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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De Hogeschool van Amsterdam, het Zijlstra Center (VU) en Hofmeier doen samen onderzoek naar de invulling van de onafhankelijke controlfunctie bij Woningcorporaties. Wij delen opvallende bevindingen en waarnemingen met u in enkele publicaties. Dit is de eerste, over de ruimte die gegeven wordt bij het inrichten van de onafhankelijke controlfunctie en het effect dat dat heeft. Voor de een geeft een ruime jas de mogelijkheid om er knus in weg te duiken, voor de ander zakt een ruime jas ongemakkelijk van de schouders. Wat kan ruimte in vereisten doen met de inrichting van de onafhankelijke control functie? Welke kansen en risico’s geeft dat corporaties in het algemeen en u als interne toezichthouder in het bijzonder? Rol ambiguïteit, rolconflicten en jobcrafting kunnen signalen zijn voor het niet goed ingericht zijn van de organisatie en daarmee mogelijk van missstanden. Het kan de RvC (en het bestuur) handvatten bieden voor toezicht via soft controls.
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Mirror neurons in the cerebral cortex have been shown to fire not onlyduring performance but also during visual and auditory observation ofactivity. This phenomenon is commonly called cerebral resonance behavior.This would mean that cortical motor regions would not only beactivated while singing, but also while listening to music. The sameshould hold true for playing a music instrument. Although most individualsare able to sing along when they hear a melody, even highlyskilled instrumentalists, however, are frequently unable to play by ear.They are score-dependent—i.e. they are only able to play a piece of musicwhen they have access to the notes—while musicians who are able to playby ear and improvise are non score-dependent; they are able to playwithout notes. Our hypothesis is that score-dependent instrumentalistswill exhibit less cerebral resonance behavior than non score-dependentmusicians while listening to music. Using fMRI to measure BOLD response,subjects listen to two-part harmony presented with headphones.The following experimental conditions are distinguished: (1) well-knownvs. unknown music (2) motor imagery vs. attentive listening. A voxelbasedanalysis of differences between the condition-related cerebral activationsis performed using Statistical Parametric Mapping.
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).
Road freight transport contributes to 75% of the global logistics CO2 emissions. Various European initiatives are calling for a drastic cut-down of CO2 emissions in this sector [1]. This requires advanced and very expensive technological innovations; i.e. re-design of vehicle units, hybridization of powertrains and autonomous vehicle technology. One particular innovation that aims to solve this problem is multi-articulated vehicles (road-trains). They have a smaller footprint and better efficiency of transport than traditional transport vehicles like trucks. In line with the missions for Energy Transition and Sustainability [2], road-trains can have zero-emission powertrains leading to clean and sustainable urban mobility of people and goods. However, multiple articulations in a vehicle pose a problem of reversing the vehicle. Since it is extremely difficult to predict the sideways movement of the vehicle combination while reversing, no driver can master this process. This is also the problem faced by the drivers of TRENS Solar Train’s vehicle, which is a multi-articulated modular electric road vehicle. It can be used for transporting cargo as well as passengers in tight environments, making it suitable for operation in urban areas. This project aims to develop a reverse assist system to help drivers reverse multi-articulated vehicles like the TRENS Solar Train, enabling them to maneuver backward when the need arises in its operations, safely and predictably. This will subsequently provide multi-articulated vehicle users with a sustainable and economically viable option for the transport of cargo and passengers with unrestricted maneuverability resulting in better application and adding to the innovation in sustainable road transport.
In this project we utilize the conversational model of delivering destination information as an experimental intervention to provide tips to a sub-group of visitor participants in one specific destination, Overijssel. By contrasting the experience of this group to a randomly assigned control group will be able to test the effectiveness of hyper-personalized information. Furthermore, we will investigate the effectiveness of integrating, in the tips provided, the policy of the DMO to direct visitors to certain places while reducing the pressure on others. For this variable as well––policy-driven vs. demand-driven information sources––random assignment to test and control groups will allow us to draw conclusions about causes of differences in tourist behavior and experience.The main question is: Does the conversational information model, as exemplified by Travel with Zoey, create the possibility to direct people to the places destination managers would like them to go, while assuring they benefit equally––or even more–from their travel experience? Partners: NBTC, Marketing Oost, Travel With Zoey.
