Automated Technology to Speed Recognition of Signs of Illness in Older Adults

Health care providers who care for older adults often become aware of subtle changes, almost developing a “sixth sense” that “something is wrong” or “something is about to happen” to a particular person. Many times, these early changes are beginning symptoms of acute illnesses or exacerbations in chronic illnesses. Our Eldertech Research Team, an interdisciplinary team of nurses, social workers, physician, informatics experts, and electrical and computer engineers, implemented a novel network of environmentally embedded sensors that generate data about older adults as they go about their normal living activities. Automated data processing methods developed by the team analyze data and alert clinicians that something “is different” and “potentially wrong” with a person in their care. The clinicians have a convenient way to visualize the data to interpret what may be occurring, within the context of the person’s health status (Alexander, Wakefield, Rantz, et al., 2011). Our team hypothesized from preliminary work (Rantz, Skubic, & Miller, 2009) that sensor data would provide cues of impending signs of illness earlier than traditional nursing assessments. We also hypothesized that patterns in the sensor data would emerge before older people become aware that something is “not right” with their health. This article reports findings of this innovative research for early illness detection and proactive chronic disease management to detect changes in health status that indicate impending acute illness or exacerbation of chronic illness before usual assessment methods. Our team chose to use unobtrusive, inexpensive, non-wearable, infrared (PIR) motion sensors that monitor people continuously while they go about daily activities in their homes. Unobtrusive bed sensors collect data about each person’s pulse, breathing, and restlessness while they sleep. We developed methods of alerting health care providers and identified key outcome variables for future research with large scale testing of non-wearable sensor technology that enables aging in place through proactive early illness detection.