Sensing technologies hold enormous potential for detecting and tracking health changes that can dramatically affect the aging experience. Embedded health assessment can improve management of chronic or acute conditions, and thus, improve quality of life. Problems in chronic disease management are often the cause of losing independence for aging Americans. Chronic disease management is further complicated when patients have Alzheimer?s Disease or Related Dementias (ADRD). In 2018, approximately 5.5 million people over the age of 65 years (10%) have been diagnosed with ADRD. These numbers rise with age; 32% of people over 85 years have been diagnosed. Sensing technologies address this challenge by detecting and tracking cognitive decline in aging so that early interventions can be offered (even when the patient cannot report the problem) when treatment is most effective, prevention of decline is still possible, and costs can be controlled. In previous work, the team developed a health alert system that captures and analyzes data from sensors embedded in the home. Sensor data are captured passively and continuously; clinical staff receive health change alerts, which are generated automatically from the system, thereby allowing early treatment. Studies have shown that the system detects changes in chronic diseases or acute illnesses on average 10 days to 2 weeks before usual assessment methods or self-reports of illness. Due to early interventions, the system was shown to increase length of stay in TigerPlace (aging-in-place senior housing with services) by 65%. In the parent NINR award, the team is expanding from the clinician-focused system to a consumer-focused system with a new interface designed for consumers (older adults and their family) and more sensitive and personalized algorithms to track health trajectories. The study includes deployment in independent living housing for seniors aged 55 and above. The views of the seniors and their designated family members will be used to inform the development of new customized alert algorithms and drive the development of a consumer- focused interface that will provide empowering tools for self-management of chronic illnesses. In the proposed supplement, the system will be extended to support a new population, namely, patients with Alzheimer?s Disease or Related Dementias. The goals of this pilot study are to (1) capture sleep, gait, and activity patterns of ADRD patients living in the community with family using in-home sensors, (2) capture user feedback from the monitored ADRD patients and family about a consumer interface that provides access to the sensor data, and (3) explore changes in the sensor data trends that could be used for guiding and tracking interventions. The long-term goal is to identify parameters from the continuous in-home sensor data that are more sensitive than episodic assessments and, thus, could be used to track cognitive decline in aging over time. This would support future research studies in which in-home sensor data can track the effectiveness of new interventions such as cognitive stimulation therapy.
We will build on our current work using intelligent, in-home sensor systems with automated health alerts to investigate a consumer-focused system that targets older adults with Alzheimer?s Disease or Related Dementias (ADRD) and their family members. New sensor parameters on sleep, gait, and activity will be integrated into the system and tested for correlation with changes in cognitive status. We will recruit ADRD patients living in the community with family to test the system and a multi-platform consumer interface to display the sensor data trends and automated health alerts on smartphones, computers, and voice assistant platforms.
