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

• 批准号: 8331820
• 负责人: BRUCE H DOBKIN
• 金额: $ 41.43万
• 依托单位: UNIVERSITY OF CALIFORNIA LOS ANGELES
• 依托单位国家: 美国
• 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/8331820
• 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; meetings; novel; post stroke; programs; randomized trial; remote sensing; remote sensor; secondary outcome; sensor; skills; spatiotemporal; stroke rehabilitation; tool; treatment as usual; wireless network

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! PUBLIC HEALTH RELEVANCE: Several important public health needs will be met by testing, within a clinical trial in the home and community, a system of wearable activity and exercise monitoring technologies that measure the type, quantity and quality of walking and exercise. Our trial aims to increase fitness and enable more functional levels of daily activities in disable persons after stroke, while providing a proof-of-principle for the utility of wireless health toolsto reliably monitor real-world physical functioning and to provide clinically meaningful outcome measures. These generic tools for daily care and research can be deployed across diseases and disabilities.

描述（由申请人提供）：“健康独立生活技术”与我们的目标相吻合，即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在家庭和社区中获得有关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。根据踝关节加速度计为每个人开发的新型机器学习算法将持续识别运动和步行的类型、数量和质量。此外，我们将测试一种为残疾人设计的廉价的、仪表化的、测力踏板，称为UCFit，以促进以家庭为基础的健身运动。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UCLA服务器进行分析。我们将进一步评估这些遥感设备在近期致残性中风患者临床试验的真实环境中的效用，对这些患者来说，正式的康复往往达不到功能性步行和心血管健康。在一项2期随机对照试验中，我们将比较两种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。高反馈组将收到日常表现数据，包括脚踏板的回合数和分钟数加上速率/RPM和使用的力量，以及在家中和社区使用的步行回合数，持续时间和平均速度和距离。最小反馈组只听到每日UCFit运动和步行时间。经过4个月的鼓励，每周进行4次30分钟的UCFit运动，我们将测试每日运动量和步行时间的50%组间差异。次要结果检查健身水平，步行速度和身体功能的变化。然后，两个小组在4个月内根据高反馈或低反馈进行交叉，然后重新评估结果。然后，没有反馈，并在12个月时测试参与者的运动量和健身情况。我们将确定哪种形式的反馈可以激发更好的自我管理，以优化健身和步行。我们将收集有关从康复医院到家庭的过渡中的移动性的独特数据;在常规护理期间量化步态训练;在比实验室更复杂的条件下评估行走技能;并在预期收益的时间内测试传感器数据的响应性作为比例尺度结果测量。这项研究将是第一次全面展示移动健康远程监测和日常身体活动的结果措施！ 公共卫生相关性：几个重要的公共卫生需求将通过在家庭和社区的临床试验中测试可穿戴活动和运动监测技术系统来满足，该系统可以测量步行和运动的类型，数量和质量。我们的试验旨在提高中风后残疾人的健康水平，使其能够进行更多功能水平的日常活动，同时为无线健康工具的实用性提供原理证明，以可靠地监测现实世界的身体功能，并提供有临床意义的结果指标。这些用于日常护理和研究的通用工具可用于各种疾病和残疾。