22 research outputs found

    A feasibility study comparing UK older adult mental health inpatient wards which use protected engagement time with other wards which do not: Study protocol

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    © 2016 Nolan et al. Background: Protected engagement time (PET) is a concept of managing staff time on mental health inpatient wards with the aim of increasing staff and patient interaction. Despite apparent widespread use of PET, there remains a dearth of evidence as to how it is implemented and whether it carries benefits for staff or patients. This protocol describes a study which is being carried out on mental health wards caring for older adults (aged over 65) in England. The study shares a large proportion of the procedures, measures and study team membership of a recently completed investigation of the impact of PET in adult acute mental health wards. The study aims to identify prevalence and components of PET to construct a model for the intervention, in addition to testing the feasibility of the measures and procedures in preparation for a randomised trial. Methods/design: The study comprises four modules and uses a mixed methods approach. Module 1 involves mapping all inpatient wards in England which provide care for older adults, including those with dementia, ascertaining how many of these provide PET and in what way. Module 2 uses a prospective cohort method to compare five older adult mental health wards that use PET with five that do not across three National Health Service (NHS) Foundation Trust sites. The comparison comprises questionnaires, observation tools and routinely collected clinical service data and combines validated measures with questions developed specifically for the study. Module 3 entails an in-depth case study evaluation of three of the participating PET wards (one from each NHS Trust site) using semi-structured interviews with patients, carers and staff. Module 4 describes the development of a model and fidelity scale for PET using the information derived from the other modules with a working group of patients, carers and staff. Discussion: This is a feasibility study to test the application of the measures and methods in inpatient wards for older adults and develop a draft model for the intervention. The next stage will prospectively involve testing of the model and fidelity scale in randomised conditions to provide evidence for the effectiveness of PET as an intervention

    High Degree of Heterogeneity in Alzheimer's Disease Progression Patterns

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    There have been several reports on the varying rates of progression among Alzheimer's Disease (AD) patients; however, there has been no quantitative study of the amount of heterogeneity in AD. Obtaining a reliable quantitative measure of AD progression rates and their variances among the patients for each stage of AD is essential for evaluating results of any clinical study. The Global Deterioration Scale (GDS) and Functional Assessment Staging procedure (FAST) characterize seven stages in the course of AD from normal aging to severe dementia. Each GDS/FAST stage has a published mean duration, but the variance is unknown. We use statistical analysis to reconstruct GDS/FAST stage durations in a cohort of 648 AD patients with an average follow-up time of 4.78 years. Calculations for GDS/FAST stages 4–6 reveal that the standard deviations for stage durations are comparable with their mean values, indicating the presence of large variations in the AD progression among patients. Such amount of heterogeneity in the course of progression of AD is consistent with the existence of several sub-groups of AD patients, which differ by their patterns of decline

    Calculating Stage Duration Statistics in Multistage Diseases

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    Many human diseases are characterized by multiple stages of progression. While the typical sequence of disease progression can be identified, there may be large individual variations among patients. Identifying mean stage durations and their variations is critical for statistical hypothesis testing needed to determine if treatment is having a significant effect on the progression, or if a new therapy is showing a delay of progression through a multistage disease. In this paper we focus on two methods for extracting stage duration statistics from longitudinal datasets: an extension of the linear regression technique, and a counting algorithm. Both are non-iterative, non-parametric and computationally cheap methods, which makes them invaluable tools for studying the epidemiology of diseases, with a goal of identifying different patterns of progression by using bioinformatics methodologies. Here we show that the regression method performs well for calculating the mean stage durations under a wide variety of assumptions, however, its generalization to variance calculations fails under realistic assumptions about the data collection procedure. On the other hand, the counting method yields reliable estimations for both means and variances of stage durations. Applications to Alzheimer disease progression are discussed
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