Self-tracking tools can support health awareness and behavior change, though sustaining engagement remains difficult. Prior work has explored qualitative visualization, customization, and collaborative features to promote engagement; but little is known about how these strategies interact when combined. We present Sprout, a mobile application that integrates qualitative, customizable, and collaborative health tracking using a garden metaphor. Sprout allows users to choose what they track, customize how data is visually encoded, and participate in anonymous communities where collective progress unlocks shared features. In a 2-week field study with N=22 participants, users reported that qualitative displays worked best as a complement to quantitative tools, customization mostly happened during app setup, social features were the most engaging though collaboration produced both motivation and frustration, and anonymity protected privacy but limited social connection. Our findings show how multiple design strategies coexist in one system, sometimes competing and sometimes aligning in supporting users' tracking needs.
Self-tracking tools are often built around the assumption that tracking the “right” health variables will lead to actionable insights and greater control over one’s health. Yet, it remains unclear how these assumptions hold up in contexts marked by uncertainty, unpredictability, and frequent fluctuations in health needs. We explore this question in the management of enigmatic diseases - conditions such as fibromyalgia, Crohn’s disease, and endometriosis that are poorly understood and highly individualized. Through interviews with 23 participants living with disparate enigmatic conditions, we examine goals, motivations, and how tracking practices evolve across different disease states. Our findings show that tracking was strongly shaped by shifting needs, with goals emerging, evolving, or being abandoned in response to health fluctuations. Tracking was often double-edged: at times empowering, fostering a sense of control, but also frustrating, leading to self-blame and negative views of everyday activities being tracked.
Self-experimentation, or using tracked data to systematically answer health and wellbeing questions via hypothesis testing, has significant potential to support personal health. However, technological support for self-experimentation has focused on expert-designed self-experiments for specific health conditions, limiting people's ability to design their own rigorous experiments. To address this gap, we developed CASEbot (Conversation Agent for Self-Experimentation), an LLM-powered chatbot using a theory-driven approach to guide users through designing well-structured, personalized, and safe self-experiments. We conducted a within-subjects, mixed-methods study with 42 participants comparing CASEbot to a traditional worksheet-based approach. When formally comparing the experiment rigor and specificity, most participants designed better experiments using CASEbot. They appreciated CASEbot's conversational approach, which prompted them to surface everyday constraints and proactively raised safety concerns, but some found the platform too rigid in its recommendations. We discuss opportunities for future generative AI self-experimentation systems for health to balance structured guidance with user autonomy.
Can self-tracking technology be designed to help us learn how our everyday activities align with our values? Existing personal informatics systems can help track time use, but rarely explicitly connect time use with personal abstract values. We designed, built, and deployed eValuATE, a personal informatics system that helps individuals reconstruct their everyday activities, annotate those activities with their values, and visualize relationships between their activities and values. Fifteen participants used the system in a think-aloud study, a 2-4 week deployment, and a closing interview. The system helped participants refine their own values, understand how those values were supported through their daily activities, view their time use differently, and in many cases change their behavior to better align with their values. We discuss design considerations for self-tracking abstract values, motivate a shift towards short term life studies over perpetual self-tracking, and present a multi-level reflection model for personal informatics system design.
New parents often turn to baby-tracking technology to monitor and reflect on the daily routines of their infants. However, we lack understanding of how tracking practices evolve as children grow and develop, with caregivers adopting, using, and eventually abandoning baby tracking. We analyze the logs of 60 parents and 71 children who used the popular baby-tracking app Huckleberry for an average of 12 months, combined with re-analyzing interviews with 20 parents who used various baby-tracking technologies. We find that parents start tracking at different ages, track habitually and intermittently, change and swap what and how they track, and often gradually abandon the practice. Through unpacking why these patterns occur, we find that parents effectively self-manage what data categories are worthwhile to continue tracking. We point out lessons that domains outside baby tracking can take from the evolving, longitudinal process, and present design recommendations to better support caregivers across phases.
This paper reports how clinicians explain personal health data to patients and the tensions which arise from this in practice, leading us to describe a set of implications for designing communication aids around personal health data. With the trend towards patient-centered care and shared decision making, it is crucial that patients understand their clinical data and respective implications during medical consultations. So, what strategies do clinicians currently use to ensure this? And how can these inform the development of successful patient communication aids? Through interviewing 19 healthcare professionals, we identify 57 techniques, painting a rich picture of current practices. However, we also note 9 tensions that arise when applying these techniques in reality; such as balancing transparency with disclosing data inappropriate for a patient's current situation. Based on the techniques and motivated by these tensions, we present a set of considerations to inform the design of technological patient communication aids consistent with current clinical practice.
Conversational agents have been studied as tools to scaffold planning and self-reflection for productivity and well-being. While prior work has demonstrated positive outcomes, we still lack a clear understanding of what drives these results and how users behave and communicate with agents that act as coaches rather than assistants. Such understanding is critical for designing interactions in which agents foster meaningful behavioral change.
We conducted a 14-day longitudinal study with 12 participants using a proactive agent that initiated regular check-ins to support daily planning and reflection. Our findings reveal diverse interaction patterns: participants accepted or negotiated suggestions, developed shared mental models, reported progress, and at times resisted or disengaged. We also identified problematic aspects of the agent's behavior, including rigidity, premature turn-taking, and overpromising. Our work contributes to understanding how people interact with a proactive, coach-like agent and offers design considerations for facilitating effective behavioral change.