Eating is complex, as is people’s relationship to food, which significantly impacts well-being and health. Mindful eating interventions have been shown to be effective in addressing problematic eating, and mindful eating has also gained increasing interest as a research topic in Human-Computer Interaction (HCI), albeit with limited grounding in health research, so that we know little about how HCI work could be informed by health research on mindful eating and its interventions. Such a theoretical foundation grounded in health research is crucial to ensure the design of safe and effective mindful eating technologies and interventions. To address this gap, we present an analysis of health research on mindful eating principles, measurement scales, and therapeutic interventions such as the MB-EAT program, which helped us identify the main aspects of mindful eating. Then, we conducted a scoping review on technologies targeting these aspects, from which we curated 16 design exemplars representing the breadth of these technologies and generated a new SmartPlate conceptual design. Then, we designed the Mindful Eating Design Critique (MEDEC) cards and reported workshops with 36 mindful eating practitioners who used the MEDEC cards to critique the set of design exemplars. Our main contributions include a solid theoretical foundation grounded in health research for the design of mindful eating technologies, the MEDEC cards as a novel critique tool consisting of 29 cards improved based on practitioners’ feedback, and a design framework to further support research and development of mindful eating technologies.

The increasing adoption of generative AI raises concerns regarding its potential to reinforce gender stereotypes, especially in content aimed at children.AI-generated images that reflect traditional gender roles can inadvertently influence children's perceptions, embedding unconscious biases.Through co-design sessions with domain experts, we developed a child-friendly generative AI web application designed with dual purpose: (1) analysing children's choices to determine potential underlying bias and whether children's own gender identity influences these choices, and (2) investigating if specific non-aesthetic traits prompt the AI to produce characters with stereotypical presentations.

HCI research on goals and behavior change has significantly increased over the past decade.However, while emerging work has synthesized personal informatics goals, fewer efforts have focused on also integrating HCI research on behavior change to chart future research directions.We conducted a systematic review of 180 papers focused on goals and behavior change from over 10 years of SIGCHI journals and conference proceedings.We further analyzed 37 papers from the data set that included evaluations of interventions' effectiveness in-the-wild.We also reported on the effectiveness of 76 of such technology-based interventions and the meta-analysis of 28 of these interventions.We find that most research has focused on goals in the health and wellbeing domains, centered on the individual, low intrinsic goals, and partial use of theoretical constructs in technology-based interventions.We highlight opportunities for supporting multiple-domain, social, high intrinsic, and qualitative goals in HCI research for behavior change, and for more effective technology-based interventions with stronger theoretical underpinning, supporting users' awareness of deep motives for qualitative goals.

A growing number of mobile apps have focused on healthy or problematic eating, albeit limited research has focused on evaluating such apps from users’ perspectives. To address this, we evaluated the functionalities of 27 apps on mindfulness eating, and eating disorders from the Apple App, and Google Play Stores, and conducted a content analysis of 1248 user reviews, totaling over 60,000 words. Findings indicate the main functionalities of tracking data on eating behaviors, emotions, thoughts, bodily sensations, symptoms, as well as triggers of eating disorders, and of providing interventions such as mindfulness, goal setting, psychoeducation, CBT, and holistic ones. Findings also highlight key usability and ethical challenges, which we used to inform five design implications namely tracking and reflecting on multiple aspects of mindfulness and healthy eating, supporting personalized interventions and AI-based ones, as well as the sensitive design for diagnosis, and for tracking and monitoring problematic data.