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The increasing prevalence of stress, anxiety, and depression has heightened demand for accessible mental health support. Mobile mental health applications offer scalable solutions, yet adoption remains uneven. This questionnaire study surveyed 61 adults in Sweden (11 users, 50 non-users) to examine perceptions and factors influencing engagement. Non-users primarily cited lack of awareness, doubts about effectiveness, and privacy concerns, while users valued accessibility and low cost but reported limited long-term engagement due to low perceived value and motivation. Both groups emphasized the need for professional endorsement, personalization, and human interaction. Overall, adoption of digital mental health tools in Sweden is shaped by awareness, trust, cultural expectations, and design quality.

BACKGROUND: Quality in use (QiU), a stakeholder-centered dimension of software quality encompassing effectiveness, efficiency, satisfaction, and freedom from risk, is essential in evaluating digital systems, particularly in health-related domains. Although QiU has been explored in various fields, its application within connected mental health (CMH) systems remains fragmented and understudied. Given the rapid rise in CMH technologies, ranging from mobile apps to teletherapy platforms, understanding how QiU is conceptualized, evaluated, and reported in this domain has become increasingly urgent.

OBJECTIVE: This study aims to systematically map and synthesize existing research on QiU in CMH applications. It seeks to identify current trends, research gaps, evaluation methods, and the range of technologies examined concerning QiU.

METHODS: A systematic mapping methodology following the guidelines by Petersen et al will be used. The process includes defining mapping questions, developing a classification scheme, and systematically searching and analyzing peer-reviewed literature from databases-Scopus, PubMed, IEEE Xplore, and ACM Digital Library. Eight mapping questions will guide the analysis, focusing on publication trends, research types, empirical evaluations, QiU characteristics and subcharacteristics, and technologies studied.

RESULTS: As this paper presents the protocol for an ongoing mapping study, results are not yet available. The literature search and data analysis are scheduled for completion in 2026. Preliminary screening suggests variability in how QiU is defined and evaluated across CMH technologies, highlighting the need for systematic synthesis.

CONCLUSIONS: This systematic mapping study will fill a critical gap by providing a comprehensive overview of QiU research in the context of CMH. By organizing and classifying the existing literature, the study will inform future research, support the development of more user-centered CMH tools, and contribute to establishing more consistent evaluation practices in this growing field.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/79611.

Research into the environmental impact of software has increased in recent years, although its assessment remains complex due to the diversity of existing technologies. This paper proposes a structured and configurable framework that enables joint assessment of environmental sustainability and a particular software quality dimension or attribute. The framework builds upon established practices in software quality evaluation and considers statistical analysis to empirically explore the interplay between both dimensions. The paper contributes by proposing a methodological approach and illustrating its application through data analysis. The framework was configured to assess environmental sustainability and usability in a set of sports news aggregators, correlating heuristic-based usability metrics with environmental sustainability indicators derived from website performance data. The results revealed notable negative correlations between heuristic H5 (error prevention) and multimedia metrics (r = -0.86), and between H4 (consistency and standards) and operation-related sustainability metrics (r = -0.70). These findings provide insights into how usability factors may influence environmental sustainability and suggest directions for improvement. Overall, the paper provides preliminary insights into environmentally aware software quality assessment, through both the design of the framework and the analytical results obtained.

Artificial intelligence (AI) is reshaping work, presenting new challenges and opportunities for professionals across industries. This study, grounded in Self-Determination Theory (SDT), examines the impact of AI integration on work engagement among IT professionals. Through qualitative investigation, our research reveals that AI both augments and complicates professionals' work lives, providing opportunities for growth while also demanding ongoing adaptation. Key findings indicate that AI tools like UiPath and GitHub Copilot enhance work efficiency by automating routine tasks, enabling professionals to concentrate on more complex aspects of their work and enhancing their perceived competence. However, this efficiency gain requires continuous learning and adaptation, posing challenges in maintaining engagement and mastery. These findings illuminate the complex balance between leveraging AI for increased efficiency and maintaining the intrinsic human elements of IT design, offering some insights for navigating AI integration in the workplace.

This study explores the integration of futuristic thinking and soft skills development within educational settings, emphasizing their potential role in equipping students to navigate the complexities of the modern workforce. In this paper, we report our insights captured from both higher education teachers and K-12 students. The findings from two experimental sessions-one involving teachers and the other with K-12 students-demonstrate not only high levels of engagement and satisfaction but also the adaptability and relevance of these methodologies across different age groups and educational stages. These results underscore the effectiveness of integrating futuristic thinking with soft skills training, highlighting their potential to be widely adopted in educational curricula.

In the EDU-AI project (Adapting Computing Education for an AI-Driven Future: Empirical Insights and Guidelines for Integrating Generative AI into Curriculum and Practice), we investigate the application of generative AI by IT professionals, how students view this technology in higher education, as well as faculty adaptation and changing teaching strategies due to generative AI. This presentation will be based on a report from the EDU-AI project where we summarize ongoing research and discuss findings, related to higher education teaching and learning. These findings include adoption patterns with generative AI, generative AI advantages (such as increased productivity, enhanced learning, and innovative problem-solving), challenges with generative AI (such as ethical considerations, data biases, and potential misuse), and possible AI competence requirements in future labor market.

Background: Many mental health professionals face work-related stress due to high job demands, limited control, and inadequate institutional support. Connected mental health (CMH) technologies such as mobile apps and teletherapy platforms are increasingly being proposed as tools to alleviate these job demands. However, their actual influence on clinicians’ work environments—here understood as the organizational, social, and psychological conditions that shape their workload, job demands, autonomy, and overall well-being—remains underexplored. Existing reviews have primarily focused on traditional organizational interventions, leaving a critical gap in understanding how CMH technologies specifically influence the work environment of mental health clinicians.

Objective: This systematic literature review aims to identify and summarize knowledge about the impact of CMH on the work environment of mental health clinicians.

Methods: A systematic literature review will be performed. The review follows PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines and has been registered in PROSPERO on April 23, 2025. A comprehensive search strategy was developed using the population, intervention, comparison, and outcome (PICO) framework in collaboration with an academic librarian. Studies will be sourced from the PubMed, Scopus, IEEE Xplore, and ACM Digital Library databases. Inclusion criteria are limited to empirical studies involving mental health clinicians using CMH tools, where outcomes explicitly relate to the work environment (eg, job demands, workload, autonomy, stress, or well-being). Eligible studies must be published in English. Data extraction will include publication trends, study methods, and types of CMH technologies. Additionally, the extraction will capture the study results, including qualitative and quantitative findings, along with the measurement instruments used. Two reviewers will independently select articles for review and extract data. Conflicts will be discussed, and a third reviewer will be consulted if a consensus cannot be reached. Descriptive statistics and thematic analysis (via NVivo) will be used to synthesize the findings.

Results: This systematic literature review seeks to explore and synthesize existing research on how CMH technologies affect clinicians’ work environments and is expected to be completed by December 2025.

Conclusions: This review will offer a comprehensive overview of how CMH technologies affect the professional work environment of clinicians.

Understanding how connected mental health (CMH) tools affect the work environment of mental health clinicians requires carefully designed qualitative inquiry. This paper presents an experience report detailing the iterative development of a semi-structured interview guide used to explore clinicians’ experiences with CMH. The process involved internal workshops, feedback from experts, and pilot testing. Each stage contributed to refining the guide’s clarity, contextual relevance, and alignment with the study’s objectives. The final guide comprised 9 screening questions, 12 background questions, and 10 main interview questions, which were reduced and refined from an initial set of 21 interview questions across three thematic areas; pilot feedback led to revisions in the form of more concrete prompts and clarified language. While semi-structured interviews are widely used in health technology research, the development process is often underreported. By presenting our iterative design process, we highlight how interview guide development can be conceived, validated, and refined to enhance methodological rigor. This paper offers practical insights for researchers conducting qualitative studies in healthcare and technology settings, particularly those working on eliciting perceptions and experiences with CMH.

Generative artificial intelligence (AI), a rapidly evolving field of AI, holds transformative potential in mental health by addressing critical challenges such as accessibility, affordability, and personalization of care. Generative AI facilitates innovative solutions in illness screening, diagnosis, treatment, and psychoeducation. Despite these promises, integrating generative AI into mental health care poses significant risks, including misinformation, algorithmic bias, data privacy concerns, and a lack of regulatory oversight. This paper examines the opportunities and risks associated with generative AI in mental health and emphasizes the need for robust safeguards. Recommendations include implementing ethical design principles, developing clear regulatory frameworks, ensuring mental health professionals’ involvement, and prioritizing data privacy and security. By balancing innovation with caution, generative AI can advance mental health care responsibly and effectively.

Health care increasingly depends on information and communication technology. This offers both opportunities and challenges when designing connected health systems. While individual studies examined particular cases, there is a limited synthesis of insights across projects. The objective of this paper is to explore these opportunities and challenges by examining 6 diverse connected health projects and synthesizing lessons from an expert workshop. To achieve this, we conducted a full-day workshop that brought together 6 connected health projects. The workshop used an iterative and participatory process which included paper submissions and presentations and facilitated discussions, and a gallery walk to enable cross-case comparison and collaborative reflection. Thematic analysis of workshop outputs was then used to synthesize key opportunities and challenges in designing connected health systems. The 6 projects represented a variety of design methods and approaches to connected health, and their discussion surfaced both opportunities and challenges in this domain. Key opportunities include improving data integration and usability, enhancing collaboration across stakeholders, using a user-centered and iterative design process, addressing complexity in sociotechnical systems, sustainability, and adopting digital infrastructures for seamless communication. Participants also identified important challenges, namely exchange of information, interoperability, and communication; ethical considerations, rules, and regulations; understanding design, evaluation, and standards; actionable data, reliability, quality, and trust in data; and stakeholder involvement. The contribution of this paper lies in the synthesis of insights across multiple projects and perspectives to provide practical guidance for researchers, designers, and policymakers. By highlighting opportunities and challenges in designing connected health systems, the findings emphasize the importance of patient-centered, sustainable, and collaborative design approaches while also pointing to the need to address persistent barriers. Advancing connected health will require adopting iterative and inclusive design processes that prioritize patient-centeredness, sustainability, and collaboration across health care systems.