The increasing commercialisation of the sports sector and changing consumer demands are some of the issues that create challenges for non-profit sports in contemporary society. It is important for managers and marketers of these organisations to innovate because innovation is a way to grow within a competitive environment and to meet customers' expectations. The present study aims to develop an explorative typology of sports federations based on their attitudes and perceptions of determinants of innovation and their innovation capacity. A cluster analysis suggested three clusters with different responses towards service innovation: traditional sports federations, financially secure sports federations and competitive sports federations. Sports federations perceiving competition in terms of financial and human resources, favouring change and paid staff involvement in decision-making processes, and with negative economic perceptions are significantly more innovative. These findings have implications for the management and marketing of non-profit sports organisations.
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Abstract Purpose: This study aimed to establish which determinants had an effect on frailty among acutely admitted patients, where frailty was identified at discharge. In particular, our study focused on associations of sex with frailty. Methods: A cross-sectional study was designed using a sample of 1267 people aged 65 years or older. The Tilburg Frailty Indicator (TFI), a user-friendly self-report questionnaire was used to measure multidimensional frailty (physical, psychological, social) and determinants of frailty (sex, age, marital status, education, income, lifestyle, life events, multimorbidity). Results: The mean age of the participants was 76.8 years (SD 7.5; range 65-100). The bivariate regression analyses showed that all determinants were associated with total and physical frailty, and six determinants were associated with psychological and social frailty. Using multiple linear regression analyses, the explained variances differed from 3.5% (psychological frailty) to 20.1% (social frailty), with p values < 0.001. Of the independent variables age, income, lifestyle, life events, and multimorbidity were associated with three frailty variables, after controlling for all the other variables in the model. At the level of both frailty domains and components, females appeared to be more frail than men. Conclusion: The present study showed that sociodemographic characteristics (sex, age, marital status, education, income), lifestyle, life events, and multimorbidity had a different effect on total frailty and its domains (physical, psychological, social) in a sample of acute admitted patients.
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Although the prevalence of cybercrime has increased rapidly, most victims do not report these offenses to the police. This is the first study that compares associations between victim characteristics and crime reporting behavior for traditional crimes versus cybercrimes. Data from four waves of a Dutch cross-sectional population survey are used (N = 97,186 victims). Results show that cybercrimes are among the least reported types of crime. Moreover, the determinants of crime reporting differ between traditional crimes and cybercrimes, between different types of cybercrime (that is, identity theft, consumer fraud, hacking), and between reporting cybercrimes to the police and to other organizations. Implications for future research and practice are discussed. doi: https://doi.org/10.1177/1477370818773610 This article is honored with the European Society of Criminology (ESC) Award for the “Best Article of the Year 2019”. Dit artikel is bekroond met de European Society of Criminology (ESC) Award for the “Best Article of the Year 2019”.
MULTIFILE
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).
The main objective of DEDIPAC is to understand the determinants of dietary, physical activity and sedentary behaviours and to translate this knowledge into a more effective promotion of a healthy diet and physical activity.The DEDIPAC KH is a multidisciplinary consortium of scientists from 68 research centers in 12 countries across Europe.
The main objective of DEDIPAC is to understand the determinants of dietary, physical activity and sedentary behaviours and to translate this knowledge into a more effective promotion of a healthy diet and physical activity.The DEDIPAC KH is a multidisciplinary consortium of scientists from 68 research centers in 12 countries across Europe.
