mStroke: Mobile Technology for Post-Stroke Recurrence Prevention and Recovery

mStroke：用于中风后复发预防和恢复的移动技术

基本信息

批准号：
8626740
负责人：
Nancy Lynn Tripp Fell
金额：
$ 38.47万
依托单位：
UNIVERSITY OF TENNESSEE CHATTANOOGA
依托单位国家：
美国
项目类别：
财政年份：
2014
资助国家：
美国
起止时间：
2014-05-01 至 2019-04-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/8626740
关键词：
Acute Adult Algorithms Appointment Appointments and Schedules Assisted Living Facilities Biomedical Technology Caregivers Caring Clinical Clinical Trials Code Cognitive Collaborations Communication Communication impairment Computer software Data Data Analyses Data Compression Data Security Databases Development Devices Ensure Environment Equilibrium Foundations Functional disorder Gait Goals Health Health Insurance Portability and Accountability Act Health Personnel Health Professional Health Services Home environment Hospitals Immune Impairment Individual Inpatients Institution Instruction Intervention Knowledge Learning Left Leg Length of Stay Long-Term Care Measurement Measures Medical Mission Monitor Motion Motor Movement National Institute of Biomedical Imaging and Bioengineering Outcome Outpatients Patient Discharge Patient Monitoring Patients Phase Physical Rehabilitation Physical therapy Physicians Principal Investigator Process Public Health Recovery Recovery of Function Recurrence Registries Rehabilitation therapy Reporting Research Risk Assessment Secure Security Solid Speed Stream Stroke Stroke prevention Students Supervision Survivors System Techniques Technology Testing Time Training United States United States National Institutes of Health Visit Wireless Technology Yang acute stroke arm base cost data acquisition data exchange disability disorder later incidence prevention encryption fall risk follow-up graduate student improved motor control multidisciplinary outcome forecast post stroke public health relevance rehabilitation management sensor stroke recovery stroke rehabilitation tool transmission process treatment as usual undergraduate student usability

项目摘要

Research Summary We aim to develop a smart system, which will monitor and evaluate motor control, fall risk, and gait speed of patients post stroke using wearable Bluetooth Low-Energy (BLE) devices. Stroke, the leading cause of disability for adults, has a high cost in inpatient care and rehabilitation ($54 billion in 2010). To reduce the cost and ultimately improve stroke-care outcomes, follow-up data is required to correlate successful versus unsuccessful recovery and determine optimal interventions. However, this data is not currently available due to difficulties involved in data acquisition and lack of centralized databanks once patients are released from acute care hospitals. Our proposed system will evaluate recovery of post-stroke patients after they leave the hospital, and will provide trustworthy customized activity analysis and statistical interpretation to support health care providers in delivering improved health services beyond usual stroke care. Our research is focused on developing a smart system that is practical, accurate, secure, and effective. Thus, we anticipate that this system will have a significant impact on stroke rehabilitation (intervention and research) and patients' long-term recovery. Significance of Our Proposed System: Post stroke functional recovery is enhanced by movement and activity practice. Providing health care professionals with patient-specific post stroke movement and activity reports, beyond those directly observed, will facilitate prescription of specific interventions and, over the long term, optimize patient recovery. In summary, the benefits of our smart system have three folds: 1) Enhancing Acute Management: Immediately post stroke (e.g., 2-4 days), when physicians are actively engaged in achieving patients' medical stability and beginning physical rehabilitation, our smart system may be used to provide movement and activity information beyond that which is typically available in usual care. This will enhance health providers' understanding of the magnitude of movement dysfunction and support prescription of the optimal acute stroke rehabilitation setting. 2) Enhancing Acute Rehabilitation: Once patients are discharged from the acute or rehabilitation hospital to home or assisted living environments, our smart system will provide objective movement and activity health information. This phase of post stroke recovery is typically managed by patients and their caregivers with occasional professional guidance (e.g., home health or outpatient visits). Physicians and therapists will have access to previously unavailable real-time data, supporting efficient and effective management strategies when patients are seen in regularly scheduled appointments. 3) Extending Care: At the end of rehab, when therapy is discontinued and physician appointments are less frequent, our smart system will provide physicians with previously unavailable real-time data, whereby medical and/or rehabilitation intervention may be triggered to support patients' optimal long-term recovery. The specific aims of this project are: Aim 1: Efficient Data Acquisition and Reliable Data Transmission Aim 2: Automated Motor Control Scoring, Fall Risk Assessment, and Gait Speed Measurements Aim 3: Health Data Security Aim 4: Demonstration of Patient Usability and Efficacy of mStroke Student Involvement: Our proposed smart system provides a dynamic learning environment for both undergraduate and graduate students. Under the supervision of PD/PIs, the students will contribute to implement the proposed algorithms and integrate them to develop the proposed automated post-stroke assessment tool. Then, students will help to collect the testing data and validate the system. This will introduce both undergraduate and graduate students to real-world challenges and excite them with the opportunity to do research, especially in fields of smart health, physical therapy, data analysis, security, and data compression.

研究摘要我们旨在开发一个智能系统，该系统将监视和评估电动机控制，下降风险和步态速度使用可穿戴蓝牙低能（BLE）设备的中风后患者。中风，主要原因成人残疾人的住院护理和康复成本很高（2010年为540亿美元）。减少成本并最终改善了中风的结果，需要进行后续数据以使成功关联相对于失败的恢复并确定最佳干预措施。但是，此数据目前不是由于在数据获取方面涉及的困难以及一旦患者缺乏集中式数据库，因此可用从急诊医院释放。我们提出的系统将评估冲程后的恢复患者离开医院后，将提供值得信赖的定制活动分析和统计解释以支持医疗保健提供者提供改进的医疗服务通常的中风护理。我们的研究专注于开发一种实用，准确，安全，有效。因此，我们预计该系统将对中风产生重大影响康复（干预和研究）以及患者的长期康复。我们提出的系统的意义：动作和活动实践增强了中风后功能恢复。提供医疗保健具有特定于患者的中风后运动和活动报告的专业人员，超出了这些报告观察到的，将有助于处方特定的干预措施，并从长远来看优化患者恢复。总而言之，我们的智能系统的好处有三倍： 1）增强急性管理：立即中风后（例如，2-4天），当医生积极从事实现患者的医疗稳定性并开始身体康复的情况下，我们的智能系统可用于提供运动和活动信息，超出通常可用的信息通常的护理。这将增强健康提供者对运动功能障碍的大小的理解并支持最佳急性中风康复设置的处方。 2）增强急性康复：一旦患者从急性或康复医院出院在家或辅助生活环境中，我们的智能系统将提供客观的运动和活动健康信息。这一中风后恢复阶段通常由患者及其管理偶尔有专业指导的护理人员（例如，家庭健康或门诊就诊）。医师和治疗师将可以访问以前无法获得的实时数据，从而支持高效在定期预约中看到患者时的管理策略。 3）延长护理：在康复结束时，停止治疗并任命医生不太频繁，我们的智能系统将为医生提供以前无法使用的实时数据，可以触发医疗和/或康复干预措施以支持患者的最佳长期恢复。该项目的具体目的是：目标1：有效的数据获取和可靠的数据传输 AIM 2：自动电动机控制评分，跌落风险评估和步态速度测量目标3：健康数据安全目标4：演示患者的可用性和效果学生参与：我们提出的智能系统为本科生和研究生。在PD/PI的监督下，学生将为实施拟议的算法并将它们整合起来，以开发提出的自动冲击后评估工具。然后，学生将帮助收集测试数据并验证系统。这将介绍两者本科生和研究生面对现实世界中的挑战，并激发他们的机会研究，特别是在智能健康，物理疗法，数据分析，安全和数据领域压缩。