Participatory research to design a novel system to support the night-time needs of people with dementia; NOCTURNAL

Strategies to support people living with dementia are broad in scope, proposing both pharmacological and non-pharmacological interventions as part of the care pathway. Assistive technologies form part of this offering as both stand-alone devices to support particular tasks and the more complex offering of the “smart home” to underpin ambient assisted living. This paper presents a technology-based system, which expands on the smart home architecture, orientated to support people with daily living. The system, NOCTURNAL, was developed by working directly with people who had dementia, and their carers using qualitative research methods. The research focused primarily on the nighttime needs of people living with dementia in real home settings. Eight people with dementia had the final prototype system installed for a three month evaluation at home. Disturbed sleep patterns, night-time wandering were a focus of this research not only in terms of detection by commercially available technology but also exploring if automated music, light and visual personalized photographs would be soothing to participants during the hours of darkness. The NOCTURNAL platform and associated services was informed by strong user engagement of people with dementia and the service providers who care for them. NOCTURNAL emerged as a holistic service offering a personalised therapeutic aspect with interactive capabilities

Monitoring and detection of agitation in dementia: towards real-time and big-data solutions

The changing demographic profile of the population has potentially challenging social, geopolitical, and financial consequences for individuals, families, the wider society, and governments globally. The demographic change will result in a rapidly growing elderly population with healthcare implications which importantly include Alzheimer type conditions (a leading cause of dementia). Dementia requires long term care to manage the negative behavioral symptoms which are primarily exhibited in terms of agitation and aggression as the condition develops. This paper considers the nature of dementia along with the issues and challenges implicit in its management. The Behavioral and Psychological Symptoms of Dementia (BPSD) are introduced with factors (precursors) to the onset of agitation and aggression. Independent living is considered, health monitoring and implementation in context-aware decision-support systems is discussed with consideration of data analytics. Implicit in health monitoring are technical and ethical constraints, we briefly consider these constraints with the ability to generalize to a range of medical conditions. We postulate that health monitoring offers exciting potential opportunities however the challenges lie in the effective realization of independent assisted living while meeting the ethical challenges, achieving this remains an open research question remains.Peer ReviewedPostprint (author's final draft

Night optimised care technology for users needing assisted lifestyles

There is growing interest in the development of ambient assisted living services to increase the quality of life of the increasing proportion of the older population. We report on the Night Optimised Care Technology for UseRs Needing Assisted Lifestyles project, which provides specialised night time support to people at early stages of dementia. This article explains the technical infrastructure, the intelligent software behind the decision-making driving the system, the software development process followed, the interfaces used to interact with the user, and the findings and lessons of our user-centred approach

Night optimised care technology for users needing assisted lifestyles

There is growing interest in the development of ambient assisted living services to increase the quality of life of the increasing proportion of the older population. We report on the Night Optimised Care Technology for UseRs Needing Assisted Lifestyles project, which provides specialised night time support to people at early stages of dementia. This article explains the technical infrastructure, the intelligent software behind the decision-making driving the system, the software development process followed, the interfaces used to interact with the user, and the findings and lessons of our user-centred approach

Monoaminergic Neuropathology in Alzheimer's disease

Acknowledgments This work was supported by The Croatian Science Foundation grant. no. IP-2014-09-9730 (“Tau protein hyperphosphorylation, aggregation, and trans-synaptic transfer in Alzheimer’s disease: cerebrospinal fluid analysis and assessment of potential neuroprotective compounds”) and European Cooperation in Science and Technology (COST) Action CM1103 (“Stucture-based drug design for diagnosis and treatment of neurological diseases: dissecting and modulating complex function in the monoaminergic systems of the brain”). PRH is supported in part by NIH grant P50 AG005138.Peer reviewedPostprin

THE ASSOCIATION BETWEEN SLEEP DURATION AND DEMENTIA: A META-ANALYSIS

In the United States, the current cases of Alzheimer’s disease will double by 2050. Therefore, it is important to study risk factors associated with dementia such as sleep duration. This meta-analysis was conducted to understand the discrepancy in study results since some demonstrated a V shaped association between duration of sleep and dementia while others found no association. If there truly is an association then sleep duration could be targeted to decrease the burdens caused by dementia. A meta-analysis of published studies was conducted to assess the association between sleep duration and the different forms of dementia. The articles were found using PubMed, Embase, Scopus, and EBSCO with the search terms (“Sleep Duration” OR “Change in Sleep Duration”) AND (Alzheimer* OR Dementia) and reviewing bibliographies. Studies were included in the analysis if they met the following criteria 1) a longitudinal study 2) a cohort, case-control, or clinical trial 3) assessed the exposure and outcome of interest 4) diagnosed dementia using established diagnostic criteria 5) provided a risk estimate and 95% confidence interval (CI) 6) in English 7) a published paper. Analyses such as test of heterogeneity, sensitivity analysis, and tests of publication bias were done using STATA15. The analysis included 11 cohort studies with a total of 48,360 participants. No significant association was found between short or long sleep duration and any form of dementia. However, there was a significant association between increase in sleep and dementia but there were only two published papers that examined this association. This study suggests that there is likely no association between sleep duration and any form of dementia which differs from results of previous meta-analyses

Machine learning methods for detecting urinary tract infection and analysing daily living activities in people with dementia

Dementia is a neurological and cognitive condition that affects millions of people around the world. At any given time in the United Kingdom, 1 in 4 hospital beds are occupied by a person with dementia, while about 22% of these hospital admissions are due to preventable causes. In this paper we discuss using Internet of Things (IoT) technologies and in-home sensory devices in combination with machine learning techniques to monitor health and well-being of people with dementia. This will allow us to provide more effective and preventative care and reduce preventable hospital admissions. One of the unique aspects of this work is combining environmental data with physiological data collected via low cost in-home sensory devices to extract actionable information regarding the health and well-being of people with dementia in their own home environment. We have worked with clinicians to design our machine learning algorithms where we focused on developing solutions for real-world settings. In our solutions, we avoid generating too many alerts/alarms to prevent increasing the monitoring and support workload. We have designed an algorithm to detect Urinary Tract Infections (UTI) which is one of the top five reasons of hospital admissions for people with dementia (around 9% of hospital admissions for people with dementia in the UK). To develop the UTI detection algorithm, we have used a Non-negative Matrix Factorisation (NMF) technique to extract latent factors from raw observation and use them for clustering and identifying the possible UTI cases. In addition, we have designed an algorithm for detecting changes in activity patterns to identify early symptoms of cognitive decline or health decline in order to provide personalised and preventative care services. For this purpose, we have used an Isolation Forest (iForest) technique to create a holistic view of the daily activity patterns. This paper describes the algorithms and discusses the evaluation of the work using a large set of real-world data collected from a trial with people with dementia and their caregivers

Emerging technologies for monitoring behavioural and psychological symptoms of dementia

(c) 2014 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.Behavioural and psychological symptoms of dementia (BPSD) are complex array of symptoms that have devastating impact on patients, carers and their loved ones. In this paper we argue that with the combined use of pervasive computing and big data, we could make significant progress in the diagnosis of the causes of BPSD, monitoring response to treatment and helping in the prevention of these symptoms. We review the available technologies, such as Cloud computing and context aware systems, and how they could help in managing and hopefully preventing the Behavioural and Psychological Symptoms of Dementia.Peer ReviewedPostprint (author's final draft

Android-Based Family Support to Improve the Quality of Sleep Patterns in the Elderly

Ageing is often associated with sleep disorders. This study aimed to describe the effect of using an Android application on family support in the sleep patterns of the elderly. The study used a cross-sectional survey design conducted among the elderly to assess the use of android-based applications in identifying the sleep pattern experienced and the perceived-family support. The research sample was 98 people selected by convenience sampling and snowball sampling techniques. Researchers enhanced family support through an Android app. The mean value of sleep patterns before using the app was 4.25 and after using the app was 8.35. The correlation test showed that perceived-family support had a significantly strong correlation with sleep patterns with a positive correlation (correlation coefficient 0.779, p-value 0.001; Table 5). The higher the family support perceived by the elderly, the better their sleep pattern. The study concluded that the elderly at risk of dementia often experience disturbed sleep patterns. Families play an important role in helping the improvement of elderly sleep patterns. Android applications can be a technological innovation in increasing family support for the elderly. Keywords: android-based application, sleep patterns, family suppor