Fitness tracking platforms increasingly integrate generative AI to interpret activity data, such as Strava’s Athlete Intelligence. These integrations raise questions about how athletes engage with AI-supported fitness self-tracking. We analyzed 297 Reddit threads and 5,692 comments from r/Strava following the company’s launch of AI features to examine user reactions to AI-generated fitness feedback. Our findings revealed four recurring tensions: (1) numerical evaluation versus contextual understanding; (2) isolated session summaries versus ongoing training narratives; (3) a fixed AI tone versus diverse emotional states; and (4) a single AI voice versus different athletic types. Across these tensions, users resisted AI feedback that constrained interpretations of their own lived experiences. These findings shed light on the implicit challenges of integrating AI into self-tracking platforms. We conclude with implications for the design of AI-supported self-tracking systems that preserve interpretive openness and user agency.

Cookie banners and privacy settings attempt to give users a sense of control over how their personal data is collected and used, but background tracking of personal information often continues unnoticed. To explore how such invisible data collection might be made more perceptible, we present DataCrumb, a physical probe that reacts in real-time to data tracking with visual and auditory feedback. Using a research-through-design approach, we deployed the artifact in three households and studied participants’ responses. Instead of providing details about what data was being tracked, the artifact introduced subtle disruptions that made background data flows harder to ignore. Participants described new forms of awareness, contradiction, and fatigue. Our findings show how sensory feedback can support reflection by drawing attention to tracking data flows that are usually hidden. We argue for designing systems that foster awareness and interpretation, especially when the users’ control and understanding are limited.

Large Language Models (LLMs) such as ChatGPT are increasingly used in education, but their general-purpose design limits support for conceptual learning. We present a study of a custom GPT, scaffolded with Bloom’s Taxonomy and framed as an educator, to examine how structured dialogue shapes learning. Using deceptive patterns as a test domain, 38 students compared the custom GPT with ChatGPT-4 and an expert-authored website. The custom GPT led to higher satisfaction, trust, and perceived learning than ChatGPT-4, though the website remained most trusted overall. Students valued structured guidance for deeper reflection but also found it demanding, and still relied on expert sources for credibility. Our contribution is threefold: we provide evidence on how these tools differ in shaping learning experiences; show how structured prompting and role framing act as pedagogical scaffolds; and propose design directions that combine scaffolding with source cues, adapt to learner needs, and complement teaching practices.

Abstract Despite the widely acknowledged importance of well-being, our well-being can regularly be under pressure from external sources. Work is often attributed as a source of stress and dissatisfaction, so, unsurprisingly, extensive efforts are made to measure and improve our well-being in this context. This paper examines opportunities to better design supportive digital solutions through two complementary studies. In the first study, we present a longitudinal assessment of a well-being-focused self-report application deployed in two organizations. Through an analysis of one year of application usage across 219 users, we find both established and novel patterns of application usage and well-being evaluation. While prior work has highlighted substantial dropout rates and daily well-being fluctuations that peak in the morning and early evening, our results highlight that substantial breaks in usage are common, suggesting that users choose to engage with well-being applications mainly when they need them. In the second study, we expand on the topic of well-being reflection at work and the use of technology for this purpose. Through a survey involving 100 participants, we identify current practices in increasing well-being at work, obstacles to sharing and discussing mental well-being states, opportunities for digital well-being solutions and reflections on transparency and communication. Our combined results highlight opportunities for HCI research and practice to address the ongoing challenges of maintaining well-being in today’s work environments.

Smart home cameras (SHCs) offer convenience and security to users, but also cause greater privacy concerns than other sensors due to constant collection and processing of sensitive data. Moreover, privacy perceptions may differ between primary users and other users at home. To address these issues, we developed three physical cover prototypes for SHCs: Manual, Hybrid, and Automatic, based on design criteria of observability, understandability, and tangibility. With 90 SHC users, we ran an online survey using video vignettes of the prototypes. We evaluated how the physical covers alleviated privacy concerns by measuring perceived creepiness and trustworthiness. Our results show that the physical covers were well received, even though primary SHC users valued always-on surveillance. We advocate for the integration of physical covers into future SHC designs, emphasizing their potential to establish a shared understanding of surveillance status. Additionally, we provide design recommendations to support this proposition.

Self-tracking technologies have long promised to enhance our well-being. However, our initial work and that of others show that most of these technologies focus on data, not the user. Based on interviews, development of mood boards, and the creation of a research product, we propose an alternative approach to self-tracking: re-humanising self-tracking technologies. Our work shows that feelings play an important role with data, that data are temporal, and associated with work and utility. We interpret four design criteria, which are applied in the creation of Frankie: a human-centred tracking device which records both quantitative (number of activities) and qualitative (perceived weight of the activity and spoken reflections) data to foster self-reflection. Through this design case we add to the discussion on re-imagining self-tracking technologies to go beyond data-centric artefacts.

Routines are an important element of day-to-day work life, supporting people in structuring their day around required tasks. Effectively managing these routines is, however, experienced as challenging by many – an issue further amplified by the current work from home lockdown measures. In this paper we present Rainmaker, a tangible device to support people in their working life in the context of their own homes. We evaluate and iterate on our prototype through two qualitative studies, spanning respectively three days (N = 11) and 15 days (N = 2). Our results highlight the perceived advantages of the use of a primarily physical rather than digital tool for work support, allowing users to stay focused on their tasks and reflect on their work achievements. We present lessons for future work in this area and publicly release the software and hardware used in the construction of Rainmaker.