Unobtrusive In-Home Detection of Time Spent Out-of-Home With Applications to Loneliness and Physical Activity

Abstract

Loneliness is a common condition in elderly associated with severe health consequences including increased mortality, decreased cognitive function, and poor quality of life. Identifying and assisting lonely individuals is therefore increasingly important—especially in the home setting—as the very nature of loneliness often makes it difficult to detect by traditional methods. One critical component in assessing loneliness unobtrusively is to measure time spent out-of-home, as loneliness often presents with decreased physical activity, decreased motor functioning, and a decline in activities of daily living, all of which may cause decreases in the amount of time spent outside the home. Using passive and unobtrusive in-home sensing technologies, we have developed a methodology for detecting time spent out-of-home based on logistic regression. Our approach was both sensitive (0.939) and specific (0.975) in detecting time out-of-home across over 41,000 epochs of data collected from 4 subjects monitored for at least 30 days each in their own homes. In addition to linking time spent out-of-home to loneliness (r=−0.44, p=0.011) as measured by the UCLA Loneliness Index, we demonstrate its usefulness in other applications such as uncovering general behavioral patterns of elderly and exploring the link between time spent out-of-home and physical activity (r=0.415, p=0.031), as measured by the Berkman Social Disengagement Index.

Longitudinal Relationship Between Loneliness and Social Isolation in Older Adults

“…These results have important consequences for future studies on loneliness, especially on the identification of lonely individuals. Because loneliness may change in response to many life events and is closely related to social isolation, it may be more timely and informative to identify lonely individuals by tracking changes in the social network or other daily behaviors using unobtrusive monitoring techniques (Kaye et al, 2010; Petersen, Austin, Kaye, Pavel, & Hayes, 2014). For example, tracking call history would enable assessment of social network size and frequency of contact—the two major components of social isolation scales (Petersen, Thielke, Austin, & Kaye, 2015).…”

“…For example, tracking call history would enable assessment of social network size and frequency of contact—the two major components of social isolation scales (Petersen, Thielke, Austin, & Kaye, 2015). Other behaviors that may relate to loneliness that can be tracked unobtrusively include time spent outside the home (Petersen et al, 2014; Petersen, Austin, Mattek, & Kaye, 2015), computer use (Amichai-Hamburger & Ben-Artzi, 2003), and sleep quality (Hawkley et al, 2010). Such approaches to loneliness identification would have dramatic consequences for the understanding of loneliness, enabling researchers to monitor and assess loneliness levels on smaller timescales such as daily or even hourly.…”

Older Adults’ Attitudes to Self-Management of Health and Wellness through Smart Home Data

“…One of the earliest introductions was by Dishman [14], who discussed the potential for such systems to collect data on behaviours, detect problems in a timely manner and support interventions. Given their potential to address many challenges associated with ageing, there is a vast amount of research within the smart home and AAL space, including to detect monitoring the onset of low mood or depression [15], 1 http://www.withings.com/eu/ [16], motor and cognitive function [2], [17]; and patterns of activity [18]. Studies have ranged in design from short stays in artificial residences occupied by researchers, to deployments of smart home technology in the permanent homes of older adults.…”

“…Their ISAAC project is large scale, with approximately 164 participants being monitored over a 5-year period [19]. Their research has focused on data analysis of nighttime activity, computer use and motor activity, including time spent outside the home [17], [18] and healthcare professionals' views on such monitoring technology [7]. From the older adult's point of view they have examined acceptability as well as willingness to share data and privacy concerns [20].…”

“…The data focused on 3 areas of wellbeing, sleep, activity and physiological monitoring, which have been shown to be important in maintaining healthy ageing [11], [18], [24][25][26]. …”