AIM: To contribute to the knowledge and understanding of the active ingredients and mechanisms of change in Motivational Interviewing (MI), to enable MI-counsellors to optimise their MI-strategies in daily practice.METHOD: The body of this dissertation are two multiple case studies, one in 14 patients with schizophrenia receiving MI for medication adherence; another in 24 patients with a coronary artery disease receiving MI for smoking cessation.FINDINGS: We found that the active ingredients of MI consist of combinations of clinician factors and patient factors, mostly built up during longer interactions. ‘Arguing oneself into change’ was the most frequently observed mechanism of change.DISCUSSION AND CONCLUSION: Active ingredients in MI consist off combinations of factors contributed by the clinician and factors contributed by the patient. These factors can be employed in a person-centred MI-strategy to trigger a mechanism of change in the patient.This dissertation adds to the understanding of MI since it provides an explanation of how MI may work. It offers a general idea how counsellors can effectively execute MI. This ‘how-possibly’ explanation may be a building block in the development of a ‘how-actually’ explanation of the interactions leading to the active ingredients and mechanisms of change in MI.--De vraagstelling van het proefschrift is hoe MGv werkt: wat zijn de actieve ingrediënten en de verandermechanismen van motiverende gespreksvoering? De twee patiëntengroepen laten zien dat de actieve ingrediënten van MGv bestaan uit een wisselende combinatie van zorgverlener- en patiëntfactoren. Actieve ingrediënten ontstaan gedurende een langer lopende interactie tussen patiënt en professional.
This paper conducted a preliminary study of reviewing and exploring bias strategies using a framework of a different discipline: change management. The hypothesis here is: If the major problem of implicit bias strategies is that they do not translate into actual changes in behaviors, then it could be helpful to learn from studies that have contributed to successful change interventions such as reward management, social neuroscience, health behavioral change, and cognitive behavioral therapy. The result of this integrated approach is: (1) current bias strategies can be improved and new ones can be developed with insight from adjunct study fields in change management; (2) it could be more sustainable to invest in a holistic and proactive bias strategy approach that targets the social environment, eliminating the very condition under which biases arise; and (3) while implicit biases are automatic, future studies should invest more on strategies that empower people as “change agents” who can act proactively to regulate the very environment that gives rise to their biased thoughts and behaviors.
Habitual behavior is often hard to change because of a lack of self-monitoring skills. Digital technologies offer an unprecedented chance to facilitate self-monitoring by delivering feedback on undesired habitual behavior. This review analyzed the results of 72 studies in which feedback from digital technology attempted to disrupt and change undesired habits. A vast majority of these studies found that feedback through digital technology is an effective way to disrupt habits, regardless of target behavior or feedback technology used.
Every year in the Netherlands around 10.000 people are diagnosed with non-small cell lung cancer, commonly at advanced stages. In 1 to 2% of patients, a chromosomal translocation of the ROS1 gene drives oncogenesis. Since a few years, ROS1+ cancer can be treated effectively by targeted therapy with the tyrosine kinase inhibitor (TKI) crizotinib, which binds to the ROS1 protein, impairs the kinase activity and thereby inhibits tumor growth. Despite the successful treatment with crizotinib, most patients eventually show disease progression due to development of resistance. The available TKI-drugs for ROS1+ lung cancer make it possible to sequentially change medication as the disease progresses, but this is largely a ‘trial and error’ approach. Patients and their doctors ask for better prediction which TKI will work best after resistance occurs. The ROS1 patient foundation ‘Stichting Merels Wereld’ raises awareness and brings researchers together to close the knowledge gap on ROS1-driven oncogenesis and increase the options for treatment. As ROS1+ lung cancer is rare, research into resistance mechanisms and the availability of cell line models are limited. Medical Life Sciences & Diagnostics can help to improve treatment by developing new models which mimic the situation in resistant tumor cells. In the current proposal we will develop novel TKI-resistant cell lines that allow screening for improved personalized treatment with TKIs. Knowledge of specific mutations occurring after resistance will help to predict more accurately what the next step in patient treatment could be. This project is part of a long-term collaboration between the ROS1 patient foundation ‘Stichting Merels Wereld’, the departments of Pulmonary Oncology and Pathology of the UMCG and the Institute for Life Science & Technology of the Hanzehogeschool. The company Vivomicx will join our consortium, adding expertise on drug screening in complex cell systems.
The bi-directional communication link with the physical system is one of the main distinguishing features of the Digital Twin paradigm. This continuous flow of data and information, along its entire life cycle, is what makes a Digital Twin a dynamic and evolving entity and not merely a high-fidelity copy. There is an increasing realisation of the importance of a well functioning digital twin in critical infrastructures, such as water networks. Configuration of water network assets, such as valves, pumps, boosters and reservoirs, must be carefully managed and the water flows rerouted, often manually, which is a slow and costly process. The state of the art water management systems assume a relatively static physical model that requires manual corrections. Any change in the network conditions or topology due to degraded control mechanisms, ongoing maintenance, or changes in the external context situation, such as a heat wave, makes the existing model diverge from the reality. Our project proposes a unique approach to real-time monitoring of the water network that can handle automated changes of the model, based on the measured discrepancy of the model with the obtained IoT sensor data. We aim at an evolutionary approach that can apply detected changes to the model and update it in real-time without the need for any additional model validation and calibration. The state of the art deep learning algorithms will be applied to create a machine-learning data-driven simulation of the water network system. Moreover, unlike most research that is focused on detection of network problems and sensor faults, we will investigate the possibility of making a step further and continue using the degraded network and malfunctioning sensors until the maintenance and repairs can take place, which can take a long time. We will create a formal model and analyse the effect on data readings of different malfunctions, to construct a mitigating mechanism that is tailor-made for each malfunction type and allows to continue using the data, albeit in a limited capacity.
Our world is changing rapidly as a result of societal and technological developments that create new opportunities and challenges. Extended Realities (XR) could provide solutions for the problems the world is facing. In this project we apply these novel solutions in food and hospitality. It aims to tackle fundamental questions on how to stimulate a healthy and vital society that is based on a sustainable and innovative economy. This project aims to answer the question: How can Extended Reality (XR) technologies be integrated in the design of immersive food experiences to stimulate sustainable consumption behavior? A multidisciplinary approach, that has demonstrated its strength in the creative industry, will be applied in the hospitality and food sector. The project investigates implications and design considerations for immersion through XR technology that can stimulate sustainable consumption behavior. Based on XR prototypes, physiological data will be collected using biometric measuring devices in combination with self-reports. The effect of stimuli on sustainable consumption behavior during the immersive experience will be tested to introduce XR implementations that can motivate long-term behavioral change in food consumption. The results of the project contribute towards developing innovations in the hospitality sector that can tackle global societal challenges by exploiting the impact of new technology and understanding of consumer behavior to promote a healthy lifestyle and economy. Next to academic publications and conference contributions, the project will develop a handbook for hospitality professionals. It will outline steps and design criteria for the implementation of XR technologies to create immersive experiences that can stimulate sustainable consumption behavior. The knowledge generated in the project will contribute to the development of the curriculum at the Academy for Hotel and Facility at Breda University of Applied Sciences by introducing a technology-driven experience design approach for the course Sustainable Strategic Business Design.
