RESUMEN
The World Health Organization estimates that approximately 10 million people are newly diagnosed with dementia each year and a global prevalence of nearly 50 million persons with dementia (PwD). The vast majority of PwD living at home receive the majority of their care from informal familial caregivers. The quality of life (QOL) of familial caregivers may be significantly impacted by their caregiving responsibilities and resultant caregiver burden. A major contributor to caregiver burden is the random occurrence of agitation in PwD and familial caregivers' lack of preparedness to manage these episodes. Caregiver burden may be reduced if it is possible to forecast impending agitation episodes. In this study, we leverage data-driven deep learning models to predict agitation episodes in PwD. We used Long Short-Term Memory (LSTM), a deep learning class of algorithms, to forecast agitations up to 30 min before actual agitation events. In particular, we managed the missing data by estimating the missing values and compensated for the class imbalance challenge by down-sampling the majority class. The simulations were based on real-world data from Alzheimer's disease (AD) caregivers and PwD dyads home environments, including ambient noise level, illumination, room temperature, atmospheric pressure (Pa), and relative humidity. Our results show the efficacy of data-driven deep learning models in predicting agitation episodes in community-dwelling AD dyads with accuracy of 98.6% and recall (sensitivity) of 84.8%.
RESUMEN
BACKGROUND AND OBJECTIVES: Caregiver burden associated with dementia-related agitation is one of the commonest reasons a community-dwelling person with dementia (PWD) transitions to a care facility. Behavioral and Environmental Sensing and Intervention for Dementia Caregiver Empowerment (BESI) is a system of body-worn and in-home sensors developed to provide continuous, noninvasive agitation assessment and environmental context monitoring to detect early signs of agitation and its environmental triggers. RESEARCH DESIGN AND METHODS: This mixed methods, remote ethnographic study is explored in a 3-phase, multiyear plan. In Phase 1, we developed and refined the BESI system and completed usability studies. Validation of the system and the development of dyad-specific models of the relationship between agitation and the environment occurred in Phase 2. RESULTS: Phases 1 and 2 results facilitated targeted changes in BESI, thus improving its overall usability for the final phase of the study, when real-time notifications and interventions will be implemented. CONCLUSION: Our results show a valid relationship between the presence of dementia related agitation and environmental factors and that persons with dementia and their caregivers prefer a home-based monitoring system like BESI.