Stroke is the second most common cause of death and the third leading cause of disability worldwide,1,2 with the burden expected to increase during the next 20 years.1 Almost 40% of the people with stroke have a recurrent stroke within 10 years,3 making secondary prevention vital.3,4 High amounts of sedentary time have been found to increase the risk of cardiovascular disease,5–11 particularly when the sedentary time is accumulated in prolonged bouts.12–15 Sedentary behavior, is defined as “any waking behavior characterized by an energy expenditure ≤1.5 Metabolic Equivalent of Task (METs) while in a sitting, reclining or lying posture”.16,17 Studies in healthy people, as well as people with diabetes and obesity, have shown that reducing the total amount of sedentary time and/or breaking up long periods of uninterrupted sedentary time, reduces metabolic risk factors associated with cardiovascular disease.6,9,10,12–15 Recent studies have shown that people living in the community after stroke spend more time each day sedentary, and more time in uninterrupted bouts of sedentary time compared to age-matched healthy peers.18–20 Reducing sedentary time and breaking up long sedentary bouts with short bursts of activity may be a promising intervention to reduce the risk of recurrent stroke and other cardiovascular diseases in people with stroke. To develop effective interventions, it is important to understand the factors associated with sedentary time in people with stroke. Previous studies have found associations between self-reported physical function after stroke and total sedentary time, but inconsistent results with regards to the relationship of age, stroke severity, and walking speed with sedentary time.20,21 These results are from secondary analyses of single-site observational studies, not powered to address associations, and inconsistent in the methods used to determine waking hours; thus making direct comparisons between studies difficult.20,21 Individual participant data pooling, with consistent processing of wake time data, allows novel exploratory analyses of larger datasets with greater power. By pooling all available individual participant data internationally, this study aimed to comprehensively explore the factors associated with sedentary time in community-dwelling people with stroke. Specifically, our research questions were: (1) What factors are associated with total sedentary time during waking hours after stroke? (2) What factors are associated with time spent in prolonged sedentary bouts during waking hours?
In this study, growth trajectories (from admission until unconditional release) of crime-related dynamic risk factors were investigated in a sample of Dutch forensic patients (N = 317), using latent growth curve modeling. After testing the unconditional model, three predictors were added: first-time offender versus recidivist, age, and treatment duration. Postanalyses were chi-square difference tests, t tests, and analyses of variance (ANOVAs) to assess differences in trajectories. Overall, on scale level, a decrease of risk factors over time was found. The predictors showed no significant slope differences although age and treatment duration differed significantly at some time points. The oldest age group performed worse, especially at later time points. Treatment duration effects were found at the second time point. Our results that forensic patients show a decrease in crime-related risk factors may indicate that treatment is effective. This study also found differences in growth rates, indicating the effect of individual differences
Background: Short-term survival after solid-organ transplantation has substantially improved, and the focus has shifted to long-term survival, including the role of physical activity (PA). Knowledge about PA and sedentary time in recipients of solid-organ transplantation is limited, and identification of the levels and associated factors is necessary for intervention development.Objective: The objectives of this study were to investigate the level of PA and sedentary time in recipients of solid-organ transplantation and to identify factors associated with these behaviors.Design: The design consisted of a cross-sectional survey.Methods: Questionnaires on PA level, sedentary time, and potential associated factors were used for recipients of solid-organ transplantation (kidney, liver, lung, and heart [N = 656]). Multiple regression analyses with a variable selection procedure were used.Results: Fewer than 60% of the recipients fulfilled the PA guideline. Factors significantly associated with a lower level of PA included being a woman, younger age (nonlinear), not actively working or being retired, physical limitations, and low expectations and self-confidence. Factors significantly associated with less sedentary time included exercise self-efficacy and not actively working or being retired. Significantly associated with more sedentary time were a high education level, fear of negative effects, physical limitations, and the motivator "health and physical outcomes." The type of transplantation did not significantly influence either of the outcome measures.Limitations: The design did not allow for causal inferences to be made. The studied associated factors were limited to individual and interpersonal factors. Self-reported measures of PA and sedentary time were used.Conclusions: In intervention development directed at increasing the level of PA and reducing sedentary time in recipients of solid-organ transplantation, attention should be paid to physical limitations, fear of negative effects, low expectations and self-confidence, health and physical outcomes, and exercise self-efficacy.
In het project “ADVICE: Advanced Driver Vehicle Interface in a Complex Environment” zijn belangrijke onderzoeksresultaten geboekt op het gebied van het schatten van de toestand en werklast van een voertuigbestuurder om hiermee systemen die informatie geven aan de bestuurder adaptief te maken om zo de veiligheid te verhogen. Een voorbeeld is om minder belangrijke informatie van een navigatiesysteem te onderdrukken, zolang de bestuurder een hoge werklast ervaart voor het autorijden en/of belangrijke informatie juist duidelijker weer te geven. Dit leidt tot een real-time werklast schatter die geografische informatie meeneemt, geavaleerd in zowel een rijsimulator als op de weg. In de ontwikkeling naar automatisch rijden is de veranderende rol van de bestuurder een belangrijk (veiligheids) onderwerp, welke sterk gerelateerd is aan de werklast van de bestuurder. Indien rijtaken meer geautomatiseerd worden, wijzigt de rol van actieve bestuurder meer naar supervisie van de rijtaken, maar tevens met de eis om snel en gericht in te grijpen indien de situatie dit vereist. Zowel deze supervisie als interventietaak zijn geen eenvoudige taken met onderling een sterk verschillende werklast (respectievelijk lage en (zeer) hoge werklast). Of een goede combinatie inclusief snelle overgangen tussen deze twee hoofdtaken veilig mogelijk is voor een bestuurder en hoe dit dan het beste ondersteund kan worden, is een belangrijk onderwerp van huidig onderzoek. De ontwikkeling naar autonoom rijden verandert niet alleen de rol van de bestuurder, maar zal ook de eisen aan het rijgedrag van het voertuig beïnvloeden, de voertuigdynamica. Voor de actieve bestuurder kunnen snelle voertuigreacties op bestuurdersinput belangrijk zijn, zeker voor een ‘sportief’ rijdende bestuurder. Indien dit voertuig ook automatische rijtaken moet uitvoeren, kan juist een meer gelijkmatig rijgedrag gewenst zijn, zodat de bestuurder ook andere taken kan uitvoeren. Dit stelt eisen aan vertaling van (automatische) input naar voertuigreactie en aan de voertuigdynamica. Mogelijk wil zelfs een sportieve bestuurder een meer comfortabel voertuiggedrag tijdens automatisch rijden. Eveneens voor deze twee voertuigtoestanden, menselijke of automatische besturing, moet gezocht worden naar een goede combinatie inclusief (veilige) overgangen tussen deze twee toestanden. Hierbij speelt de werklast en toestand van de bestuurder een doorslaggevende rol. In de geschetste ontwikkelingen in automatisch rijden kunnen de onderzoeksresultaten van ADVICE een goede ondersteuning bieden. Veel van deze ontwikkelingen worstelen met het schatten van de werklast van de bestuurder als cruciaal (veiligheids) aspect van automatisch rijden. De ADVICE resultaten zijn echter gepresenteerd voor beperkt publiek en gepubliceerd op conferenties, waarvan de artikelen veelal slechts tegen betaling toegankelijk zijn. Daarnaast zijn dergelijke artikelen gelimiteerd in aantal pagina’s waardoor de over te dragen informatie beperkt is. Om een betere doorwerking van ADVICE aan ‘iedereen’ te realiseren en tevens de mogelijkheden hiervan in de toekomst van automatisch rijden te plaatsen, willen wij top-up gebruiken om hierover een artikel te schrijven en dit in een peer-reviewed Open Access tijdschrift online toegankelijk te maken. Hierdoor wordt de informatie voor iedereen, gratis toegankelijk (open access), is de inhoud uitgebreider aan te geven (tijdschriftartikel) en is de inhoud en kwaliteit goed en relevant voor het vakgebied (peer-reviewed).
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).
In order to stay competitive and respond to the increasing demand for steady and predictable aircraft turnaround times, process optimization has been identified by Maintenance, Repair and Overhaul (MRO) SMEs in the aviation industry as their key element for innovation. Indeed, MRO SMEs have always been looking for options to organize their work as efficient as possible, which often resulted in applying lean business organization solutions. However, their aircraft maintenance processes stay characterized by unpredictable process times and material requirements. Lean business methodologies are unable to change this fact. This problem is often compensated by large buffers in terms of time, personnel and parts, leading to a relatively expensive and inefficient process. To tackle this problem of unpredictability, MRO SMEs want to explore the possibilities of data mining: the exploration and analysis of large quantities of their own historical maintenance data, with the meaning of discovering useful knowledge from seemingly unrelated data. Ideally, it will help predict failures in the maintenance process and thus better anticipate repair times and material requirements. With this, MRO SMEs face two challenges. First, the data they have available is often fragmented and non-transparent, while standardized data availability is a basic requirement for successful data analysis. Second, it is difficult to find meaningful patterns within these data sets because no operative system for data mining exists in the industry. This RAAK MKB project is initiated by the Aviation Academy of the Amsterdam University of Applied Sciences (Hogeschool van Amsterdan, hereinafter: HvA), in direct cooperation with the industry, to help MRO SMEs improve their maintenance process. Its main aim is to develop new knowledge of - and a method for - data mining. To do so, the current state of data presence within MRO SMEs is explored, mapped, categorized, cleaned and prepared. This will result in readable data sets that have predictive value for key elements of the maintenance process. Secondly, analysis principles are developed to interpret this data. These principles are translated into an easy-to-use data mining (IT)tool, helping MRO SMEs to predict their maintenance requirements in terms of costs and time, allowing them to adapt their maintenance process accordingly. In several case studies these products are tested and further improved. This is a resubmission of an earlier proposal dated October 2015 (3rd round) entitled ‘Data mining for MRO process optimization’ (number 2015-03-23M). We believe the merits of the proposal are substantial, and sufficient to be awarded a grant. The text of this submission is essentially unchanged from the previous proposal. Where text has been added – for clarification – this has been marked in yellow. Almost all of these new text parts are taken from our rebuttal (hoor en wederhoor), submitted in January 2016.
