Automatic stress tracking has become increasingly available on wearable devices. Research has investigated its use for individual stress management, largely within the traditional data-as-care framing. However, its use for stress sharing in social relationships, particularly close relationships, is still under explored. Inspired by the idea of “caring-through-data”, which focuses on mediating the social and emotional experiences of the collective “us” with data, this paper presents a design study with a prototype called IntimaSea, a display featuring illustrative stress data in collective forms to be shared among close relationships. The field trials with nine groups of intimately-connected users (N=19) highlight its potential on stress awareness, interpretation and management, as well as intimacy promotion. We end by discussing sharing stress for social ways of stress management, stress data as a meaningful social cue mediating relationships, as well as design implications for caring-through-data.

Background information is very important for news reading and understanding, especially with today’s rapid new media development for news production and consumption. When researchers actively develop tools to automatically provide back-ground information for people, we still have little understanding of how people, from readers’ perspective, engage with news and what background information is needed. To address such a gap, we conducted a study with a news reading platform, observed participants’ news reading behaviors using the platform and interviewed them over the process. Our study highlighted that news reading is a learning activity between leisure and study, and their background information searching behavior is part of Gestalt knowing – to make their existing knowledge more complete, by deepening their prior understandings, broadening their horizons, filling the gaps, clearing their confusion, as well as tracking the progress of an event as a whole, so a more holistic understanding could be achieved. We end by discussing implications of our findings.

With the recent advancement in computer vision, Artificial Intelligence (AI), and mobile technologies, it has become technically feasible for computerized Face Reading Technologies (FRTs) to learn about one's health in everyday settings. However, how to design FRT-based applications for everyday health practices remains unexplored. This paper presents a design study with a technology probe called Faced, a mobile health checkup application based on the facial diagnosis method from Traditional Chinese Medicine (TCM). A field trial of Faced with 10 participants suggests potential usage modes and highlights a number of critical design issues in the use of FRTs for everyday health, including adaptability, practicality, sensitivity, and trustworthiness. We end by discussing design implications to address the unique challenges of fully integrating FRTs into everyday health practices.