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This study explores the phenomenon of energy and self-tracking technologies, moving beyond the context of managing chronic conditions. Our approach to designing from the experiences of people with disability is informed by crip theory, which challenges societal norms of health and ability. We analysed 50 survey responses and 15 interviews with wearable tracker users and found that self-tracking shapes interpretation of energy and self-care strategies. Our findings indicate that tracking significantly affect perceptions and judgments of bodily activity, energy and rest. We found a notable disconnect between the metrics provided by the trackers and the subjective understanding of personal energy meanings, especially during events of bodily and contextual changes such as travelling, illness, or menstrual cycle. This research contributes to discourses on energy in self-tracking technologies and advocates for designing more inclusive, crip futures for everyone that celebrate irregularity, fluctuation, and change, accommodating diverse bodily rhythms in energy tracking practices.

How can large language models (LLMs) support critical design explorations for describing, articulating, and responding to bodily phenomena? This study investigates the integration of code-generating LLMs across design explorations in four different self-tracked or bio-sensed domains: learning, chronic illness, posture control, and knee injury. We synthesise our collaborative research through design inquiries into three themes: 1) forced articulation of fuzzy phenomena, 2) difficulty of escaping local optima, and 3) unexpected fragility of generated sketches. These themes, though potentially applicable in other domain contexts, are grounded in our challenges with code-generating LLMs on embodied and embodiment subjects, with a particular focus on working with technology and data relevant to embodied experiences. We propose two implications for design of LLM-based sketching tools anchored on our design inquiry journeys.

There is a growing interest in navigating the tensions between emerging technologies designed for movement and physical activity, and how people (mis)use and appropriate them. These include whether technologies are helpful or harmful, enhance or distort lived experiences, and the gap between what data captures, how it is represented and visualized, and what it inevitably renders invisible. This 1-day workshop aims to bring together interaction designers, HCI researchers, and practitioners who engage in movement and physical activity to unpack, explore, and critique these tensions (and beyond) through movement. Together, we will participate in a guided movement session, collect various forms of data, and craft representations of these data to foster reflection and critique. Our workshop aims to contribute to developing a shared design agenda to interrogate, challenge, and reshape the future of digital technologies and data that foster engagement with movement and physical activity beyond quantification and static data representations.

CSS ConceptsAs AI and data systems often falter when encountering queer identities and knowledge, reinforcing existing oppressions, queer people have resisted such systems and their normalizing tendencies. This pictorial explores tactics of queering AI through a collaborative zine-making project (i.e. zineography) that challenges generative AI and data systems. We share how we workshopped and materialized queering tactics in zine spreads; analyzed these spreads according to materials, content, and tone; and visualized our analysis as thematic collages. We contribute: (1) tangible characteristics of queering AI and data systems (i.e. materials, tones, and aesthetics); and (2) design opportunities for using zineographies as a radical method for building and collectively sharing knowledge about a marginalized community, including recommendations for enacting queer zineographies. By materializing queering tactics through zine-making, we invite embodied, action-oriented critiques that question dominant techno-solutionist movements and trace queer possibilities outside of their normalizing narratives.

People with energy-limiting conditions, such as chronic fatigue syndrome (ME/CFS) and Long COVID, need to limit their activity levels and balance exertion with rest and restorative activities. This practice is known as “pacing”. There is an opportunity for technology to help people with this process, but conducting research with this population can be difficult given their limited and unpredictable energy levels. This research explores how we can use crip theory to inform the development of co-design methods suitable for this cohort, and as an analytical lens to explore how these tools should be designed outside of normative and abelist assumptions about fatigue and productivity. This is done through a 5 week Asynchronous Remote Community study utilising various co-design techniques. These findings point to future designs of pacing technologies and contribute insights about developing more accessible approaches to conducting research with people with energy-limiting conditions.

Energy limiting conditions (ELC), such as long COVID and ME/CFS, require the careful monitoring and pacing of activity and rest to avoid over-exertion. Commercially available fitness tracking technologies are currently being “misused” to manage these conditions. Based on co-design research with people with ELC, we conducted a research-through-design process to ideate upon what ELC pacing technologies could be. Our ongoing design process is informed by crip theories that highlight the social and political, rather than medical, aspects of disability and chronic conditions. In an attempt to offer non-medicalising pacing technologies, we explored integrating bronze casting as a jewelry making technique within the prototyping process. We also explore how we can present quantitative pacing data gathered from wearable sensors through felt vibrations on the body in a way that can be therapeutic and allow for the user to calibrate the quantitative data with their own felt sense of fatigue

This paper critically examines the machine learning (ML) modeling of humans in three case studies of well-being technologies. Through a critical technical approach, it examines how these apps were experienced in daily life (technology in use) to surface breakdowns and to identify the assumptions about the "human"body entrenched in the ML models (technology design). To address these issues, this paper applies agential realism to decenter foundational assumptions, such as body regularity and health/illness binaries, and speculates more inclusive design and ML modeling paths that acknowledge irregularity, human-system entanglements, and uncertain transitions. This work is among the first to explore the implications of decentering theories in computational modeling of human bodies and well-being, offering insights for more inclusive technologies and speculations toward posthuman-centered ML modeling.