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.

Person-centered care provides a critical framework for dementia support in long-term care facilities, emphasizing attention to each resident's evolving needs, personal history, preferences, and values. Achieving this level of personalized attention depends on coordination among staff and active family involvement to record and share this essential context. However, fragmented communication, caregiver burnout, limited resources, and regulatory complexity frequently disrupt the flow of information, creating conditions in which person-centered care becomes difficult to sustain in practice. To address these challenges, we introduce Family In The Loop (FITL), an AI-enabled web platform that enhances continuity and collaboration for person-centered dementia care. FITL automatically synthesizes diverse care data-including video, notes, and records-into contextually relevant, emotionally resonant updates tailored for staff and family members. It offers four interlinked views: ''Reels'' provides families with concise, meaningful video updates; ''Essentials'' supplies caregivers with resident-specific summaries for immediate use; ''Explanations'' presents narrative insights into care decisions; and ''Archives'' maintains a searchable, longitudinal record of care activities. Together, these views enable flexible yet coordinated information sharing, respecting the fluid nature of family involvement and supporting continuity across caregiver roles and shifts. Through an iterative co-creation process spanning two years and seven resource-constrained facilities, we designed, developed, and deployed FITL in real-world dementia care contexts. Findings indicate that FITL improved staff access to critical resident information and facilitated sustained family engagement without increasing caregiver burdens. By surfacing timely, personalized, and meaningful information, FITL strengthened trust between families and staff, reduced misunderstandings, and bridged communication gaps. Our work highlights the potential of AI tools to stitch fragmented care interactions into cohesive, person-centered caregiving ecosystems. We conclude the paper by discussing inherent tensions around automation, oversight, and privacy, and we propose design principles for adaptive, collaborative, and person-centered care technologies.

Collaborative technology provides powerful opportunities to engage young people in active learning experiences that are inclusive, immersive, and personally meaningful. In particular, interactive narratives have proven to be effective scaffolds for learning, and learnersourcing has emerged as a promising student-driven approach to enable personalized education and quality control at-scale. We introduce the first synthesis of these ideas in the context of teaching artificial intelligence (AI), which is now seen as a critical component of 21st-century education. Specifically, we explore the design of a narrative-based learnersourcing platform where engagement is centered around a learner-made choose-your-own-adventure story. In grounding our approach, we draw from pedagogical literature, digital storytelling, and recent work on learnersourcing. We report on our iterative, learner-centered design process as well as our study findings that demonstrate the platform’s positive effects on knowledge gains, interest in AI concepts, and the overall user experience of narrative-based learnersourcing technology.

On-demand mental health services-including counseling, crisis hotlines, and peer support programs-are vital to the healthcare system, providing acute and ongoing support through telephone, online chats, and text messaging. Although such services have proven effective at reducing hopelessness, psychological pain, and suicidality, they put the providers of these services at high risk of burnout, secondary traumatic stress, and compassion fatigue. Our interviews with professionals from four mental health organizations revealed that while these workers have a strong motivation to help clients with mental health care needs, they face various challenges themselves, particularly regarding heavy caseloads, difficult crisis clients, and coping with repeated exposure to abuse and harassment. To overcome challenges, participants identify the need to be self-reliant and engage in self-care practices ranging from socializing with coworkers to yoga and meditation. Although organizations spend significant time training workers prior to their involvement with clients, the training typically lacks components on self-compassion and self-care. Designers might see technology as an opportunity to promote such practices; however, while technology is an integral part of their work routine, participants, irrespective of age, had misapprehensions regarding technology use in the mental health care space, including for managing their psychological well-being. We recommend design guidelines for HCI researchers, including developing contextualized just-in-time adaptive interventions to promote self-compassion and educating workers regarding the use of various technologies to manage their well-being.

Computational thinking (CT) education reaches only a fraction of young children, in part because CT learning tools often require expensive hardware or fluent literacy. Informed by needfinding interviews, we developed a voice-guided smartphone application leveraging storytelling as a creative activity by which to teach CT concepts to 5- to 8-year-old children. The app includes two storytelling games where users create and listen to stories as well as four CT games where users then modify those stories to learn about sequences, loops, events, and variables. We improved upon the app design through wizard-of-oz testing (N = 28) and iterative design testing (N = 22) before conducting an evaluation study (N = 22). Children were successfully able to navigate the app, effectively learn about the target computing concepts, and, after using the app, children demonstrated above-chance performance on a near transfer CT concept recognition task.

A key challenge in education is effectively engaging children in learning activities. We investigated how a narrative story impacts engagement and learning, as well as how feedback can provide further benefits. To do so, we created an interactive, tablet-based learning platform with a multi-step math task designed using Common Core State Standards. Subjects completed a pretest and then were assigned to a condition, either one of three variations of the system (narratives, narratives with hints, and narratives with a tutoring chatbot using wizard-of-oz techniques) or a control system that has children complete the same learning task without narratives nor feedback, before the subjects completed a post test. 72 children in U.S. grades 3--5 participated. Our results showed that embedding learning activities into narratives boosted children's engagement as evaluated by coding video responses and surveys, and the integration of a tutoring chatbot improved learning outcomes on the assessment. These results provide evidence that a narrative-based tutoring system with chatbot-mediated help may support effective learning experiences for children.

Numerous technologies now exist for promoting more active lifestyles. However, while quantitative data representations (e.g., charts, graphs, and statistical reports) typify most health tools, growing evidence suggests such feedback can not only fail to motivate behavior but may also harm self-integrity and fuel negative mindsets about exercise. Our research seeks to devise alternative, more qualitative schemes for encoding personal information. In particular, this paper explores the design of data-driven narratives, given the intuitive and persuasive power of stories. We present WhoIsZuki, a smartphone application that visualizes physical activities and goals as components of a multi-chapter quest, where the main character's progress is tied to the user's. We report on our design process involving online surveys, in-lab studies, and in-the-wild deployments, aimed at refining the interface and the narrative and gaining a deep understanding of people's experiences with this type of feedback. From these insights, we contribute recommendations to guide future development of narrative-based applications for motivating healthy behavior.

Research in personal informatics (PI) calls for systems to sup- port social forms of tracking, raising questions about how privacy can and should support intentionally sharing sensitive health information. We focus on the case of personal data related to the self-tracking of bipolar disorder (BD) in order to explore the ways in which disclosure activities intersect with other privacy experiences. While research in HCI of- ten discusses privacy as a disclosure activity, this does not reflect the ways in which privacy can be passively experienced. In this paper we broaden conceptions of privacy by defining transparency experiences and contributing factors in contrast to disclosure activities and preferences. Next, we ground this theoretical move in empirical analysis of personal narratives shared by people managing BD. We discuss the resulting emer- gent model of transparency in terms of implications for the design of socially-enabled PI systems. CAUTION: This paper contains references to experiences of mental illness, including self-harm, depression, suicidal ideation, etc.

Advances in conversational AI have the potential to enable more engaging and effective ways to teach factual knowledge. To investigate this hypothesis, we created QuizBot, a dialogue-based agent that helps students learn factual knowledge in science, safety, and English vocabulary. We evaluated QuizBot with 76 students through two within-subject studies against a flashcard app, the traditional medium for learning factual knowledge. Though both systems used the same algorithm for sequencing materials, QuizBot led to students recognizing (and recalling) over 20% more correct answers than when students used the flashcard app. Using a conversational agent is more time consuming to practice with, but in a second study, of their own volition, students spent 2.6x more time learning with QuizBot than with flashcards and reported preferring it strongly for casual learning. Our results in this second study showed QuizBot yielded improved learning gains over flashcards on recall. These results suggest that educational chatbot systems may have beneficial use, particularly for learning outside of traditional settings.

People with invisible chronic illnesses (ICIs) can use social media to seek both informational and emotional support, but these individuals also face social and health-related challenges in posting about their often-stigmatized conditions online. To understand how they evaluate different platforms for disclosure, we interviewed 19 people with ICIs who post on general social media about their illnesses, such as Facebook, Instagram, and Twitter. We present a cross-platform analysis of how platforms varied in their suitability to achieve participants' goals, as well as the challenges posed by each platform. We also found that as participants' ICIs progressed, their goals, challenges, and social media use similarly evolved over time. Our findings highlight how people with ICIs select platforms from a broader ecology of social media and suggest a general need to understand shifts in social media use for populations with chronic but changing health concerns.

Issues of social identity, attitudes towards self-disclosure, and potentially biased approaches to what is considered "typical" or "normal" are critical factors when designing visualizations for personal informatics systems. This is particularly true when working with vulnerable populations like those who self-track to manage serious mental illnesses like bipolar disorder (BD). We worked with individuals diagnosed with BD to 1) better understand sense-making challenges related to the representation and interpretation of personal data and 2) probe the benefits, risks, and limitations of participatory approaches to designing personal data visualizations that better reflect their lived experiences. We describe our co-design process, present a series of emergent visual encoding schemas resulting from these activities, and report on the assessment of these speculative designs by participants. We conclude by summarizing important considerations and implications for designing personal data visualizations for (and with) people who self-track to manage serious mental illness.

Motivated by the need to support those self-managing chronic pain, we report on the development and evaluation of a novel pressure-based tangible user interface (TUI) for the self-report of scalar values representing pain intensity. Our TUI consists of a conductive foam-based, force-sensitive resistor (FSR) covered in a soft rubber with embedded signal conditioning, an ARM Cortex-M0 microprocessor, and Bluetooth Low Energy (BLE). In-lab usability and feasibility studies with 28 participants found that individuals were able to use the device to make reliable reports with four degrees of freedom as well map squeeze pressure to pain level and visual feedback. Building on insights from these studies, we further redesigned the FSR into a wearable device with multiple form factors, including a necklace, bracelet, and keychain. A usability study with an additional 7 participants from our target population, elderly individuals with chronic pain, found high receptivity to the wearable design, which offered a number of participant-valued characteristics (e.g., discreetness) along with other design implications that serve to inform the continued refinement of tangible devices that support pain self-assessment.

Personal informatics systems for supporting health largely grew out of a "self"-centric orientation: self-tracking, self-reflection, self-knowledge, self-experimentation, self-improvement. Health management, however, even when self-driven, is inherently social and depends on a person's direct relationships and broader sociocultural contexts, as an emerging line of research is coming to recognize, study, and support. This is particularly true in the case of mental health. In this paper, we engage with individuals managing the serious mental illness bipolar disorder and members of their support circles to (a) identify key social relations and the roles they play in condition management, (b) characterize patients' complex interactions with these relations (e.g., positive or negative, direct or peripheral, steady or unstable), and (c) understand how personal informatics mediates these recovery relations. Based on these insights, we offer a model of this social ecology, along with design implications for personal informatics systems that are sensitive to these interpersonal contexts.

To better support the self-management of chronic pain, this paper investigates how those living with the condition prefer to self-assess their pain levels using smartphones. Our work consists of three stages: design ideation and review, an in-lab user study with 10 participants resulting in nine candidate interfaces, and a 3 week field trial of two further honed measures with 12 participants. This research firstly yields a better understanding of participants' strong and sometimes contrasting preferences regarding their self-assessment of pain intensity. We additionally contribute two novel interfaces that support accurate, quick, and repeated use along with other participant-valued interactions (e.g., familiar, relatable, and highly usable). In particular, we focus on designing tailored measures that both enhance respondent motivation as well as minimize the difficulty of meaningful self-assessment by supporting the cog-nitive effort in translating a subjective experience into a single numerical value.

There has been a recent increase in the development of digital self-tracking tools for managing mental illness. Most of these tools originate from clinical practice and are, as a result, largely clinician oriented. As a consequence, little is known about the self-tracking practices and needs of individuals living with mental illness. This understanding is important to guide the design of future tools to enable people to play a greater role in managing their health. In this article, we present a qualitative study focusing on the self-tracking practices of 10 people with bipolar disorder. We seek to understand the role self-tracking has played as they have come to grips with their diagnosis and attempted to self-manage their health. A central motivation for these participants is to identify risky patterns that may be harbingers of mood episodes, as well as positive trends that support recovery. What emerges is a fragmented picture of self-tracking, with no clear delineation between clinician-initiated and self-initiated practices, as well as considerable challenges participants face in making observations of themselves when their sense of self and emotional state is in flux, uncertain, and unreliable. Informed by these observations, we discuss the merits of a new form of self-tracking that combines manual and automated methods, addresses both clinician and individual needs, helps engage people with bipolar disorder in treatment, and seeks to overcome the significant challenges they face in self-monitoring.

Often, attention to “community” focuses on motivating core members or helping newcomers become regulars. However, much of the traffic to online communities comes from people who visit only briefly. We hypothesize that their personal characteristics, design elements of the site, and others' activity all affect the contributions these "one-timers" make. We present the results from an experiment asking Amazon Mechanical Turk (“AMT”) workers to comment on the AMT participation agreement in a discussion forum. One-timers with stronger ties to other Turkers or feelings of trust for Amazon are more likely to leave more --- but shorter and less relevant --- comments, while those with higher self-efficacy leave longer and more relevant comments. The phrasing of prompts also matters; a general appeal for personally-reflective contributions leads to comments that are less relevant to community discussion topics. Finally, activity matters too; synchronous activity begets responses, while pre-existing content tends to suppress them. These findings suggest design moves that can help communities harness this “long tail” of contribution.

Throughout the day, our alertness levels change and our cognitive performance fluctuates. The creation of technology that can adapt to such variations requires reliable measurement with ecological validity. Our study is the first to collect alertness data in the wild using the clinically validated Psychomotor Vigilance Test. With 20 participants over 40 days, we find that alertness can oscillate approximately 30% depending on time and body clock type and that Daylight Savings Time, hours slept, and stimulant intake can influence alertness as well. Based on these findings, we develop novel methods for unobtrusively and continuously assessing alertness. In estimating response time, our model achieves a root-mean-square error of 80.64 milliseconds, which is significantly lower than the 500ms threshold used as a standard indicator of impaired cognitive ability. Finally, we discuss how such real-time detection of alertness is a key first step towards developing systems that are sensitive to our biological variations.

Our body clock causes considerable variations in our behavioral, mental, and physical processes, including alertness, throughout the day. While much research has studied technology usage patterns, the potential impact of underlying biological processes on these patterns is under-explored. Using data from 20 participants over 40 days, this paper presents the first study to connect patterns of mobile application usage with these contributing biological factors. Among other results, we find that usage patterns vary for individuals with different body clock types, that usage correlates with rhythms of alertness, that app use features such as duration and switching can distinguish periods of low and high alertness, and that app use reflects sleep interruptions as well as sleep duration. We conclude by discussing how our findings inform the design of biologically-friendly technology that can better support personal rhythms of performance.

OBJECTIVE: To understand self-monitoring strategies used independently of clinical treatment by individuals with bipolar disorder (BD), in order to recommend technology design principles to support mental health management. MATERIALS AND METHODS: Participants with BD (N = 552) were recruited through the Depression and Bipolar Support Alliance, the International Bipolar Foundation, and WeSearchTogether.org to complete a survey of closed- and open-ended questions. In this study, we focus on descriptive results and qualitative analyses. RESULTS: Individuals reported primarily self-monitoring items related to their bipolar disorder (mood, sleep, finances, exercise, and social interactions), with an increasing trend towards the use of digital tracking methods observed. Most participants reported having positive experiences with technology-based tracking because it enables self-reflection and agency regarding health management and also enhances lines of communication with treatment teams. Reported challenges stem from poor usability or difficulty interpreting self-tracked data. DISCUSSION: Two major implications for technology-based self-monitoring emerged from our results. First, technologies can be designed to be more condition-oriented, intuitive, and proactive. Second, more automated forms of digital symptom tracking and intervention are desired, and our results suggest the feasibility of detecting and predicting emotional states from patterns of technology usage. However, we also uncovered tension points, namely that technology designed to support mental health can also be a disruptor. CONCLUSION: This study provides increased understanding of self-monitoring practices, attitudes, and needs of individuals with bipolar disorder. This knowledge bears implications for clinical researchers and practitioners seeking insight into how individuals independently self-manage their condition as well as for researchers designing monitoring technologies to support mental health management.

The increasing adoption of social media provides unprecedented opportunities to gain insight into human nature at vastly broader scales. Regarding the study of population-wide sentiment, prior research commonly focuses on text-based analyses and ignores a treasure trove of sentiment-laden content: images. In this paper, we make methodological and computational contributions by introducing the Smile Index as a formalized measure of societal happiness. Detecting smiles in 9 million geo-located tweets over 16 months, we validate our Smile Index against both text-based techniques and self-reported happiness. We further make observational contributions by applying our metric to explore temporal trends in sentiment, relate public mood to societal events, and predict economic indicators. Reflecting upon the innate, language-independent aspects of facial expressions, we recommend future improvements and applications to enable robust, global-level analyses. We conclude with implications for researchers studying and facilitating the expression of collective emotion through socio-technical systems.

By nature, we are circadian creatures whose bodies' biological clocks drive numerous physiological, mental, and behavioral rhythms. Simultaneously, we are social beings. Accordingly, our internal circadian timings experience interference from externally determined factors such as work schedules and social engagements, and digital connectivity imports additional social constraints that can further misalign our individual body clocks. Misalignment between biological and social time causes social jet lag [50], which has serious physical and mental health consequences. It particularly impacts our sleep processes and neurobehavioral functioning. Examining the interplay between biological rhythms and technology-mediated social interactions, we find that technology may both modulate and reflect circadian rhythms. We also leverage such social-sensor data to infer sleep-related behaviors and disruptions and to analyze variations in attention, cognitive performance, and mood following (in)adequate sleep. We conclude with recommendations for designing technologies attuned to our innate biological traits.

Analysis of smokers' posts and behaviors on Twitter reveals factors impacting abstinence and relapse during cessation attempts. Combining automatic and crowdsourced techniques, we detect users trying to quit smoking and analyze tweet and network data from a sample of 653 individuals over a two-year window of quitting. Guided by theory and practice, we derive behavioral, social, and emotional measures to compare users who abstain and relapse. We also examine the cessation process, demonstrating that Twitter can help chronicle how some people go about quitting. Among other results, we show that those who fail in their smoking cessation are far heavier posters and use relatively less positive language, while those who succeed are more social in both network ties and in directed communication. We conclude with insights on how intelligent intervention systems can harness these signals to provide tailored behavior change support.

We often think of ourselves as individuals with steady capabilities. However, converging strands of research indicate that this is not the case. Our biochemistry varies significantly over the course of a 24 hour period. Consequently our levels of alertness, productivity, physical activity, and even sensitivity to pain fluctuate throughout the day. This offers a considerable opportunity for the UbiComp community to identify novel measurements and interventions that can leverage these daily variations. To illustrate this potential, we present results from an empirical study with 9 participants over 97 days investigating whether such variations manifest in low-level smartphone use, focusing on daily rhythms related to sleep. Our findings demonstrate that phone usage patterns can be used to detect and predict individual daily variations indicative of temporal preference, sleep duration, and deprivation. We also identify opportunities and challenges for measuring and enhancing well-being using these simple and effective markers of circadian rhythms.