Game jams are time-bounded collaborative events that range from one to few days, bringing together teams to create game prototypes. Previous research has emphasized the positive aspects of these events. Fewer studies focused on their limitations, usually focusing on a small number of participants in specific contexts, which provides a limited view of the negative experiences faced by event participants. To address this gap, we qualitatively analyzed 866 open-ended responses about the difficulties faced by the same group of participants in two subsequent game jams. We report a taxonomy with 42 difficulties structured in five categories: Social Dynamics, Project Management, Personal, Event Format, and Technical and Infrastructural. We then discuss how to use this taxonomy in different scenarios, and how the difficulties are intertwined with participants emotional experiences and may even influence whether they decide to return to these events.

Gender disparity persists in science, technology, engineering, and mathematics (STEM), impacting teamwork, productivity, and innovation. In the gaming industry, women constitute only 30% of professionals, despite comprising half of the players, raising concerns about effective game design and marketing for a diverse audience. Time-bounded collaborative events, such as hackathons and game jams, have been used to promote inclusion, providing opportunities for networking and skill development, among other benefits. However, game jams have low participation rates from minorities. This paper describes a qualitative study using card sorting and interviews with nine women game jam participants to elucidate their difficulties. We explore the interplay of these difficulties, the strategies employed to overcome them, and the subsequent impact on women's experiences. By shedding light on these issues, we aim to contribute insights that help mitigate barriers, fostering a more inclusive environment in the gaming industry and similar domains.

Fertility tracking and technology are characterized by logging varied health-related data potentially associated with female fertility cycles. Such data are often seen as private and restricted to the individual level. We conducted an interview study with 21 people (16 in the U.S.) facing challenges to conceive and 5 U.S. healthcare providers specialized in infertility to analyze (in)fertility experiences with data. Our findings suggest that although fertility data are considered personal and private, they are embedded in larger ecological systems of use, influencing and being influenced by different stakeholders, institutional contexts, and sociocultural factors. Leveraging the Ecological Systems Theory, we analyze the relationships and factors shaping individuals’ fertility trajectories, discussing how the different layers influence the work individuals have to engage and the burden imposed on them through various social, institutional, and cultural boundaries. We propose an ecological perspective on fertility data practices and discuss opportunities to counter-influence broader environmental systems through data tracking.

OBJECTIVE: Qualitative methods are particularly well-suited to studying the complexities and contingencies that emerge in the development, preparation, and implementation of technological interventions in real-world clinical practice, and much remains to be done to use these methods to their full advantage. We aimed to analyze how qualitative methods have been used in health informatics research, focusing on objectives, populations studied, data collection, analysis methods, and fields of analytical origin. METHODS: We conducted a scoping review of original, qualitative empirical research in JAMIA from its inception in 1994 to 2019. We queried PubMed to identify relevant articles, ultimately including and extracting data from 158 articles. RESULTS: The proportion of qualitative studies increased over time, constituting 4.2% of articles published in JAMIA overall. Studies overwhelmingly used interviews, observations, grounded theory, and thematic analysis. These articles used qualitative methods to analyze health informatics systems before, after, and separate from deployment. Providers have typically been the main focus of studies, but there has been an upward trend of articles focusing on healthcare consumers. DISCUSSION: While there has been a rich tradition of qualitative inquiry in JAMIA, its scope has been limited when compared with the range of qualitative methods used in other technology-oriented fields, such as human-computer interaction, computer-supported cooperative work, and science and technology studies. CONCLUSION: We recommend increased public funding for and adoption of a broader variety of qualitative methods by scholars, practitioners, and policy makers and an expansion of the variety of participants studied. This should lead to systems that are more responsive to practical needs, improving usability, safety, and outcomes.

Telerehabilitation technology has the potential to support the work of patients and clinicians by collecting and displaying patients' data to inform, motivate, and support decision-making. However, few studies have investigated data-driven practices in telerehabilitation. In this qualitative study, we conducted interviews and a focus group with the use of data visualization probes to investigate the experience of stroke survivors and healthcare providers with game-based telerehabilitation involving physical and occupational therapy. We find that \hlstudy participants saw potential value in the data to support their work. However, they experienced challenges when interpreting data to arrive at meaningful insights and actionable information. Further, patients' personal relationships with their goals and data stand in contrast with clinicians' more matter-of-fact perspectives. Informed by these results, we discuss implications for telerehabilitation technology design.

Abstract Personal informatics tools can help users self-reflect on their experiences. When reflective thought occurs, it sometimes leads to negative thought and emotion cycles. To help explain these cycles, we draw from Psychology to introduce the concept of rumination—anxious, perseverative cognition focused on negative aspects of the self—as a result of engaging with personal data. Rumination is an important concept for the Human Computer Interaction community because it can negatively affect users’ well-being and lead to maladaptive use. Thus, preventing and mitigating rumination is beneficial. In this conceptual paper, we differentiate reflection from rumination. We also explain how self-tracking technologies may inadvertently lead to rumination and the implications this has for design. Our goal is to expand self-tracking research by discussing these negative cycles and encourage researchers to consider rumination when studying, designing, and promoting tools to prevent adverse unintended consequences among users.

Medical practices have always been data oriented: healthcare providers decisions are based on data, both clinically generated and patient reported. With the increased use of patient-generated health data (PGHD), other types of data are entering providers? practices and influencing patient-provider collaboration. We studied the use of PGHD and its related data practices in the context of fertility, a health concern that is uncertain, complex, and data intensive. We interviewed 14 patients who are facing or have faced challenges to conceive and 5 healthcare providers specialized in infertility. Our findings show that patients and providers use PGHD in different ways but with the common goal of exploring 'the unknown' generated by the uncertainties of fertility. Providers use patients? data in a rational protocol, aiming to identify possible causes of infertility and define a treatment course. Patients use data in a much more emotional way, learning about their bodies while struggling with data interpretation challenges. By analyzing these data practices, we discuss the principles behind their differences and describe how they have individual benefits for each specific group. We then suggest that fertility technologies need to consider such principles, highlight the existing boundary between patients' and providers' data practices, and focus on bridging instead of merging them in order to facilitate collaboration and maintain their independent benefits.

Self-tracking data is often seen as a means to reflect and achieve a goal, usually focusing on positive insights and actions. Lately, some studies have discussed the negative consequences of self-tracking, suggesting that people interact with personal data in different ways. We explored how self-tracking activities and the emotional context characterize how people engage with personal health data through the analysis of a complex and emotionally-loaded use case: fertility self-tracking. We qualitatively analyzed patient-generated content in an online health community dedicated to fertility. We found five distinct types of engagement with data: positive, burdened, obsessive, trapped, and abandoning. Each of them is composed of an action and an emotional component that mutually influence each other. We discuss how the interplay of these components characterize a person's engagement with data, how the online forum made these issues visible, and how they are embedded in the self-tracking culture. We also provide insights into the implications of these issues for self-tracking tools. Finally, we hypothesize how people transition through the types of relationships with data, suggesting directions for future research in the area.

Infertility is a global health concern that affects countless couples trying to conceive a child. Effective fertility treatment requires continuous monitoring of a wide range of health indicators through self-tracking. The process of collecting and interpreting data and information about fertility is complex, and much of the burden falls on women. In this study, we analyzed patient-generated content in a popular online health community dedicated to fertility issues. The objective was to understand the process in which women engage in tracking relevant information, and the challenges they face. Leveraging the Personal Informatics Model, we describe women's self-tracking experiences during their fertility cycles. We discuss how a complex and highly personalized context leads to responsibility, pressure, and emotional burden on women performing self-tracking activities, as well as the role of collaboration in creating individualized solutions. Finally, we provide implications for technologies aiming to support women with fertility care needs.