View clinical trials related to Dementia.
Filter by:The purpose of the present study is to investigate the effectiveness of mental imagery (MI) in subjects with early stage of dementia. The hypothesis of the study is that MI will have a beneficial effect in motor, cognitive and emotional state in this clinical population.
The purpose of this study is to learn more about the effectiveness of palliative care training for community physicians and telemedicine support services for patients and carepartners with Parkinson's disease and Lewy Body Dementia (LBD) or related conditions and their care partners. Palliative care is a treatment approach focused on improving quality of life by relieving suffering in the areas of physical symptoms such as pain, psychiatric symptoms such as depression, psychosocial issues and spiritual needs. Telemedicine is the use of technology that allows participants to interact with a health care provider without being physically near the provider.
Caregivers of a person living with dementia (PLWD) experience high levels of prolonged stress that can lead to chronic problems with health, including increased risk of cardiovascular disease that is linked to autonomic dysregulation. Heart rate variability (HRV), measures of autonomic cardiovascular regulation, is decreased (worse) in caregivers of a person living with dementia. Autonomic function is linked to lateralization in the brain, and emerging neuromodulation methods that target lateralized signals in the brain, like Cereset (CR), may be able to improve heart rate variability. Therefore, this pilot study aims to test whether CR can improve HRV in caregivers of a person living with dementia experiencing stress, anxiety, or insomnia, as well as improve self-report measures of stress, sleep and caregiver burden.
This research investigates the effect of active versus sham tACS paired with cognitive exercises in a regimen protocol of weeks, everyday. The study design is a one-time crossing over design in which participants are randomized into two groups: One group will receive active treatment for 4 weeks, then 8 weeks of no treatment followed by another 4 weeks of treatment but with sham. The other group will start with sham and end up with active treatment. The study is double-blind; thus, neither participants, nor the assessors know the group assignment. The Intervention is applying tACS either active or sham paired with cognitive exercises (MindTriggers app games). Note: The above study will run at both University of Manitoba (U of M) and Aster Gardens residence. However, at Aster Gardens Residence only questionnaire type of assessments will be run, while in U of M, the investigators will have three other major assessments as well. In addition, the investigators will run the assessments at the Aster Gardens Residence through online sessions.
Global dementia prevalence is rising. Alzheimer's disease (AD), the most common cause, has devastating effects on people's quality of life. AD has a preclinical (pre-AD) period of 10-20 years when brain pathology silently progresses before any cognitive symptoms appear. Current tests for pre-AD are invasive, costly and unsuitable for screening at population level. Similar to screening for pre-diabetes and carcinoma in situ, it is important to detect AD at the preclinical stage in order to offer early interventions before the pathology progresses to the irrerversible degenerative stage. In the study, research will develop a new scalable test (TAS Test) by combining two innovative ideas: hand-movement tests to detect pre-AD >10 years before cognitive symptoms begin; and computer vision so people can "self-test" online using home computers. This unique approach builds on recent discoveries that hand-movement patterns change in pre-AD. The research team will use exquisitely precise computer vision methods to automatically analyse movement data from thousands of participants, and combine this with machine learning of overall motor-cognitive performance. The project team has access to 3 well-phenotyped cohorts, >10,000 existing participants and a cutting-edge assay for a blood AD biomarker, ptau181. The research team will develop a TAS Test algorithm to classify hand-movement and cognitive test data for pre-AD risk (p-taua181 levels) and determine TAS Test's precision to prospectively predict 5-year risks of cognitive decline and AD.
Alzheimer's disease (AD) may currently be diagnosed using molecular biomarkers in cerebrospinal fluid (CSF) and/or positron emission tomography (PET). These diagnostic procedures are highly accurate, but the high cost and low availability hamper their feasibility. Recently, ultrasensitive blood tests predicting Alzheimer pathologies in the brain have been developed. These tests have a reliable ability to differentiate AD from other neurodegenerative disorders and identify AD across the clinical continuum with high sensitivity and specificity in research cohorts with a high prevalence of AD. This project will assess the predictive value of these tests in a general practice population. The hypothesis is that the actual blood panel will have high positive predictive value for a diagnosis of Alzheimer's disease in the primary health care setting.
The Multidisciplinary Expert System for the Assessment & Management of Complex Brain Disorders (MES-CoBraD) is an interdisciplinary project combining Real-World Data (RWD) from multiple clinical and consumer sources through comprehensive, cost-efficient, and fast protocols towards improving diagnostic accuracy and therapeutic outcomes in people with Complex Brain Disorders (CoBraD), as reflected in Neurocognitive (Dementia), Sleep, and Seizure (Epilepsy) disorders and their interdependence. It brings together internationally recognized experts in medicine, engineering, computer science, social health science, law, and marketing and communication from across Europe, and combines clinical information and scientific research in CoBraD with technical innovation in secure data-sharing platforms, artificial intelligence algorithms, and expert systems of precision and personalized care, with a primary focus on improving the quality of life of patients, their caregivers, and the society at large. It leverages RWD from diverse CoBraD populations across cultural, socioeconomic, educational, and health system backgrounds, with special attention on including vulnerable populations and minorities in an equitable manner and engaging key stakeholders to maximize project impact.
The overall objective of this study is to compare knowledge, decisional conflict, preferences, and caregiver burden over time caregivers of Alzheimer's Disease and Related Dementias (ADRD) patients by comparing the effectiveness of a video decision aid intervention and enhanced usual care.
Neuropsychiatric symptoms are the most difficult, distressing, and burdensome aspects of dementia care and a catalyst for long-term care placement. Intervention studies have largely focused on helping caregivers manage these symptoms. However, little has been done with regard to persons at the earliest stages of dementia, nor have persons with dementia played a direct and active, central role in helping to design intervention studies. This study focuses on building, pilot testing, and evaluating a tailored activity plan developed with persons with early-stage dementia. The goal of the intervention is to provide persons at this early stage meaningful activities and a plan for adaptation with disease progression.
Every three seconds someone in the world develops dementia. There are over 50 million people worldwide living with dementia and by 2030 this figure is expected to reach 82 million. Besides time-consuming patient investigations with low discriminative power for dementia risk, current treatment options focus on late symptom management. By screening brain connectivity and dementia risk estimation in people affected by mild cognitive impairment, the European Union (EU) funded AI-Mind project will open the door to extending the 'dementia-free' period by offering proper diagnosis and early intervention. AI-Mind will develop two artificial intelligence-based digital tools that will identify dysfunctional brain networks and assess dementia risk. Personalised patient reports will be generated, potentially opening new windows for intervention possibilities.