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Sensor Activity Monitoring, Feedback, and Outcome Measures, Stroke Rehabilitation

传感器活动监测、反馈和结果测量、中风康复

基本信息

批准号：
8686617
负责人：
BRUCE H DOBKIN
金额：
$ 36.29万
依托单位：
UNIVERSITY OF CALIFORNIA LOS ANGELES
依托单位国家：
美国
项目类别：
财政年份：
2012
资助国家：
美国
起止时间：
2012-07-20 至 2015-05-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/8686617
关键词：
Address Adherence Aerobic Aerobic Exercise Algorithms Ankle Beds California Cardiovascular system Caregivers Caring Chronic Disease Clinic Clinical Clinical Trials Communication Communities Complex Continuity of Patient Care Data Devices Disabled Persons Disease Effectiveness Evaluation Event Exercise Feedback Gait Generic Drugs Genetic Crossing Over Glosso-Sterandryl Goals Health Hearing Home environment Hospitals Independent Living Inpatients Internet Intervention Laboratories Lower Extremity Machine Learning Measurement Measures Medical Monitor Movement Outcome Outcome Measure Outpatients Participant Patients Pattern Performance Persons Phase Physical Function Physical activity Pilot Projects Procedures Public Health Questionnaires Randomized Randomized Clinical Trials Randomized Controlled Trials Regimen Rehabilitation therapy Relative (related person)Research Research Personnel Resistance Self Management Speed Staging Stroke System Technology Testing Time Torque Training Universities Visit Walking Wireless Technology arm base conditioning cost data acquisition design disability falls fitness improved insight instrument mHealth meetings novel post stroke programs randomized trial remote sensing remote sensor secondary outcome sensor skills spatiotemporal stroke rehabilitation tool treatment as usual wireless network

项目摘要

DESCRIPTION (provided by applicant): "Technologies for Healthy Independent Living" coincides with our goal to test and further develop inexpensive wireless sensors with communication and analysis platforms to monitor everyday activities, such as walking and exercise, in disabled persons. Our Medical Daily Activity Wireless Network (MDAWN) aims to enable researchers, trialists, and clinicians to acquire reliable data about daily physical activites in the home and community, rather than only during clinic and laboratory visits or by questionnaires. Novel machine-learning algorithms developed for each person from ankle accelerometers will identify the type, quantity and quality of exercise and walking on a continuous basis. In addition, we will test an inexpensive, instrumented, ergometric pedaler designed for disabled persons, called the UCFit, to promote home-based fitness exercise. The MDAWN and UFCFit sensors send data to an Android smartphone, then over the Internet to a UCLA server for analysis. We will nest further evaluation of the utility of these remote sensing devices within the real-world setting of a clinical trial of patients with recent disabling stroke,for whom formal rehabilitation often falls short of enabling functional walking and cardiovascular fitness. In a phase 2, randomized controlled trial, we will compare two levels of weekly telephonic feedback about daily performance, made possible for the first time by remote data acquisition. The high feedback group will receive everyday performance data that includes the number of bouts and minutes of pedaling plus rate/RPMs and forces used, as well as the number of bouts of walking, duration, and average speed and distance used in home and community. The minimal feedback group only hears about total daily UCFit exercise and walking time. After 4 months of encouraging up to four 30-min sessions per week of UCFit exercise, we will test for a 50% between-group difference in amount of daily exercise and walking time. Secondary outcomes examine change in level of fitness, walking speed, and physical functioning. The two groups then cross over to high or low feedback for 4 more months, before outcomes are reassessed. Then, no feedback is given and participants are tested for amount of exercise and fitness at 12 months. We will determine which form of feedback motivates better self-management to optimize fitness and walking. We will gather unique data about mobility in the transition from rehabilitation hospital to home; quantify gait training durin usual care; assess walking skills under more complex conditions than in a laboratory; and test the responsiveness of sensor data as a ratio scale outcome measurement during a time of expected gains. This study will be the first comprehensive demonstration of mHealth remote monitoring and outcome measures of daily physical activity!

描述(申请人提供)：“健康独立生活的技术”与我们的目标不谋而合，我们的目标是测试和进一步开发具有通信和分析平台的廉价无线传感器，以监测残疾人的日常活动，如步行和锻炼。我们的医疗日常活动无线网络(MDAWN)旨在使研究人员、试验者和临床医生能够获得有关家庭和社区日常身体活动的可靠数据，而不仅仅是在诊所和实验室访问期间或通过问卷调查。通过脚踝加速计为每个人开发的新型机器学习算法将识别持续进行的运动和步行的类型、数量和质量。此外，我们将测试一款专为残疾人设计的廉价、仪表化、测功式踏板车，名为UCFIT，以推广家居健身运动。MDAWN和UFCFIT传感器将数据发送到Android智能手机，然后通过互联网发送到加州大学洛杉矶分校的服务器进行分析。我们将在最近的致残中风患者的临床试验的真实环境中，对这些遥感设备的实用性进行进一步的评估，对这些患者来说，正式的康复往往无法实现功能性行走和心血管健康。在第二阶段的随机对照试验中，我们将比较两个水平的每周电话反馈的日常表现，这是第一次通过远程数据采集实现的。高反馈组将收到日常表现数据，包括骑行次数和分钟数加脚踏率/RPM和使用的力量，以及行走次数、持续时间以及家庭和社区使用的平均速度和距离。最小反馈组只听到UCFIT每天的总运动量和步行时间。经过4个月的鼓励，UCFIT每周进行四次30分钟的锻炼，我们将测试每天运动量和步行时间在组间的50%差异。次要结果检查健康水平、行走速度和身体功能的变化。然后，两组人在4个多月的时间里进行高反馈或低反馈，然后重新评估结果。然后，不给出任何反馈，参与者在12个月时接受运动量和健康测试。我们将确定哪种形式的反馈能激励更好的自我管理，以优化健身和步行。我们将收集从康复医院到家庭过渡过程中有关机动性的独特数据；量化日常护理中的步态训练；评估比实验室中更复杂的条件下的行走技能；并测试传感器数据的响应性，作为预期收益期间的比率衡量结果。这项研究将是mHealth远程监测和日常体力活动结果测量的第一个全面演示！