Featured past projects
Ambient Independence Measures for Guiding Care Transitions
Investigator: Jeff Kaye, M.D.
Funding Period: 2013 to 2018
Funding Source: National Institute on Aging
Our rapidly aging population will result in an increasing number of people at risk for loss of independence through dementia, frailty and other syndromes of aging. The high cost of health care to assist the dependent elderly is expected to soar by 2030, when over 20% of the U.S. population will be over the age of 65, many of whom will be unable to live independently. Some of this cost can be avoided by helping individuals remain independent and at home for as long as possible. The long-term objective of our research is to develop systems that improve our ability to unobtrusively monitor important health changes due to chronic disease and aging, allowing timely intervention to prevent avoidable health deterioration or loss of independence. Using technologies that we have developed and tested over the past five years in hundreds of seniors' homes, we will determine if such Ambient Independence Measures (AIMs), collected using unobtrusive sensors distributed throughout an individual's natural living environment, aid in making decisions about transitions to different levels of care.
Conversational Engagement Study
Full Project Title: Detecting changes in the levels of social engagement: Conversational Pilot Study
Investigator: Hiroko Dodge, Ph.D.
Funding Period: 2011 to 2015
Funding Source: National Institute on Aging
We examined whether frequent conversations using webcam and internet could improve thinking abilities among the elderly. First, we examined who are likely to participate in our trial by distributing surveys to local seniors. Over 2000 surveys were distributed. The results were used to estimate the potential sample selection bias in our trial (the publication #1 below). Our randomized controlled trial showed high adherence (over 89%) and improved language-based executive functions among the experimental group in comparison with the control group (publication #2 below). By using the recorded conversations, we also found interaction patterns differ by participants' cognitive status (publication #3 below). We plan to extend the study to a larger population in the near future.
New investigator projects
ORCATECH also supports graduate students and other new investigators who are performing important research at OHSU.
Adriana Seelye, Ph.D., completed a project entitled, "Constructing unobtrusive objective measures of everyday cognition and function." The goal of her research project was to develop a computer use activity as one component of a novel, everyday cognition assessment suite embedded in a pervasive computing environment. She determined which markers embedded within the activity are most sensitive to early decline or changes in health status, and to demonstrate the activity’s feasibility as a real-world objective measure of everyday cognition for cognitively intact older adults and those with mild cognitive impairment.
Julia Leach worked on integrating a balance assessment system into ORCATECH’s in-home technological platform to extract frequent, longitudinal, objective measures of postural sway (both in elders who are cognitively intact and elders with mild cognitive impairment, or MCI). The objectives of her dissertation research were to determine how abnormalities and longitudinal changes in postural sway relate to cognitive decline and falls in older adults with MCI.
Johanna Petersen: Using unobtrusive sensors in the home, Johanna worked at developing methods to detect loneliness in seniors as they begin to become lonely. By developing methods to monitor behaviors relating to loneliness, Johanna worked to identify changes in behavior that signify an individual is experiencing loneliness. She also worked to validating an intervention to help reduce loneliness so identified seniors can be enrolled in a program that meets their specific needs.
Daniel Austin, Ph.D., is a graduate of the OHSU Department of Biomedical Engineering. While working as a research instructor in the Department of Neurology, his work focused on fusing multiple data streams captured in a person’s own home (walking speed, sleep, time spent out-of-home, computer use, mobility, etc.) into a cohesive behavioral model to assess current health levels and predict future adverse health outcomes. Two projects included: 1) Assessment of a person’s current pain level and affect from a behavioral model, and 2) Understanding the behavioral correlates of older adults’ transitions to advanced care (e.g., when a person transitions from living independently to having a nurse regularly visit the home) and assessing the risk of future transitions from change in behavior over time.
Zach Beattie, Ph.D., is a graduate of the OHSU Department of Biomedical Engineering. When he was a senior research associate in the Department of Psychiatry, Zach researched the use of force sensors placed under the supports of the bed to unobtrusively detect sleep apnea and other health characteristics.