Physiotherapists should take a primary role in relation to the prevention and management of all diseases that are associated with low levels of physical activity. The benefits of regular physical activity on health, longevity, and well being easily surpass the effectiveness of any drugs or other medical treatment. Physical activity has many beneficial effects on the body, helps prevent the development of many chronic diseases and is a useful complement to drug treatment in many diseases. As the importance of physical activity for health might well be underrated and undervalued even by manual therapists we describe the physiological consequences and health dangers of being inactive in this paper.
Purpose: The objective of this study was to develop a questionnaire to assess confidence in wheelchair mobility in Dutch youth (WheelCon-Mobility Dutch Youth). Methods: (1) A forward–backward translation process was used to translate the original WheelCon-M from English to Dutch. (2) Items related to wheelchair mobility in Dutch youth were selected and adapted based on focus groups with youth, parents and health care professionals to create the WheelCon-Mobility Dutch Youth. (3) The WheelCon-Mobility Dutch Youth and the Utrecht Pediatric Wheelchair Mobility Skills Test 2.0 (UP-WMST 2.0) were administered to 62 participants to evaluate internal consistency and construct validity. Results: Translation and cultural adaptation led to general adaptations in instructions, sentence structure and response scale. At the item level, 24 items were included with (n = 17) and without (n = 7) adaptation, 10 items were deleted and 7 new items were included. The WheelCon-Mobility Dutch Youth had an excellent Cronbach’s alpha of 0.924 and a significant correlation (r = 0.44, p < .001) with the UP-WMST 2.0. Conclusions: This study resulted in the adaptation of the WheelCon-M into the WheelCon-Mobility for Dutch youth using a manual wheelchair. Our study suggests there is evidence supporting the internal consistency and construct validity of the WheelCon-Mobility Dutch Youth. Implications for Rehabilitation The WheelCon-Mobility Dutch Youth is a newly developed tool for assessing confidence in wheelchair mobility in Dutch youth using a manual wheelchair. It is important to assess performance and confidence in wheelchair mobility in paediatric rehabilitation.
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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.
The maximum capacity of the road infrastructure is being reached due to the number of vehicles that are being introduced on Dutch roads each day. One of the plausible solutions to tackle congestion could be efficient and effective use of road infrastructure using modern technologies such as cooperative mobility. Cooperative mobility relies majorly on big data that is generated potentially by millions of vehicles that are travelling on the road. But how can this data be generated? Modern vehicles already contain a host of sensors that are required for its operation. This data is typically circulated within an automobile via the CAN bus and can in-principle be shared with the outside world considering the privacy aspects of data sharing. The main problem is, however, the difficulty in interpreting this data. This is mainly because the configuration of this data varies between manufacturers and vehicle models and have not been standardized by the manufacturers. Signals from the CAN bus could be manually reverse engineered, but this process is extremely labour-intensive and time-consuming. In this project we investigate if an intelligent tool or specific test procedures could be developed to extract CAN messages and their composition efficiently irrespective of vehicle brand and type. This would lay the foundations that are required to generate big data-sets from in-vehicle data efficiently.
In the last decade, the automotive industry has seen significant advancements in technology (Advanced Driver Assistance Systems (ADAS) and autonomous vehicles) that presents the opportunity to improve traffic safety, efficiency, and comfort. However, the lack of drivers’ knowledge (such as risks, benefits, capabilities, limitations, and components) and confusion (i.e., multiple systems that have similar but not identical functions with different names) concerning the vehicle technology still prevails and thus, limiting the safety potential. The usual sources (such as the owner’s manual, instructions from a sales representative, online forums, and post-purchase training) do not provide adequate and sustainable knowledge to drivers concerning ADAS. Additionally, existing driving training and examinations focus mainly on unassisted driving and are practically unchanged for 30 years. Therefore, where and how drivers should obtain the necessary skills and knowledge for safely and effectively using ADAS? The proposed KIEM project AMIGO aims to create a training framework for learner drivers by combining classroom, online/virtual, and on-the-road training modules for imparting adequate knowledge and skills (such as risk assessment, handling in safety-critical and take-over transitions, and self-evaluation). AMIGO will also develop an assessment procedure to evaluate the impact of ADAS training on drivers’ skills and knowledge by defining key performance indicators (KPIs) using in-vehicle data, eye-tracking data, and subjective measures. For practical reasons, AMIGO will focus on either lane-keeping assistance (LKA) or adaptive cruise control (ACC) for framework development and testing, depending on the system availability. The insights obtained from this project will serve as a foundation for a subsequent research project, which will expand the AMIGO framework to other ADAS systems (e.g., mandatory ADAS systems in new cars from 2020 onwards) and specific driver target groups, such as the elderly and novice.
