Despite the increased use of activity trackers, little is known about how they can be used in healthcare settings. This study aimed to support healthcare professionals and patients with embedding an activity tracker in the daily clinical practice of a specialized mental healthcare center and gaining knowledge about the implementation process. An action research design was used to let healthcare professionals and patients learn about how and when they can use an activity tracker. Data collection was performed in the specialized center with audio recordings of conversations during therapy, reflection sessions with the therapists, and semi-structured interviews with the patients. Analyses were performed by directed content analyses. Twenty-eight conversations during therapy, four reflection sessions, and eleven interviews were recorded. Both healthcare professionals and patients were positive about the use of activity trackers and experienced it as an added value. Therapists formulated exclusion criteria for patients, a flowchart on when to use the activity tracker, defined goals, and guidance on how to discuss (the data of) the activity tracker. The action research approach was helpful to allow therapists to learn and reflect with each other and embed the activity trackers into their clinical practice at a specialized mental healthcare center.
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Abstract from article: The Dutch healthcare system has changed towards a system of regulated competition to contain costs and to improve efficiency and quality of care. This paper provides: (1) a brief as-is overview of the changes for primary care, based on explorative literature reviews; (2) provides noteworthy remarks as for the way primary and secondary healthcare is organised; (3) an example of an E-health portal illustrating implemented processes within the Dutch context and (4) a proposed research agenda on various e-health topics. Noteworthy remarks are: (1) government, insurer, healthcare provider and patient are main actors within the Dutch healthcare system; (2) general practitioners (GP’s) are gatekeepers to secondary and other care providers; (3) the illustrated portal with a patient oriented design, provides access to applications implemented at care providers resulting in increased electronic availability and increased patient satisfaction; (4) a variety of fragmented information systems at health care providers exists, which leaves room for standardisation and increased efficiency. We end with suggestions for future research.
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Developing a framework that integrates Advanced Language Models into the qualitative research process.Qualitative research, vital for understanding complex phenomena, is often limited by labour-intensive data collection, transcription, and analysis processes. This hinders scalability, accessibility, and efficiency in both academic and industry contexts. As a result, insights are often delayed or incomplete, impacting decision-making, policy development, and innovation. The lack of tools to enhance accuracy and reduce human error exacerbates these challenges, particularly for projects requiring large datasets or quick iterations. Addressing these inefficiencies through AI-driven solutions like AIDA can empower researchers, enhance outcomes, and make qualitative research more inclusive, impactful, and efficient.The AIDA project enhances qualitative research by integrating AI technologies to streamline transcription, coding, and analysis processes. This innovation enables researchers to analyse larger datasets with greater efficiency and accuracy, providing faster and more comprehensive insights. By reducing manual effort and human error, AIDA empowers organisations to make informed decisions and implement evidence-based policies more effectively. Its scalability supports diverse societal and industry applications, from healthcare to market research, fostering innovation and addressing complex challenges. Ultimately, AIDA contributes to improving research quality, accessibility, and societal relevance, driving advancements across multiple sectors.
Granular materials (GMs) are simply a collection of individual particles, e.g., rice, coffee, iron-ore. Although straightforward in appearance, GMs are key to several processes in chemical-pharmaceutical, high-tech, agri-food and energy industry. Examples include laser sintering in additive manufacturing, tableting in pharma or just mixing of your favourite crunchy muesli mix in food industry. However, these bulk material handling processes are notorious for their inefficiency and ineffectiveness. Thereby, affecting the overall expenses and product quality. To understand and enhance the quality of a process, GMs industries utilise computer-simulations, much like how cars and aeroplanes have been designed and optimised since the 1990s. Just as how engineers utilise advanced computer-models to develop our fuel-efficient vehicle design, energy-saving granular processes are also developed utilising physics-based simulation-models, using a computer. Although physics-based models can effectively optimise large-scale processes, creating and simulating a fully representative virtual prototype of a GMs process is very iterative, computationally expensive and time intensive. On the contrary, given the available data, this is where machine learning (ML) could be of immense value. Like how ML has transformed the healthcare, energy and other top sectors, recent ML-based developments for GMs show serious promise in faster virtual prototyping and reduced computational cost. Enabling industries to rapidly design and optimise, enhancing real-time data-driven decision making. GranML aims to empower the GMs industries with ML. We will do so by (i) performing an in-depth GMs-ML literature review, (ii) developing open-access ML implementation guidelines; and (iii) an open-source proof-of-concept for an industry-relevant use case. Eventually, our follow-up mission is to build upon this vital knowledge by (i) expanding the consortium; (ii) co-developing a unified methodology for efficient computer-prototyping, unifying physics- and ML-based technologies for GMs; (iii) enhancing the existing computer-modelling infrastructure; and (iv) validating through industry focused demonstrators.
Lymphedema is one of the most poorly understood, relatively underestimated and least researched complications of cancer, or its treatment. Lymphedema is a chronic condition that causes abnormal build up of fluid under the skin resulting in painful swelling, commonly in the arms and legs. Limpressive Compression Garments have designed and conceptualised an active and smart compression sleeve that integrates pioneering smart materials and sensor technology to be used to treat and evaluate lymphedema. The Limpressive garments can be used as a research tool while replacing existing compression sleeves and pneumatic compression apparatus. There is currently no product on the market that is integrating both the actuator and sensor technology to treat, let alone quantify lymphedema. It is thus imperative that the Limpressive Compression Garments team are allowed the opportunity through funding to investigate the feasibility of the technology and its integration into healthcare, the business structures and processes needed to enter and be successful in the marketplace and the value to both the consumer and to the organisations dedicated to developing a greater understanding of the disease. Limpressive will complete an extensive and detailed business plan and a complete product design by the end of the Take-off Phase. The business plan and complete product design will be complemented by a proof of function prototype.
