Ignite Creation Date:
2025-12-25 @ 5:04 AM
Ignite Modification Date:
2025-12-26 @ 4:06 AM
Study NCT ID:
NCT07027618
Status:
NOT_YET_RECRUITING
Last Update Posted:
2025-06-18
First Post:
2025-06-11
Is NOT Gene Therapy:
False
Has Adverse Events:
False
Brief Title:
AI-Driven Personalization of End-of-Life Care for the Elderly
Sponsor:
Organization:
Raw JSON
{'hasResults': False, 'derivedSection': {'miscInfoModule': {'versionHolder': '2025-12-24'}, 'conditionBrowseModule': {'meshes': [{'id': 'D003704', 'term': 'Dementia'}], 'ancestors': [{'id': 'D001927', 'term': 'Brain Diseases'}, {'id': 'D002493', 'term': 'Central Nervous System Diseases'}, {'id': 'D009422', 'term': 'Nervous System Diseases'}, {'id': 'D019965', 'term': 'Neurocognitive Disorders'}, {'id': 'D001523', 'term': 'Mental Disorders'}]}}, 'protocolSection': {'designModule': {'phases': ['NA'], 'studyType': 'INTERVENTIONAL', 'designInfo': {'allocation': 'RANDOMIZED', 'maskingInfo': {'masking': 'SINGLE', 'whoMasked': ['OUTCOMES_ASSESSOR']}, 'primaryPurpose': 'PREVENTION', 'interventionModel': 'PARALLEL', 'interventionModelDescription': 'Participants will be randomly assigned to one of two parallel groups: an intervention group receiving AI-based palliative care through a personalized software system, and a control group receiving standard end-of-life care without the AI intervention. Randomization will be conducted using block randomization to ensure balance in baseline characteristics. Although the study is open-label, outcome assessors will be blinded to group assignments to minimize assessment bias.'}, 'enrollmentInfo': {'type': 'ESTIMATED', 'count': 140}}, 'statusModule': {'overallStatus': 'NOT_YET_RECRUITING', 'startDateStruct': {'date': '2025-07', 'type': 'ESTIMATED'}, 'expandedAccessInfo': {'hasExpandedAccess': False}, 'statusVerifiedDate': '2025-06', 'completionDateStruct': {'date': '2026-09', 'type': 'ESTIMATED'}, 'lastUpdateSubmitDate': '2025-06-11', 'studyFirstSubmitDate': '2025-06-11', 'studyFirstSubmitQcDate': '2025-06-11', 'lastUpdatePostDateStruct': {'date': '2025-06-18', 'type': 'ACTUAL'}, 'studyFirstPostDateStruct': {'date': '2025-06-18', 'type': 'ACTUAL'}, 'primaryCompletionDateStruct': {'date': '2026-08', 'type': 'ESTIMATED'}}, 'outcomesModule': {'primaryOutcomes': [{'measure': 'Change in Quality of Life Score', 'timeFrame': 'Up to 3 months after intervention start', 'description': 'Assessed using a validated instrument such as the Quality of Life at the End of Life (QUAL-E) scale to measure overall well-being, comfort, and satisfaction with care during end-of-life.'}]}, 'oversightModule': {'oversightHasDmc': True, 'isFdaRegulatedDrug': False, 'isFdaRegulatedDevice': False}, 'conditionsModule': {'conditions': ['Artificial Intelligence (AI)', 'Dementia', 'Palliative Care']}, 'descriptionModule': {'briefSummary': 'This study aims to evaluate the effectiveness of an artificial intelligence (AI)-based software in personalizing high-quality end-of-life care for elderly patients. As the elderly population grows, providing tailored and quality care during the final stages of life becomes increasingly important. This AI software continuously monitors vital signs and behaviors through wearable sensors, offers smart medication reminders, alerts the care team to potential risks, and provides personalized care plans along with psychological and social support.\n\nThe study is designed as a randomized controlled trial comparing two groups: one receiving standard end-of-life care and the other using the AI software. Key outcomes include improving quality of life, reducing adverse events like falls and emergency hospitalizations, increasing patient and family satisfaction, improving medication management, and reducing caregiver burden. Data will be collected over six months to assess these effects. The results will help determine whether AI technology can enhance end-of-life care for seniors and support families and healthcare providers.', 'detailedDescription': "This section provides a comprehensive overview of the study design, objectives, population, interventions, and methods without repeating information already included in other sections of the record. It elaborates on the rationale for using AI-based software to personalize end-of-life care for elderly patients, details the randomized controlled trial setup, explains inclusion and exclusion criteria, intervention specifics, outcome measures, data collection methods, and planned statistical analyses. This description ensures a clear understanding of the study's scope and methodology."}, 'eligibilityModule': {'sex': 'ALL', 'stdAges': ['ADULT', 'OLDER_ADULT'], 'minimumAge': '60 Years', 'healthyVolunteers': False, 'eligibilityCriteria': 'Inclusion Criteria:\n\nAge ≥ 60 years\n\nClinical diagnosis of being in the end-of-life stage, based on criteria such as the Karnofsky Performance Scale or Palliative Performance Scale\n\nInformed consent obtained from the participant or legal representative\n\nAbility to use technology independently or with support provided by the research team\n\nAccess to necessary equipment for the intervention (e.g., wearable sensors, smartphone/tablet)\n\nExclusion Criteria:\n\nPresence of severe cognitive impairment preventing software use\n\nVoluntary withdrawal from the study at any stage\n\nCritical medical deterioration or death during the study\n\nPoor adherence or insufficient engagement with the intervention software in the intervention group'}, 'identificationModule': {'nctId': 'NCT07027618', 'acronym': 'PEACE-AI', 'briefTitle': 'AI-Driven Personalization of End-of-Life Care for the Elderly', 'organization': {'class': 'OTHER', 'fullName': 'Baqiyatallah Medical Sciences University'}, 'officialTitle': '"Evaluating the Effectiveness of an AI-Based Software in Personalizing High-Quality End-of-Life Care for the Elderly: A Randomized Controlled Clinical Trial"', 'orgStudyIdInfo': {'id': 'BUMS'}}, 'armsInterventionsModule': {'armGroups': [{'type': 'EXPERIMENTAL', 'label': 'AI-based Personalized End-of-Life Care', 'description': 'Participants in this group will receive personalized end-of-life care supported by an AI-based software. The software monitors vital signs and behavior through wearable sensors, provides intelligent medication reminders, issues preventive alerts to the care team, delivers personalized care plans, and offers psychological and social support through communication features.', 'interventionNames': ['Behavioral: AI-based Software for Personalized End-of-Life Care']}, {'type': 'ACTIVE_COMPARATOR', 'label': 'Standard End-of-Life Care', 'description': 'Participants in this group will receive standard end-of-life care without the use of the AI-based software. Care is provided according to usual clinical practices and guidelines.', 'interventionNames': ['Behavioral: AI-based Software for Personalized End-of-Life Care']}], 'interventions': [{'name': 'AI-based Software for Personalized End-of-Life Care', 'type': 'BEHAVIORAL', 'description': 'This intervention involves the use of an AI-powered software system designed to personalize end-of-life care for elderly patients. The software continuously monitors vital signs and behavior through wearable sensors, sends smart medication reminders, issues preventive alerts to care providers, delivers customized care plans, and provides psychological and social support through communication features.', 'armGroupLabels': ['AI-based Personalized End-of-Life Care', 'Standard End-of-Life Care']}]}, 'sponsorCollaboratorsModule': {'leadSponsor': {'name': 'Baqiyatallah Medical Sciences University', 'class': 'OTHER'}, 'responsibleParty': {'type': 'PRINCIPAL_INVESTIGATOR', 'investigatorTitle': 'Professor', 'investigatorFullName': 'Amir Vahedian-Azimi', 'investigatorAffiliation': 'Baqiyatallah Medical Sciences University'}}}}