Movement behaviors, that is, both physical activity and sedentary behavior, are independently associated with health risks. Although both behaviors have been investigated separately in people after stroke, little is known about the combined movement behavior patterns, differences in these patterns between individuals, or the factors associated with these patterns. Therefore, the objectives of this study are (1) to identify movement behavior patterns in people with first-ever stroke discharged to the home setting and (2) to explore factors associated with the identified patterns.
Background Movement behaviors (i.e., physical activity levels, sedentary behavior) in people with stroke are not self-contained but cluster in patterns. Recent research identified three commonly distinct movement behavior patterns in people with stroke. However, it remains unknown if movement behavior patterns remain stable and if individuals change in movement behavior pattern over time. Objectives 1) To investigate the stability of the composition of movement behavior patterns over time, and 2) determine if individuals change their movement behavior resulting in allocation to another movement behavior pattern within the first two years after discharge to home in people with a first-ever stroke. Methods Accelerometer data of 200 people with stroke of the RISE-cohort study were analyzed. Ten movement behavior variables were compressed using Principal Componence Analysis and K-means clustering was used to identify movement behavior patterns at three weeks, six months, one year, and two years after home discharge. The stability of the components within movement behavior patterns was investigated. Frequencies of individuals’ movement behavior pattern and changes in movement behavior pattern allocation were objectified. Results The composition of the movement behavior patterns at discharge did not change over time. At baseline, there were 22% sedentary exercisers (active/sedentary), 45% sedentary movers (inactive/sedentary) and 33% sedentary prolongers (inactive/highly sedentary). Thirty-five percent of the stroke survivors allocated to another movement behavior pattern within the first two years, of whom 63% deteriorated to a movement behavior pattern with higher health risks. After two years there were, 19% sedentary exercisers, 42% sedentary movers, and 39% sedentary prolongers. Conclusions The composition of movement behavior patterns remains stable over time. However, individuals change their movement behavior. Significantly more people allocated to a movement behavior pattern with higher health risks. The increase of people allocated to sedentary movers and sedentary prolongers is of great concern. It underlines the importance of improving or maintaining healthy movement behavior to prevent future health risks after stroke.
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Purpose (1) To investigate the differences in the course of participation up to one year after stroke between distinct movement behavior patterns identified directly after discharge to the home setting, and (2) to investigate the longitudinal association between the development of movement behavior patterns over time and participation after stroke. Materials and methods 200 individuals with a first-ever stroke were assessed directly after discharge to the home setting, at six months and at one year. The Participation domain of the Stroke Impact Scale 3.0 was used to measure participation. Movement behavior was objectified using accelerometry for 14 days. Participants were categorized into three distinct movement behavior patterns: sedentary exercisers, sedentary movers and sedentary prolongers. Generalized estimating equations (GEE) were performed. Results People who were classified as sedentary prolongers directly after discharge was associated with a worse course of participation up to one year after stroke. The development of sedentary prolongers over time was also associated with worse participation compared to sedentary exercisers. Conclusions The course of participation after stroke differs across distinct movement behavior patterns after discharge to the home setting. Highly sedentary and inactive people with stroke are at risk for restrictions in participation over time. Implications for rehabilitation The course of participation in people with a first-ever stroke up to one year after discharge to the home setting differed based on three distinct movement behavior patterns, i.e., sedentary exercisers, sedentary movers and sedentary prolongers. Early identification of highly sedentary and inactive people with stroke after discharge to the home setting is important, as sedentary prolongers are at risk for restrictions in participation over time. Supporting people with stroke to adapt and maintain a healthy movement behavior after discharge to the home setting could prevent potential long-term restrictions in participation.
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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).
This proposal aims to explore a radically different path towards a more sustainable fashion future through technology. Most research on fashion and technology focuses on high tech innovation and, as a result, overlooks knowledge that is already available and has been used, tested and improved for centuries. The proposed research project, however, looks backward to move forward. It aims to investigate ‘the blindingly obvious’ and asks the question how historical technologies could be used to solve contemporary environmental issues in fashion. It thus argues that technology from the past could inspire both designers and technologists to come up with new and exciting solutions to make the future of fashion more sustainable. The current fast fashion system has changed the relationship consumers have with their clothing. Clothing has become a throwaway object and this has severe environmental implications. This research project aims to find a solution by exploring historical technologies - such as folding, mending and reassembling-, because in the past a ‘sustainable’ attitude towards fashion was the norm simply because cloth and garments were expensive. It wants to examine what happens when consumers, fashion designers and technologists are confronted with these techniques. What would, for example, materialize when an aeronautical engineer takes the technique of folding as a starting point and aims to create clothes that can grow with babies and toddlers? The answer is the signature suit of the brand Petit Pli: a special folding technique allows their signature suit to grow with children from 3 months to 3 years. Much like the age-old folding techniques applied in traditional Dutch dress, which allowed the size women’s jackets to be altered, by simply adjusting the pleats. Similarly, this project aims to investigate how high tech solutions, can be initiated through historical techniques.
Performance feedback is an important mechanism of adaptation in learning theories, as it provides one of the motivations for organizations to learn (Pettit, Crossan, and Vera 2017). Embedded in the behavioral theory of the firm, organizational learning from performance feedback predicts the probability for organizations to change with an emphasis on organizational aspirations, which serve as a threshold against which absolute performance is evaluated (Cyert and March 1963; Greve 2003). It postulates that performance becomes a ‘problem’, or the trigger to search for alternative procedures, strategies, products and behaviors, when performance is below that threshold. This search is known as problemistic search. Missing from this body of research, is empirically grounded understanding if the characteristics of performance feedback over time matter for the triggering function of the feedback. I explore this gap. This investigation adds temporality as a dimension of the performance feedback concept guided by a worldview of ongoing change and flux where conditions and choices are not given, but made relevant by actors and enacted upon (Tsoukas and Chia 2002). The general aim of the study is to complement the current knowledge of performance feedback as a trigger for problemistic search with an explicit process temporal approach. The main question guiding this project is how temporal patterns of performance feedback influence organizational change, which I answer in four chapters, each zooming into one sub-question.First, I focus on the temporal order of performance feedback by examining performance feedback and change sequences organizations go through. In this section time is under study and the goal is to explore how feedback patterns have evolved over time, just as the change states organizations pass through. Second, I focus on the plurality of performance feedback by investigating performance feedback from multiple aspiration levels (i.e. multiple qualitatively different metrics and multiple reference points) and how over time clusters of performance feedback sequences have evolved. Next, I look into the rate and scope of change relative to performance feedback sequences and add an element of signal strength to the feedback. In the last chapter, time is a predictor (in the sequences), and, it is under study (in the timing of responses). I focus on the timing of organizational responses in relation to performance feedback sequences of multiple metrics and reference points.In sum, all chapters are guided by the timing problem of performance feedback, meaning that performance feedback does not come ‘available’ at a single point in time. Similarly to stones with unequal weight dropped in the river, performance feedback with different strength comes available at multiple points in time and it is plausible that sometimes it is considered by decision-makers as problematic and sometimes it is not, because of the sequence it is part of. Overall, the investigation is grounded in the general principles of organizational learning from performance feedback, and the concept of time as duration, sequences and timing, with a focus on specification of when things happen. The context of the study is universities of applied sciences and hotels in The Netherlands. Project partner: Tilburg University, School of Social and Behavioral Sciences, Department of Organization Studies
