "This arm, the AI-Supported Mobile Education Group, receives the experimental intervention for a period of 6 weeks. The intervention consists of a web-based mobile application that delivers specialized education to Hemodialysis (HD) patients. The content, developed with expert opinions, covers six key areas, including AVF care, diet adherence, fluid management, and general treatment adherence. The application is supported by Artificial Intelligence (AI), which is used to store patient data, track the patient's usage time, and automatically send reminders and motivational messages to encourage compliance. After receiving their login credentials and an orientation from the researcher, patients are expected to access and complete the education modules independently over the 6-week period.", 'interventionNames': ['Behavioral: AI-Supported Mobile Education']}, {'type': 'NO_INTERVENTION', 'label': 'Control Group', 'description': 'Patients receiving routine care and education.'}], 'interventions': [{'name': 'AI-Supported Mobile Education', 'type': 'BEHAVIORAL', 'description': "This arm, the AI-Supported Mobile Education Group, receives the experimental intervention for a period of 6 weeks. The intervention consists of a web-based mobile application that delivers specialized education to Hemodialysis (HD) patients. The content, developed with expert opinions, covers six key areas, including AVF care, diet adherence, fluid management, and general treatment adherence. The application is supported by Artificial Intelligence (AI), which is used to store patient data, track the patient's usage time, and automatically send reminders and motivational messages to encourage compliance. After receiving their login credentials and an orientation from the researcher, patients are expected to access and complete the education modules independently over the 6-week period.", 'armGroupLabels': ['AI-Supported Mobile Education Group']}]}, 'contactsLocationsModule': {'centralContacts': [{'name': 'Mehtap Kavurmacı, prof. dr.', 'role': 'CONTACT', 'email': 'mehtap.kavurmaci@atauni.edu.tr', 'phone': '+905309327579'}], 'overallOfficials': [{'name': 'Mehtap KAVURMACI, Prof. Dr.', 'role': 'STUDY_DIRECTOR', 'affiliation': 'Ataturk University'}]}, 'ipdSharingStatementModule': {'ipdSharing': 'NO', 'description': 'Individual Participant Data (IPD) will not be shared with other researchers outside of the primary study team. This decision is based on the data being collected specifically for the completion of a doctoral thesis and for publication of the study results. The ethical approval and informed consent procedures primarily cover the use of the data for this singular project. To ensure the strictest adherence to patient privacy and confidentiality agreements, the raw data will remain securely restricted to the principal investigators.'}, 'sponsorCollaboratorsModule': {'leadSponsor': {'name': 'Ataturk University', 'class': 'OTHER'}, 'responsibleParty': {'type': 'PRINCIPAL_INVESTIGATOR', 'investigatorTitle': 'research assistant', 'investigatorFullName': 'yakup dilbilir', 'investigatorAffiliation': 'Ataturk University'}}}}