Automated, Assistive, Non-Contact Sleep Quality Monitor for Individuals with Alzheimer Disease

项目总结/摘要 提出了一种睡眠监测系统，该系统专门为阿尔茨海默氏症患者的护理提供者设计， 疾病和阿尔茨海默病相关痴呆（AD/ADRD），以监测睡眠障碍和趋势。 意义：研究表明，大约三分之一的AD/ADRD患者患有睡眠和其他疾病。 夜间干扰，如失眠和睡眠中断、徘徊、昼夜混淆，反复 离开床做噩梦和幻觉睡眠障碍往往会导致健康状况下降和质量下降 AD/ADRD患者和长期护理（LTC）环境中的患者的生命周期给工作人员带来了负担。近一半的 LTC环境中的人诊断为AD/ADRD。不幸的是，LTC的临床提供者和工作人员 由于目前的睡眠质量评估方法 不准确、耗时或不适合用于AD/ADRD患者。假设：我们 假设一种允许对患者的睡眠质量度量进行实际的长期监测的系统 与AD/ADRD将导致更多的睡眠问题的文件，更多的处方，适当的 非药物治疗，改善睡眠质量和日间活动。具体目标： 将第一阶段的原型转换为生产就绪的产品，并评估第一阶段系统的功效 II IDL将1）创建具有更新的硬件、固件和机械设计的生产就绪系统2） 创建支持云的后端，实现Web /移动的界面，并完善睡眠指标，以及3）评估 系统在前后临床试验中的真实可行性、可接受性和实用性。

Project Summary/Abstract Proposed is a sleep monitoring system specifically designed for care providers of persons with Alzheimer’s disease and Alzheimer’s Disease Related Dementias (AD/ADRD) to monitor sleep disturbances and trends. Significance: Studies indicate that approximately 1 in 3 patients with AD/ADRD suffer from sleep and other nighttime disturbances such as insomnia and disruption of sleep, wandering, day/night confusion, repeatedly leaving bed, nightmares, and hallucinations. Sleep disturbances often lead to reduced health and lower quality of life for persons with AD/ADRD and in Long Term Care (LTC) settings place burden on staff. Nearly half of all people in LTC settings have a diagnosis of AD/ADRD. Unfortunately, clinical providers and staff in LTCs are limited in their ability to identify and treat these conditions as current methods of sleep quality assessment are either inaccurate, time consuming, or inappropriate for use with those who have AD/ADRD. Hypothesis: We hypothesize that a system which allows for practical, long-term monitoring of sleep quality metrics for patients with AD/ADRD will lead to greater documentation of sleep problems, greater prescribing of appropriate nonpharmacological treatment, and improvements in sleep quality and daytime inactivity. Specific Aims: To transition the Phase I prototype to a production ready product and evaluate the efficacy of the system in Phase II IDL will 1) Create production-ready system with updated hardware, firmware, and mechanical design 2) Create cloud-enabled backend, implement web / mobile interfaces, and refine sleep metrics and 3) Evaluate system’s real-world feasibility, acceptability, and utility in a before-after clinical trial.