General rightsThis document is made available in accordance with publisher policies. Please cite only the published version using the reference above. Full terms of use are available: http://www.bristol.ac.uk/pure/about/ebr-terms
ABSTRACTDigital self-tracking technologies offer many potential benefits over self-tracking with paper notebooks. However, they are often too rigid to support people's practical and emotional needs in everyday settings. To inform the design of more flexible self-tracking tools, we examine bullet journaling: an analogue and customisable approach for logging and reflecting on everyday life. Analysing a corpus of paper bullet journal photos and related conversations on Instagram, we found that individuals extended and adapted bullet journaling systems to their changing practical and emotional needs through: (1) creating and combining personally meaningful visualisations of different types of trackers, such as habit, mood, and symptom trackers; (2) engaging in mindful reflective thinking through design practices and self-reflective strategies; and (3) posting photos of paper journals online to become part of a selftracking culture of sharing and learning. We outline two interrelated design directions for flexible and mindful selftracking: digitally extending analogue self-tracking and supporting digital self-tracking as a mindful design practice.
We present a case study that informs the creation of a 'companion guide' providing transparency to potential non-expert users of a ubiquitous machine learning (ML) platform during the initial onboarding. Ubiquitous platforms (e.g., smart home systems, including smart meters and conversational agents) are increasingly commonplace and increasingly apply complex ML methods. Understanding how non-ML experts comprehend these platforms is important in supporting participants in making an informed choice about if and how they adopt these platforms. To aid this decision-making process, we created a companion guide for a home health platform through an iterative user-centred-design process, seeking additional input from platform experts at all stages of the process to ensure the accuracy of explanations. This user-centred and expert informed design process highlights the need to present the platform's entire ecosystem at an appropriate level for those with differing backgrounds to understand, in order to support informed consent and decision making.
Co-design is a widely applied design process with well-documented values, including mutual learning and collective creativity. However, the real-world challenges of conducting multidisciplinary co-design research to inform the design of self-care technologies are not well established. We provide a qualitative account of a multidisciplinary project that aimed to co-design machine learning applications for Type 1 Diabetes (T1D) self-management. Through retrospective interviews, we identify not only perceived social, technological and strategic benefits of co-design but also organisational, translational and pragmatic design challenges: participants with T1D experienced difficulties in co-designing systems that met their individual self-care needs as part of group design activities; HCI and AI researchers described challenges collaborating to apply co-design outcomes to data-driven ML work; and industry collaborators highlighted academic data sharing regulations as cross-organisational challenges that can impede co-design efforts. Based on this understanding, we discuss opportunities for supporting multidisciplinary collaborations and aligning individual health needs with collaborative co-design activities.CCS Concepts: • Human-centered computing → Collaborative and social computing; Empirical studies in collaborative and social computing;
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.