Large language models (LLMs) are increasingly integrated into daily life through conversational interfaces, processing user data via natural language inputs and exhibiting advanced reasoning capabilities, which raises new concerns about user control over privacy. While much research has focused on potential privacy risks, less attention has been paid to the data control mechanisms these platforms provide. This study examines six conversational LLM platforms, analyzing how they define and implement features for users to access, edit, delete, and share data. Our analysis reveals an emerging paradigm of data control in conversational LLM platforms, where user data is generated and derived through interaction itself, natural language enables flexible yet often ambiguous control, and multi-user interactions with shared data raise questions of co-ownership and governance. Based on these findings, we offer practical insights for platform developers, policymakers, and researchers to design more effective and usable privacy controls in LLM-powered conversational interactions.

Eating disorder (ED) is a psychiatric condition that involves behaviors like binge and restrictive eating with severe health consequences, particularly prevalent among young women. While technology interventions exist, they typically focus on retrospective reflection or general management, missing the time window when an ED behavior is taking place. In this work, we conducted co-design sessions with 22 young women experiencing EDs to develop ideas for Just-in-Time (JIT) interventions, followed by interviews with five experts specialized in ED treatment. We found that eating plays varied roles in participants’ lives—from a means of gaining autonomy to automatic physiological responses—leading to design ideas including behavioral warnings, appetite management, food option redirection, psychological support systems, etc. By examining the characteristics of these designs with expert perspectives, we discuss what JIT support means for ED care and how to make it effective and sustainable. © 2026 Copyright held by the owner/author(s).

While previous research has attempted to link features of individuals’ drawings to their emotional states, it often overlooks the deeply personal and context-driven nature of visual expression. To bridge the gap, we conducted a two‑week diary study with 21 participants, who used a custom‑built app to track daily emotions through free drawings, followed by interviews reflecting on their artwork. Among the 252 drawings gathered, we found no strong correlations between reported emotions and measurable drawing behaviors; instead, participants expressed emotions through diverse approaches, from illustrations of emotion sources (e.g., events, objects) and metaphors, to emojis, literal text and spontaneous, random mark-making. Participants developed consistent personal styles and described drawing as an intuitive, playful, and safe outlet, though some faced challenges with the ambiguity of visual expressions and interpreting their creations afterwards. With the lessons learned, we discuss opportunities for designing expression-centered emotion tracking technologies that embrace individuality and creativity.

For university students transitioning to an independent and flexible lifestyle, having ADHD poses multiple challenges to their academic task management, which are closely tied to their metacognitive struggles—difficulties in awareness and regulation of one’s own thinking processes. The recently surged Generative AI shows promise to mitigate these gaps with its advanced information understanding and generation capabilities. As an exploratory step, we conducted co-design sessions with 20 university students diagnosed with ADHD, followed by interviews with five experts specialized in ADHD intervention. Adopting a metacognitive lens, we examined participants’ ideas on GenAI-based task management support and experts’ assessments, which led to three design directions: providing cognitive scaffolding to enhance task and self-awareness, promoting reflective task execution for building metacognitive abilities, and facilitating emotional regulation to sustain task engagement. Drawing on these findings, we discuss opportunities for GenAI to support the metacognitive needs of neurodivergent populations, offering future directions for both research and practice.

The HCI community has been actively developing and studying the use of Personal Informatics (PI) systems.While celebrating the headways, researchers have uncovered many unintended consequences of using PI systems, such as data-induced stress and obsessive tracking, but there has been a lack of systematic analysis of these consequences and their underlying causes.In this work, we reviewed 172 PI research articles, highlighting that tracking and interacting with personal data can adversely affect individuals' cognitive load, emotional well-being, social acts, and behaviors, while bringing practical challenges.By synthesizing the pathways through which these consequences occur, we recognized issues in the data-centric design ideology, variations across tracking needs and literacy, the evolving social dynamics, and individuals' intention-behavior gap.Reflecting on the findings, we discuss how to best leverage personal data in our lives and propose a practice-oriented research agenda to mitigate these unintended consequences.

Grandparents play a central role in co-parenting in many Asian households, particularly when both parents work full-time.While this intergenerational caregiving arrangement offers emotional and practical benefits, it also introduces communication challenges rooted in generational differences in parenting values and expectations.Therefore, we explore how grandparents and parents in Hong Kong navigate communication around young children's upbringing, and how technology mediates these interactions.Based on semi-structured interviews with ten preliminary participants (five grandparents and five parents), our early findings reveal key areas of communication and conflict, especially around educational decisions and caregiving responsibilities.Participants also shared the use of digital tools, such as messaging apps, in facilitating coparenting communication.This work highlights the need for more inclusive, elderly-friendly technological interventions and offers early design considerations for tools that support clearer, more empathetic intergenerational dialogue in child caregiving contexts.These insights contribute to ongoing efforts to design culturally sensitive technologies for multigenerational families.

The Illusion of Sunset" (Nature Science) Content preview that re-organizes the text into Cornell-style Notes based on the reader's prior knowledge and reading habits (P1)."Quantum Entanglement" (Physics) Background music that simulates the sounds in an atomic world, while blocking external noise to help the reader stay focused (P7)."The Illusion of Sunset" (Nature Science) An "attention flow" panel highlighting the reader's current focused text area while fading other text, powered by an eye-tracking system (P6)."Bamboo Creek" (Ancient Chinese Literary) Tactile, sound, and olfactory stimuli that enable the reader to feel, hear, and smell the bamboo forest described in the article (P7)."Category Theory" (Mathematics) Bright, colourful visual decorations (e.g., small flowers) that make the reading material appealing, and thus sustain the reader's motivation (P10)."Quantum Entanglement" (Physics) A background simulating internet virus with intense colors, urging the readers to finish reading as soon as possible (P4)

As social service robots become commonplace, it is essential for them to effectively interpret human signals, such as verbal, gesture, and eye gaze, when people need to focus on their primary tasks to minimize interruptions and distractions. Toward such a socially acceptable Human-Robot Interaction, we conducted a study ($N=24$) in an AR-simulated context of a coffee chat. Participants elicited social cues to signal intentions to an anthropomorphic, zoomorphic, grounded technical, or aerial technical robot waiter when they were speakers or listeners. Our findings reveal common patterns of social cues over intentions, the effects of robot morphology on social cue position and conversational role on social cue complexity, and users' rationale in choosing social cues. We offer insights into understanding social cues concerning perceptions of robots, cognitive load, and social context. Additionally, we discuss design considerations on approaching, social cue recognition, and response strategies for future service robots.

Personalized support is essential to fulfill individuals' emotional needs and sustain their mental well-being. Large language models (LLMs), with great customization flexibility, hold promises to enable individuals to create their own emotional support agents. In this work, we developed ChatLab, where users could construct LLM-powered chatbots with additional interaction features including voices and avatars. Using a Research through Design approach, we conducted a week-long field study followed by interviews and design activities (N = 22), which uncovered how participants created diverse chatbot personas for emotional reliance, confronting stressors, connecting to intellectual discourse, reflecting mirrored selves, etc. We found that participants actively enriched the personas they constructed, shaping the dynamics between themselves and the chatbot to foster open and honest conversations. They also suggested other customizable features, such as integrating online activities and adjustable memory settings. Based on these findings, we discuss opportunities for enhancing personalized emotional support through emerging AI technologies.

Large language models (LLMs) could extend AI support for mental well-being with their unprecedented language understanding and generation ability. While we have seen individuals who lack access to professional care utilizing LLMs for mental health support, it is unclear how they prompt and interact with LLMs given their individualized emotional needs and life situations. In this work, we analyzed 49 threads and 7,538 comments on Reddit, aiming to understand how people seek mental health support from GPT by creating and crafting various prompts. Despite GPT explicitly disclaiming that it is not an alternative to professional care, we found that users continued to use it for support and devised different prompts to bypass the safety guardrails. Meanwhile, users actively refined and shared their prompts to make GPT more human-like by specifying nuanced communication styles and cultivating in-depth discussions. They also came up with several strategies to make GPT communicate more efficiently to enrich the customized personas on the fly or gain multiple perspectives. Reflecting on these findings, we discuss the tensions associated with using LLMs for mental health support and the implications for designing safer and more empowering human-LLM interactions.

Amidst the increasingly prevalent smartphone addiction, we introduce StayFocused, a mobile app to help people focus on their tasks at hand by reducing compulsive smartphone use. Besides guiding people to set focus sessions for non-screen time, we incorporated reflective prompts probing individuals’ phone-checking intentions whenever they check their phones and a chatbot to deliver these prompts. To examine the effects of the reflective prompts and the chatbot support, we designed three versions of StayFocused: baseline, reflection, and reflection-chatbot, and conducted a stage-based between-subjects study with 36 college students over five weeks. We found that participants who received the reflective prompts were able to focus longer and resist distractions, and those with chatbot support seemed to better maintain their smartphone use reduction. By highlighting how participants reflected on their focus session activities and their preferences for the chatbot, we discuss the implications of designing persuasive conversational interfaces to reduce unintended behaviors.

Hand gestures are widely used in daily life for expressing emotions, yet gesture input is not part of existing emotion tracking systems. To seek a practical and effortless way of using gestures to inform emotions, we explore the relationships between gestural features and commonly experienced emotions by focusing on single-hand gestures that are easy to perform and capture. First, we collected 756 gestures (in photo and video pairs) from 63 participants who expressed different emotions in a survey, and then interviewed 11 of them to understand their gesture-forming rationales. We found that the valence and arousal level of the expressed emotions significantly correlated with participants’ finger-pointing direction and their gesture strength, and synthesized four channels through which participants externalized their expressions with gestures. Reflecting on the findings, we discuss how emotions can be characterized and contextualized with gestural cues and implications for designing multimodal emotion tracking systems and beyond.

Speech as a natural and low-burden input modality has great potential to support personal data capture. However, little is known about how people use speech input, together with traditional touch input, to capture different types of data in self-tracking contexts. In this work, we designed and developed NoteWordy, a multimodal self-tracking application integrating touch and speech input, and deployed it in the context of productivity tracking for two weeks (N = 17). Our participants used the two input modalities differently, depending on the data type as well as personal preferences, error tolerance for speech recognition issues, and social surroundings. Additionally, we found speech input reduced participants' diary entry time and enhanced the data richness of the free-form text. Drawing from the findings, we discuss opportunities for supporting efficient personal data capture with multimodal input and implications for improving the user experience with natural language input to capture various self-tracking data.

Patient-generated data (PGD) show great promise for informing the delivery of personalized and patient-centered care. However, patients' data tracking does not automatically lead to data sharing and discussion with clinicians, which can make it difficult to utilize and derive optimal benefit from PGD. In this paper, we investigate whether and how patients share their PGD with clinicians and the types of challenges that arise within this context. We describe patients' immediate experiences of PGD sharing with clinicians, based on our short onsite interviews with 57 patients who had just met with a clinician at a university health center. Our analyses identified overarching patterns in patients' PGD sharing practices and the associated challenges that arise from the information asymmetry between patients and clinicians and from patients' reliance on their memory to share their PGD. We discuss the implications of our findings for designing PGD-integrated health IT systems in ways to support patients' tracking of relevant PGD, clinicians' effective engagement with patients around PGD, and the efficient sharing and review of PGD within clinical settings.

The factors influencing people’s food decisions, such as one’s mood and eating environment, are important information to foster self-reflection and to develop personalized healthy diet. But, it is difficult to consistently collect them due to the heavy data capture burden. In this work, we examine how speech input supports capturing everyday food practice through a week-long data collection study (N = 11). We deployed FoodScrap, a speech-based food journaling app that allows people to capture food components, preparation methods, and food decisions. Using speech input, participants detailed their meal ingredients and elaborated their food decisions by describing the eating moments, explaining their eating strategy, and assessing their food practice. Participants recognized that speech input facilitated self-reflection, but expressed concerns around re-recording, mental load, social constraints, and privacy. We discuss how speech input can support low-burden and reflective food journaling and opportunities for effectively processing and presenting large amounts of speech data.

Smart speakers such as Amazon Echo present promising opportunities for exploring voice interaction in the domain of in-home exercise tracking. In this work, we examine if and how voice interaction complements and augments a mobile app in promoting consistent exercise. We designed and developed TandemTrack, which combines a mobile app and an Alexa skill to support exercise regimen, data capture, feedback, and reminder. We then conducted a four-week between-subjects study deploying TandemTrack to 22 participants who were instructed to follow a short daily exercise regimen: one group used only the mobile app and the other group used both the app and the skill. We collected rich data on individuals' exercise adherence and performance, and their use of voice and visual interactions, while examining how TandemTrack as a whole influenced their exercise experience. Reflecting on these data, we discuss the benefits and challenges of incorporating voice interaction to assist daily exercise, and implications for designing effective multimodal systems to support self-tracking and promote consistent exercise.

We report co-design workshops with registered dietitians conducted to identify opportunities for designing customizable food trackers. Dietitians typically see patients who have different dietary problems, thus having different information needs. However, existing food trackers such as paper-based diaries and mobile apps are rarely customizable, making it difficult to capture necessary data for both patients and dietitians. During the co-design sessions, dietitians created representative patient personas and designed food trackers for each persona. We found a wide range of potential tracking items such as food, reflection, symptom, activity, and physical state. Depending on patients' dietary problems and dietitians' practice, the necessity and importance of these tracking items vary. We identify opportunities for patients and healthcare providers to collaborate around data tracking and sharing through customization. We also discuss how to structure co-design workshops to solicit the design considerations of self-tracking tools for patients with specific health problems.

Extended periods of uninterrupted sedentary behavior are detrimental to long-term health. While prolonged sitting is prevalent among information workers, it is difficult for them to break prolonged sedentary behavior due to the nature of their work. This work aims to understand information workers' intentions & practices around standing or moving breaks. We developed Time for Break, a break prompting system that enables people to set their desired work duration and prompts them to stand up or move. We conducted an exploratory field study (N = 25) with Time for Break to collect participants' work & break intentions and behaviors for three weeks, followed by semi-structured interviews. We examined rich contexts affecting participants' receptiveness to standing or moving breaks, and identified how their habit strength and self-regulation are related to their break-taking intentions & practices. We discuss design implications for interventions to break up periods of prolonged sedentary behavior in workplaces.

Insomnia can drastically affect individuals' overall well-being and work performance, with substantial costs to society and industry. Cognitive behavioral therapy for insomnia (CBT-I) is a psychotherapeutic treatment, which requires patients to track sleep and share the data with CBT-I clinicians. However, the number of specialists who can provide CBT-I limits the number of patients who can receive it. In this paper, we aim to identify opportunities to leverage technology to assist clinicians in delivering quality and effective CBT-I services to broader populations. Toward this goal, we conducted formative studies, including 11 CBT-I clinic observations and 17 semi-structured interviews, to understand the current workflow of CBT-I and associated challenges. We discuss how technology can assist clinicians and patients throughout the various steps of CBT-I workflow while addressing some of the identified challenges, and more broadly, how technology can make space for clinicians and patients to build quality therapeutic relationships.