Objective: We aimed to identify published classification systems with a targeted treatment approach (treatment-based classification systems (TBCSs)) for patients with non-specific neck pain, and assess their quality and effectiveness. Design: Systematic review. Data sources: MEDLINE, CINAHL, EMBASE, PEDro and the grey literature were systematically searched from inception to December 2019. Study appraisal and synthesis: The main selection criterium was a TBCS for patients with non-specific neck pain with physiotherapeutic interventions. For data extraction of descriptive data and quality assessment we used the framework developed by Buchbinder et al. We considered as score of ≤3 as low quality, a score between 3 and 5 as moderate quality and a score ≥5 as good quality. To assess the risk of bias of studies concerning the effectiveness of TBCSs (only randomized clinical trials (RCTs) were included) we used the PEDro scale. We considered a score of ≥ six points on this scale as low risk of bias. Results: Out of 7664 initial references we included 13 studies. The overall quality of the TBCSs ranged from low to moderate. We found two RCTs, both with low risk of bias, evaluating the effectiveness of two TBCSs compared to alternative treatments. The results showed that both TBCSs were not superior to alternative treatments. Conclusion: Existing TBCSs are, at best, of moderate quality. In addition, TBCSs were not shown to be more effective than alternatives. Therefore using these TBCSs in daily practice is not recommended.
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Office well-being aims to explore and support a healthy, balanced and active work style in office environments. Recent work on tangible user interfaces has started to explore the role of physical, tangible interfaces as active interventions to explore how to tackle problems such as inactive work and lifestyles, and increasingly sedentary behaviours. We identify a fragmented research landscape on tangible Office well-being interventions, missing the relationship between interventions, data, design strategies, and outcomes, and behaviour change techniques. Based on the analysis of 40 papers, we identify 7 classifications in tangible Office well-being interventions and analyse the intervention based on their role and foundation in behaviour change. Based on the analysis, we present design considerations for the development of future tangible Office well-being design interventions and present an overview of the current field and future research into tangible Office well-being interventions to design for a healthier and active office environment.
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This paper proposes an amendment of the classification of safety events based on their controllability and contemplates the potential of an event to escalate into higher severity classes. It considers (1) whether the end-user had the opportunity to intervene into the course of an event, (2) the level of end-user familiarity with the situation, and (3) the positive or negative effects of end-user intervention against expected outcomes. To examine its potential, we applied the refined classification to 296 aviation safety investigation reports. The results suggested that pilots controlled only three-quarters of the occurrences, more than three-thirds of the controlled cases regarded fairly unfamiliar situations, and the flight crews succeeded to mitigate the possible negative consequences of events in about 71% of the cases. Further statistical tests showed that the controllability-related characteristics of events had not significantly changed over time, and they varied across regions, aircraft, operational and event characteristics, as well as when fatigue had contributed to the occurrences. Overall, the findings demonstrated the value of using the controllability classification before considering the actual outcomes of events as means to support the identification of system resilience and successes. The classification can also be embedded in voluntary reporting systems to allow end-users to express the degree of each of the controllability characteristics so that management can monitor them over time and perform internal and external benchmarking. The mandatory reports concerned, the classification could function as a decision-making parameter for prioritising incident investigations.
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Horse riding falls under the “Sport for Life” disciplines, where a long-term equestrian development can provide a clear pathway of developmental stages to help individuals, inclusive of those with a disability, to pursue their goals in sport and physical activity, providing long-term health benefits. However, the biomechanical interaction between horse and (disabled) rider is not wholly understood, leaving challenges and opportunities for the horse riding sport. Therefore, the purpose of this KIEM project is to start an interdisciplinary collaboration between parties interested in integrating existing knowledge on horse and (disabled) rider interaction with any novel insights to be gained from analysing recently collected sensor data using the EquiMoves™ system. EquiMoves is based on the state-of-the-art inertial- and orientational-sensor system ProMove-mini from Inertia Technology B.V., a partner in this proposal. On the basis of analysing previously collected data, machine learning algorithms will be selected for implementation in existing or modified EquiMoves sensor hardware and software solutions. Target applications and follow-ups include: - Improving horse and (disabled) rider interaction for riders of all skill levels; - Objective evidence-based classification system for competitive grading of disabled riders in Para Dressage events; - Identifying biomechanical irregularities for detecting and/or preventing injuries of horses. Topic-wise, the project is connected to “Smart Technologies and Materials”, “High Tech Systems & Materials” and “Digital key technologies”. The core consortium of Saxion University of Applied Sciences, Rosmark Consultancy and Inertia Technology will receive feedback to project progress and outcomes from a panel of international experts (Utrecht University, Sport Horse Health Plan, University of Central Lancashire, Swedish University of Agricultural Sciences), combining a strong mix of expertise on horse and rider biomechanics, veterinary medicine, sensor hardware, data analysis and AI/machine learning algorithm development and implementation, all together presenting a solid collaborative base for derived RAAK-mkb, -publiek and/or -PRO follow-up projects.
Organisations are increasingly embedding Artificial Intelligence (AI) techniques and tools in their processes. Typical examples are generative AI for images, videos, text, and classification tasks commonly used, for example, in medical applications and industry. One danger of the proliferation of AI systems is the focus on the performance of AI models, neglecting important aspects such as fairness and sustainability. For example, an organisation might be tempted to use a model with better global performance, even if it works poorly for specific vulnerable groups. The same logic can be applied to high-performance models that require a significant amount of energy for training and usage. At the same time, many organisations recognise the need for responsible AI development that balances performance with fairness and sustainability. This KIEM project proposal aims to develop a tool that can be employed by organizations that develop and implement AI systems and aim to do so more responsibly. Through visual aiding and data visualisation, the tool facilitates making these trade-offs. By showing what these values mean in practice, which choices could be made and highlighting the relationship with performance, we aspire to educate users on how the use of different metrics impacts the decisions made by the model and its wider consequences, such as energy consumption or fairness-related harms. This tool is meant to facilitate conversation between developers, product owners and project leaders to assist them in making their choices more explicit and responsible.
About half of the e-waste generated in The Netherlands is properly documented and collected (184kT in 2018). The amount of PCBs in this waste is projected to be about 7kT in 2018 with a growth rate of 3-4%. Studies indicate that a third of the weight of a PCB is made or recoverable and critical metals which we need as resources for the various societal challenges facing us in the future. Recycling a waste PCB today means first shredding it and then processing it for material recovery mostly via non-selective pyrometallurgical methods. Sorting the PCBs in quality grades (wastebins) before shredding would however lead to more flexibility in selecting when and which recovery metallurgy is to be used. The yield and diversity of the recovered metals increases as a result, especially when high-grade recycling techniques are used. Unfortunately, the sorting of waste PCBs is not easily automated as an experienced operator eye is needed to classify the very inhomogeneous waste-PCB stream in wastebins. In this project, a knowledge institution partners with an e-waste processor, a high-grade recycling technology startup and a developer of waste sorting systems to investigate the efficiency of methods for sensory sorting of waste PCBs. The knowledge gained in this project will lead towards a waste PCB sorting demonstrator as a follow-up project.
