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      Smart Environments and Social Robots for Age-Friendly Integrated Care Services

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          Abstract

          The world is facing major societal challenges because of an aging population that is putting increasing pressure on the sustainability of care. While demand for care and social services is steadily increasing, the supply is constrained by the decreasing workforce. The development of smart, physical, social and age-friendly environments is identified by World Health Organization (WHO) as a key intervention point for enabling older adults, enabling them to remain as much possible in their residences, delay institutionalization, and ultimately, improve quality of life. In this study, we survey smart environments, machine learning and robot assistive technologies that can offer support for the independent living of older adults and provide age-friendly care services. We describe two examples of integrated care services that are using assistive technologies in innovative ways to assess and deliver of timely interventions for polypharmacy management and for social and cognitive activity support in older adults. We describe the architectural views of these services, focusing on details about technology usage, end-user interaction flows and data models that are developed or enhanced to achieve the envisioned objective of healthier, safer, more independent and socially connected older people.

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          Most cited references144

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          Factors influencing acceptance of technology for aging in place: a systematic review.

          To provide an overview of factors influencing the acceptance of electronic technologies that support aging in place by community-dwelling older adults. Since technology acceptance factors fluctuate over time, a distinction was made between factors in the pre-implementation stage and factors in the post-implementation stage.
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            Assistive social robots in elderly care: a review

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              The Use and Effectiveness of Mobile Apps for Depression: Results From a Fully Remote Clinical Trial

              Background Mobile apps for mental health have the potential to overcome access barriers to mental health care, but there is little information on whether patients use the interventions as intended and the impact they have on mental health outcomes. Objective The objective of our study was to document and compare use patterns and clinical outcomes across the United States between 3 different self-guided mobile apps for depression. Methods Participants were recruited through Web-based advertisements and social media and were randomly assigned to 1 of 3 mood apps. Treatment and assessment were conducted remotely on each participant’s smartphone or tablet with minimal contact with study staff. We enrolled 626 English-speaking adults (≥18 years old) with mild to moderate depression as determined by a 9-item Patient Health Questionnaire (PHQ-9) score ≥5, or if their score on item 10 was ≥2. The apps were (1) Project: EVO, a cognitive training app theorized to mitigate depressive symptoms by improving cognitive control, (2) iPST, an app based on an evidence-based psychotherapy for depression, and (3) Health Tips, a treatment control. Outcomes were scores on the PHQ-9 and the Sheehan Disability Scale. Adherence to treatment was measured as number of times participants opened and used the apps as instructed. Results We randomly assigned 211 participants to iPST, 209 to Project: EVO, and 206 to Health Tips. Among the participants, 77.0% (482/626) had a PHQ-9 score >10 (moderately depressed). Among the participants using the 2 active apps, 57.9% (243/420) did not download their assigned intervention app but did not differ demographically from those who did. Differential treatment effects were present in participants with baseline PHQ-9 score >10, with the cognitive training and problem-solving apps resulting in greater effects on mood than the information control app (χ22=6.46, P=.04). Conclusions Mobile apps for depression appear to have their greatest impact on people with more moderate levels of depression. In particular, an app that is designed to engage cognitive correlates of depression had the strongest effect on depressed mood in this sample. This study suggests that mobile apps reach many people and are useful for more moderate levels of depression. ClinicalTrial Clinicaltrials.gov NCT00540865; https://www.clinicaltrials.gov/ct2/show/NCT00540865 (Archived by WebCite at http://www.webcitation.org/6mj8IPqQr)
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                Author and article information

                Journal
                Int J Environ Res Public Health
                Int J Environ Res Public Health
                ijerph
                International Journal of Environmental Research and Public Health
                MDPI
                1661-7827
                1660-4601
                27 May 2020
                June 2020
                : 17
                : 11
                : 3801
                Affiliations
                Computer Science Department, Technical University of Cluj-Napoca, Memorandumului 28, 400114 Cluj-Napoca, Romania; ionut.anghel@ 123456cs.utcluj.ro (I.A.); dorin.moldovan@ 123456cs.utcluj.ro (D.M.); marcel.antal@ 123456cs.utcluj.ro (M.A.); claudia.pop@ 123456cs.utcluj.ro (C.D.P.); ioan.salomie@ 123456cs.utcluj.ro (I.S.); cristina.pop@ 123456cs.utcluj.ro (C.B.P.); viorica.chifu@ 123456cs.utcluj.ro (V.R.C.)
                Author notes
                [* ]Correspondence: tudor.cioara@ 123456cs.utcluj.ro ; Tel.: +40-264-202-352
                Author information
                https://orcid.org/0000-0002-7559-3862
                https://orcid.org/0000-0002-4886-3572
                https://orcid.org/0000-0002-3194-080X
                Article
                ijerph-17-03801
                10.3390/ijerph17113801
                7312538
                32471108
                5a02c945-3e26-4276-bda8-9bd3b63de2e9
                © 2020 by the authors.

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( http://creativecommons.org/licenses/by/4.0/).

                History
                : 14 April 2020
                : 26 May 2020
                Categories
                Communication

                Public health
                social robots,ambient assisted living,machine learning,older adults care,daily life activities monitoring,technology limitation and acceptance,care services models

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