Living With Intelligent Sensors: Older Adult and Family Member Perceptions.

This qualitative study is part of a larger randomized prospective intervention study that examined the clinical and cost effectiveness of using sensor data from an environmentally embedded sensor system for early illness recognition. It explored the perceptions of older adults and family members on the sensor system's usefulness, impact on daily routine, privacy, and sharing of health information. This study was conducted in 13 assisted-living facilities in Missouri, and 55 older adults were interviewed. Data were collected over five points in time with a total of 188 interviews. From these five participant interview iterations, the following themes emerged: (1) understanding and purpose, (2) daily life and benefits, (3) impact on privacy, and (4) sharing of information. Three themes emerged from one round of family interviews: (1) benefits of bed sensors, (2) family involvement/staff interaction, and (3) privacy protection versus sensor benefits. The sensor suite was regarded as helpful in maintaining independence, health, and physical functioning. Responses suggest that the willingness to adopt the sensor suite was motivated by both a decline in functional status and a desire to remain independent. Participants were willing to share their health data with providers and select family members. Recommendations for future practice are provided.

Randomized Trial of Intelligent Sensor System for Early Illness Alerts in Senior Housing.

OBJECTIVES: Measure the clinical effectiveness and cost effectiveness of using sensor data from an environmentally embedded sensor system for early illness recognition. This sensor system has demonstrated in pilot studies to detect changes in function and in chronic diseases or acute illnesses on average 10 days to 2 weeks before usual assessment methods or self-reports of illness. DESIGN: Prospective intervention study in 13 assisted living (AL) communities of 171 residents randomly assigned to intervention (n=86) or comparison group (n=85) receiving usual care. METHODS: Intervention participants lived with the sensor system an average of one year. MEASUREMENTS: Continuous data collected 24 hours/7 days a week from motion sensors to measure overall activity, an under mattress bed sensor to capture respiration, pulse, and restlessness as people sleep, and a gait sensor that continuously measures gait speed, stride length and time, and automatically assess for increasing fall risk as the person walks around the apartment. Continuously running computer algorithms are applied to the sensor data and send health alerts to staff when there are changes in sensor data patterns. RESULTS: The randomized comparison group functionally declined more rapidly than the intervention group. Walking speed and several measures from GaitRite, velocity, step length left and right, stride length left and right, and the fall risk measure of functional ambulation profile (FAP) all had clinically significant changes. The walking speed increase (worse) and velocity decline (worse) of 0.073 m/s for comparison group exceeded 0.05 m/s, a value considered to be a minimum clinically important difference. No differences were measured in health care costs. CONCLUSIONS: These findings demonstrate that sensor data with health alerts and fall alerts sent to AL nursing staff can be an effective strategy to detect and intervene in early signs of illness or functional decline.