Behavioral changes are critical for addressing sustainability challenges, which have become increasingly urgent due to the growing impact of global greenhouse gas emissions on ecosystems and human livelihoods. However, translating awareness into meaningful action requires practical tools to bridge this gap. Mobile applications, utilizing strategies from human–computer interaction (HCI) such as gamification, nudging, and persuasive technologies, have proven to be powerful in promoting sustainable behaviors. To support designers in developing effective apps of this kind, theory-based design guidelines were created, drawing on established theories and design approaches aimed at shaping and encouraging virtuous user behaviors fostering sustainability. To make these guidelines more accessible and enhance their usability during the design phase, this study presents their transformation into the SBAM card deck, a deck of 11 design cards. The SBAM cards aim to simplify theoretical concepts, stimulate creativity, and provide structured support for design discussions, helping designers generate solutions tailored to specific project contexts. This study also evaluates the effectiveness of the SBAM cards in the design process through two workshops with design students. Results show that the cards enhance ideation, foster creativity, and improve designers’ perceived self-efficacy compared to the exploitation of the same design guidelines information presented in traditional textual formats. This paper discusses the SBAM cards design and evaluation methodology, findings, and implications, offering insights into how the SBAM design cards can bridge the gap between theory and practice in sustainability-focused mobile app development. To ensure broader accessibility, the SBAM cards have been made available to the public through a dedicated website.

Purpose: This study examines patients' perspectives on the integration of artificial intelligence (AI) in radiology through focus groups, aiming to identify the main issues and areas for improvement. It is part of a larger research project that employs various methodologies to explore the views of both patients and radiologists regarding AI tools. Methods: We conducted two focus groups using a narrative story and vignettes: one with patients who self-assessed as AI experts and the other with non-AI experts. Results: The focus groups revealed diverse opinions on AI use in diagnostics, focusing on six main topics: acceptance, concerns, communication between radiologists and patients, explainability of AI, medical records, and emotional aspects. Conclusions: The findings underscore the importance of developing patient-centric AI solutions to build trust in AI-assisted diagnostic tools, considering emotional and communicational aspects and addressing both physician and patient concerns to facilitate smoother integration of AI in radiology.