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: 8514028
• 负责人: BRUCE H DOBKIN
• 金额: $ 38.34万
• 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/8514028
• 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!

描述（由申请人提供）：“健康独立生活技术”与我们的目标一致，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，例如步行和锻炼。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取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行走的类型、数量和质量。此外，我们还将测试一种专为残疾人设计的廉价、仪器化、测功踏板，称为 UCFit，以促进家庭健身锻炼。 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。我们将在近期致残中风患者的临床试验的现实环境中进一步评估这些遥感设备的实用性，对这些患者来说，正式康复往往无法实现功能性步行和心血管健康。在第二阶段随机对照试验中，我们将比较关于日常表现的每周电话反馈的两个级别，这是首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，包括踩踏的次数和分钟数、速率/转速和所用的力量，以及在家和社区中步行的次数、持续时间、平均速度和距离。最低限度反馈组仅听说每日 UCFit 总锻炼量和步行时间。经过 4 个月鼓励每周进行 4 次每次 30 分钟的 UCFit 锻炼后，我们将测试组间每日锻炼量和步行时间是否存在 50% 的差异。次要结果检查健康水平、步行速度和身体机能的变化。然后，在重新评估结果之前，两组将在 4 个月内交叉进行高或低反馈。然后，不提供任何反馈，并在 12 个月时对参与者进行运动量和健康状况测试。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过渡过程中移动性的独特数据；量化日常护理期间的步态训练；在比实验室更复杂的条件下评估步行技能；并测试传感器数据的响应能力，作为预期增益期间的比例结果测量。这项研究将是首次全面展示 mHealth 远程监测和日常身体活动结果测量！