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

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.

Home monitoring of daily living activities and prediction of agitation risk in a cohort of people living with dementia.

BACKGROUND: People living with dementia (PLWD) have an increased susceptibility to developing adverse physical and psychological events. Internet of Things (IoT) technologies provides new ways to remotely monitor patients within the comfort of their homes, particularly important for the timely delivery of appropriate healthcare. Presented here is data collated as part of the on-going UK Dementia Research Institute's Care Research and Technology Centre cohort and Technology Integrated Health Management (TIHM) study. There are two main aims to this work: first, to investigate the effect of the COVID-19 quarantine on the performance of daily living activities of PLWD, on which there is currently little research; and second, to create a simple classification model capable of effectively predicting agitation risk in PLWD, allowing for the generation of alerts with actionable information by which to prevent such outcomes. METHOD: A within-subject, date-matched study was conducted on daily living activity data using the first COVID-19 quarantine as a natural experiment. Supervised machine learning approaches were then applied to combined physiological and environmental data to create two simple classification models: a single marker model trained using ambient temperature as a feature, and a multi-marker model using ambient temperature, body temperature, movement, and entropy as features. RESULT: There are 102 PLWD total included in the dataset, with all patients having an established diagnosis of dementia, but with ranging types and severity. The COVID-19 study was carried out on a sub-group of 21 patient households. In 2020, PLWD had a significant increase in daily household activity (p = 1.40e-08), one-way repeated measures ANOVA). Moreover, there was a significant interaction between the pandemic quarantine and patient gender on night-time bed-occupancy duration (p = 3.00e-02, two-way mixed-effect ANOVA). On evaluating the models using 10-fold cross validation, both the single and multi-marker model were shown to balance precision and recall well, having F1-scores of 0.80 and 0.66, respectively. CONCLUSION: Remote monitoring technologies provide a continuous and reliable way of monitoring patient day-to-day wellbeing. The application of statistical analyses and machine learning algorithms to combined physiological and environmental data has huge potential to positively impact the delivery of healthcare for PLWD.

Conferences in the time of COVID: notes on organizing and delivering the first Brain Conference.

To further fulfil their missions of promoting teaching, education and research in neurology and related clinical-academic disciplines, the Guarantors of Brain and the Brain journal family invited delegates to the first Brain Conference in Spring of this year. This event aimed to deliver excellent teaching and scientific presentations across a broad spectrum of neuroscience fields, with the key aim of making the content as accessible as possible. We hoped to capitalize on the benefits of an online format, whilst trying to capture a little of the joy of the in-person meeting. This article reports on the approach and practical choices made to achieve these goals, and we hope this will provide some guidance and advice to others organizing their own online conference.

Author Correction: A role for Biofoundries in rapid development and validation of automated SARS-CoV-2 clinical diagnostics.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Dementia wellbeing and COVID-19: Review and expert consensus on current research and knowledge gaps.

OBJECTIVES: In response to a commissioned research update on dementia during the COVID-19 pandemic, a UK-based working group, comprising dementia researchers from a range of fields and disciplines, aimed to describe the impact of the pandemic on dementia wellbeing and identify priorities for future research. METHODS: We supplemented a rapid literature search (including unpublished, non-peer reviewed and ongoing studies/reports) on dementia wellbeing in the context of COVID-19 with expert group members' consensus about future research needs. From this we generated potential research questions the group judged to be relevant that were not covered by the existing literature. RESULTS: Themes emerged from 141 studies within the six domains of the NHS England COVID-19 Dementia Wellbeing Pathway: Preventing Well, Diagnosing Well, Treating Well, Supporting Well, Living Well and Dying Well. We describe current research findings and knowledge gaps relating to the impact on people affected by dementia (individuals with a diagnosis, their carers and social contacts, health and social care practitioners and volunteers), services, research activities and organisations. Broad themes included the potential benefits and risks of new models of working including remote healthcare, the need for population-representative longitudinal studies to monitor longer-term impacts, and the importance of reporting dementia-related findings within broader health and care studies. CONCLUSIONS: The COVID-19 pandemic has had a disproportionately negative impact on people affected by dementia. Researchers and funding organisations have responded rapidly to try to understand the impacts. Future research should highlight and resolve outstanding questions to develop evidence-based measures to improve the quality of life of people affected by dementia.

A role for Biofoundries in rapid development and validation of automated SARS-CoV-2 clinical diagnostics.

The SARS-CoV-2 pandemic has shown how a rapid rise in demand for patient and community sample testing can quickly overwhelm testing capability globally. With most diagnostic infrastructure dependent on specialized instruments, their exclusive reagent supplies quickly become bottlenecks, creating an urgent need for approaches to boost testing capacity. We address this challenge by refocusing the London Biofoundry onto the development of alternative testing pipelines. Here, we present a reagent-agnostic automated SARS-CoV-2 testing platform that can be quickly deployed and scaled. Using an in-house-generated, open-source, MS2-virus-like particle (VLP) SARS-CoV-2 standard, we validate RNA extraction and RT-qPCR workflows as well as two detection assays based on CRISPR-Cas13a and RT-loop-mediated isothermal amplification (RT-LAMP). In collaboration with an NHS diagnostic testing lab, we report the performance of the overall workflow and detection of SARS-CoV-2 in patient samples using RT-qPCR, CRISPR-Cas13a, and RT-LAMP. The validated RNA extraction and RT-qPCR platform has been installed in NHS diagnostic labs, increasing testing capacity by 1000 samples per day.