Document Type


Degree Name



Dept. of Biomedical Engineering


Oregon Health & Science University


Socialization is a very important part of healthy aging, but due to normal changes in health and life style, elderly individuals are at increased risk of becoming lonely—a qualitative state characterized by a subjective deficit in the social relationships. In the elderly, loneliness predicts morbidity and mortality [1-3], is associated with decreased cognitive functioning [4], impairs sleep quality [5, 6], decreases mobility [7, 8], and reduces quality of life. As a result, it is increasingly important to identify and assist lonely individuals. While surveys exist to measure loneliness [9, 10], they are given in a sporadic, infrequent, and impersonal fashion, making early detection of loneliness difficult. It is therefore necessary to improve current methods to identify lonely individuals. Recently, we and others have developed techniques to continually monitor older adults in their home environment using unobtrusive sensing technologies [12, 13] designed to help older adults maintain independence [14, 15] by tracking activities and behavioral patterns in the home on a daily basis. Unobtrusive technologies allow us to collect many types of data that relate to a person’s interaction with others, but this information has never been related back to the current state of the art loneliness assessment. The focus of this thesis is to develop techniques to assess loneliness based on data from motion, contact, phone and computer sensors in the home, setting the framework for unobtrusively measuring loneliness among older adults. In this way, the model of loneliness will be a great contribution to the paradigm of continuous assessment, allowing for a well-rounded view of functional ability and independence in the aging population. The first step in unobtrusively assessing loneliness is to understand which behaviors are both associated with loneliness and can be monitored unobtrusively. However, many of the variables we can monitor using this array of sensors, such as frequency of visitor contact and phone use, relate directly to the level of social isolation, not the loneliness level per say. Social isolation is a quantitative construct that captures the number of personal contacts and the frequency of interactions an individual has. However, the association between loneliness and social isolation is not clear as relatively few longitudinal studies analyzing the relationship between loneliness and social isolation have been performed. Thus, we first investigate the longitudinal relationship between both the level of social isolation and deviations (relative to an individual’s median) in the level of social isolation and loneliness using innovative longitudinal analysis techniques, and demonstrate that loneliness is closely related to both the overall level of isolation and deviations in that level. Next, we develop methods to monitor behaviors associated with loneliness, including phone use, time out-of-home, and visitors to the home. Using these developed metrics (among others), we analyze the relationship between the behavior and loneliness of 16 older adults monitored in their own homes for 8 months. Here, we demonstrate the close relationship between in-home behavior and loneliness (R2 = 0.428), suggesting in-home technology can be used for continuous assessment of loneliness in older adults.




School of Medicine



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