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.
Introduction: Hardly any research exists on the relationship between substance use and sexual behaviors in patients with a substance use disorder. This study aimed to examine this relation by looking into perceived positive effects on sexual behavior, perceived negative effects and risky sexual behavior due to substance use in patient groups of users of alcohol, stimulants, sedatives and Gamma hydroxybutyrate (GHB). In addition, the current study aimed to address the question whether sexual behavior (e.g. number of sexual partners, sexualactivity) differs between these patient groups.Method: A total of 180 patients with a substance use disorder (i.e. alcohol, amphetamine, cannabis, cocaine, GHB and opiates) participated. A self-report questionnaire was administered with questions on substance use,sexual behaviors (e.g. sexual activity, masturbation, use of pornography) and statements about the perceived changes in sexual functioning and behavior under influence of the primary substance of abuse.Results: All four groups reported changes in sexual thoughts, feelings and behavior due to the use of their primary substance. More than half of the patients reported enhancements in sexual domains (i.e. sexual pleasure,sexual arousal, sexual behavior), but also decrements or risky behaviors and about a quarter stated that their sexual thoughts, feelings and behaviors were often associated with the use of their primary substance of abuse.Patients with a GHB use disorder reported the strongest relation between drug use and sexual behavior. Users of HB not only reported more enhancement in several sexual domains, but also less decline in sexual domains compared to the other patient groups and more risky behavior or more sexual activity than some of the other groups of patients.Conclusions: The results underline the importance of addressing the relationship between substance use and sexual behavior in treatment programs, as patients may be hesitant to stop their use of substances when they experience many positive effects in their sexual behavior. Future research directions are suggested.
While activity-based working is gaining popularity worldwide, research shows that workers frequently experience a misfit between the task at hand and their work setting. In the current study, experience sampling data were used to examine how perceived fit in activity-based work environments is related to user behavior (i.e., the use of work settings and setting-switching). We found that workers’ perceived fit was higher when they used closed rather than open work settings for individual high-concentration work. Furthermore, more frequent setting-switching was related to higher perceived fit. Unexpectedly, however, this relation was observed only among workers low in activity-switching. These findings indicate that user behavior may indeed be relevant to creating fit in activity-based work environments. To optimize workers’ perceived fit, it seems to be particularly important to facilitate and stimulate the use of closed work settings for individual high-concentration work.
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A world where technology is ubiquitous and embedded in our daily lives is becoming increasingly likely. To prepare our students to live and work in such a future, we propose to turn Saxion’s Epy-Drost building into a living lab environment. This will entail setting up and drafting the proper infrastructure and agreements to collect people’s location and building data (e.g. temperature, humidity) in Epy-Drost, and making the data appropriately available to student and research projects within Saxion. With regards to this project’s effect on education, we envision the proposal of several derived student projects which will provide students the opportunity to work with huge amounts of data and state-of-the-art natural interaction interfaces. Through these projects, students will acquire skills and knowledge that are necessary in the current and future labor-market, as well as get experience in working with topics of great importance now and in the near future. This is not only aligned with the Creative Media and Game Technologies (CMGT) study program’s new vision and focus on interactive technology, but also with many other education programs within Saxion. In terms of research, the candidate Postdoc will study if and how the data, together with the building’s infrastructure, can be leveraged to promote healthy behavior through playful strategies. In other words, whether we can persuade people in the building to be more physically active and engage more in social interactions through data-based gamification and building actuation. This fits very well with the Ambient Intelligence (AmI) research group’s agenda in Augmented Interaction, and CMGT’s User Experience line. Overall, this project will help spark and solidify lasting collaboration links between AmI and CMGT, give body to AmI’s new Augmented Interaction line, and increase Saxion’s level of education through the dissemination of knowledge between researchers, teachers and students.
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.
Movebite aims to combat the issue of sedentary behavior prevalent among office workers. A recent report of the Nederlandse Sportraad reveal a concerning trend of increased sitting time among Dutch employees, leading to a myriad of musculoskeletal discomforts and significant health costs for employers due to increased sick leave. Recognizing the critical importance of addressing prolonged sitting in the workplace, Movebite has developed an innovative concept leveraging cutting-edge technology to provide a solution. The Movebite app seamlessly integrates into workplace platforms such as Microsoft Teams and Slack, offering a user-friendly interface to incorporate movement into their daily routines. Through scalable AI coaching and real-time movement feedback, Movebite assists individuals in scheduling and implementing active micro-breaks throughout the workday, thereby mitigating the adverse effects of sedentary behavior. In collaboration with the Avans research group Equal Chance on Healthy Choices, Movebite conducts user-centered testing to refine its offerings and ensure maximum efficacy. This includes testing initiatives at sports events, where the diverse crowd provides invaluable feedback to fine-tune the app's features and user experience. The testing process encompasses both quantitative and qualitative approaches based on the Health Belief Model. Through digital questionnaires, Movebite aims to gauge users' perceptions of sitting as a health threat and the potential benefits of using the app to alleviate associated risks. Additionally, semi-structured interviews delve deeper into user experiences, providing qualitative insights into the app's usability, look, and feel. By this, Movebite aims to not only understand the factors influencing adoption but also to tailor its interventions effectively. Ultimately, the goal is to create an environment encouraging individuals to embrace physical activity in small, manageable increments, thereby fostering long-term engagement promoting overall well-being.Through continuous innovation and collaboration with research partners, Movebite remains committed to empowering individuals to lead healthier, more active lifestyles, one micro-break at a time.
