Kaye Jeffrey, Reynolds Christina, Bowman Molly, Sharma Nicole, Riley Thomas, Golonka Ona, Lee Jonathan, Quinn Charlie, Beattie Zachary, Austin Johanna, Seelye Adriana, Wild Katherine, Mattek Nora
Department of Neurology, ORCATECH - Oregon Center for Aging & Technology, Oregon Health & Science University;
Department of Neurology, ORCATECH - Oregon Center for Aging & Technology, Oregon Health & Science University.
J Vis Exp. 2018 Jul 27(137):56942. doi: 10.3791/56942.
An end-to-end suite of technologies has been established for the unobtrusive and continuous monitoring of health and activity changes occurring in the daily life of older adults over extended periods of time. The technology is aggregated into a system that incorporates the principles of being minimally obtrusive, while generating secure, privacy protected, continuous objective data in real-world (home-based) settings for months to years. The system includes passive infrared presence sensors placed throughout the home, door contact sensors installed on exterior doors, connected physiological monitoring devices (such as scales), medication boxes, and wearable actigraphs. Driving sensors are also installed in participants' cars and computer (PC, tablet or smartphone) use is tracked. Data is annotated via frequent online self-report options that provide vital information with regard to the data that is difficult to infer via sensors such as internal states (e.g., pain, mood, loneliness), as well as data referent to activity pattern interpretation (e.g., visitors, rearranged furniture). Algorithms have been developed using the data obtained to identify functional domains key to health or disease activity monitoring, including mobility (e.g., room transitions, steps, gait speed), physiologic function (e.g., weight, body mass index, pulse), sleep behaviors (e.g., sleep time, trips to the bathroom at night), medication adherence (e.g., missed doses), social engagement (e.g., time spent out of home, time couples spend together), and cognitive function (e.g., time on computer, mouse movements, characteristics of online form completion, driving ability). Change detection of these functions provides a sensitive marker for the application in health surveillance of acute illnesses (e.g., viral epidemic) to the early detection of prodromal dementia syndromes. The system is particularly suitable for monitoring the efficacy of clinical interventions in natural history studies of geriatric syndromes and in clinical trials.
已经建立了一套端到端的技术，用于在较长时间内对老年人日常生活中发生的健康和活动变化进行不干扰的持续监测。这些技术被整合到一个系统中，该系统遵循最小干扰原则，同时在现实世界（家庭）环境中持续数月至数年生成安全、受隐私保护的客观数据。该系统包括安装在整个家中的被动红外存在传感器、安装在外门上的门接触传感器、联网的生理监测设备（如体重秤）、药盒和可穿戴活动记录仪。还在参与者的汽车中安装了驾驶传感器，并对计算机（个人电脑、平板电脑或智能手机）的使用情况进行跟踪。通过频繁的在线自我报告选项对数据进行注释，这些选项提供了关于难以通过传感器推断的数据的重要信息，如内部状态（如疼痛、情绪、孤独感），以及与活动模式解释相关的数据（如访客、重新布置的家具）。利用所获得的数据开发了算法，以识别对健康或疾病活动监测至关重要的功能领域，包括 mobility（如房间转换、步数、步态速度）、生理功能（如体重、体重指数、脉搏）、睡眠行为（如睡眠时间、夜间上厕所次数）、药物依从性（如漏服剂量）、社交参与（如外出时间、夫妻共处时间）和认知功能（如在电脑上花费的时间、鼠标移动、在线表格填写特征、驾驶能力）。这些功能的变化检测为在急性疾病（如病毒流行）的健康监测到前驱性痴呆综合征的早期检测中的应用提供了一个敏感指标。该系统特别适用于在老年综合征的自然史研究和临床试验中监测临床干预的效果。
