Purpose (1) To investigate the differences in the course of participation up to one year after stroke between distinct movement behavior patterns identified directly after discharge to the home setting, and (2) to investigate the longitudinal association between the development of movement behavior patterns over time and participation after stroke. Materials and methods 200 individuals with a first-ever stroke were assessed directly after discharge to the home setting, at six months and at one year. The Participation domain of the Stroke Impact Scale 3.0 was used to measure participation. Movement behavior was objectified using accelerometry for 14 days. Participants were categorized into three distinct movement behavior patterns: sedentary exercisers, sedentary movers and sedentary prolongers. Generalized estimating equations (GEE) were performed. Results People who were classified as sedentary prolongers directly after discharge was associated with a worse course of participation up to one year after stroke. The development of sedentary prolongers over time was also associated with worse participation compared to sedentary exercisers. Conclusions The course of participation after stroke differs across distinct movement behavior patterns after discharge to the home setting. Highly sedentary and inactive people with stroke are at risk for restrictions in participation over time. Implications for rehabilitation The course of participation in people with a first-ever stroke up to one year after discharge to the home setting differed based on three distinct movement behavior patterns, i.e., sedentary exercisers, sedentary movers and sedentary prolongers. Early identification of highly sedentary and inactive people with stroke after discharge to the home setting is important, as sedentary prolongers are at risk for restrictions in participation over time. Supporting people with stroke to adapt and maintain a healthy movement behavior after discharge to the home setting could prevent potential long-term restrictions in participation.
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Objective: This study aimed to investigate which characteristics of athlete, wheelchair and athlete-wheelchair interface are the best predictors of wheelchair basketball mobility performance. Design: A total of 60 experienced wheelchair basketball players performed a wheelchair mobility performance test to assess their mobility performance. To determine which variables were the best predictors of mobility performance, forward stepwise linear regression analyses were performed on a set of 33 characteristics, including 10 athlete, 19 wheelchair, and 4 athlete-wheelchair interface characteristics. Results: A total of 8 of the characteristics turned out to be significant predictors of wheelchair basketball mobility performance. Classification, experience, maximal isometric force, wheel axis height, and hand rim diameter—which both are interchangeable with each other and wheel diameter—camber angle, and the vertical distance between shoulder and rear wheel axis—which was interchangeable with seat height—were positively associated with mobility performance. The vertical distance between the front seat and the footrest was negatively associated with mobility performance. Conclusion: With this insight, coaches and biomechanical specialists are provided with statistical findings to determine which characteristics they could focus on best to improve mobility performance. Six out of 8 predictors are modifiable and can be optimized to improve mobility performance. These adjustments could be carried out both in training (maximal isometric force) and in wheelchair configurations (eg, camber angle). https://doi.org/10.1123/jsr.2017-0142 LinkedIn: https://www.linkedin.com/in/annemarie-de-witte-9582b154/ https://www.linkedin.com/in/moniqueberger/ https://www.linkedin.com/in/rienkvdslikke/
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Background Movement behaviors (i.e., physical activity levels, sedentary behavior) in people with stroke are not self-contained but cluster in patterns. Recent research identified three commonly distinct movement behavior patterns in people with stroke. However, it remains unknown if movement behavior patterns remain stable and if individuals change in movement behavior pattern over time. Objectives 1) To investigate the stability of the composition of movement behavior patterns over time, and 2) determine if individuals change their movement behavior resulting in allocation to another movement behavior pattern within the first two years after discharge to home in people with a first-ever stroke. Methods Accelerometer data of 200 people with stroke of the RISE-cohort study were analyzed. Ten movement behavior variables were compressed using Principal Componence Analysis and K-means clustering was used to identify movement behavior patterns at three weeks, six months, one year, and two years after home discharge. The stability of the components within movement behavior patterns was investigated. Frequencies of individuals’ movement behavior pattern and changes in movement behavior pattern allocation were objectified. Results The composition of the movement behavior patterns at discharge did not change over time. At baseline, there were 22% sedentary exercisers (active/sedentary), 45% sedentary movers (inactive/sedentary) and 33% sedentary prolongers (inactive/highly sedentary). Thirty-five percent of the stroke survivors allocated to another movement behavior pattern within the first two years, of whom 63% deteriorated to a movement behavior pattern with higher health risks. After two years there were, 19% sedentary exercisers, 42% sedentary movers, and 39% sedentary prolongers. Conclusions The composition of movement behavior patterns remains stable over time. However, individuals change their movement behavior. Significantly more people allocated to a movement behavior pattern with higher health risks. The increase of people allocated to sedentary movers and sedentary prolongers is of great concern. It underlines the importance of improving or maintaining healthy movement behavior to prevent future health risks after stroke.
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Dutch Cycling Intelligence (DCI) embodies all Dutch cycling knowledge to enhances customer-oriented cycling policy. Based on the data-driven cycle policy enhancement tools and knowledge of the Breda University of Applied Sciences, DCI is the next step in creating a learning community between road authorities, consultants, cycling industry, and knowledge institutes with their students. The DCI consists of three pilars:- Connecting- Accelerating knowledge- Developing knowledgeConnecting There are many stakeholders and specialists in the cycling domain. Specialists with additional knowledge about socio-cultural impacts, geo-special knowledge, and technical traffic solutions. All of these specialists need each other to ensure a perfect balance between the (electric) bicycle, the cyclist and the cycle path in its environment. DCI connects and brings together all kind of different specialists.Accelerating knowledge Many bicycle innovations take place in so-called living labs. Within the living lab, the triple helix collaboration between road authorities the industry and knowledge institutes is key. Being actively involved in state-of-the-art innovations creates an inspiring work and learning environment for students and staff. A practical example of a successful living lab is the cycle superhighway F261 between Tilburg and Waalwijk, where BUAS tested new cycle route signage. Next, the Cycling Lab F58 is created, where the road authorities Breda and Tilburg opened up physical cycling infrastructure for entrepreneurs in the bicycle domain and knowledge institutes to develop e-cycling innovation. The living labs are test environments where pilots can be carried out in practice and an excellent environment for students to conduct scientifically applied research.Developing knowledge Ultimately, data and information must be translated into knowledge. With a team of specialists and partners Breda University of applied sciences developed knowledge and tools to monitor and evaluate cycling behavior. By participating in (inter)national research programs BUAS has become one of the frontrunners in data-driven cycle policy enhancement. In close collaboration with road authorities, knowledge institutes as well as consultants, new insights and answers are developed in an international context. By an active knowledge contribution to the network of the Dutch Cycling Embassy, BUAS aims to strengthen its position and add to the global sustainability challenges. Partners: Province Noord-Brabant, Province Utrecht, Vervoerregio Amsterdam, Dutch Cycling Embassy, Tour de Force, University of Amsterdam, Technical University Eindhoven, Technical University Delft, Utrecht University, DTV Capacity building, Dat.mobility, Goudappel Coffeng, Argaleo, Stratopo, Move.Mobility Clients:Province Noord-Brabant, Province Utrecht, Province Zuid-Holland, Tilburg, Breda, Tour de Force
Road freight transport contributes to 75% of the global logistics CO2 emissions. Various European initiatives are calling for a drastic cut-down of CO2 emissions in this sector [1]. This requires advanced and very expensive technological innovations; i.e. re-design of vehicle units, hybridization of powertrains and autonomous vehicle technology. One particular innovation that aims to solve this problem is multi-articulated vehicles (road-trains). They have a smaller footprint and better efficiency of transport than traditional transport vehicles like trucks. In line with the missions for Energy Transition and Sustainability [2], road-trains can have zero-emission powertrains leading to clean and sustainable urban mobility of people and goods. However, multiple articulations in a vehicle pose a problem of reversing the vehicle. Since it is extremely difficult to predict the sideways movement of the vehicle combination while reversing, no driver can master this process. This is also the problem faced by the drivers of TRENS Solar Train’s vehicle, which is a multi-articulated modular electric road vehicle. It can be used for transporting cargo as well as passengers in tight environments, making it suitable for operation in urban areas. This project aims to develop a reverse assist system to help drivers reverse multi-articulated vehicles like the TRENS Solar Train, enabling them to maneuver backward when the need arises in its operations, safely and predictably. This will subsequently provide multi-articulated vehicle users with a sustainable and economically viable option for the transport of cargo and passengers with unrestricted maneuverability resulting in better application and adding to the innovation in sustainable road transport.
Automated driving nowadays has become reality with the help of in-vehicle (ADAS) systems. More and more of such systems are being developed by OEMs and service providers. These (partly) automated systems are intended to enhance road and traffic safety (among other benefits) by addressing human limitations such as fatigue, low vigilance/distraction, reaction time, low behavioral adaptation, etc. In other words, (partly) automated driving should relieve the driver from his/her one or more preliminary driving tasks, making the ride enjoyable, safer and more relaxing. The present in-vehicle systems, on the contrary, requires continuous vigilance/alertness and behavioral adaptation from human drivers, and may also subject them to frequent in-and-out-of-the-loop situations and warnings. The tip of the iceberg is the robotic behavior of these in-vehicle systems, contrary to human driving behavior, viz. adaptive according to road, traffic, users, laws, weather, etc. Furthermore, no two human drivers are the same, and thus, do not possess the same driving styles and preferences. So how can one design of robotic behavior of an in-vehicle system be suitable for all human drivers? To emphasize the need for HUBRIS, this project proposes quantifying the behavioral difference between human driver and two in-vehicle systems through naturalistic driving in highway conditions, and subsequently, formulating preliminary design guidelines using the quantified behavioral difference matrix. Partners are V-tron, a service provider and potential developer of in-vehicle systems, Smits Opleidingen, a driving school keen on providing state-of-the-art education and training, Dutch Autonomous Mobility (DAM) B.V., a company active in operations, testing and assessment of self-driving vehicles in the Groningen province, Goudappel Coffeng, consultants in mobility and experts in traffic psychology, and Siemens Industry Software and Services B.V. (Siemens), developers of traffic simulation environments for testing in-vehicle systems.
