[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-studies-list:{\"overallStatus\":[\"RECRUITING\",\"AVAILABLE\",\"NOT_YET_RECRUITING\"],\"leadSponsorName\":\"Getúlio Vargas University Hospital\",\"orderBy\":\"LastUpdateSubmitDate:desc\",\"size\":25,\"offset\":0}":3,"health-study-condition:":76},{"pageToken":4,"total":5,"offset":6,"count":5,"results":7},null,2,0,[8,49],{"id":9,"slug":10,"hasResults":11,"nctId":12,"briefTitle":13,"officialTitle":13,"acronym":4,"eligibilityCriteria":14,"healthyVolunteers":11,"sex":15,"minAge":16,"maxAge":4,"enrollmentInfo":17,"targetDuration":4,"studyType":20,"phases":4,"briefSummary":21,"conditions":22,"keywords":24,"overallStatus":36,"whyStopped":4,"lastUpdateSubmitDate":37,"lastUpdatePostDateStruct":38,"startDateStruct":41,"completionDateStruct":43,"leadSponsor":45,"locationsCount":48},"100619844","predicting-hospital-readmission-for-surgical-patients-using-deep-learning-models-with-smart-watch-and-smart-ring-sensors-data-100619844",false,"NCT07349901","Predicting Hospital Readmission for Surgical Patients Using Deep Learning Models With Smart Watch and Smart Ring Sensors Data","Inclusion Criteria:\n\n* Adults over 18 years of age;\n* Hospitalization for medium and\u002For large elective surgery at HUGV;\n* Conscious and oriented patients who have sufficient understanding to answer questionnaires and use wearable devices for the study;\n* Have minimal skills in the use of wearable technologies;\n* Patients who have agreed to participate voluntarily in the research by signing the Informed Consent Form (ICF).\n\nNon-inclusion Criteria:\n\n* Presence of tattoos or any other skin condition (skin pathologies or skin diseases such as vitiligo, lupus, and atopic dermatitis, among others) that affects the area of the wrist or finger where the wearable sensors are located;\n* Presence of any type of sensitivity or allergic reaction, of any degree, to the materials of the wearables (smartwatch and smartring);\n* Pregnant and lactating women;\n* Participants with implantable cardiac devices, such as pacemakers, cardioverter defibrillators, and resynchronization devices;\n* Participants in drugs abuse;\n\nExclusion Criteria:\n\n* Severe medical conditions and decompensations prior to surgery;\n* Patients who die before hospital discharge;\n* Patients with an expected postoperative hospital stay of more than 10 days.","ALL","18 Years",{"count":18,"type":19},300,"ESTIMATED","OBSERVATIONAL","Hospital readmissions are an important measure of healthcare quality and safety. These events create a substantial burden for patients, families, and health systems because they may increase costs, extend recovery time, and lead to more serious postoperative complications. Predicting which patients are at higher risk of readmission remains difficult, as many complications begin silently and are not easily identified in routine clinical evaluations.\n\nThis study aims to evaluate whether artificial intelligence (AI) can help predict hospital readmissions in surgical patients by analyzing physiological and behavioral data collected before and after surgery. To achieve this, participants will use wearable devices-specifically a smartwatch and a smart ring-capable of continuously monitoring health biomarkers such as heart rate, electrocardiogram (ECG), oxygen saturation, sleep patterns, blood pressure trends, body composition through bioimpedance, and stress indicators. These devices are provided through a technology partnership and sponsorship from Samsung, which supports the study with advanced health technologies.\n\nThis is a prospective, single-center cohort study conducted at the main tertiary hospital in the state of Amazonas. Approximately 225 to 300 adults undergoing medium- or large-scale elective surgeries will be invited to participate over a 25-month period. All participants will provide informed consent. After enrollment, the study will collect demographic information, preoperative assessments, validated sleep questionnaires, comorbidity indexes such as the Charlson Comorbidity Index, laboratory exams, pulmonary function tests, intraoperative and postoperative data, and hospital discharge information.\n\nParticipants will be continuously monitored using wearable devices during their hospital stay-including the first 48 hours in the intensive care unit when applicable-and for 30 days after hospital discharge. These physiological data will be integrated with clinical and laboratory information to create a comprehensive dataset.\n\nThe primary objective is to develop and test artificial intelligence models capable of predicting 30-day hospital readmission following elective surgery. Both deep learning approaches and classical machine-learning techniques will be evaluated. By analyzing large volumes of continuous physiologic data, these models may identify early signs of postoperative deterioration that would otherwise go unnoticed.\n\nIf successful, this study may improve postoperative care, support earlier clinical intervention, reduce complications, and help healthcare teams provide safer recovery pathways for surgical patients.",[23],"Patient Readmission",[25,26,27,28,29,30,31,32,33,34,35],"Hospital Readmission","Postoperative Complications","Artificial Intelligence","Machine Learning","Wearable Electronic Devices","Physiological Monitoring","Sleep Disorders","Surgical Procedures, Operative","Perioperative Care","Amazon Region","Predictive Modeling","RECRUITING","2026-03-14",{"date":39,"type":40},"2026-03-17","ACTUAL",{"date":42,"type":40},"2026-03-04",{"date":44,"type":19},"2028-06",{"name":46,"class":47},"Getúlio Vargas University Hospital","OTHER_GOV",1,{"id":50,"slug":51,"hasResults":11,"nctId":52,"briefTitle":53,"officialTitle":53,"acronym":4,"eligibilityCriteria":54,"healthyVolunteers":55,"sex":15,"minAge":56,"maxAge":4,"enrollmentInfo":57,"targetDuration":4,"studyType":20,"phases":4,"briefSummary":59,"conditions":60,"keywords":64,"overallStatus":67,"whyStopped":4,"lastUpdateSubmitDate":68,"lastUpdatePostDateStruct":69,"startDateStruct":71,"completionDateStruct":73,"leadSponsor":75,"locationsCount":48},"100623038","use-of-wearables-for-identifying-factors-associated-with-mild-cognitive-impairment-and-early-stage-alzheimers-disease-100623038","NCT07391436","Use of Wearables for Identifying Factors Associated With Mild Cognitive Impairment and Early-Stage Alzheimer's Disease","Inclusion criteria Age 65 years or older; Clinical suspicion of MCI, early AD Patients who are conscious, oriented, and able to respond to questionnaires; Ability and willingness to use wearable devices for 30 days; Signature of the Informed Consent Form (ICF)\n\nNon-inclusion criteria Advanced dementia Severe uncontrolled psychiatric disorder Severe visual or hearing impairment that prevents communication Severe physical or cognitive inability to use wearables Pacemaker incompatible with the devices\n\nExclusion criteria Unconfirmed diagnosis of MCI or early AD; Voluntary withdrawal at any stage of the study; Death during the research period; Inappropriate use or non-adherence to the use of wearable devices Refusal or inability to fully perform the requested tests and exams.",true,"65 Years",{"count":58,"type":19},150,"Cognitive decline affects millions of older adults worldwide and has a profound impact on individuals, families, and healthcare systems. Mild Cognitive Impairment (MCI) is often an early stage of Alzheimer's disease (AD), a condition for which there is currently no cure. Identifying individuals at risk at the earliest possible stage remains a major challenge. Traditional diagnostic approaches, such as laboratory biomarkers, neuroimaging, and neuropsychological testing, are usually performed at a single point in time and may fail to detect subtle or early changes in brain function and daily behavior.\n\nRecent advances in wearable technology, such as smartwatches and smart rings, allow continuous and noninvasive monitoring of physiological and behavioral patterns in daily life. These devices can capture data related to physical activity, sleep, heart rate, and other parameters that may change before clear cognitive symptoms become evident. When combined with clinical, laboratory, neuropsychological, neuroimaging, and electroencephalographic (EEG) information, these data may help identify early signs of cognitive decline.\n\nThe objective of this study is to develop and validate models capable of detecting early indicators of MCI and early-stage Alzheimer's disease by integrating multiple sources of data, including clinical assessments, blood tests, neuropsychological evaluations, brain imaging, EEG recordings, and continuous data obtained from wearable devices.\n\nThis is an observational, analytical, single-center, prospective cohort study that will include 150 participants of both sexes, aged 65 years or older. Participants will be recruited from the Dementia Outpatient Clinic of Getúlio Vargas University Hospital (HUGV), through referrals from external neurologists, or via study dissemination on social media. To achieve the target sample size, up to 250 individuals may be approached using a non-probabilistic, convenience-based recruitment strategy. After providing informed consent, participants will undergo a comprehensive medical evaluation, standardized and validated neuropsychological testing, laboratory and imaging examinations, and EEG recording. Participants will also receive training to use wearable devices for continuous monitoring in their daily routines. A control group of older adults without cognitive impairment will be included for comparison.\n\nAll collected data will be securely stored in a centralized database and used to develop and validate analytical models aimed at identifying patterns associated with cognitive decline. The results of this study may support earlier identification of individuals at risk for MCI and Alzheimer's disease, help guide timely interventions, and potentially delay disease progression and early institutionalization, contributing to improved quality of life for older adults and their families.",[61,62,63],"Mild Cognitive Impairment","Alzheimer's Disease","Alzheimer's Disease Dementia",[65,61,66,63,28],"Wearable Devices","Alzheimer's disease","NOT_YET_RECRUITING","2026-01-29",{"date":70,"type":40},"2026-02-05",{"date":72,"type":19},"2026-03",{"date":74,"type":19},"2027-12",{"name":46,"class":47},""]