Search

Background: Adult Day care centres provide an important aspect of care provision through all phases of the dementia illness from diagnosis to the end of life (Dabelko HI 2008) supporting the well-being of both older people living with dementia and their care partners. Services within adult day care settings are designed to provide biopsychosocial health benefits to participants as well as care partner respite. Objective: To examine research studies, literature reviews and grey literature and identify and map the literature on psychosocial interventions used in day care services for older people living with dementia and chart their use, evaluation and outcomes. The research review question is "what are the psychosocial interventions used in day care service for older people living with dementia?" Psychosocial interventions are important non-pharmacological interventions which support people's wellbeing. Methods: Inclusion/Exclusion criteria were identified and guided the search strategy. Participants were people aged 60 years and over living with dementia attending day care services. The use of psychosocial interventions for this cohort was the focus of the review. Databases were searched (Cochrane Reviews, CINAHL, Embase, Medline EBSCO, Medline Ovid, Medline PubMed, PsycINFO, Scopus, Open Grey, Lenus and WHO Global Index Medicus databases) using keywords/terms with Boolean operators from 2011 to 2023. Rayyan was used to extract and manage the data. Results: The findings present a narrative and charting of the data from the 45 papers that met the review criteria, and this data is mapped onto the five objectives. Within this review, interventions were grouped into five broad types: nature (n = 6 papers), memory/cognitive (n = 11 papers), social (n = 17 papers), animal (n = 4 papers), or physical/sensory (n = 7 papers) based interventions. Conclusions: This review has illustrated the wide variety in the types, range and facilitation of psychosocial interventions within adult day care services. This review highlights the potential benefits of these interventions. However, findings must be considered in the context that many were provided as brief intervention studies with little evidence of continuation after the study and further research is required given the complex and diverse range of interventions. Results will be of interest to practitioners planning to implement or evaluate psychosocial interventions used in day care services for older people living with dementia. [ABSTRACT FROM AUTHOR]

In this months editorial, the PLOS Medicine Editors announce an upcoming Special Issue and call for papers, with Guest Editors Carol Brayne and Bruce Miller, on dementia across the lifespan and around the globe. [ABSTRACT FROM AUTHOR]

Virtual Reality (VR) has emerged as a promising tool in many domains of therapy and rehabilitation, and has recently attracted the attention of researchers and clinicians working with elderly people with MCI, Alzheimer’s disease and related disorders. Here we present a study testing the feasibility of using highly realistic image-based rendered VR with patients with MCI and dementia. We designed an attentional task to train selective and sustained attention, and we tested a VR and a paper version of this task in a single-session within-subjects design. Results showed that participants with MCI and dementia reported to be highly satisfied and interested in the task, and they reported high feelings of security, low discomfort, anxiety and fatigue. In addition, participants reported a preference for the VR condition compared to the paper condition, even if the task was more difficult. Interestingly, apathetic participants showed a preference for the VR condition stronger than that of non-apathetic participants. These findings suggest that VR-based training can be considered as an interesting tool to improve adherence to cognitive training in elderly people with cognitive impairment. [ABSTRACT FROM AUTHOR]

Background: Almost 44 million people are currently living with dementia worldwide. This number is set to increase threefold by 2050, posing a serious threat to the sustainability of healthcare systems. Overuse of antipsychotic drugs for the management of the symptoms of dementia carries negative consequences for patients while also increasing the health expenditures for society. Supportive care (SC) interventions could be considered a safer and potentially cost-saving option. In this paper we provide a systematic review of the existing evidence regarding the cost-effectiveness and cost-utility of SC interventions targeted towards persons living with dementia and their caregivers., Methods: A systematic literature review was performed between February 2019 and December 2021 through searches of the databases PubMed (MEDLINE), Cochrane Library, CENTRAL, Embase and PsycINFO. The search strategy was based on PRISMA 2020 recommendations. We considered studies published through December 2021 with no lower date limit. We distinguished between five categories of SC strategies: cognitive therapies, physical activity, indirect strategies (organisational and environmental changes), interventions primarily targeted towards family caregivers, and multicomponent interventions., Results: Of the 5,479 articles retrieved, 39 met the inclusion criteria. These studies analysed 35 SC programmes located at different stages of the dementia care pathway. Eleven studies provided evidence of high cost-effectiveness for seven interventions: two multicomponent interventions; two indirect interventions; two interventions aimed at caregivers of community-dwelling persons with dementia; one community-based cognitive stimulation and occupational programme., Conclusion: We find that the most promising SC strategies in terms of cost-effectiveness are multicomponent interventions (targeted towards both nursing home residents and day-care service users), indirect strategies (group living and dementia care management at home), some forms of tailored occupational therapy, together with some psychosocial interventions for caregivers of community-dwelling persons with dementia. Our results suggest that the adoption of effective SC interventions may increase the economic sustainability of dementia care., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2023 Guzzon et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Background: Many classification methods have been proposed based on magnetic resonance images. Most methods rely on measures such as volume, the cerebral cortical thickness and grey matter density. These measures are susceptible to the performance of registration and limited in representation of anatomical structure. This paper proposes a two-stage local feature fusion method, in which deformable registration is not desired and anatomical information is represented from moderate scale. Methods: Keypoints are firstly extracted from scale-space to represent anatomical structure. Then, two kinds of local features are calculated around the keypoints, one for correspondence and the other for representation. Scores are assigned for keypoints to quantify their effect in classification. The sum of scores for all effective keypoints is used to determine which group the test subject belongs to. Results: We apply this method to magnetic resonance images of Alzheimer's disease and Parkinson's disease. The advantage of local feature in correspondence and representation contributes to the final classification. With the help of local feature (Scale Invariant Feature Transform, SIFT) in correspondence, the performance becomes better. Local feature (Histogram of Oriented Gradient, HOG) extracted from 16×16 cell block obtains better results compared with 4×4 and 8×8 cell block. Discussion: This paper presents a method which combines the effect of SIFT descriptor in correspondence and the representation ability of HOG descriptor in anatomical structure. This method has the potential in distinguishing patients with brain disease from controls. [ABSTRACT FROM AUTHOR]

Participatory research approaches have developed in response to the growing emphasis on translation of research evidence into practice. However, there are few published examples of stakeholder engagement strategies, and little guidance specific to larger ongoing research programs or those with a rural focus. This paper describes the evolution, structure, and processes of an annual Rural Dementia Summit launched in 2008 as an engagement strategy for the Rural Dementia Action Research (RaDAR) program and ongoing for more than 10 years; and reports findings from a parallel mixed-methods study that includes stakeholder and researcher perspectives on the Summit's value and impact. Twelve years of stakeholder evaluations were analyzed. Rating scale data were summarized with descriptive statistics; open-ended questions were analyzed using an inductive thematic analysis. A thematic analysis was also used to analyze interviews with RaDAR researchers. Rating scale data showed high stakeholder satisfaction with all aspects of the Summit. Five themes were identified in the qualitative data: hearing diverse perspectives, building connections, collaborating for change, developing research and practice capacity, and leaving recharged. Five themes were identified in the researcher data: impact on development as a researcher, understanding stakeholder needs, informing research design, deepening commitment to rural dementia research, and building a culture of engagement. These findings reflect the key principles and impacts of stakeholder engagement reported in the literature. Additional findings include the value stakeholders place on connecting with stakeholders from diverse backgrounds, how the Summit was revitalizing, and how it developed stakeholder capacity to support change in their communities. Findings indicate that the Summit has developed into a community of practice where people with a common interest come together to learn and collaborate to improve rural dementia care. The Summit's success and sustainability are linked to RaDAR's responsiveness to stakeholder needs, the trust that has been established, and the value that stakeholders and researchers find in their participation., Competing Interests: The authors have declared that no competing interests exist.

Background: Sleep disturbances affect 38% of care home residents living with dementia. They are often treated with medication, but non-pharmacological interventions may be safer and effective yet more difficult to implement. In the SIESTA study (Sleep problems In dEmentia: interviews with care home STAff) we explored care home staffs' experience of managing sleep disturbances in their residents living with dementia., Methods: We conducted one-to-one semi-structured interviews in four UK care homes, and purposively recruited a maximum variation sample of 18 nurses and care assistants, who were each interviewed once. We used a topic guide and audio-recorded the interviews. Two researchers independently analysed themes from transcribed interviews., Results: Staff used a range of techniques that often worked in improving or preventing residents' sleep disturbance. During the daytime, staff encouraged residents to eat well, and be physically active and stimulated to limit daytime sleep. In the evening, staff settled residents into dark, quiet, comfortable bedrooms often after a snack. When residents woke at night, they gave them caffeinated tea or food, considered possible pain and discomfort, and reassured residents they were safe. If residents remained unsettled, staff would engage them in activities. They used telecare to monitor night-time risk. Staff found minimising daytime napping difficult, described insufficient staffing at night to attend to reorient and guide awake residents and said residents frequently did not know it was night-time., Conclusions: Some common techniques, such as caffeinated drinks, may be counterproductive. Future non-pharmacological interventions should consider practical difficulties staff face in managing sleep disturbances, including struggling to limit daytime napping, identifying residents' night-time needs, day-night disorientation, and insufficient night-time staffing., Competing Interests: LW reports that the analyses in this paper were undertaken as part of their ESRC funded PhD studentship. SGC reports receiving research funding from Alzheimer’s Research UK Grant, European Research Council and Dunhill Medical Trust. GL reports participating on the Lancet Healthy Longevity Board and receiving research funding from Alzheimer’s Society, Wellcome trust, and NIHR. This does not alter our adherence to PLOS ONE policies on sharing data and materials.

Introduction: It is well established that the actions and behaviour of dementia care workers are fundamental to the wellbeing of the people they care for. Not only do they deal with basic healthcare needs, but they also perform a vital psycho-social function by providing–through their regular presence–an underlying continuity for residents. This has been shown to improve well-being, particularly for those in the advanced stages of dementia. It has also been suggested that there are additional psycho-social benefits of such contact which can directly influence the need for anti-psychotic medication. However, unlike most other healthcare and medical settings, the specialised and often difficult interactions that dementia care workers handle every day have not yet been the subject of detailed micro-level analysis. This is particularly significant because much of the impact that care-workers have relates to the way in which they interact with the people they care for. Not having a clear understanding of how their interactions 'work' at the micro-level–particularly ones that are specific to dementia care settings, and that care workers report to be difficult or challenging–means that any training interventions that are developed may not resonate with their real-world experience, and ultimately run the risk of failing. This video-based observational study aims to provide a detailed micro-exploration of problematic and challenging interactions involving care-workers and people living with dementia. Setting and methods: The study is based in the UK and will involve up to 20 dementia care staff and 60 people living with dementia. Fieldwork will be conducted in 5 dementia care home and community-based dementia day care settings using naturalistic observational methods (primarily video-ethnography). Data will be analysed using Conversation Analysis (CA). [ABSTRACT FROM AUTHOR]

Background: There is uncertainty around the health impact and economic costs of the recent slowing of the historical decline in cardiovascular disease (CVD) incidence and the future impact on dementia and disability., Methods: Previously validated IMPACT Better Ageing Markov model for England and Wales, integrating English Longitudinal Study of Ageing (ELSA) data for 17,906 ELSA participants followed from 1998 to 2012, linked to NHS Hospital Episode Statistics. Counterfactual design comparing two scenarios: Scenario 1. CVD Plateau-age-specific CVD incidence remains at 2011 levels, thus continuing recent trends. Scenario 2. CVD Fall-age-specific CVD incidence goes on declining, following longer-term trends. The main outcome measures were age-related healthcare costs, social care costs, opportunity costs of informal care, and quality adjusted life years (valued at £60,000 per QALY)., Findings: The total 10 year cumulative incremental net monetary cost associated with a persistent plateauing of CVD would be approximately £54 billion (95% uncertainty interval £14.3-£96.2 billion), made up of some £13 billion (£8.8-£16.7 billion) healthcare costs, £1.5 billion (-£0.9-£4.0 billion) social care costs, £8 billion (£3.4-£12.8 billion) informal care and £32 billion (£0.3-£67.6 billion) value of lost QALYs., Interpretation: After previous, dramatic falls, CVD incidence has recently plateaued. That slowdown could substantially increase health and social care costs over the next ten years. Healthcare costs are likely to increase more than social care costs in absolute terms, but social care costs will increase more in relative terms. Given the links between COVID-19 and cardiovascular health, effective cardiovascular prevention policies need to be revitalised urgently., Competing Interests: All authors have completed the Unified Competing Interest form (available on request from the corresponding author) and declare: no support from any organisation for the submitted work; no financial relationships with any organisations that might have an interest in the submitted work in the previous three years. Dr Collins is currently on secondment as Head of Health Economics in Welsh Government; this paper does not represent any views of Welsh Government. Dr Pearson-Stuttard is also Head of Health Analytics at a commercial company, Lane Clark & Peacock LLP, vice-chair of the Royal Society for Public Health and reports personal fees from Novo Nordisk A/S, all outside of the submitted work. This work was completed as part of Dr Pearson-Stuttard’s academic appointment at Imperial College London.

Background: Due to the disease’s progressive nature, advance care planning (ACP) is recommended for people with early stage dementia. General practitioners (GPs) should initiate ACP because of their longstanding relationships with their patients and their early involvement with the disease, however ACP is seldom applied. Aim: To determine the barriers and facilitators faced by GPs related to ACP with people with dementia. Data sources: We systematically searched the relevant databases for papers published between January 1995 and December 2016, using the terms: primary healthcare, GP, dementia, and ACP. We conducted a systematic integrative review following Whittemore and Knafl’s method. Papers containing empirical data about GP barriers and/or facilitators regarding ACP for people with dementia were included. We evaluated quality using the Mixed-Method-Appraisal-Tool and analyzed data using qualitative content analysis. Results: Ten qualitative, five quantitative, and one mixed-method paper revealed four themes: timely initiation of ACP, stakeholder engagement, important aspects of ACP the conversation, and prerequisites for ACP. Important barriers were: uncertainty about the timing of ACP, how to plan for an uncertain future, lack of knowledge about dementia, difficulties assessing people with dementia’s decisional capacities, and changing preferences. Facilitators for ACP were: an early start when cognitive decline is still mild, inclusion of all stakeholders, and discussing social and medical issues aimed at maintaining normal life. Conclusion: Discussing future care is difficult due to uncertainties about the future and the decisional capacities of people with dementia. Based on the facilitators, we recommend that GPs use a timely and goal-oriented approach and involve all stakeholders. ACP discussions should focus on the ability of people with dementia to maintain normal daily function as well as on their quality of life, instead of end-of-life-discussions only. GPs need training to acquire knowledge and skills to timely initiate collaborative ACP discussions. [ABSTRACT FROM AUTHOR]

Background: Social care is vital to quality of life for people with young onset dementia and their families. Yet care is hugely variable, frequently lacking and poorly coordinated. We aim to establish current practice in English social care for people with young onset dementia and co-produce evidence-based recommendations and resources for improvement. Methods and analysis: In Work-Package 1, we will gather qualitative data from 25 people with young onset dementia and/or main supporters residing in England. We will ask them about their experiences of social care (broadly defined, including independent and voluntary sector provision) and suggestions for improvement. In Work-Package 2, we will conduct a short on-line survey with a wide range of staff with a role in adult social care in England. We will find out about current awareness, knowledge and practice and suggestions for improvements. Quantitative and qualitative analysis will provide a picture of current practice. In Work-Package 3, we will use convergence analysis to synthesise the findings from Work-Packages 1 and 2 and present the findings to a stakeholder workshop, to identify feasible priorities for improvement. We will establish what is already known about good practice relating to these key priorities using a scoping review and interviews with professionals. This knowledge will then feed into the co-production of resources and recommendations with key stakeholders to improve social care for people with young onset dementia and their families. Discussion: This study seeks to address a gap in our understanding of social care provision for people with young onset dementia and develop recommendations and practical resources for improvements. The findings will help people with young onset dementia and supporters to receive higher quality social care. Trial registration: Study registration number:ISRCTN10653250. [ABSTRACT FROM AUTHOR]

Background: Alzheimer’s disease neuroimaging initiative (ADNI) is a landmark imaging and omics study in AD. ADNI research literature has increased substantially over the past decade, which poses challenges for effectively communicating information about the results and impact of ADNI-related studies. In this work, we employed advanced information visualization techniques to perform a comprehensive and systematic mapping of the ADNI scientific growth and impact over a period of 12 years. Methods: Citation information of ADNI-related publications from 01/01/2003 to 05/12/2015 were downloaded from the Scopus database. Five fields, including authors, years, affiliations, sources (journals), and keywords, were extracted and preprocessed. Statistical analyses were performed on basic publication data as well as journal and citations information. Science mapping workflows were conducted using the Science of Science (Sci2) Tool to generate geospatial, topical, and collaboration visualizations at the micro (individual) to macro (global) levels such as geospatial layouts of institutional collaboration networks, keyword co-occurrence networks, and author collaboration networks evolving over time. Results: During the studied period, 996 ADNI manuscripts were published across 233 journals and conference proceedings. The number of publications grew linearly from 2008 to 2015, so did the number of involved institutions. ADNI publications received much more citations than typical papers from the same set of journals. Collaborations were visualized at multiple levels, including authors, institutions, and research areas. The evolution of key ADNI research topics was also plotted over the studied period. Conclusions: Both statistical and visualization results demonstrate the increasing attention of ADNI research, strong citation impact of ADNI publications, the expanding collaboration networks among researchers, institutions and ADNI core areas, and the dynamic evolution of ADNI research topics. The visualizations presented here can help improve daily decision making based on a deep understanding of existing patterns and trends using proven and replicable data analysis and visualization methods. They have great potential to provide new insights and actionable knowledge for helping translational research in AD. [ABSTRACT FROM AUTHOR]

Background: Evidence for the potential prevention of dementia through lifestyle risk factor modification is growing and has prompted examination of implementation approaches. Understanding the general population's perspectives regarding dementia risk reduction is key to implementation. This may provide useful insights into more effective and efficient ways to help people change relevant beliefs, motivations and behaviour patterns. We conducted a systematic review and thematic synthesis of qualitative evidence to develop an integrated model of general population dementia risk reduction perspectives and the implications for intervention in research and implementation contexts., Methods and Findings: We searched electronic databases, supplemented by lateral search techniques, to identify studies published since 1995 reporting qualitative dementia risk reduction perspectives of the non-expert general population who do not have dementia. Thematic synthesis, incorporating an expert panel discussion, was used to identify overarching themes and develop an integrated model to guide intervention to support individuals to adopt and maintain dementia risk reduction behaviour patterns. Quality of included studies and confidence in review findings were systematically appraised. We included 50 papers, reflecting the views of more than 4,500 individuals. Main themes were: 1) The need for effective education about a complex topic to prevent confusion and facilitate understanding and empowerment; 2) Personally relevant short- and long-term benefits of dementia risk reduction behaviour patterns can generate value and facilitate action; 3) Individuals benefit from trusted, reliable and sensitive support to convert understanding to personal commitment to relevant behaviour change; 4) Choice, control and relevant self-regulatory supports help individuals take-action and direct their own progress; 5) Collaborative and empowering social opportunities can facilitate and propagate dementia risk reduction behaviour change; 6) Individual behaviour patterns occur in social contexts that influence beliefs through heuristic processes and need to be understood. Findings indicate that, for intervention: 1) education is key, but both content and delivery need to be tailored; 2) complementary interventions to support self-regulation mechanisms and social processes will increase education effectiveness; 3) co-design principles should guide intervention design and delivery processes; 4) all interventions need to be supported by context-specific data., Conclusions: This systematic review and thematic synthesis provides a comprehensive, integrated model of the dementia risk reduction perspectives of the general population and intervention approaches to support behaviour change that can be applied in clinical trial and real-world implementation settings. Findings extend existing knowledge and may assist more effective intervention design and delivery., Competing Interests: The authors have declared that no competing interests exist.

This paper describes a randomized controlled trial on the Online Life Story Book (OLSB), a digital reminiscence intervention for people with (very) mild dementia living at home. The aim of the study was to investigate the effectiveness of the OLSB on (i) neuropsychiatric symptoms (NPS) in persons with dementia and (ii) the distress and quality of life (QOL) of primary informal caregivers. A randomized controlled trial with individual randomization to one of two conditions was conducted: 1) intervention "Online Life Story Book"; 2) wait list control condition. In the intervention OLSB, a trained volunteer guided the participants through the process of creating an OLSB in approximately 5 meetings within a period of 8-10 weeks. Participants in the control condition received care as usual while they waited for 6 months before starting. Outcomes on NPS and distress and QOL of the informal caregiver were assessed at baseline (baseline, T0), 3 months (T1) and 6 months (T2) post baseline. Of the 42 persons with dementia, 23 were female and 19 were male. They had a mean age of 80 years, ranging from 49 to 95. The total drop-out rate was 14.3 percent. Small but insignificant effects on NPS, caregiver distress and QOL of caregivers were found with the exception of self-rated caregiver distress that reduced significantly during the intervention. One reason to explain the results might be that the included participants were in relatively good health. Practical challenges during the intervention could have affected the results as well. It might also be that the intervention caused effects on other outcomes than NPS and caregiver distress. In future research, it is important to study the effects in persons with more complaints and higher distress and to be careful in the selection of outcome variables in relation to the reminiscence functions served by the intervention., Competing Interests: The authors have declared that no competing interests exist.

Methods have been developed for Mendelian randomization that can obtain consistent causal estimates under weaker assumptions than the standard instrumental variable assumptions. The median-based estimator and MR-Egger are examples of such methods. However, these methods can be sensitive to genetic variants with heterogeneous causal estimates. Such heterogeneity may arise from over-dispersion in the causal estimates, or specific variants with outlying causal estimates. In this paper, we develop three extensions to robust methods for Mendelian randomization with summarized data: 1) robust regression (MM-estimation); 2) penalized weights; and 3) Lasso penalization. Methods using these approaches are considered in two applied examples: one where there is evidence of over-dispersion in the causal estimates (the causal effect of body mass index on schizophrenia risk), and the other containing outliers (the causal effect of low-density lipoprotein cholesterol on Alzheimer’s disease risk). Through an extensive simulation study, we demonstrate that robust regression applied to the inverse-variance weighted method with penalized weights is a worthwhile additional sensitivity analysis for Mendelian randomization to provide robustness to variants with outlying causal estimates. The results from the applied examples and simulation study highlight the importance of using methods that make different assumptions to assess the robustness of findings from Mendelian randomization investigations with multiple genetic variants. [ABSTRACT FROM AUTHOR]

The prevalence of persons living with dementia and at risk of going missing is rising. In this study, we engaged persons living with dementia, care partners, police services, search and rescue organizations, and health and social service providers to develop Community ASAP, a mobile alert system that engages community citizens, as volunteers, to look out for persons with dementia reported missing. We completed three phases of development and evaluation of the usability and functionality of the alert system with stakeholders in three Canadian provinces. In this paper we describe features of the Community ASAP and the findings of these evaluation phases., Competing Interests: The authors have declared that no competing interests exist.

Accelerometry data has been widely used to measure activity and the circadian rhythm of individuals across the health sciences, in particular with people with advanced dementia. Modern accelerometers can record continuous observations on a single individual for several days at a sampling frequency of the order of one hertz. Such rich and lengthy data sets provide new opportunities for statistical insight, but also pose challenges in selecting from a wide range of possible summary statistics, and how the calculation of such statistics should be optimally tuned and implemented. In this paper, we build on existing approaches, as well as propose new summary statistics, and detail how these should be implemented with high frequency accelerometry data. We test and validate our methods on an observed data set from 26 recordings from individuals with advanced dementia and 14 recordings from individuals without dementia. We study four metrics: Interdaily stability (IS), intradaily variability (IV), the scaling exponent from detrended fluctuation analysis (DFA), and a novel nonparametric estimator which we call the proportion of variance (PoV), which calculates the strength of the circadian rhythm using spectral density estimation. We perform a detailed analysis indicating how the time series should be optimally subsampled to calculate IV, and recommend a subsampling rate of approximately 5 minutes for the dataset that has been studied. In addition, we propose the use of the DFA scaling exponent separately for daytime and nighttime, to further separate effects between individuals. We compare the relationships between all these methods and show that they effectively capture different features of the time series., Competing Interests: The authors have declared that no competing interests exist.

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. [ABSTRACT FROM AUTHOR]

Early diagnosis is critical for individuals with Alzheimer's disease (AD) in clinical practice because its progress is irreversible. In the existing literature, support vector machine (SVM) has always been applied to distinguish between AD and healthy controls (HC) based on neuroimaging data. But previous studies have only used a single SVM to classify AD and HC, and the accuracy is not very high and generally less than 90%. The method of random support vector machine cluster was proposed to classify AD and HC in this paper. From the Alzheimer's Disease Neuroimaging Initiative database, the subjects including 25 AD individuals and 35 HC individuals were obtained. The classification accuracy could reach to 94.44% in the results. Furthermore, the method could also be used for feature selection and the accuracy could be maintained at the level of 94.44%. In addition, we could also find out abnormal brain regions (inferior frontal gyrus, superior frontal gyrus, precentral gyrus and cingulate cortex). It is worth noting that the proposed random support vector machine cluster could be a new insight to help the diagnosis of AD. [ABSTRACT FROM AUTHOR]

Background and Objectives: Agitation is common in people living with dementia especially at the end of life. We examined how staff interpreted agitation behavior in people with dementia nearing end of life, how this may influence their responses and its impact on the quality of care., Research Design: Ethnographic study. Structured and semi-structured non-participant observations (referred to subsequently in this paper as "structured observations") of people living with dementia nearing the end of life in hospital and care homes (south-east England) and in-depth interviews with staff, conducted August 2015-March 2017., Methods: Three data sources: 1) detailed field notes, 2) observations using a structured tool and checklist for behaviors classed as agitation and staff and institutional responses, 3) staff semi-structured qualitative interviews. We calculated the time participants were agitated and described staff responses. Data sources were analyzed separately, developed continuously and relationally during the study and synthesized where appropriate., Results: We identified two main 'ideal types' of staff explanatory models for agitation: In the first, staff attribute agitated behaviors to the person's "moral judgement", making them prone to rejecting or punitive responses. In the second staff adopt a more "needs-based" approach in which agitation behaviors are regarded as meaningful and managed with proactive and investigative approaches. These different approaches appear to have significant consequences for the timing, frequency and quality of staff response. While these models may overlap they tend to reflect distinct organizational resources and values., Conclusions: Care worker knowledge about agitation is not enough, and staff need organizational support to care better for people living with dementia towards end of life. Positional theory may help to explain much of the cultural-structural context that produces staff disengagement from people with dementia, offering insights on how agitation behavior is reframed by some staff as dangerous. Such behavior may be associated with low-resource institutions with minimal staff training where the personhood of staff may be neglected., Competing Interests: The authors have declared that no competing interests exist.

This study identified and analyzed metaphors related to the lived experience of young-onset dementia that were used in nine illness narratives written by people with the condition. A final set of 1111 MEs sorted into 30 source domain categories were grouped according to six target domain categories reflecting the biologic (the person with dementia's body/brain), psychologic (suffering with dementia, coping with dementia, dementia itself, the person with dementia), and social (the social experience of dementia) aspects of having dementia. Notably, many of the metaphors were similar to previously reported metaphors of illness, such as fight and journey, and other metaphors of embodiment, as well as disease as enemy, body as container, and body as machine. In addition, although negative conceptualizations were in the majority, almost one-third of the metaphoric expressions, belonging to the fight and journey source domain categories, reflected mainly positive images. The commonality of metaphor types with those of other illness experiences supports the notion of shared metaphors across illness contexts. Moreover, in contrast to the dehumanizing and stigmatizing terms that have previously been used to socially construct dementia, the positivity of metaphoric images identified here indicates the authors' proactive and affirming conceptualizations of their experience of dementia. Health care professionals can draw on this study's findings to help their own patients make sense of and cope with dementia. [ABSTRACT FROM AUTHOR]

Dementia is characterized by a decline in memory and thinking that is significant enough to impair function in activities of daily living. Patients seen in dementia specialty clinics are highly heterogenous with a variety of different symptoms that progress at different rates. Recent research has focused on finding data-driven subtypes for revealing new insights into dementia's underlying heterogeneity, rather than assuming that the cohort is homogenous. However, current studies on dementia subtyping have the following limitations: (i) focusing on AD-related dementia only and not examining heterogeneity within dementia as a whole, (ii) using only cross-sectional baseline visit information for clustering and (iii) predominantly relying on expensive imaging biomarkers as features for clustering. In this study, we seek to overcome such limitations, using a data-driven unsupervised clustering algorithm named SillyPutty, in combination with hierarchical clustering on cognitive assessment scores to estimate subtypes within a real-world clinical dementia cohort. We use a longitudinal patient data set for our clustering analysis, instead of relying only on baseline visits, allowing us to explore the ongoing temporal relationship between subtypes and disease progression over time. Results showed that subtypes with very mild or mild dementia were more heterogenous in their cognitive profiles and risk of disease progression. [ABSTRACT FROM AUTHOR]

The number of people diagnosed with dementia is expected to rise in the coming years. Given that there is currently no definite cure for dementia and the cost of care for this condition soars dramatically, slowing the decline and maintaining independent living are important goals for supporting people with dementia. This paper discusses a study that is called Technology Integrated Health Management (TIHM). TIHM is a technology assisted monitoring system that uses Internet of Things (IoT) enabled solutions for continuous monitoring of people with dementia in their own homes. We have developed machine learning algorithms to analyse the correlation between environmental data collected by IoT technologies in TIHM in order to monitor and facilitate the physical well-being of people with dementia. The algorithms are developed with different temporal granularity to process the data for long-term and short-term analysis. We extract higher-level activity patterns which are then used to detect any change in patients' routines. We have also developed a hierarchical information fusion approach for detecting agitation, irritability and aggression. We have conducted evaluations using sensory data collected from homes of people with dementia. The proposed techniques are able to recognise agitation and unusual patterns with an accuracy of up to 80%.

Introduction: Possible dementia is usually identified in primary care by general practitioners (GPs) who refer to specialists for diagnosis. Only two-thirds of dementia cases are currently recorded in primary care, so increasing the proportion of cases diagnosed is a strategic priority for the UK and internationally. Variables in the primary care record may indicate risk of developing dementia, and could be combined in a predictive model to help find patients who are missing a diagnosis. We conducted a meta-analysis to identify clinical entities with potential for use in such a predictive model for dementia in primary care., Methods and Findings: We conducted a systematic search in PubMed, Web of Science and primary care database bibliographies. We included cohort or case-control studies which used routinely collected primary care data, to measure the association between any clinical entity and dementia. Meta-analyses were performed to pool odds ratios. A sensitivity analysis assessed the impact of non-independence of cases between studies. From a sift of 3836 papers, 20 studies, all European, were eligible for inclusion, comprising >1 million patients. 75 clinical entities were assessed as risk factors for all cause dementia, Alzheimer's (AD) and Vascular dementia (VaD). Data included were unexpectedly heterogeneous, and assumptions were made about definitions of clinical entities and timing as these were not all well described. Meta-analysis showed that neuropsychiatric symptoms including depression, anxiety, and seizures, cognitive symptoms, and history of stroke, were positively associated with dementia. Cardiovascular risk factors such as hypertension, heart disease, dyslipidaemia and diabetes were positively associated with VaD and negatively with AD. Sensitivity analyses showed similar results., Conclusions: These findings are of potential value in guiding feature selection for a risk prediction tool for dementia in primary care. Limitations include findings being UK-focussed. Further predictive entities ascertainable from primary care data, such as changes in consulting patterns, were absent from the literature and should also be explored in future studies.

Background: In the UK, there are currently 800 000 people living with dementia. This number is expected to double in the next 20 years. Two-thirds of people with dementia live in the community supported by informal carers. Caring for a person with dementia has adverse effects on psychological, physical, social wellbeing and quality of life. The measurement of quality of life of carers of people with dementia is increasingly of interest to health and social care practitioners and commissioners, policymakers, and carers themselves. However, there is lack of consensus on the most suitable instrument(s) for undertaking this., Methods: A systematic review of the literature using COSMIN methodology. Searching of electronic databases (Medline, PsycINFO, CINAHL and Web of Science), reference list and citation searching of key papers was undertaken. COSMIN methodology was used to simultaneously extract data from and assess methodological quality of included studies, and make a recommendation for the instrument with the most high quality evidence for its measurement properties., Results: Ten instruments were suitable for inclusion in this review. The Carer well-being and support questionnaire (CWS) has the best quality evidence for the greatest number of measurement of properties. The Caregiver Well-Being Scale is also worthy of consideration. There is not presently a measure which could be recommended for use in economic evaluations, however the Impact of Alzheimer's Disease on the Caregiver questionnaire (IADCQ) could potentially be used following further investigation of its measurement properties in a representative population., Conclusion: The CWS is the most appropriate instrument to recommend for the assessment of quality of life in informal carers of people with dementia at present. All instruments included in this review would benefit from more rigorous evaluation of their measurement properties.

In recent years, several high-dimensional, accurate, and effective classification methods have been proposed for the automatic discrimination of the subject between Alzheimer’s disease (AD) or its prodromal phase {i.e., mild cognitive impairment (MCI)} and healthy control (HC) persons based on T1-weighted structural magnetic resonance imaging (sMRI). These methods emphasis only on using the individual feature from sMRI images for the classification of AD, MCI, and HC subjects and their achieved classification accuracy is low. However, latest multimodal studies have shown that combining multiple features from different sMRI analysis techniques can improve the classification accuracy for these types of subjects. In this paper, we propose a novel classification technique that precisely distinguishes individuals with AD, aAD (stable MCI, who had not converted to AD within a 36-month time period), and mAD (MCI caused by AD, who had converted to AD within a 36-month time period) from HC individuals. The proposed method combines three different features extracted from structural MR (sMR) images using voxel-based morphometry (VBM), hippocampal volume (HV), and cortical and subcortical segmented region techniques. Three classification experiments were performed (AD vs. HC, aAD vs. mAD, and HC vs. mAD) with 326 subjects (171 elderly controls and 81 AD, 35 aAD, and 39 mAD patients). For the development and validation of the proposed classification method, we acquired the sMR images from the dataset of the National Research Center for Dementia (NRCD). A five-fold cross-validation technique was applied to find the optimal hyperparameters for the classifier, and the classification performance was compared by using three well-known classifiers: K-nearest neighbor, support vector machine, and random forest. Overall, the proposed model with the SVM classifier achieved the best performance on the NRCD dataset. For the individual feature, the VBM technique provided the best results followed by the HV technique. However, the use of combined features improved the classification accuracy and predictive power for the early classification of AD compared to the use of individual features. The most stable and reliable classification results were achieved when combining all extracted features. Additionally, to analyze the efficiency of the proposed model, we used the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset to compare the classification performance of the proposed model with those of several state-of-the-art methods. [ABSTRACT FROM AUTHOR]

The path signature is a means of feature generation that can encode nonlinear interactions in data in addition to the usual linear terms. It provides interpretable features and its output is a fixed length vector irrespective of the number of input points or their sample times. In this paper we use the path signature to provide features for identifying people whose diagnosis subsequently converts to Alzheimer’s disease. In two separate classification tasks we distinguish converters from 1) healthy individuals, and 2) individuals with mild cognitive impairment. The data used are time-ordered measurements of the whole brain, ventricles and hippocampus from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). We find two nonlinear interactions which are predictive in both cases. The first interaction is change of hippocampal volume with time, and the second is a change of hippocampal volume relative to the volume of the whole brain. While hippocampal and brain volume changes are well known in Alzheimer’s disease, we demonstrate the power of the path signature in their identification and analysis without manual feature selection. Sequential data is becoming increasingly available as monitoring technology is applied, and the path signature method is shown to be a useful tool in the processing of this data. [ABSTRACT FROM AUTHOR]

Alzheimer’s disease (AD) affects millions of people and is a major rising problem in health care worldwide. Recent research suggests that AD could have different subtypes, presenting differences in how the disease develops. Characterizing those subtypes could be key to deepen the understanding of this complex disease. In this paper, we used a multivariate, non-supervised clustering method over blood-based markers to find subgroups of patients defined by distinctive blood marker profiles. Our analysis on ADNI database identified 4 possible subgroups, each with a different blood profile. More importantly, we show that subgroups with different profiles have a different relationship between brain phenotypes detected in magnetic resonance imaging and disease condition. [ABSTRACT FROM AUTHOR]

Background: Early diagnosis of Alzheimer’s disease (AD) and Mild Cognitive Impairment (MCI) is essential for timely treatment. Machine learning and multivariate pattern analysis (MVPA) for the diagnosis of brain disorders are explicitly attracting attention in the neuroimaging community. In this paper, we propose a voxel-wise discriminative framework applied to multi-measure resting-state fMRI (rs-fMRI) that integrates hybrid MVPA and extreme learning machine (ELM) for the automated discrimination of AD and MCI from the cognitive normal (CN) state. Materials and methods: We used two rs-fMRI cohorts: the public Alzheimer’s disease Neuroimaging Initiative database (ADNI2) and an in-house Alzheimer’s disease cohort from South Korea, both including individuals with AD, MCI, and normal controls. After extracting three-dimensional (3-D) patterns measuring regional coherence and functional connectivity during the resting state, we performed univariate statistical t-tests to generate a 3-D mask that retained only voxels showing significant changes. Given the initial univariate features, to enhance discriminative patterns, we implemented MVPA feature reduction using support vector machine-recursive feature elimination (SVM-RFE), and least absolute shrinkage and selection operator (LASSO), in combination with the univariate t-test. Classifications were performed by an ELM, and its efficiency was compared to linear and nonlinear (radial basis function) SVMs. Results: The maximal accuracies achieved by the method in the ADNI2 cohort were 98.86% (p<0.001) and 98.57% (p<0.001) for AD and MCI vs. CN, respectively. In the in-house cohort, the same accuracies were 98.70% (p<0.001) and 94.16% (p<0.001). Conclusion: From a clinical perspective, combining extreme learning machine and hybrid MVPA applied on concatenations of multiple rs-fMRI biomarkers can potentially assist the clinicians in AD and MCI diagnosis. [ABSTRACT FROM AUTHOR]

Time-dependent data collected in studies of Alzheimer’s disease usually has missing and irregularly sampled data points. For this reason time series methods which assume regular sampling cannot be applied directly to the data without a pre-processing step. In this paper we use a random forest to learn the relationship between pairs of data points at different time separations. The input vector is a summary of the time series history and it includes both demographic and non-time varying variables such as genetic data. To test the method we use data from the TADPOLE grand challenge, an initiative which aims to predict the evolution of subjects at risk of Alzheimer’s disease using demographic, physical and cognitive input data. The task is to predict diagnosis, ADAS-13 score and normalised ventricles volume. While the competition proceeds, forecasting methods may be compared using a leaderboard dataset selected from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) and with standard metrics for measuring accuracy. For diagnosis, we find an mAUC of 0.82, and a classification accuracy of 0.73 compared with a benchmark SVM predictor which gives mAUC = 0.62 and BCA = 0.52. The results show that the method is effective and comparable with other methods. [ABSTRACT FROM AUTHOR]

Background: Dementias in high income countries are set to be the third major burden of disease even as older people are increasingly required to think for themselves how to provide for their lives in retirement. Meanwhile the period of older age continues to extend with increase in life expectancy. This challenge demands an understanding of how cognition changes over an extended period in later life. But studying cognitive ageing in the population faces a difficulty from the fact that older respondents are liable to leave (attrite) before study completion. This study tested three hypotheses: trajectories of cognitive ageing in Britain show an improvement beyond the age of 50; and they are lifted by secular improvement in cognition across cohorts; lastly they are susceptible to distortion due to attrition. Methods and Findings: Using the English Longitudinal Study of Ageing, this paper studied trajectories of episodic memory of Britons aged 50–89 from 2002 to 2013 (N = 5931). Using joint models the analysis found that levels of episodic memory follow a curvilinear shape, not a steady decline, in later life. The findings also revealed secular improvement in cognitive ageing such that as a cohort is being replaced episodic memory levels in the population improve. The analysis lastly demonstrated that failure to simultaneously model attrition can produce distorted pictures of cognitive ageing. Conclusion: Old age in this century is not necessarily a period dominated by cognitive decline. In identifying behavioural factors associated with better cognitive ageing, such as social connections of traditional and online kinds, the paper raises possibilities of mustering an adequate response to the cognition challenge. [ABSTRACT FROM AUTHOR]

Background: Dementia is a complex disorder characterized by poor outcomes for the patients and high costs of care. After decades of research little is known about its mechanisms. Having prognostic estimates about dementia can help researchers, patients and public entities in dealing with this disorder. Thus, health data, machine learning and microsimulation techniques could be employed in developing prognostic estimates for dementia., Objective: The goal of this paper is to present evidence on the state of the art of studies investigating and the prognosis of dementia using machine learning and microsimulation techniques., Method: To achieve our goal we carried out a systematic literature review, in which three large databases-Pubmed, Socups and Web of Science were searched to select studies that employed machine learning or microsimulation techniques for the prognosis of dementia. A single backward snowballing was done to identify further studies. A quality checklist was also employed to assess the quality of the evidence presented by the selected studies, and low quality studies were removed. Finally, data from the final set of studies were extracted in summary tables., Results: In total 37 papers were included. The data summary results showed that the current research is focused on the investigation of the patients with mild cognitive impairment that will evolve to Alzheimer's disease, using machine learning techniques. Microsimulation studies were concerned with cost estimation and had a populational focus. Neuroimaging was the most commonly used variable., Conclusions: Prediction of conversion from MCI to AD is the dominant theme in the selected studies. Most studies used ML techniques on Neuroimaging data. Only a few data sources have been recruited by most studies and the ADNI database is the one most commonly used. Only two studies have investigated the prediction of epidemiological aspects of Dementia using either ML or MS techniques. Finally, care should be taken when interpreting the reported accuracy of ML techniques, given studies' different contexts.

Background: Adherence to medication is vital for disease management while simultaneously reducing healthcare expenditure. Older persons with cognitive impairment (CI) are at risk for non-adherence as cognitive processes are needed to manage medications. This systematic review focuses on the relationship between medication non-adherence and specific cognitive domains in persons with CI, and explores determinants of medication non-adherence. When available, relationships and factors are compared with cognitively intact populations., Methods: A seven database systematic search of studies published between 1 January 1949-31 December 2015 examining medication non-adherence in community dwelling persons with CI or dementia was conducted. Articles reporting medication non-adherence in people with CI or dementia in the community, with or without caregiver supports were eligible for inclusion. Papers reporting adherence to treatments in cognitively intact populations, populations from hospital or institutional settings, for non-prescribed medication or those describing dementia as a factor predicting medication non-adherence were excluded. Data on study and population characteristics, research design, data sources and analysis, specific cognitive domains, non-adherence prevalence, measurement of adherence, salient findings, factors associated with adherence and strategies to improve medication adherence were extracted. Study limitations included inconsistencies between data sources and definitions, resulting in a loss of fidelity in the value and comprehensiveness of data, as well as exclusion of non-pharmacological treatments and regimens., Findings: Fifteen studies met inclusion criteria. Adherence among CI subjects ranged from 10.7%-38% with better rates of adherence in non-CI individuals. Medication non-adherence definitions varied considerably. New-learning, memory and executive functioning were associated with improved adherence and formed the focus of most studies. Multiple factors were identified as modulators of non-adherence., Conclusion: This review highlights a gap in knowledge on how specific cognitive domains contribute to medication non-adherence amongst CI populations, and demonstrates the current focus is limited to two domains: memory and executive functioning., Competing Interests: The authors have declared that no competing interests exist.

This paper estimates the influence of inadequate access to healthcare services on the rate of Emergency Room (ER) hospital visits in Australia. We take micro-data on different types of healthcare shortfalls from the 2012 Australian Survey of Disability, Aging and Carers, and employ Propensity Score Matching (PSM) techniques to identify their effects on ER visits. We find that shortfalls in access to various medical services increases ER visits for individuals with mental and physical conditions by about the same degree. Conversely, inadequate community care services significantly predict ER visits for individuals with physical conditions, but not for persons with mental conditions. The lack of predictive power for inadequate community care for persons with mental health problems is surprising, as “acopia” is thought to be a significant driver of crises that require emergency treatment. We discuss some of the mechanisms that may underpin this finding and address the policy implications of our results. Lastly a number of robustness checks and diagnostics tests are presented which confirm that our modelling assumptions are not violated and that our results are insensitive to the choice of matching algorithms. [ABSTRACT FROM AUTHOR]

Case-control Genome-Wide Association Studies (GWAS) provide a rich resource for studying the genetic architecture of complex diseases. A key is to elucidate how the genetic effects vary by the environment, what is traditionally defined by Gene-Environment interactions (GxE). The overlooked complication is that multiple, distinct pathophysiologic mechanisms may lead to the same clinical diagnosis and often these mechanisms have distinct genetic bases. In this paper, we first show that using the clinically diagnosed status can lead to severely biased estimates of GxE interactions in situations when the frequency of the pathologic diagnosis of interest, as compared to other diagnoses, depends on the environment. We then propose a pseudo-likelihood solution to correct the bias. Finally, we demonstrate our method in extensive simulations and in a GWAS of Alzheimer’s disease. [ABSTRACT FROM AUTHOR]

Personhood discourses in dementia care have gained prominence and current care home standards mandate that care should be “person-centred”. However, it is unclear how the personhood of staff is construed within the care relationship. This paper aims to explore how the personhood of paid carers of people with dementia can be understood by focussing on the views and experiences of care home staff. We undertook a secondary qualitative analysis of interviews with 25 paid care staff in England, conducted as part of the MARQUE (Managing Agitation and Raising QUality of lifE) study. The authors inductively developed themes around the topic of personhood for staff, contrasting management and care staff perspectives. We found that many care staff are not identified as persons in their own right by their employing institutions, and that there is a general lack of acknowledgment of the moral work of caring that occurs within formal care work. This oversight can reduce the complex relationships of care work to a series of care tasks, challenges care workers’ self-worth and self-efficacy, and impede their efforts to deliver person-centred care. We conclude that care staff status as persons in their own right should be explicitly considered in quality standards and supported by employers’ policies and practices, not simply for their role in preserving the personhood of people with dementia but for their own sense of valued personhood. Enhancing staff personhood may also result in improved care. [ABSTRACT FROM AUTHOR]

Objectives: Demographic change has led to an increase of older people in need of long-term care in nearly all European countries. Informal carers primarily provide the care and support needed by dependent people. The supply and willingness of individuals to act as carers are critical to sustain informal care resources as part of the home health care provision. This paper describes a longitudinal study of informal care in six European countries and reports analyses that determine those factors predicting the outcomes of family care over a one-year period. Methods: Analyses are based on data from the EUROFAMCARE project, a longitudinal survey study of family carers of older people with baseline data collection in 2004 and follow-up data collection a year later in six European countries (Germany, Greece, Italy, Poland, Sweden, and the United Kingdom), N = 3,348. Descriptive statistics of the sample characteristics are reported. Binary logistic random-intercept regressions were computed, predicting the outcome of change of the care dyad’s status at follow-up. Results: Where care is provided by a more distant family member or by a friend or neighbour, the care-recipient is significantly more likely to be cared for by someone else (OR 1.62) or to be in residential care (OR 3.37) after one year. The same holds true if the care-recipient has memory problems with a dementia diagnosis (OR 1.79/OR 1.84). Higher dependency (OR 1.22) and behavioural problems (OR 1.76) in the care-recipient also lead to a change of care dyad status. Country of residence explained a relatively small amount of variance (8%) in whether a care-recipient was cared for by someone else after one year, but explained a substantial amount of variance (52%) in whether a care-recipient was in residential care. Particularly in Sweden, care-recipients are much more likely to be cared for by another family or professional carer or to be in residential care, whereas in Greece the status of the care dyad is much less likely to change. Discussion: The majority of family carers continued to provide care to their respective older relatives over a one-year period, despite often high levels of functional, cognitive and behavioural problems in the care-recipient. Those family carers could benefit most from appropriate support. The carer/care-recipient relationship plays an important role in whether or not a family care dyad remains intact over a one-year period. The support of health and social care services should be particularly targeted toward those care dyads where there is no partner or spouse acting as carer, or no extended family network that might absorb the caring role when required. Distant relatives, friends or acquaintances who are acting as carers might need substantial intervention if their caregiving role is to be maintained. [ABSTRACT FROM AUTHOR]

Introduction: Incontinence is a major problem for people with dementia (PWD) and their family/friend caregivers, often causing substantial harm, including residential care admission. The incontinence needs of PWD are complex and different from those of people without dementia. The aim of this study was to investigate carer and nurse perceptions of continence service provision and potential improvements. Methods: A secondary analysis of qualitative data was undertaken. Semi-structured interviews (n = 45) were undertaken with PWD, family caregivers and healthcare professionals (continence or dementia nurses) in the UK. PWD and caregivers were recruited via www.joindementiaresearch.nihr.ac.uk and via dementia/carer groups. Nurses were recruited via their employers. Framework analysis was used. The COREQ Research guideline statement assists reporting. Results: Four themes were found. Firstly, there was a lack of awareness of the service and waiting time. Many caregivers were unaware of continence services and dementia nurses often viewed it as a pad provision service. Caregivers reported long waits not meeting their urgent needs. Secondly, product provision was often inadequate. Most caregivers self-purchased all or many products and substantial variation in product provision was found. The number of products provided was often inadequate. Thirdly, a sense that "nothing can be done" was observed by some nurses and caregivers. Caregivers believed that, if nothing else, care information should be provided. Finally, suggestions for improvements were made, including proactive service signposting, joint clinics with dementia services, improved information before crisis point, dementia training for continence nurses and improved product provision. Conclusion: Continence service inadequacies for PWD and caregivers have been reported for many years. This study demonstrates service provision remains unsatisfactory in the UK. Stakeholders propose a range of service improvements. It highlights that listening to the voices of PWD, caregivers and nurses is crucial for services seeking to improve continence services for PWD living at home. [ABSTRACT FROM AUTHOR]

Objectives: This study examined the impact of the Center of Healthy Aging Program (CHAP) on the cognitive function and economic burden associated with dementia. Methods: This observational study utilised Iwaki cohort data. We included participants with mini-mental state examination (MMSE) scores and categorised them into pre- and post-CHAP groups based on their year of entry into the cohort (before 2013 or after) (index year). We defined participants with suspected severe cognitive impairment and suspected mild cognitive impairment using their MMSE scores, with their incidence being the first observation meeting these definitions during the follow-up period. We compared the incidence rates between the pre- and post-CHAP groups using Cox proportional hazard analysis. Medical and caregiving costs were estimated based on the projected number of residents in Iwaki area with suspected mild cognitive impairment and sSCI during hypothetical 10 years of the CHAP implemented or not and compared. Results: Of the 2,569 participants, 1716 and 853 were included in the pre- and post-CHAP groups, respectively. The incidence rate of suspected mild cognitive impairment was significantly lower in the post-CHAP group even after adjusted known factors associated with cognitive disorders. No cases of suspected severe cognitive impairment occurred in the post-CHAP group during the follow-up period. Estimated costs of JPY 1,628,450 (USD 11562.00 or EUR 10259.24, JPY 100 = USD 0.71 or EUR 0.63) and JPY 789,560 (USD 5605.88 or EUR 4974.23) per person per year were projected after 10 years with and without the CHAP, respectively. Conclusions: We demonstrated a reduction in the incidence rate of suspected mild cognitive impairment among residents who participated in the CHAP and a decrease in the medical and caregiving costs associated with suspected severe cognitive impairment. [ABSTRACT FROM AUTHOR]

Introduction: Health policy in the UK and globally regarding dementia, emphasises prevention and risk reduction. These goals could be facilitated by automated assessment of dementia risk in primary care using routinely collected patient data. However, existing applicable tools are weak at identifying patients at high risk for dementia. We set out to develop improved risk prediction models deployable in primary care. Methods: Electronic health records (EHRs) for patients aged 60–89 from 393 English general practices were extracted from the Clinical Practice Research Datalink (CPRD) GOLD database. 235 and 158 practices respectively were randomly assigned to development and validation cohorts. Separate dementia risk models were developed for patients aged 60–79 (development cohort n = 616,366; validation cohort n = 419,126) and 80–89 (n = 175,131 and n = 118,717). The outcome was incident dementia within 5 years and more than 60 evidence-based risk factors were evaluated. Risk models were developed and validated using multivariable Cox regression. Results: The age 60–79 development cohort included 10,841 incident cases of dementia (6.3 per 1,000 person-years) and the age 80–89 development cohort included 15,994 (40.2 per 1,000 person-years). Discrimination and calibration for the resulting age 60–79 model were good (Harrell's C 0.78 (95% CI: 0.78 to 0.79); Royston's D 1.74 (1.70 to 1.78); calibration slope 0.98 (0.96 to 1.01)), with 37% of patients in the top 1% of risk scores receiving a dementia diagnosis within 5 years. Fit statistics were lower for the age 80–89 model but dementia incidence was higher and 79% of those in the top 1% of risk scores subsequently developed dementia. Conclusion: Our models can identify individuals at higher risk of dementia using routinely collected information from their primary care record, and outperform an existing EHR-based tool. Discriminative ability was greatest for those aged 60–79, but the model for those aged 80–89 may also be clinical useful. [ABSTRACT FROM AUTHOR]

Background: Multi-component CS programs incorporating practice of activities of daily living (ADL) into intervention have reported benefits for ADL outcomes in individuals living with mild-to-moderate dementia. A randomised controlled trial (RCT) within community occupational therapy services in Ireland, is planned to evaluate the effects of CS-ADL, an ADL-focused, multi-component CS program, on ADL outcomes for individuals living with mild-to-moderate dementia. Method: A single-blind RCT with a calculated sample size of 34 participants has been planned to compare the effects of CS-ADL versus treatment as usual on the outcomes of basic ADLs and instrumental ADLs. Cognition, mood, communication, and quality of life will also be evaluated as secondary outcomes. CS-ADL sessions will run once weekly for a total of seven weeks, lasting approximately two hours each. Outcome data will be collected at baseline, within sessions and post-intervention at week eight. Descriptive statistics will be used to analyse the data. This study has been registered at clinicaltrials.gov (NCT06147479). Discussion: CS programs are commonly conducted by occupational therapists working with individuals living with mild-to-moderate dementia. This study aims to demonstrate the effectiveness of a multi-component CS program delivered through an occupational therapy lens, potentially influencing the approach to CS and ADL interventions undertaken by occupational therapists. [ABSTRACT FROM AUTHOR]

Background: Smoking, alcohol consumption, poor diet and low levels of physical activity significantly contribute to the burden of illness in developed countries. Whilst the links between specific and multiple risk behaviours and individual chronic conditions are well documented, the impact of these behaviours in mid-life across a range of later life outcomes has yet to be comprehensively assessed. This review aimed to provide an overview of behavioural risk factors in mid-life that are associated with successful ageing and the primary prevention or delay of disability, dementia, frailty and non-communicable chronic conditions., Methods: A literature search was conducted to identify cohort studies published in English since 2000 up to Dec 2014. Multivariate analyses and a minimum follow-up of five years were required for inclusion. Two reviewers screened titles, abstracts and papers independently. Studies were assessed for quality. Evidence was synthesised by mid-life behavioural risk for a range of late life outcomes., Findings: This search located 10,338 individual references, of which 164 are included in this review. Follow-up data ranged from five years to 36 years. Outcomes include dementia, frailty, disability and cardiovascular disease. There is consistent evidence of beneficial associations between mid-life physical activity, healthy ageing and disease outcomes. Across all populations studied there is consistent evidence that mid-life smoking has a detrimental effect on health. Evidence specific to alcohol consumption was mixed. Limited, but supportive, evidence was available relating specifically to mid-life diet, leisure and social activities or health inequalities., Conclusions: There is consistent evidence of associations between mid-life behaviours and a range of late life outcomes. The promotion of physical activity, healthy diet and smoking cessation in all mid-life populations should be encouraged for successful ageing and the prevention of disability and chronic disease.

Background and objective: There has been a growing interest in using computerized cognitive assessment to detect age-related cognitive disorders. We have developed a tablet-based cancellation test (e-CT), previously shown as a reliable measure of executive functions and free of effect of familiarity with computer-based devices in healthy older adults. This study aimed to investigate the influence of demographics and current daily use of computer-based devices in older adults with Mild Cognitive Impairment (MCI) and Alzheimer’s disease (AD). We further studied the ability of the e-CT to discriminate MCI and AD patients from older adults with normal cognition (NC). Methods: The e-CT was administered to 325 older adults (NC = 112, MCI = 129, AD = 84). Subjects also performed the K-T test, a paper-and-pencil cancellation test from which the e-CT was developed. Multiple linear regression analyses were conducted to assess the contribution of demographics and current daily use of computer-based devices on the e-CT in patient groups. The Receiver Operating Characteristic (ROC) curves and the Area Under the Curve (AUC) were established to compare the efficacy of the e-CT and the K-T test to classify subjects into diagnostic groups. Results: In the MCI group, age (B = -0.37, p<0.001) and current daily use of computer-based devices (B = 5.85, p<0.001) were associated with the number of correct cancellations of the e-CT. In the AD group, only current daily use of a computer-based device was a significant contributor (B = 6.28, p<0.001). The e-CT (AUC = 0.811; 95% confidence interval [CI]: 0.756–0.867) and the K-T (AUC = 0.837; CI: 0.787–0.887) showed good and comparable diagnostic accuracy to discriminate between MCI and NC subjects. To discriminate between NC and AD, both tests showed high diagnostic accuracy, with the AUC values of 0.923 (CI: 0.876–0.971) and 0.929 (95%CI: 0.886–0.972) for the e-CT and the K-T, respectively. Conclusion: The e-CT presents satisfying discriminative validity and is a promising tool for detection of early cognitive impairment in older adults. [ABSTRACT FROM AUTHOR]

Detecting early morphological changes in the brain and making early diagnosis are important for Alzheimer’s disease (AD). High resolution magnetic resonance imaging can be used to help diagnosis and prediction of the disease. In this paper, we proposed a machine learning method to discriminate patients with AD or mild cognitive impairment (MCI) from healthy elderly and to predict the AD conversion in MCI patients by computing and analyzing the regional morphological differences of brain between groups. Distance between each pair of subjects was quantified from a symmetric diffeomorphic registration, followed by an embedding algorithm and a learning approach for classification. The proposed method obtained accuracy of 96.5% in differentiating mild AD from healthy elderly with the whole-brain gray matter or temporal lobe as region of interest (ROI), 91.74% in differentiating progressive MCI from healthy elderly and 88.99% in classifying progressive MCI versus stable MCI with amygdala or hippocampus as ROI. This deformation-based method has made full use of the pair-wise macroscopic shape difference between groups and consequently increased the power for discrimination. [ABSTRACT FROM AUTHOR]

Objective: This study began as a single-blind randomized controlled trial (RCT) to investigate the efficacy and safety of electroconvulsive therapy (ECT) for severe treatment-refractory agitation in advanced dementia. The aims are to assess agitation reduction using the Cohen-Mansfield Agitation Inventory (CMAI), evaluate tolerability and safety outcomes, and explore the long-term stability of agitation reduction and global functioning. Due to challenges encountered during implementation, including recruitment obstacles and operational difficulties, the study design was modified to an open-label format and other protocol amendments were implemented. Methods: Initially, the RCT randomized participants 1:1 to either ECT plus usual care or simulated ECT plus usual care (S-ECT) groups. As patients were enrolled, data were collected from both ECT and simulated ECT (S-ECT) patients. The study now continues in an open-label study design where all patients receive actual ECT, reducing the targeted sample size from 200 to 50 participants. Results: Study is ongoing and open to enrollment. Conclusion: The transition of the ECT-AD study design from an RCT to open-label design exemplifies adaptive research methodologies in response to real-world challenges. Data from both the RCT and open-label phases of the study will provide a unique perspective on the role of ECT in managing severe treatment-refractory agitation in dementia, potentially influencing future clinical practices and research approaches. [ABSTRACT FROM AUTHOR]

Older people living with dementia or mild cognitive impairment (MCI) are more vulnerable to experiencing social isolation and loneliness due to their cognitive and physical impairments. Increasingly integrating technology into group exercises contributed to the improved resilience and well-being of older adults living with dementia and MCI. The purpose of this scoping review was to identify the various types, feasibility, outcome measures, and impacts of technology-based group exercise interventions for people with dementia or MCI. We utilized the Joanna Briggs Institute approach, a three-step process. A comprehensive literature search on five databases—CINAHL, MEDLINE, Embase, Web of Science, and PsycInfo—until January 2024 yielded 1,585 publications; the final review included 14 publications that recruited a total of 379 participants, with mean age of 69 (SD = 4.21) years to 87.07 (SD = 3.92) years. Analysis of data showed three types of technology-based group exercise interventions for people with dementia or MCI: (a) exergames, (b) virtual cycling or kayak paddling, and (c) video-conferencing platforms. In addition, we identified three key impacts: (a) feasibility and accessibility; (b) physical, psychosocial, and cognitive benefits; and (c) adaptations necessary for persons with dementia or MCI. Our study suggests that technology-based group exercise interventions are feasible and acceptable to persons with dementia or MCI. Future studies should involve individuals with dementia and their caregivers in the design and implementation of technology-based group exercise programs. [ABSTRACT FROM AUTHOR]

Introduction: Dementia, a major global health concern, is an acquired disorder that causes a progressive decline in cognitive abilities, affecting learning and memory, language, executive function, complex attention, perceptual-motor skills, and social cognition. Our study aims to evaluate the knowledge and attitudes regarding dementia and Alzheimer's disease among medical students. Methods: This cross-sectional study was conducted among 393 medical students in Palestine from August 2023 to November 2023. The assessment of knowledge and attitude toward dementia was measured using the Alzheimer's Disease Knowledge Scale (ADKS) and Dementia Attitude Scale (DAS). The data were analyzed using SPSS version 26, and the Mann-Whitney U-test and the Kruskal-Wallis test were used to compare the mean between the groups with a 5% significance level. Results: The overall mean score of the student's knowledge of dementia measured by the ADKS was 18.91 (±3.32 SD) out of 30. The mean score of the student's attitude toward dementia measured by the DAS was 91.68 (±3.32 SD). Clinical students had higher ADKS scores than pre-clinical students (p-value < 0.001). No significant differences in the knowledge and attitudes toward dementia were found between males and females. The medical students' knowledge and attitude scores were positively correlated (ρ = 0.227, p-value <0.001). Conclusion: Palestinian medical student's knowledge about Alzheimer's disease and dementia is insufficient, with students in the clinical phase showing better understanding than pre-clinical students. The findings highlight a necessity for enhancing the dementia curriculum and conducting further studies to evaluate training's impact on students' knowledge and attitudes. [ABSTRACT FROM AUTHOR]

Background: Recognised as essential for high-quality dementia service, person-centred care aims to understand and respect the unique needs of each individual. Self-experience practices may offer caregivers an opportunity to acquire knowledge, empathy, and skills related to person-centred care, especially through recreating experiences similar to dementia. Given the need to enhance the understanding of self-experience practices in dementia care, a more comprehensive investigation of these training interventions for (future) caregivers is needed. Methods: We conducted a scoping review to map the evidence on the use of self-experience practices in dementia training. We systematically searched Cochrane Library, MEDLINE via PubMed, CINAHL, and Web of Science. We also searched for grey literature, as well as registry entries, and conducted backward citation tracking of included reviews. We analysed data on intervention characteristics, factors influencing the implementation, and learning outcomes based on Kirkpatrick's model. Results: We included 44 reports across 30 intervention programmes. The majority of reports (91%) were published from 2016 onwards, with 32% originating from the USA and 25% from the UK. We identified passive, interactive, immersive, and multicomponent self-experience interventions in dementia education and training. Learning outcomes based on Kirkpatrick's model were fairly distributed across all identified modalities. Both consumers and providers emphasised aspects related to the development and implementation of practices, particularly organisational-related considerations such as temporal and spatial planning of trainings. Conclusions: Our review highlights diverse interventions incorporating self-experience practices, with an increasing role for technological tools. While self-experience interventions engage participants, the impact on individuals with dementia and organisational levels remain largely unreported. Our overview, informed by current literature, underscores unique considerations and challenges associated with dementia-related self-experience practices. Implementing and evaluating complex training interventions using self-experience practices should consider ethical aspects. Trial registration: Registry: Registered within the Open Science Framework (available at https://osf.io/fycxa/). [ABSTRACT FROM AUTHOR]

Introduction: A Job Exposure Matrix (JEM) for psychosocial job stressors allows assessment of these exposures at a population level. JEMs are particularly useful in situations when information on psychosocial job stressors were not collected individually and can help eliminate the biases that may be present in individual self-report accounts. This research paper describes the development of a JEM in the Australian context. Methods: The Household Income Labour Dynamics in Australia (HILDA) survey was used to construct a JEM for job control, job demands and complexity, job insecurity, and fairness of pay. Population median values of these variables for all employed people (n = 20,428) were used to define individual exposures across the period 2001 to 2012. The JEM was calculated for the Australian and New Zealand Standard Classification of Occupations (ANZSCO) at the four-digit level, which represents 358 occupations. Both continuous and binary exposures to job stressors were calculated at the 4-digit level. We assessed concordance between the JEM-assigned and individually-reported exposures using the Kappa statistic, sensitivity and specificity assessments. We conducted regression analysis using mental health as an outcome measure. Results: Kappa statistics indicate good agreement between individually-reported and JEM-assigned dichotomous measures for job demands and control, and moderate agreement for job insecurity and fairness of pay. Job control, job demands and security had the highest sensitivity, while specificity was relatively high for the four exposures. Regression analysis shows that most individually reported and JEM measures were significantly associated with mental health, and individually-reported exposures produced much stronger effects on mental health than the JEM-assigned exposures. Discussion: These JEM-based estimates of stressors exposure provide a conservative proxy for individual-level data, and can be applied to a range of health and organisational outcomes. [ABSTRACT FROM AUTHOR]