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.

Agitation episodes influence the quality of life of both the person with dementia (PWD) and caregivers. Caregiving is challenging, with high workloads and barriers such as fatigue, lack of sleep, and unpredictable episodes of agitation among PWD. Advanced technologies such as cyber-human systems are increasingly considered as means to alleviate some of the stress experienced by caregivers of PWD.

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, non-invasive agitation assessment and environmental context monitoring to detect early signals of agitation and environmental triggers. The goal to detect early stages of agitation in persons with dementia (PWD) opens up new and promising technological development of cyber-human systems to enable early caregiver intervention. Caregivers shoulder most of the burden of dementia caregiving, and the BESI project seeks to reduce burden and improve caregiver self-efficacy. This mixed methods, remote ethnographic study is explored in a 3-phase, multi-year plan. In Phase 1 we developed the BESI system, completed usability studies in Alzheimer's Disease support groups using the Systems Usability Scale (SUS), and refined the system. Dyads (caregivers + PWD) who live at home are studied for 30 days in Phase 2 with continuous data collection during the deployment period. A tablet application for caregivers is used to log PWD activities, agitation events, and input markers of caregiver self-efficacy. Using wearable wrist technology (e.g. Pebble®), agitation severity level, physical, behavioral, and social activities of the PWD are captured. Post-deployment surveys of all ten dyads provided data on the system usability from questions posed with Likert-type scaling response ratings between 1 and 6 plus qualitative feedback. Between phases 1 & 2, the tablet application was updated to enhance interface usability for caregivers. Scores for the ten questions (rated 1-6) on ease of use of the tablet were in the very easy range (5.11 - 5.90). Agreement on use of the table device yielded SUS scores (rated 1-5) with range of 2.67 - 4.56. Preliminary analysis of the subjective feedback indicates overall positive impressions in working with the technologies. Phase 2 results facilitated targeted changes in BESI, improving overall usability for the final phase of the study. Caregivers consistently demonstrated willingness to help – including working with technologies previously unfamiliar. These subject-oriented design decisions influenced the team in understanding caregiver and PWD dyad interactions with technologies. The full qualitative report will be available in June 2019.

Dementia caregiver burden associated with patient agitation is one of the most common reasons for the institutionalization of a person with dementia. We developed an integrative sensing, analytics, modeling, and intervention system that detects early signs of agitation and notifies the caregiver to intervene before escalation.