A grand challenge for computing is to better understand fundamental human needs and their satisfaction. In this work, we design a personal informatics technology probe that scaffolds reflection on how time-use satisfies Max-Neef’s fundamental needs of being, having, doing, and interacting via self-aspects, relationships and organizations, activities, and environments. Through a combination of a think-aloud study (N = 10) and a week-long in situ deployment (N = 7), participants used the probe to complete self- aspect elicitation and Day Reconstruction Method tasks. Participants then interacted with network visualizations of their daily lives, and discovered insights about their lives. During the study, we collected a dataset of 662 activities annotated with need satisfaction ratings. Despite challenges in operationalizing a theory of need through direct elicitation from individuals, personal informatics systems show potential as a participatory and individually meaningful approach for understanding need satisfaction in everyday life.

This study explores the potential benefits of an interactive system that supports individuals in collecting data and reflecting on their self-concept and self-aspects in daily life. Through a think-aloud study (N = 10) and in-situ deployment (N = 7), we design, deploy, and evaluate a self-tracking technology probe. The results suggest that participants found benefit in participating in the study and tracking their self-aspects, with all seven participants in the in-situ deployment expressing interest in continuing to use the system after the study. The study highlights the usefulness of supporting self-reflection at various temporal scales, and has implications for the design of personal informatics systems utilizing the multiple self-aspects framework and Day Reconstruction Method. This research contributes to the understanding of the potential benefits of interactive systems in supporting self-tracking of the experience of self-aspects in daily life.

We identify features of mood-tracking apps for managing mental health that foster engagement and sustained use by adolescents—a population that expresses a preference for digital apps over face-to-face support, yet demonstrates low levels of engagement with such apps. We developed a prototype of an adolescent-focused mood-tracking app, informed by literature about existing apps’ approaches to recording patients’ symptoms, the role of data representations in long-term mental health management, and the potential benefits of peer support tools. We then conducted a survey (n = 88) to assess adolescents’ preferences for various aspects of this prototype. We found that participants prefer tools for self-reflection and self-awareness over those for gamification or social support, and that they value function over entertainment when choosing wellness apps, especially among participants who disclosed a history of managing mental health. Qualitative analysis of open-ended responses revealed that customization and self-reflection are important design themes. Our findings have implications for the design of mental health apps that cater to the specific needs and preferences of adolescent users.

Technology plays an increasingly pivotal role in mediating mental health support in people's everyday lives. However, it is not clear how that mediation is occurring, to what end, and what technologies are implicated. In this study, we examine these questions with a mixed-methods analysis of conversations among participants in several Bipolar Disorder (BD) communities on Reddit. Analyzing posts produced over four years, we identify a wide variety of technologies that people employ to manage their mental conditions, such as communication technologies, online communities and tracking tools. Using this taxonomy of technologies as a framework, we then summarize three technology-mediated management strategies that these technologies enable, including serving as community, episode, and information mediators. We argue that with a comprehensive and nuanced understanding of people's in situ technology use, we can identify research and design opportunities for designing human-centered technologies to help people manage mental health challenges more effectively.

Personal informatics (PI) systems have been developed to support reflection. While reflection is considered an indispensable activity in PI use, how and when reflection occurs is still under-studied. In this paper, we present an analysis of the interactive features of 123 commercial PI apps, revealing that reflective practices are unevenly supported. The lack of features that encourage user-driven reflection, scaffolding for setting goals and configuring data collection and presentation, and consideration of wider implications stand to limit meaning-making and frustrate nuanced insight generation based on lived experiences. Based on our findings, we discuss how reflection is currently misrepresented in personal informatics tools, identify and characterize the gaps between theoretical research on reflection and interface features in current apps, and offer suggestions about how reflection could be better supported.

Background Food practice plays an important role in health. Food practice data collected in daily living settings can inform clinical decisions. However, integrating such data into clinical decision-making is burdensome for both clinicians and patients, resulting in poor adherence and limited utilization. Automation offers benefits in this regard, minimizing this burden resulting in a better fit with a patient's daily living routines, and creating opportunities for better integration into clinical workflow. Although the literature on patient-generated health data (PGHD) can serve as a starting point for the automation of food practice data, more diverse characteristics of food practice data provide additional challenges. Objectives We describe a series of steps for integrating food practices into clinical decision-making. These steps include the following: (1) sensing food practice; (2) capturing food practice data; (3) representing food practice; (4) reflecting the information to the patient; (5) incorporating data into the EHR; (6) presenting contextualized food practice information to clinicians; and (7) integrating food practice into clinical decision-making. Methods We elaborate on automation opportunities and challenges in each step, providing a summary visualization of the flow of food practice-related data from daily living settings to clinical settings. Results We propose four implications of automating food practice hereinafter. First, there are multiple ways of automating workflow related to food practice. Second, steps may occur in daily living and others in clinical settings. Food practice data and the necessary contextual information should be integrated into clinical decision-making to enable action. Third, as accuracy becomes important for food practice data, macrolevel data may have advantages over microlevel data in some situations. Fourth, relevant systems should be designed to eliminate disparities in leveraging food practice data. Conclusion Our work confirms previously developed recommendations in the context of PGHD work and provides additional specificity on how these recommendations apply to food practice.

Self-tracking technologies, especially those facilitating support from social systems, are becoming more common for treating serious mental illnesses in both clinical and informal contexts. A recently proposed feature is co-tracking, where data is gathered not only from the perspective of the user managing their condition, but also from their close contacts. The proposed system therefore supports multiple perspectives (data streams) about the same variable of interest (i.e., an individual’s mood). However, the subjective and reciprocal nature of mental health data gives rise to challenges in visualizing uncertainty that must be addressed before clinical use. Here, we create an application-specific typology of uncertainty for visualizing multi-source mental health data, and propose design solutions to communicate this uncertainty. Via a case study of mood tracking with bipolar disorder, we present an interactive visualization prototype for understanding dynamic mood states in close relationships, moving toward a real-world implementation of a co-tracking informatics system.

We present the research area of personal dream informatics: studying the self-information systems that support dream engagement and communication between the dreaming self and the wakeful self. Through a survey study of 281 individuals primarily recruited from an online community dedicated to dreaming, we develop a dream-information systems view of dreaming and dream tracking as a type of self-information system. While dream-information systems are characterized by diverse tracking processes, motivations, and outcomes, they are universally constrained by the ephemeral dreamset—the short period of time between waking up and rapid memory loss of dream experiences. By developing a system dynamics model of dreaming we highlight feedback loops that serve as high leverage points for technology designers, and suggest a variety of design considerations for crafting technology that best supports dream recall, dream tracking, and dreamwork for nightmare relief and personal development.

Global crowdsourcing teams who conduct humanitarian response use temporal narratives as a sensemaking device when time is a critical element of the data story. In dynamic situations in which the flow of online information is rapid, fluid, and disordered, the process of how distributed teams construct a temporal narrative is not well understood nor well supported by information and communication technologies (ICTs). Here, we examine an intense need for temporal sensemaking: time- and safety-critical information work during the 2017 Hurricane Maria crisis response in Puerto Rico. Our analysis of semi-structured interviews reveals how members of a global digital humanitarian group, The Standby Task Force (SBTF), use a process of triage, evaluation, negotiation, and synchronization to construct collective temporal narratives in their high-tempo, distributed information work. Informed by these empirical insights, we reflect on the design implications for cloud-based, collaborative ICTs used in time- and safety-critical remote work.

We use a sociotechnical perspective to expand upon prior characterizations of deploying end-to-end urban sensor networks that focus primarily on the technical aspects of such systems. Via exploratory, semi-structured interviews with those deploying a number of urban sensor networks in a single American city, we identify ways that human decision-making and collaborative processes influence how these infrastructures are built. We synthesize these findings into a framework in which sociotechnical factors show up across the phases of data collection, management, analysis, and impacts within smart city projects. Each phase can display variability in immediacy, automation, geographic scope, and ownership. Finally, we use our situated work to discuss a generalizable tension within smart city projects between cross-domain data integration and fragmentation and provide implications for CSCW research, the design of smart city data platforms, and municipal policy.

The rise of big data has led to the creation of large datasets that require teams to collaborate to analyze data effectively. Unfortunately, the software systems that collect and analyze large datasets are not often designed to support this kind of collaboration. Accordingly, our work investigates issues related to supporting collaboration in big data analysis systems. We use the domain of crisis informatics and the software infrastructure of Project EPIC as a case study to gain insight into the features that analysts need to effectively perform analysis at scale. This paper focuses on supporting asynchronous collaboration among analysts who work in small distributed teams on big data software systems. It describes the challenges faced by researchers who work collaboratively to analyze large crisis datasets (consisting, typically, of Twitter data). It then describes the work performed to redesign an existing big data analysis environment to substantially improve its support for collaboration. The impact of this research lies in its ability to improve the work of similar teams performing large-scale data analysis. While our work is based on insights gleaned from crisis informatics, we believe that our design, results, and lessons learned are broadly applicable to other application domains.

Numerous studies have highlighted a range of potential benefits of teletherapy for clients. Nonetheless, researchers have found that many therapists are reluctant to adopt teletherapy in their work practice. There is a dearth of research about how therapists have appropriated telehealth platforms, either to understand teletherapy practice or to understand the challenges and opportunities for system design. The COVID-19 pandemic offers an unfortunate but unique opportunity to learn more about the experiences of therapists who use a range of therapeutic interventions with a range of client populations. In this work, we explore the following research question: in what ways do telehealth platforms support and challenge the work of teletherapy? We present results of semi-structured interviews conducted with 14 mental health therapists during the first six months of the pandemic in the United States. We present a descriptive account of their experiences as well as a discussion of the ways in which the multi-layered and interdependent nature of two facets of therapeutic work---the therapeutic alliance and the therapeutic interventions---made the transition to computer-supported cooperative work particularly challenging. We then offer a suite of design implications for systems that better support the nuanced and unique work of teletherapy.

Learning how to cook presents at least two significant challenges. First, it can be difficult for novices to find appropriate recipes based on the ingredients available in one's pantry and/or refrigerator. Second, it can be difficult to focus on cooking tasks and following a recipe at the same time. In this poster, we present the design process and implementation of a system that uses deep learning to address the first of these two problems. Our initial design work focuses on streamlining the process of entering and tracking potential ingredients on hand and determining appropriate recommendations for recipes that utilize these ingredients. Here, we present the current state of our project, explaining in particular our contributions to minimizing the overhead of tracking kitchen ingredients and converting this inventory information into effective recipe recommendations using a multimodal machine learning approach.

In this research, we explored the design of navigation technology for wildland firefighters. We worked within a set of empirically informed design constraints to create prototypes of a wearable system that provides peripheral navigation cues via visual and haptic feedback. We used physical and interactive prototypes of this system as technology probes to provoke discussions with wildland firefighters about their navigation and location technology needs. Our pilot study results indicate that our prototypes helped to uncover ideas for future technical work in the domain of wildland firefighting, as well as on mobile and wearable navigation systems, more broadly.

We discuss the importance of designing self-tracking technologies for serious mental illness (SMI) that allow individuals with SMI to collect, share, and sense-make over data with a dynamic set of support system members. Our collaborative work with individuals diagnosed with bipolar disorder has suggested the following design and technical challenges for supporting social practices around personal data in long-term mental health management: allowing for fine-grained control over data disclosure by individuals with SMI, supporting dynamism in relationships and roles over long-term use of a system, and allowing individuals flexibility in the variables that they self-track. We discuss these challenges and how they relate to the goals of predictive modelling and intervention in mental health personal informatics systems.

The ability to build a construct that organizes work from different devices and information resources is as complex as it is invaluable.