ATLAS discovery potential for Higgs bosons beyond the Standard Model

This article describes the potential of the ATLAS experiment at the LHC to discover Higgs bosons in Beyond the Standard Model (BSM) scenarios. Two examples are discussed. First the discovery potential for Higgs bosons in the Minimal Supersymmetric Standard Model (MSSM) is evaluated. At least one Higgs boson can be observed for the whole accessible parameter space. Second the case of invisibly decaying Higgs bosons is discussed. A Higgs boson with a large branching ratio into invisible particles can also be discovered

The Role of Gamification in Health Behavior Change: A Review of Theory-driven Studies

Gamification is increasingly being recognized as a tool to support a change in individuals’ health behaviors. However, how and under which circumstances gamification is able to support health behavior change is still largely unexplored. This study follows the call for more theory-driven research on gamification by investigating the role of gamification in health behavior change theories (HBCTs). In order to do so, we conducted a systematic review of extant literature and identified 25 studies that explore the role of gamification in the process of health behavior change to some extent. We found large discrepancies in how the authors of these studies conceptualized the role of gamification in their theory-driven health interventions. To further strengthen theory-driven research on gamification in health and well-being, we additionally propose concrete research questions. These may guide future researchers to identify valuable avenues for further explaining and predicting the influences of gamification on health behavior change

Conceptualizing Narratives in Gamified Information Systems

Converging hedonic and utilitarian elements under the label of gamification has become an important phenomenon in information systems over the last decade. Yet, academic discourse on narratives in gamified IS remains scarce. To advance scholarly engagement, this study recontextualizes the concept of narratives for gamified IS. Based on the theoretical lens of hedonic and utilitarian consumption, we conducted a hermeneutic literature review in which we engaged with existing conceptualizations of narratives in a total of 84 studies across various disciplines. Results include a basic conceptualization of narratives complemented by six claims that may shape our way of thinking about narratives in gamified IS. Our findings contribute to a more comprehensive understanding of narratives in gamified IS that goes beyond that of traditional game elements. It may serve as a cornerstone for further discourse on narratives and how to meaningfully design them in gamified IS

Conceptual Ambiguity Surrounding Gamification and Serious Games in Health Care: Literature Review and Development of Game-Based Intervention Reporting Guidelines (GAMING)

Background: In health care, the use of game-based interventions to increase motivation, engagement, and overall sustainability of health behaviors is steadily becoming more common. The most prevalent types of game-based interventions in health care research are gamification and serious games. Various researchers have discussed substantial conceptual differences between these 2 concepts, supported by empirical studies showing differences in the effects on specific health behaviors. However, researchers also frequently report cases in which terms related to these 2 concepts are used ambiguously or even interchangeably. It remains unclear to what extent existing health care research explicitly distinguishes between gamification and serious games and whether it draws on existing conceptual considerations to do so. Objective: This study aims to address this lack of knowledge by capturing the current state of conceptualizations of gamification and serious games in health care research. Furthermore, we aim to provide tools for researchers to disambiguate the reporting of game-based interventions. Methods: We used a 2-step research approach. First, we conducted a systematic literature review of 206 studies, published in the Journal of Medical Internet Research and its sister journals, containing terms related to gamification, serious games, or both. We analyzed their conceptualizations of gamification and serious games, as well as the distinctions between the two concepts. Second, based on the literature review findings, we developed a set of guidelines for researchers reporting on game-based interventions and evaluated them with a group of 9 experts from the field. Results: Our results show that less than half of the concept mentions are accompanied by an explicit definition. To distinguish between the 2 concepts, we identified four common approaches: implicit distinction, synonymous use of terms, serious games as a type of gamified system, and distinction based on the full game dimension. Our Game-Based Intervention Reporting Guidelines (GAMING) consist of 25 items grouped into four topics: conceptual focus, contribution, mindfulness about related concepts, and individual concept definitions. Conclusions: Conceptualizations of gamification and serious games in health care literature are strongly heterogeneous, leading to conceptual ambiguity. Following the GAMING can support authors in rigorous reporting on study results of game-based interventions

Designing Gamification Concepts for Expert Explainable Artificial Intelligence Evaluation Tasks: A Problem Space Exploration

Artificial intelligence (AI) models are often complex and require additional explanations for use in high-stakes decision-making contexts like healthcare. To this end, explainable AI (XAI) developers must evaluate their explanations with domain experts to ensure understandability. As these evaluations are tedious and repetitive, we look at gamification as a means to motivate and engage experts in XAI evaluation tasks. We explore the problem space associated with gamified expert XAI evaluation. Based on a literature review of 22 relevant studies and seven interviews with experts in XAI evaluation, we elicit knowledge about affected stakeholders, eight needs, eight goals, and seven requirements. Our results help us understand better the problems associated with expert XAI evaluation and paint a broad application potential for gamification to improve XAI expert evaluations. In doing so, we lay the foundation for the design of successful gamification concepts for expert XAI evaluation

Be Mindful of User Preferences: An Explorative Study on Game Design Elements in Mindfulness Applications

Mindfulness practices are valuable exercises for physical and mental health. Various digital applications exist that support individuals in practicing mindfulness. Following the trend of gamifying utilitarian systems, many mindfulness applications (MAs) incorporate game design elements (GDEs). However, little is known about users’ GDE preferences in this unique context. In line with extant research that investigated users’ GDE preferences in other contexts, we conducted an online survey among 168 potential users of MAs. The results indicate that users generally prefer progress, levels, and goals in MAs, while leaderboards and avatars are not highly rated. Furthermore, we identified four context-independent and three context-dependent rationales that help explain users’ GDE preferences. By providing first insights into MAs as a peculiar application context for gamification, our work contributes to advancing knowledge of contextual differences in users’ GDE preferences while challenging the extant research assumptions regarding the dominance of contextual factors in forming user preferences

Be Mindful of User Preferences: An Explorative Study on Game Design Elements in Mindfulness Applications

Mindfulness practices are valuable exercises for physical and mental health. Various digital applications exist that support individuals in practicing mindfulness. Following the trend of gamifying utilitarian systems, many mindfulness applications (MAs) incorporate game design elements (GDEs). However, little is known about users’ GDE preferences in this unique context. In line with extant research that investigated users’ GDE preferences in other contexts, we conducted an online survey among 168 potential users of MAs. The results indicate that users generally prefer progress, levels, and goals in MAs, while leaderboards and avatars are not highly rated. Furthermore, we identified four context-independent and three context-dependent rationales that help explain users’ GDE preferences. By providing first insights into MAs as a peculiar application context for gamification, our work contributes to advancing knowledge of contextual differences in users’ GDE preferences while challenging the extant research assumptions regarding the dominance of contextual factors in forming user preferences

Understanding the Role of Expert Intuition in Medical Image Annotation: A Cognitive Task Analysis Approach

To improve contemporary machine learning (ML) models, research is increasingly looking at tapping in and incorporating the knowledge of domain experts. However, expert knowledge often relies on intuition, which is difficult to formalize for incorporation into ML models. Against this backdrop, we investigate the role of intuition in the context of expert medical image annotation. We apply a cognitive task analysis approach, where we observe and interview six expert medical image annotators to gain insights into pertinent decision cues and the role of intuition during annotation. Our results show that intuition plays an important role in various steps of the medical image annotation process, particularly in the appraisals of very easy or very difficult images, and in case purely cognitive appraisals remain inconclusive. Overall, we contribute to a better understanding of expert intuition in medical image annotation and provide possible interfaces to incorporate said intuition into ML models

Understanding the Role of Expert Intuition in Medical Image Annotation: A Cognitive Task Analysis Approach

​To improve contemporary machine learning (ML) models, research is increasingly looking at tapping in and incorporating the knowledge of domain experts. However, expert knowledge often relies on intuition, which is difficult to formalize for incorporation into ML models. Against this backdrop, we investigate the role of intuition in the context of expert medical image annotation. We apply a cognitive task analysis approach, where we observe and interview six expert medical image annotators to gain insights into pertinent decision cues and the role of intuition during annotation. Our results show that intuition plays an important role in various steps of the medical image annotation process, particularly in the appraisals of very easy or very difficult images, and in case purely cognitive appraisals remain inconclusive. Overall, we contribute to a better understanding of expert intuition in medical image annotation and provide possible interfaces to incorporate said intuition into ML models