View clinical trials related to Alzheimer Disease.
Filter by:Alzheimer's disease (AD) is the most common dementia and has been one of the most expensive diseases with the highest lethality. With the rapid increase of the aging population, more and more burdens will be posed on society and economics. The manifestations of AD are the progressive loss of memory, language and visuospatial function, executive and daily living abilities, and so forth. The Pathophysiological changes of AD occur 10-20 years before the clinical symptoms, while there is still a lack of effective strategy for early diagnosis. Mild cognitive impairment (MCI) is considered to be a transitional state between healthy aging and the clinical diagnosis of dementia and has received increasing attention as a separate diagnostic entity. To make the diagnosis, doctors ought to compressively consider the multimodal medical information including clinical symptoms, neuroimages, neuropsychological tests, laboratory examinations, etc. Multimodal deep learning has risen to this challengeļ¼ which could integrate the various modalities of biological information and capture the relationships among them contributing to higher accuracy and efficiency. It has been widely applied in imaging, tumor pathology, genomics, etc. Recently, the studies on AD based on deep learning still mainly focused on multimodal neuroimaging, while multimodal medical information requires comprehensive integration and intellectual analysis. Moreover, studies reveal that some imperceptible symptoms in MCI and the early stage of AD may also play an effective role in diagnosis and assessment, such as gait disorder, facial expression identification dysfunction, and speech and language impairment. However, doctors could hardly detect the slight and complex changes, which could rely on the full mining of the video and audio information by multimodal deep learning. In conclusion, we aim to explore the features of gait disorder, facial expression identification dysfunction, and speech and language impairment in MCI and AD, and analyze their diagnostic efficiency. We would identify the different degrees of dependency on multimodal medical information in diagnosis and finally build an optimal multimodal diagnostic method utilizing the most convenient and economical information. Besides, based on follow-up observations on the changes in multimodal medical information with the progress of AD and MCI, we expect to establish an effective and convenient diagnostic strategy.
Healthcare systems around the world, including within the United States, have long-established shortages of trained caregivers. The American Health Care Association states that "the health care system has experienced a shortage of trained caregivers for critical roles for some time." This scarcity directly impacts the 45,800 Long-Term Care (LTC) communities throughout the U.S. Concurrent with this staff shortage, more than half of LTC residents have some form of dementia. These two issues create a serious public health concern, since dementia is associated with a variety of behavioral expressions, such as aggression, anxiety, and agitation. Behavioral expressions of dementia can be successfully managed with the use of tailored, psychosocial interventions and communication support. Unfortunately, existing staff shortages make the facilitation of such interventions challenging. One powerful and often-overlooked approach to ameliorating staffing shortages involves the utilization of retired volunteers to facilitate interventions for persons with dementia (PWD). Based on the nearly universal love of music and a promising pilot study, the product to be developed and tested in this STTR will build upon the combined prior work of the Principal Investigators. Making Connections Thru Music (MCTM), an urgently needed product, will enable retired volunteers to facilitate an evidence-based music and discussion intervention with PWD. MCTM aims to improve engagement, enhance quality of life, and reduce behavioral expressions in PWD. The intervention will consist of two main components: (1) a comprehensive online training course for volunteers, which will provide a general overview of dementia, demonstrate effective communication strategies to use with PWD, and instruct volunteers to effectively facilitate MCTM sessions, and (2) an app containing a structured MCTM intervention protocol and toolkit, which will be the means by which volunteers facilitate MCTM. MCTM will be marketed to LTC communities.
The purpose of this study is to improve the care of persons living with dementia (PLWD) and their informal care partners by addressing emergency and post-emergency care through different combinations of three PLWD-care partner dyad focused interventions. The primary aims are to use coaching to help connect PLWD and their care partners with community support and services to improve transitional care, quality of care, care satisfaction and reduce future ED visits and hospitalizations.
Dementia with Lewy body disease (DLB) is the second leading cause of degenerative cognitive disorder after Alzheimer's disease (AD). Its variable clinical expression makes diagnosis difficult. To date, there is no validated DLB diagnostic biomarker, despite several biomarkers in development (EEG, MRI, biology). Studies have shown that an improvement in diagnostic performance could be obtained by combining different modalities biomarkers using machine learning. The aim of this research is to identify the best combination of multimodal biomarkers for the diagnosis of DLB (EEG, MRI, biology, cognitive scores), using a machine learning approach applied to a clinical cohort.
Damages in frontal area present in neurodegenerative disease (frontotemporal degeneration, frontal variant of Alzheimer disease) and in psychiatric disease (bipolar disorder) can affect behavior and cognition including social cognition. Symptoms vary both quantitatively and qualitatively from disease to another and from person to person. It cannot be completely excluded that in some cases, factors of susceptibility such as premorbid personality traits lead to frontal fragility. The study will assess the relationship between premorbid profile using NEO-PI 3 inventory and cognitive and behavioral/psychobehavioral manifestations in patients with behavioral variant of frontotemporal disorder (bvFTD), phenocopy frontotemporal dementia (phFTD), frontal variant of Alzheimer disease, bipolar disorder characterized with frontal damages.
The primary objective of this study is to evaluate the efficacy of DMTS on frequency and severity of agitation associated with dementia of the Alzheimer's type, compared with placebo.
This study will test G:DATA, a simple computer game designed to diagnose Alzheimer's Disease, in three different groups of people, some of whom have Alzheimer's Disease. It will look at whether the results of G:DATA match the results of tests that are used to diagnose people with Alzheimer's Disease now. The Investigators will also ask patients and healthcare staff for participant views on the G:DATA game.
This study seeks to evaluate the utility and efficacy of the Non-Contact Sleep Quality Monitor System when used to monitor the sleep quality of individuals living in long-term care (LTC) with either Alzheimer's Disease (AD) or Alzheimer's Disease Related Dementia (ADRD). This before-after comparison trial will be conducted in several LTC facilities to evaluate the effect access to System Sleep Quality Data has on documentation of sleep disorders or treatments and sleep quality change over time for AD/ADRD participants in the intervention group as compared to the control group. All subjects will undergo sleep quality monitoring for 4-weeks. At the end of the first 2-weeks, research staff and LTC facility staff and medical providers will receive access to sleep monitoring data. We hypothesize that when real-time System Sleep Data is shared with LTC staff or healthcare providers, that sleep disturbances will be more readily detected, leading to timelier, better tailored treatment interventions for sleep disturbances, thereby improving sleep quality and decreasing daytime physical inactivity.
This study will contribute to creating a prospective and robust automated preoperative risk assessment algorithm for 30-day mortality, major adverse cardiac and cerebrovascular events (MACCE) and perioperative neurocognitive disorders (PND) outcomes following elective general, orthopedic, cardiac, or vascular surgery. It will help to identify correlations between perioperative factors and Alzheimer's Disease (AD) or AD-related dementias (ADRD). Lastly, this study will create effective, validated multi-modal interventions to improve perioperative health. This study will explore two main hypotheses: 1. Preoperative prehabilitation and proactive cognitive/behavioral interventions will effectively improve postoperative cognitive outcomes, morbidities, and mortality, and; 2. The proactive bundled interventions are superior to current standard of care in reducing postoperative cognitive outcomes, MACCE and mortality. Expected Outcome: Improved EHR algorithm will have higher predictive accuracy for MACCE and mortality while predicting postoperative cognitive outcomes.
Care for America's Aging is a randomized pilot study investigating whether a home health aide training intervention consisting of enhanced dementia-specific curriculum content will improve: 1) behavioral symptoms of older adult persons living with dementia or cognitive impairment (PLWD/CI) and 2) global health-related quality of life among PLWD/CI and their care partners.