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

阿尔茨海默病中的躁动：使用数字行为标记和室内环境因素进行识别和预测

• 批准号: 10595595
• 负责人: Wan-Tai Au-Yeung
• 金额: $ 14.64万
• 依托单位: OREGON HEALTH & SCIENCE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-05-15 至 2026-02-28
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10595595
• 关键词: Address; Affect; Aggressive behavior; Aging; Agitation; Alzheimer' s Disease; Area; Assisted Living Facilities; Award; Beds; Behavior; Behavior assessment; Behavior monitoring; Behavioral; Caregiver Burden; Caregivers; Clinical; Clinical Research; Controlled Environment; Cues; Data; Dementia; Development; Devices; Diagnosis; Early Diagnosis; Early Intervention; Early identification; Educational workshop; Effectiveness; Elderly; Environment; Environmental Risk Factor; Ethics; Event; Family Caregiver; Formal caregiver; Goals; Health care facility; Home; Human; Humidity; Intervention; Label; Light; Long-Term Care; Machine Learning; Measures; Mental Health; Mentors; Methods; Monitor; Motor Activity; Noise; Nursing Records; Oregon; Participant; Patient Monitoring; Patients; Pattern; Persons; Pharmacological Treatment; Physical aggression; Population; Prevention strategy; Questionnaires; Research; Research Project Grants; Residential Treatment; Resources; Stress; Symptoms; Techniques; Technology; Temperature; Time; Time Series Analysis; Training; Work; Wrist; actigraphy; aging and technology; analytical method; barometric pressure; burnout; care providers; career; caregiver strain; deep learning; dementia care; detection method; digital; experience; improved; informal care; informal caregiver; intervention cost; memory care; model building; motion sensor; multimodality; neuropsychiatric symptom; novel; pharmacologic; physical conditioning; poor sleep; pressure; research and development; research study; residence; risk prediction model; sensor; side effect; skills; sleep quality; standard of care; verbal

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.

项目摘要 激越是一种最常见的和难以管理的神经精神症状的人经历的 痴呆症（PWD），影响了这个不断增长的人口的45-83%。焦虑会带来很多压力， 对患者和护理人员造成伤害。激越的治疗通常是药物干预， 有副作用非常需要识别早期行为预警信号， 环境沉淀物的搅动，使它可以铺平道路，积极主动的管理搅动， 减轻护理人员的负担。该项目的总体目标是通过以下方式解决这一关键的未满足需求： 申请人的拟议研究和指导培训。俄勒冈州老龄化与技术中心 （ORCATECH）在Kaye博士（拟议的主要导师）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， 分析老年人临床背景下收集的数据的经验。这项研究的科学目标 建议是开发数字行为标记，以识别激动发作，识别早期行为， 预警信号和环境沉淀物的躁动，并建立风险预测模型的事件， 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拌 分析.申请人将从10名患有晚期痴呆症的研究参与者收集行为数据， 记忆护理单位和10名患有晚期痴呆症的研究参与者使用被动式记忆护理住在自己家里 红外运动传感器、可穿戴活动记录仪和床压垫，并跟踪2年。 这样的行为数据将用于识别指示或预测性行为发作的数字行为标记。 激动申请人还将收集环境数据（环境光照水平、噪音水平、温度， 相对湿度和气压），这些数据将用于 识别环境中的扰动沉淀物。为了进行拟议的研究，并准备一个 独立的研究生涯，申请人将通过参加课程和参加研讨会， （1）残疾人士的不同诊断及护理标准，他们的神经精神症状 （2）在痴呆症研究中使用技术的方法;（3）从深层研究的新方法。 学习和时间序列分析，用于建立搅动风险预测模型;（4）开发 专业技能进行成功的和道德上负责的临床研究。拟议的小组 导师和顾问各自提供一个或多个领域的专业知识，并共同致力于 协助申请人的培训。申请人将把这些新技能应用于拟议的 研究项目，并获得R 01的支持，以使用检测和预测发作的方法， 激越，以创建和探索早期干预PWD激越的有效性。这些发现 可能导致改善减少和检测激动发作的方法，并最终有助于保护 照顾者的身心健康，同时改善痴呆症护理。