Abstract
Modern sensor technology is increasingly used in older adults to not only provide additional safety but also to monitor health status, often by means of sensor derived digital measures or biomarkers. Social isolation is a known risk factor for late-life depression, and a potential component of social-isolation is the lack of home visits. Therefore, home visits may serve as a digital measure for social isolation and late-life depression. Late-life depression is a common mental and emotional disorder in the growing population of older adults. The disorder, if untreated, can significantly decrease quality of life and, amongst other effects, leads to increased mortality. Late-life depression often goes undiagnosed due to associated stigma and the incorrect assumption that it is a normal part of ageing. In this work, we propose a visit detection system that generalizes well to previously unseen apartments - which may differ largely in layout, sensor placement, and size from apartments found in the semi-annotated training dataset. We find that by using a self-training-based domain adaptation strategy, a robust system to extract home visit information can be built (ROC AUC = 0.773). We further show that the resulting visit information correlates well with the common geriatric depression scale screening tool ( ρ = -0.87, p = 0.001), providing further support for the idea of utilizing the extracted information as a potential digital measure or even as a digital biomarker to monitor the risk of late-life depression.
Highlights
IntroductionMuri is with the Department of Neurology, University Neurorehabilitation Unit, University Hospital Bern (Inselspital), University of Bern, Bern, Switzerland, and with the ARTORG Center for Biomedical Engineering Research, University of Bern, Bern, Switzerland
ROC AUC values represent the performance on the nurse-visit detection task, while associations with GDS values represent a proxy for more general realworld visit detection performance
Based on the hypothesis that home visits can be an indicator for social isolation and associated geriatric depression, we introduce ST-DA, a self-training based domain adaptation approach that is tailored towards the scenario of having only nurse-visit labels available for training a visit-detection system in a multi-source domain adaptation scenario with heterogeneous feature spaces
Summary
Muri is with the Department of Neurology, University Neurorehabilitation Unit, University Hospital Bern (Inselspital), University of Bern, Bern, Switzerland, and with the ARTORG Center for Biomedical Engineering Research, University of Bern, Bern, Switzerland One area where such objective markers might have significant potential is late-life depression, a common condition in older adults that significantly decreases quality of life [14] and is associated with a wide variety of negative health outcomes, including increased risk of mortality [15] or cardiovascular disease [16]. Indicators for social isolation, measurable by pervasive computing systems, could be time spent outside the home or frequency and duration of home visits - for older adults living alone In this context, the former has been shown to be associated with perceived loneliness [21]. With declining mobility, this source of social interaction may become increasingly difficult to
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