View clinical trials related to Stroke.
Filter by:This study is designed to quantify the ventricular stasis in patients with different forms of cardiomyopathy and at risk of stroke (ischemic, non-ischemic dilated cardiomyopathy and hypertrophic cardiomyopathy) by post-processing of 2D color Doppler echocardiography and phase contrast-magnetic resonance images in order to establish the relationship between quantitative variables of intraventricular stasis and the prevalence of silent embolic events and/or intraventricular mural thrombosis.
The purpose of this investigator-initiated study is to establish if powered exoskeleton therapy as a part of a rehabilitation program can be used as a standard of care in stroke rehabilitation.
The aim of the study is to evaluate the effects and costs of an intensified rehabilitation for stroke patients. Using novel technologies the patients will receive an augmented exercise therapy during the first weeks after stroke. The effects on restoration of functions as well as on the costs incurred from social and health care services will be assessed and compared to the conventional rehabilitation over 6 month follow-up.
Stroke is the third most common cause of death worldwide and the leading cause of disability. High blood pressure is an important risk factor for stroke. Lowering a person's blood pressure reduces the risk of future stroke or heart attack, and current guidelines recommend treatment to a target of <130mmHg for secondary prevention. Home blood pressure measurement and telemonitoring are acceptable to patients, but there is uncertainty over the use of out of office blood pressure measurements in stroke patients in guidelines. This is a study designed to establish the feasibility of a larger clinical trial, comparing home blood pressure monitoring, telemonitoring and medication titration with standard care. The study hypothesis is that home BP measurement and telemonitoring with medication titration may lead to improved BP control compared to standard of care clinical practice.
People with spinal cord injury (SCI), stroke and other neurodegenerative disorders can follow two pathways for regaining independence and quality of life. One is through clinical interventions, including therapeutic exercises. The other is provided by assistive technologies, such as wheelchairs or robotic systems. In this study, we combine these two paths within a single framework by developing a new generation of body-machine interfaces (BoMI) supporting both assistive and rehabilitative goals. In particular, we focus on the recovery of muscle control by including a combination of motion and muscle activity signals in the operation of the BoMI.
Stroke - still the second commonest cause of death and principal cause of adult neurological disability in the Western World - is characterised by rapid changes over time and marked variability in outcomes. A patient may improve or deteriorate over minutes, and the resultant disability may range from an obvious complete paralysis to subtle, task dependent incoordination of a single limb. Unlike many other neurological disorders, stroke can be exquisitely sensitive to prompt and intelligently tailored treatment, rewarding innovation in the delivery of care with real-world, tangible impact on patient outcomes. Optimal treatment therefore requires both detailed characterisation of the patient's clinical picture and its pattern of change over time. Arguably the most important aspect of the patient's clinical picture -- body movement -- remains remarkably poorly documented: quantified only subjectively and at infrequent intervals in the patient's clinical evolution. The combination of artificial intelligence with high-performance computing now enables automatic extraction of a patient's skeletal frame resolved down to major joints, like that of a stick-man, to be delivered simply, safely, and inexpensively, without the use of cumbersome body worn markers. Central to this technology is patient privacy, with the skeletal frame extracted in real time, ensuring no video data, from which patients can be identified, to be stored or transmitted by the device. Our motion categorisation system -- MoCat -- will be used to study the rapid dynamics of acute stroke, seamlessly embedded in the clinical stream. By quantifying the change in motor deficit over time we shall examine the relationship between these trajectories with clinical outcomes and develop predictive models that can support clinical management and optimise service delivery.
Multi-centre prospective study evaluating computed tomography angiography studies performed for stroke patients as a diagnostic and prognostic imaging biomarker.
The Hong Kong Jockey Club Charities Trust has supported CUHK to launch a three-year project 'CUHK Jockey Club HOPE4Care Programme' to implement four evidence-based advanced rehabilitation technologies in 40 local elderly day care centres and rehabilitation centres, to benefit the community. Our research team had developed the "Augmented Reality (AR) Rehabilitation Training System" that can be used as tools for rehabilitation by individuals who have suffered from a stroke or elderly. The system facilitates an active rehabilitative exercise.
tACS has the potential to directly induce cortical alterations in the intrinsic neural oscillation at specific frequencies, and the brain could mirror the induced frequencies of the external source of oscillations from the stimulation. Hence, tACS with matching stimulation frequency could be an effective means of enhancing brain oscillatory activity to potentially induce synaptic plasticity for restoration of damaged brain functions. However from the existing studies of applying tACS over the M1 in healthy and diseased brains, there is a wide range of applied stimulation frequencies and varied neuromodulation effects on motor behavior or cortical excitability at different frequencies. In this proposal, subject-specific stimulation frequency and latency will be identified.
Stroke is a worldwide major cause of disability with great social and economic burdens. The recovery of motor function is crucial for the patient to regain independence and quality of life. Identifying early predictors of motor recovery and outcomes is useful for planning personalized rehabilitation programs and increasing their efficiency. However, making predictions using a single clinical assessment is often difficult and a combination of multiple tools is required. In the last decades, were validated two predictive algorithms for upper limb function and independent walking that can be easily implemented into clinical practice with the aim of increasing knowledge on expected outcome after stroke in patients, families and rehabilitation teams. This will be the first single-site randomized control trial that will test the implementation of such tools in a rehabilitation setting in Italy.