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

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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.