Background: Modern modeling techniques may potentially provide more accurate predictions of dichotomous outcomes than classical techniques. Objective: In this study, we aimed to examine the predictive performance of eight modeling techniques to predict mortality by frailty. Methods: We performed a longitudinal study with a 7-year follow-up. The sample consisted of 479 Dutch community-dwelling people, aged 75 years and older. Frailty was assessed with the Tilburg Frailty Indicator (TFI), a self-report questionnaire. This questionnaire consists of eight physical, four psychological, and three social frailty components. The municipality of Roosendaal, a city in the Netherlands, provided the mortality dates. We compared modeling techniques, such as support vector machine (SVM), neural network (NN), random forest, and least absolute shrinkage and selection operator, as well as classical techniques, such as logistic regression, two Bayesian networks, and recursive partitioning (RP). The area under the receiver operating characteristic curve (AUROC) indicated the performance of the models. The models were validated using bootstrapping. Results: We found that the NN model had the best validated performance (AUROC=0.812), followed by the SVM model (AUROC=0.705). The other models had validated AUROC values below 0.700. The RP model had the lowest validated AUROC (0.605). The NN model had the highest optimism (0.156). The predictor variable “difficulty in walking” was important for all models. Conclusions: Because of the high optimism of the NN model, we prefer the SVM model for predicting mortality among community-dwelling older people using the TFI, with the addition of “gender” and “age” variables. External validation is a necessary step before applying the prediction models in a new setting.
International comparative analysis of former textile cities and their 'comeback strategies'. In this chapter results are showed from the design workshop by students from the Tilburg Academy of Architecture and Urbanism
Purpose: To examine the development of multidimensional frailty, including physical, psychological and socialcomponents, over a period of seven years. To determine the effects of sociodemographic factors (gender, age, marital status, education, income) on the development of frailty. Methods: : This longitudinal study was conducted in sample of 479 community-dwelling people aged ≥ 75 years living in the municipality of Roosendaal, the Netherlands. The Tilburg Frailty Indicator (TFI), a self-report questionnaire, was used to collect data about frailty. Frailty was assessed annually. Results: : Frailty increased significantly over seven years among the people who completed the entire TFI all years (n = 121), the average score was 3.75 (SD 2.80) at baseline and 5.05 (SD 3.18) after seven years. Regarding frailty transitions, most participants remained unchanged from their baseline status. The transition from non-frail to frail was present in 8.3% to 12.6% of the participants and 5.1% to 10.7% made a transition from frail to nonfrail. Gender (woman), age (≥80 years), marital status (not married/cohabiting), high level of education, and incomes from €601-€1800 were significantly associated with a higher frailty score. Conclusion: : This study showed that multidimensional frailty, assessed with the TFI, increased among Dutch community-dwelling people aged ≥75 years using a follow-up of seven years. Gender, age, marital status, education, and income were associated with frailty transitions. These findings provide healthcare professionals clues to identify people at increased risk of frailty, and target interventions which aim to prevent or delay frailty and its adverse outcomes, such as disability and mortality.
Program Director MSc Leisure and Tourism Studies, Senior Researcher and Lecturer
Versterken symbiose tussen Vlaanderen en Nederland in de regio tussen Turnhout en Tilburg door het verzilveren van het duurzaam toeristisch potentieel. Het beter ontsluiten en verbinden van activiteiten en nieuwe belevenissen voor verschillende doelgroepen: bezoekers, bewoners, ondernemers, jongeren, senioren, gezinnen.Collaborative partnersAPB Toerisme Provincie Antwerpen, Gemeente Alphen-Chaam, Gemeente Arendonk, Gemeente Baarle-Hertog, Gemeente Baarle-Nassau, Gemeente Goirle, Gemeente Merksplas, Gemeente Ravels, Gemeente Tilburg, Stad Hoogstraten, Stad Turnhout, Thomas More Mechelen-Antwerpen.
The livability of the cities and attractiveness of our environment can be improved by smarter choices for mobility products and travel modes. A change from current car-dependent lifestyles towards the use of healthier and less polluted transport modes, such as cycling, is needed. With awareness campaigns, cycling facilities and cycle infrastructure, the use of the bicycle will be stimulated. But which campaigns are effective? Can we stimulate cycling by adding cycling facilities along the cycle path? How can we design the best cycle infrastructure for a region? And what impact does good cycle infrastructure have on the increase of cycling?To find answers for these questions and come up with a future approach to stimulate bicycle use, BUas is participating in the InterReg V NWE-project CHIPS; Cycle Highways Innovation for smarter People transport and Spatial planning. Together with the city of Tilburg and other partners from The Netherlands, Belgium, Germany and United Kingdom we explore and demonstrate infrastructural improvements and tackle crucial elements related to engaging users and successful promotion of cycle highways. BUas is responsible for the monitoring and evaluation of the project. To measure the impact and effectiveness of cycle highway innovations we use Cyclespex and Cycleprint.With Cyclespex a virtual living lab is created which we will use to test several readability and wayfinding measures for cycle infrastructure. Cyclespex gives us the opportunity to test different scenario’s in virtual reality that will help us to make decisions about the final solution that will be realized on the cycle highway. Cycleprint will be used to develop a monitoring dashboard where municipalities of cities can easily monitor and evaluate the local bicycle use.
Events play an increasingly big role in our society. Whereas events were mainly considered entertainment in the past, the social function of events is becoming more and more apparent, in particular, in the field of social bonding and in creating a feeling of solidarity.During an event, visitors identify with a theme or topic, and interact with each other about it. Thanks to social media, they can continue these interactions online, which leads to a hybrid network of individuals sharing the same interests. Eventually, this may lead to forming new communities, who communicate with each other both online and offline. However, it is not clear yet how exactly these new communities are being created.This PhD research studies the online and offline interaction rituals of various events and online communities. Through interviews and participating observations at events such as Redhead Days and the Elfia fantasy event, processes are mapped out that result in forming communities at and around events.Partner: Tilburg University
