Deze casestudie geeft inzicht in verschillende soorten kennis die kenmerkend zijn voor applied design research. Er wordt onderscheid gemaakt tussen kennis over de huidige situatie, over wenselijke alternatieven en over effectieve oplossingen om daar te komen. Ofwel, kennis hoe het is, kennis over hoe het kan zijn en kennis over hoe het zal zijn als we effectieve oplossingen toepassen. Elk van deze soorten kennis heeft andere kwaliteitscriteria.
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Introduction: Sensor-feedback systems can be used to support people after stroke during independent practice of gait. The main aim of the study was to describe the user-centred approach to (re)design the user interface of the sensor feedback system “Stappy” for people after stroke, and share the deliverables and key observations from this process. Methods: The user-centred approach was structured around four phases (the discovery, definition, development and delivery phase) which were fundamental to the design process. Fifteen participants with cognitive and/or physical limitations participated (10 women, 2/3 older than 65). Prototypes were evaluated in multiple test rounds, consisting of 2–7 individual test sessions. Results: Seven deliverables were created: a list of design requirements, a personae, a user flow, a low-, medium- and high-fidelity prototype and the character “Stappy”. The first six deliverables were necessary tools to design the user interface, whereas the character was a solution resulting from this design process. Key observations related to “readability and contrast of visual information”, “understanding and remembering information”, “physical limitations” were confirmed by and “empathy” was additionally derived from the design process. Conclusions: The study offers a structured methodology resulting in deliverables and key observations, which can be used to (re)design meaningful user interfaces for people after stroke. Additionally, the study provides a technique that may promote “empathy” through the creation of the character Stappy. The description may provide guidance for health care professionals, researchers or designers in future user interface design projects in which existing products are redesigned for people after stroke.
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Poster and poster presentation for internal/external KCO profiling meeting.
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This poster sketches the outlines of a theoretical research framework to assess whether and on what grounds certain behavioral effects may be attributed to particular game mechanics and game play aspects. It is founded on the Elaboration Likelihood Model of Persuasion (ELM), which is quite appropriate to guide the evaluation structure for interventions that either aim at short term or long term attitude and behavior change.
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Game User Research is an emerging field that ties together Human Computer Interaction, Game Development, and Experimental Psychology, specifically investigating the interaction between players and games. The community of Game User Research has been rapidly evolving for the past few years, extending and modifying existing methodologies used by the HCI community to the environment of digital games. In this workshop, we plan to investigate the different methodologies currently in practice within the field as well as their utilities and drawbacks in measuring game design issues or gaining insight about the players' experience. The outcome of the workshop will be a collection of lessons from the trenches and commonly used techniques published in a public online forum. This will extend the discussion of topics beyond the workshop, and serve as a platform for future work. The workshop will be the first of its kind at CHI, tying together HCI research and Game User Research.
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In order to empower more people to become more selfreliant in society, interactive products and services should better match the skills and values of diverse user groups. In inclusive design, relevant end-user groups are involved early on and throughout the design and development process, leading to a better user experience. However, for IT businesses not operating in the academic domain, getting access to appropriate user research methods is difficult. This paper describes the design and prototype development of the Include Toolbox, in close cooperation with practitioners of small to medium sized enterprises (SMEs) in IT. It consists of an interactive app paired with a book. The app helps to find suitable research methods for diverse user groups such as older people, people with low literacy, and children. The book offers background information on the advantages of inclusive design, information on different user groups, and best practices shared by other companies.
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Background: During the process of decision-making for long-term care, clients are often dependent on informal support and available information about quality ratings of care services. However, clients do not take ratings into account when considering preferred care, and need assistance to understand their preferences. A tool to elicit preferences for long-term care could be beneficial. Therefore, the aim of this qualitative descriptive study is to understand the user requirements and develop a web-based preference elicitation tool for clients in need of longterm care. Methods: We applied a user-centred design in which end-users influence the development of the tool. The included end-users were clients, relatives, and healthcare professionals. Data collection took place between November 2017 and March 2018 by means of meetings with the development team consisting of four users, walkthrough interviews with 21 individual users, video-audio recordings, field notes, and observations during the use of the tool. Data were collected during three phases of iteration: Look and feel, Navigation, and Content. A deductive and inductive content analysis approach was used for data analysis. Results: The layout was considered accessible and easy during the Look and feel phase, and users asked for neutral images. Users found navigation easy, and expressed the need for concise and shorter text blocks. Users reached consensus about the categories of preferences, wished to adjust the content with propositions about well-being, and discussed linguistic difficulties. Conclusion: By incorporating the requirements of end-users, the user-centred design proved to be useful in progressing from the prototype to the finalized tool ‘What matters to me’. This tool may assist the elicitation of client’s preferences in their search for long-term care.
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The concept of immersion has been widely used for the design and evaluation of user experiences. Augmented, virtual and mixed-reality environments have further sparked the discussion of immersive user experiences and underlying requirements. However, a clear definition and agreement on design criteria of immersive experiences remains debated, creating challenges to advancing our understanding of immersive experiences and how these can be designed. Based on a multidisciplinary Delphi approach, this study provides a uniform definition of immersive experiences and identifies key criteria for the design and staging thereof. Thematic analysis revealed five key themes – transition into/out of the environment, in-experience user control, environment design, user context relatedness, and user openness and motivation, that emphasise the coherency in the user-environment interaction in the immersive experience. The study proposes an immersive experience framework as a guideline for industry practitioners, outlining key design criteria for four distinct facilitators of immersive experiences–systems, spatial, empathic/social, and narrative/sequential immersion. Further research is proposed using the immersive experience framework to investigate the hierarchy of user senses to optimise experiences that blend physical and digital environments and to study triggered, desired and undesired effects on user attitude and behaviour.
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Emergency care (from ambulance to emergency room) is focused on somatic care: fixing the body. When a patient with mental dysregulation who experiences ‘disproportionate feelings like fear, anger, sadness or confusion, possibly with associated behaviours’ (Van de Glind et al. 2023) does not get appropriate attention, this can result in the disruption of treatment and even psychological trauma upon trauma. To improve the emergency care process, the authors of this paper - health researchers and design researchers engaged in a project based on the experience-based co-design (EBCD) approach (Donetto et al. 2015; Bate and Robert 2007). EBCD is a method used to design better experiences in healthcare settings, in cooperation with (former) patients and healthcare professionals. The process of EBCD involves partnerships between stakeholders and the discovery and sensemaking of experiences through specialized methods to gain an understanding of the interface between user and service, to design new experiences (Bate and Robert 2007, 31). There is, however, an interesting challenge in bringing patients and care professionals together. In emergency care, patients depend greatly on their healthcare providers. The patients in this study had existing mental vulnerabilities and may have been traumatized by previous visits. We needed to enable these stakeholders to be equal partners with ownership and power, one of the characteristics of co-design in EBCD (Donetto et al. 2015). In this paper, we describe how we adapted and applied the EBCD method, with a focus on creating equal partnerships. We also reflect on the extent of our success and the diBiculties we encountered in attaining this objective.
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This guide was developed for designers and developers of AI systems, with the goal of ensuring that these systems are sufficiently explainable. Sufficient here means that it meets the legal requirements from AI Act and GDPR and that users can use the system properly. Explainability of decisions is an important requirement in many systems and even an important principle for AI systems [HLEG19]. In many AI systems, explainability is not self-evident. AI researchers expect that the challenge of making AI explainable will only increase. For one thing, this comes from the applications: AI will be used more and more often, for larger and more sensitive decisions. On the other hand, organizations are making better and better models, for example, by using more different inputs. With more complex AI models, it is often less clear how a decision was made. Organizations that will deploy AI must take into account users' need for explanations. Systems that use AI should be designed to provide the user with appropriate explanations. In this guide, we first explain the legal requirements for explainability of AI systems. These come from the GDPR and the AI Act. Next, we explain how AI is used in the financial sector and elaborate on one problem in detail. For this problem, we then show how the user interface can be modified to make the AI explainable. These designs serve as prototypical examples that can be adapted to new problems. This guidance is based on explainability of AI systems for the financial sector. However, the advice can also be used in other sectors.
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