Agitation in Alzheimer's Disease: Identification and Prediction Using Digital Behavioral Markers and Indoor Environmental Factors

项目总结 躁动是人类最常见和最难以控制的神经精神症状之一。 患有痴呆症(PWD)，影响到这一不断增长的人口的45%-83%。激动会带来很大的压力和 对病人和照顾者不利。躁动的治疗通常是药物干预，它可以 有不良的副作用。非常需要识别早期行为预警迹象和 激荡的环境沉淀物，以便为主动管理激荡和 减轻照顾者的负担。该项目的总体目标是通过以下方式解决这一未得到满足的关键需求 申请人的拟议研究和辅导培训。俄勒冈州老龄与科技中心 (ORCATECH)，在Kaye博士(建议的初级导师)的指导下，拥有十多年的 在老年人家中开发和部署数字行为评估平台的经验 在老年人的临床背景下分析收集的数据的经验。它的科学目标是 建议是开发数字行为标记物来识别激动的情节，识别早期行为 骚动警示信号和环境沉淀物，建立突发事件风险预测模型 使用环境和行为传感器以及来自机器学习和时间序列的技术进行搅动 分析。申请者将收集10名患有晚期痴呆症的研究参与者的行为数据 记忆护理单元和10名患有晚期痴呆症的研究参与者生活在自己的家中，使用被动 红外线运动传感器、可穿戴式动作记录仪和床压垫，并跟踪它们2年。 这样的行为数据将被用来识别指示或预测发作的数字行为标记 激动不安。申请者还将收集环境数据(环境光线水平、噪音水平、温度、 相对湿度和气压)，这些数据将被用来 确定环境中引发骚动的沉淀物。为了进行拟议的研究并为 独立研究生涯，申请者将通过参加课程和参加研讨会 以下方面：(1)PWD的不同诊断和护理标准，以及他们的神经精神症状 以及它们的沉淀剂；(2)在痴呆症研究中使用技术的方法；(3)来自深层的新方法 用于建立搅动风险预测模型的学习和时间序列分析；以及(4) 进行成功和道德负责的临床研究的专业技能。拟议的团队 导师和顾问各自提供上述一个或多个领域的专业知识，并共同致力于 协力协助申请者的培训。申请者将把这些新技能应用于建议的 研究项目并获得R01支持，以便使用检测和预测疾病发作的方法 鼓动以创造和探索对PWD患者激越的早期干预的有效性。这些发现是 可能导致改进减少和检测骚动事件的方法，并最终帮助保护 护理人员的身心健康，同时改善痴呆症护理。

PROJECT SUMMARY Agitation is one of the most common and unmanageable neuropsychiatric symptoms experienced by persons with dementia (PWD), affecting 45-83% of this ever-growing population. Agitation brings much stress and detriment to patients and caregivers. Treatment of agitation is often pharmacological intervention which can have adverse side effects. There is a great need for identification of early behavioral warning signs and environmental precipitants of agitation so that it can pave the way for proactive management of agitation and lower the burden on caregivers. The overall goal of this project is to address this critical unmet need through the proposed research and mentored training of the applicant. The Oregon Center for Aging & Technology (ORCATECH), under the direction of Dr. Kaye (proposed primary mentor), has more than a decade of experience developing and deploying a digital behavioral assessment platform in older adults' homes and has the experience analyzing the data collected in the clinical context of older adults. The scientific goals of this proposal are to develop digital behavioral markers that identify episodes of agitation, identify early behavioral warning signs and environmental precipitants of agitation, and build a risk prediction model of episodes of agitation using environmental and behavioral sensors and techniques from machine learning and time series analysis. The applicant will collect behavioral data from 10 study participants with later-stage dementia living in memory care units and 10 study participants with later-stage dementia living at their own homes using passive infrared motion sensors, wearable actigraphy devices, and bed pressure mats and follow them for 2 years. Such behavioral data will be used to identify digital behavioral markers that indicate or predict episodes of agitation. The applicant will also collect environmental data (ambient light level, noise level, temperature, relative humidity, and barometric pressure) from their living environments, and such data will be used to identify environmental precipitants of agitation. In order to conduct the proposed study and prepare for an independent research career, the applicant will be trained through taking courses and attending workshops in the following areas: (1) the different diagnosis and standard of care for PWD, their neuropsychiatric symptoms and their precipitants; (2) methods of using technology in dementia research; (3) novel methods from deep learning and time series analysis for building risk prediction models of agitation; and (4) development of professional skills for conducting successful and ethically responsible clinical research. The proposed team of mentors and consultant each provide expertise in one or more of these areas and are together committed to collaboratively facilitating the applicant's training. The applicant will apply these new skills to the proposed research project and obtain R01 support in order to use the methods for detecting and predicting episode of agitation to create and explore the effectiveness of early interventions for agitation in PWD. Such findings are likely to lead to improve methods for reducing and detecting episodes of agitation and ultimately help protect caregivers' physical and mental health while improving dementia care.