Department of Neurology, Geffen UCLA School of Medicine, Los Angeles, CA, USA.
Neurorehabil Neural Repair. 2011 Nov-Dec;25(9):788-98. doi: 10.1177/1545968311425908.
Mobile health tools that enable clinicians and researchers to monitor the type, quantity, and quality of everyday activities of patients and trial participants have long been needed to improve daily care, design more clinically meaningful randomized trials of interventions, and establish cost-effective, evidence-based practices. Inexpensive, unobtrusive wireless sensors, including accelerometers, gyroscopes, and pressure-sensitive textiles, combined with Internet-based communications and machine-learning algorithms trained to recognize upper- and lower-extremity movements, have begun to fulfill this need. Continuous data from ankle triaxial accelerometers, for example, can be transmitted from the home and community via WiFi or a smartphone to a remote data analysis server. Reports can include the walking speed and duration of every bout of ambulation, spatiotemporal symmetries between the legs, and the type, duration, and energy used during exercise. For daily care, this readily accessible flow of real-world information allows clinicians to monitor the amount and quality of exercise for risk factor management and compliance in the practice of skills. Feedback may motivate better self-management as well as serve home-based rehabilitation efforts. Monitoring patients with chronic diseases and after hospitalization or the start of new medications for a decline in daily activity may help detect medical complications before rehospitalization becomes necessary. For clinical trials, repeated laboratory-quality assessments of key activities in the community, rather than by clinic testing, self-report, and ordinal scales, may reduce the cost and burden of travel, improve recruitment and retention, and capture more reliable, valid, and responsive ratio-scaled outcome measures that are not mere surrogates for changes in daily impairment, disability, and functioning.
移动健康工具使临床医生和研究人员能够监测患者和试验参与者日常活动的类型、数量和质量,从而改善日常护理,设计更具临床意义的干预措施随机试验,并建立具有成本效益的、基于证据的实践。廉价、不引人注目的无线传感器,包括加速度计、陀螺仪和压力敏感纺织品,结合基于互联网的通信和经过训练以识别上下肢运动的机器学习算法,已经开始满足这一需求。例如,来自脚踝三轴加速度计的连续数据可以通过 WiFi 或智能手机从家庭和社区传输到远程数据分析服务器。报告可以包括每次步行的速度和持续时间、腿部之间的时空对称性,以及运动过程中的类型、持续时间和能量。在日常护理中,这种易于获取的真实世界信息流使临床医生能够监测运动的数量和质量,以进行危险因素管理和技能实践中的依从性。反馈可能会激发更好的自我管理,并为家庭康复努力提供支持。监测患有慢性疾病的患者以及在因日常活动减少而住院或开始新药物治疗后,可能有助于在需要再次住院之前发现医疗并发症。对于临床试验,在社区中进行的关键活动的重复实验室质量评估,而不是通过诊所测试、自我报告和有序量表进行评估,可能会降低旅行成本和负担,提高招募和保留率,并获得更可靠、更有效、更敏感的比例量表测量结果,而不仅仅是日常损伤、残疾和功能变化的替代指标。