Objective To evaluate the effects of a psycho-educational intervention on caregiver burden in partners of patients with postoperative heart failure. Background Since partners of cardiac surgery patients play a significant role in the patient’s recovery, it is important to address their needs during hospitalization and after discharge. Methods Forty-two patients with postoperative heart failure and their partners participated in a randomized controlled pilot study. Dyads in the intervention group received psycho-educational support from a multidisciplinary team. Dyads in the control group received usual care. Results No significant differences were found in the performance of caregiving tasks and perceived caregiver burden in the control versus the intervention group. Conclusion A pilot study exploring the effects of a psycho-educational intervention in patients and their partners did not reveal significant effects with regard to reduced feelings of burden in partners. Alleviating caregiver burden in partners may need a more intense or specific approach.
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The need for care will increase in the coming years. Most people with a disability or old age receive support from an informal caregiver. Caring for a person with dementia can be difficult because of the BPSD (Behavioral and Psychological Symptoms of Dementia). BPSD, including sleep disturbance, is an important factor for a higher care load. In this scoping review, we aim to investigate whether technology is available to support the informal caregiver, to lower the care burden, improve sleep quality, and therefore influence the reduction of social isolation of informal caregivers of people with dementia. A scoping review is performed following the methodological framework by Arksey and O'Mally and Rumrill et al., the scoping review includes scientific and other sources (unpublished literature, websites, reports, etc.). The findings of the scoping review shows that there are technology applications available to support the informal caregiver of a person with dementia. The technology applications mostly contribute to lower the care burden and/or improve sleep quality and therefore may contribute to reduce social isolation. The technology applications found target either the person with dementia, the informal caregiver, or both.
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afstudeerscriptie van studente Psychologie Yasmin Gharavi gepubliceerd in BMC Psychiatry: Background: Family members who care for patients with severe mental illness experience emotional distress and report a higher incidence of mental illness than those in the general population. They report feeling inadequately prepared to provide the necessary practical and emotional support for these patients. The MAT training, an Interaction- Skills Training program (IST) for caregivers, was developed to meet those needs. This study used a single-arm pretestposttest design to examine the impact of the training on caregivers’ sense of competence (self-efficacy) and burden.
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Huntington’s disease (HD) and various spinocerebellar ataxias (SCA) are autosomal dominantly inherited neurodegenerative disorders caused by a CAG repeat expansion in the disease-related gene1. The impact of HD and SCA on families and individuals is enormous and far reaching, as patients typically display first symptoms during midlife. HD is characterized by unwanted choreatic movements, behavioral and psychiatric disturbances and dementia. SCAs are mainly characterized by ataxia but also other symptoms including cognitive deficits, similarly affecting quality of life and leading to disability. These problems worsen as the disease progresses and affected individuals are no longer able to work, drive, or care for themselves. It places an enormous burden on their family and caregivers, and patients will require intensive nursing home care when disease progresses, and lifespan is reduced. Although the clinical and pathological phenotypes are distinct for each CAG repeat expansion disorder, it is thought that similar molecular mechanisms underlie the effect of expanded CAG repeats in different genes. The predicted Age of Onset (AO) for both HD, SCA1 and SCA3 (and 5 other CAG-repeat diseases) is based on the polyQ expansion, but the CAG/polyQ determines the AO only for 50% (see figure below). A large variety on AO is observed, especially for the most common range between 40 and 50 repeats11,12. Large differences in onset, especially in the range 40-50 CAGs not only imply that current individual predictions for AO are imprecise (affecting important life decisions that patients need to make and also hampering assessment of potential onset-delaying intervention) but also do offer optimism that (patient-related) factors exist that can delay the onset of disease.To address both items, we need to generate a better model, based on patient-derived cells that generates parameters that not only mirror the CAG-repeat length dependency of these diseases, but that also better predicts inter-patient variations in disease susceptibility and effectiveness of interventions. Hereto, we will use a staggered project design as explained in 5.1, in which we first will determine which cellular and molecular determinants (referred to as landscapes) in isogenic iPSC models are associated with increased CAG repeat lengths using deep-learning algorithms (DLA) (WP1). Hereto, we will use a well characterized control cell line in which we modify the CAG repeat length in the endogenous ataxin-1, Ataxin-3 and Huntingtin gene from wildtype Q repeats to intermediate to adult onset and juvenile polyQ repeats. We will next expand the model with cells from the 3 (SCA1, SCA3, and HD) existing and new cohorts of early-onset, adult-onset and late-onset/intermediate repeat patients for which, besides accurate AO information, also clinical parameters (MRI scans, liquor markers etc) will be (made) available. This will be used for validation and to fine-tune the molecular landscapes (again using DLA) towards the best prediction of individual patient related clinical markers and AO (WP3). The same models and (most relevant) landscapes will also be used for evaluations of novel mutant protein lowering strategies as will emerge from WP4.This overall development process of landscape prediction is an iterative process that involves (a) data processing (WP5) (b) unsupervised data exploration and dimensionality reduction to find patterns in data and create “labels” for similarity and (c) development of data supervised Deep Learning (DL) models for landscape prediction based on the labels from previous step. Each iteration starts with data that is generated and deployed according to FAIR principles, and the developed deep learning system will be instrumental to connect these WPs. Insights in algorithm sensitivity from the predictive models will form the basis for discussion with field experts on the distinction and phenotypic consequences. While full development of accurate diagnostics might go beyond the timespan of the 5 year project, ideally our final landscapes can be used for new genetic counselling: when somebody is positive for the gene, can we use his/her cells, feed it into the generated cell-based model and better predict the AO and severity? While this will answer questions from clinicians and patient communities, it will also generate new ones, which is why we will study the ethical implications of such improved diagnostics in advance (WP6).
