A Community-in-the-Loop Approach to Smart Home Monitoring for Aging in Place: Mixed Methods Evaluation of a...

The population of adults aged 65 and older is rapidly increasing, while the availability of caregivers is declining. Smart homes that provide unobtrusive, continuous monitoring and alerting on clinically relevant changes in daily activity patterns offer a potentially innovative solution for aging in place.

This study aims to evaluate the barriers and facilitators to the adoption of a low-cost smart home embedded within a community-based approach to health monitoring for older adults with multiple chronic conditions and who are experiencing poverty.

Using a prospective, mixed methods design and iterative community co-design, 46 older adults from 7 different language groups were continuously monitored for 6 months with ambient sensors installed in their homes. Two older adults were monitored for 4 and 5 months, respectively, resulting in a total sample of 48. The system generated alerts based on movement pattern changes and escalated notifications to participants, support persons, community health workers, and nurses. Sensor data were analyzed descriptively to quantify alert patterns and response rates, while written text-based data from in-the-moment surveys, community health workers' and registered nurses' notes, and semistructured interviews underwent qualitative descriptive analysis and reflexive thematic coding.

The system generated 37 million sensor readings condensed into 1.2 million high-level events and 4719 novel alerts. Qualitative data comprised 34,086 words of text. Participants responded to 1.57% (74) of the initial email alerts and 7.79% (368) of the follow-up SMS text message alerts sent when no email response was received. Community health workers and registered nurses responded to 78.36% (n=3698) of the escalated alerts, resulting in 1060 contacts with participants in response to alerts. Clinical contacts resulted in 72 interventions. Three major qualitative themes emerged: (1) Alone, (2) Trust, and (3) Human Connection. Subthemes included Safety, Personalization, and Digital Distress defined as stress associated with interacting with digital health-monitoring systems. Participants rated the system highly (mean likelihood-to-recommend rating 8.68/10, SD 1.68); however, they expressed a strong preference for phone calls over automated alerts. Cultural expectations influenced adoption, particularly in multigenerational households.

Communities can effectively engage in technology-delivered health care. Future research is needed to improve technical aspects of smart home monitoring systems, including accurate alerting using machine learning, data visualizations for older adults and health care workers, and culturally sensitive features. Additional work should address how and when to communicate automated messaging, engage older adults with their own data, and integrate sensor-based monitoring into health care workflows. Research should also explore personalization through advanced computational approaches such as machine learning and strategies to reduce digital distress.