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      The effect of COVID-19 on the home behaviours of people affected by dementia

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          Abstract

          The COVID-19 pandemic has dramatically altered the behaviour of most of the world’s population, particularly affecting the elderly, including people living with dementia (PLwD). Here we use remote home monitoring technology deployed into 31 homes of PLwD living in the UK to investigate the effects of COVID-19 on behaviour within the home, including social isolation. The home activity was monitored continuously using unobtrusive sensors for 498 days from 1 December 2019 to 12 April 2021. This period included six distinct pandemic phases with differing public health measures, including three periods of home ‘lockdown’. Linear mixed-effects modelling is used to examine changes in the home activity of PLwD who lived alone or with others. An algorithm is developed to quantify time spent outside the home. Increased home activity is observed from very early in the pandemic, with a significant decrease in the time spent outside produced by the first lockdown. The study demonstrates the effects of COVID-19 lockdown on home behaviours in PLwD and shows how unobtrusive home monitoring can be used to track behaviours relevant to social isolation.

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          lmerTest Package: Tests in Linear Mixed Effects Models

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            Smart Homes for Elderly Healthcare—Recent Advances and Research Challenges

            Advancements in medical science and technology, medicine and public health coupled with increased consciousness about nutrition and environmental and personal hygiene have paved the way for the dramatic increase in life expectancy globally in the past several decades. However, increased life expectancy has given rise to an increasing aging population, thus jeopardizing the socio-economic structure of many countries in terms of costs associated with elderly healthcare and wellbeing. In order to cope with the growing need for elderly healthcare services, it is essential to develop affordable, unobtrusive and easy-to-use healthcare solutions. Smart homes, which incorporate environmental and wearable medical sensors, actuators, and modern communication and information technologies, can enable continuous and remote monitoring of elderly health and wellbeing at a low cost. Smart homes may allow the elderly to stay in their comfortable home environments instead of expensive and limited healthcare facilities. Healthcare personnel can also keep track of the overall health condition of the elderly in real-time and provide feedback and support from distant facilities. In this paper, we have presented a comprehensive review on the state-of-the-art research and development in smart home based remote healthcare technologies.
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              The effects of the COVID-19 pandemic on people with dementia

              The COVID-19 pandemic has posed unique risks to people with Alzheimer disease and dementia. Research from 2020 has shown that these people have a relatively high risk of contracting severe COVID-19, and are also at risk of neuropsychiatric disturbances as a result of lockdown measures and social isolation.
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                Author and article information

                Contributors
                david.sharp@imperial.ac.uk
                Journal
                NPJ Digit Med
                NPJ Digit Med
                NPJ Digital Medicine
                Nature Publishing Group UK (London )
                2398-6352
                17 October 2022
                17 October 2022
                2022
                : 5
                : 154
                Affiliations
                [1 ]GRID grid.511435.7, UK Dementia Research Institute, , Care Research and Technology Centre, ; London, UK
                [2 ]GRID grid.7445.2, ISNI 0000 0001 2113 8111, Dyson School of Design Engineering, , Imperial College London, ; London, UK
                [3 ]GRID grid.7445.2, ISNI 0000 0001 2113 8111, Department of Brain Sciences, , Imperial College London, ; London, UK
                Author information
                http://orcid.org/0000-0001-9468-8237
                http://orcid.org/0000-0001-8591-9638
                http://orcid.org/0000-0002-7415-1907
                http://orcid.org/0000-0003-2238-0684
                Article
                697
                10.1038/s41746-022-00697-4
                9575641
                36253530
                0811cfef-55b9-4bdd-b03a-c49919a32f97
                © The Author(s) 2022

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 17 February 2022
                : 29 September 2022
                Funding
                Funded by: UK Dementia Research Institute, Care Research & Technology Centre; the UK Department of Health; and Surrey and Borders Partnership NHS Foundation Trust
                Funded by: UKRI CDT in AI for Healthcare http://ai4health.io (Grant No. P/S023283/1)
                Funded by: UK Dementia Research Institute, Care Research & Technology Centre; the UK Department of Health; and Surrey and Borders Partnership NHS Foundation Trust
                Funded by: UK Dementia Research Institute, Care Research & Technology Centre; the UK Department of Health; and Surrey and Borders Partnership NHS Foundation Trust
                Funded by: UK Dementia Research Institute, Care Research & Technology Centre; the UK Department of Health; and Surrey and Borders Partnership NHS Foundation Trust
                Categories
                Article
                Custom metadata
                © The Author(s) 2022

                predictive markers,dementia,geriatrics,occupational health

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