Loss of decisional capacity, coupled with the increasing absence of reliable human proxies, raises urgent questions about how individuals' values can be represented in Advance Care Planning (ACP). To probe this fraught design space of high-risk, high-subjectivity decision support, we built an experience prototype (\acpagent{}) and asked 15 participants in 4 workshops to train it to be their personal ACP proxy. We analysed their coping strategies and feature requests and mapped the results onto axes of agent autonomy and human control. Our findings show a surprising 86.7\% agreement with \acpagent{}, arguing for a potential new role of AI in ACP where agents act as personal advocates for individuals, building mutual intelligibility over time. We propose that the key areas of future risk that must be addressed are the moderation of users' expectations and designing accountability and oversight over agent deployment and cutoffs.
Breaking negative mental health cycles, including rumination and recurring regrets, requires reflection that translates awareness into behavioral change. Grounded in the Transtheoretical Model (TTM) and Gross’s Emotion Regulation (ER) Process Model, we examine how Technologies Supporting Self-Reflection (TSR) bridge reflection and action. In a 15-day in-the-wild study (N = 20), participants used a voice-based journaling system to capture regrets and wishes and engaged in WhatIf-Planning, a novel structured reflection module that integrates counterfactual thinking with if–then planning. Participants were randomized to either a free-form condition or Gross-guided condition, which maps the five processes of Gross’s ER model into explicit journaling prompts. We contribute (1) a unified reflection-to-action TSR system that operationalizes the Preparation stage of TTM to bridge Contemplation and Action, and (2) triangulated empirical evidence from an in-the-wild journaling study that operationalizes Gross’s Process Model, revealing effects on coping flexibility and emotion regulation in daily life. Results show significant pre–post improvements in coping flexibility across conditions, indicating adaptive self-regulation, with the Gross-guided group generating more counterfactual alternatives, articulating more concrete if–then action plans, and implementing more plans for self-driven change.
Advances in ubiquitous and wearable sensing and HCI research have made stress monitoring increasingly accessible, enabling the development of personalized stress management technologies. Yet, stress is a subjective and contextual experience, making effective intervention design challenging. Prior studies often isolate stress detection or intervention, without providing an integrated view of how these components connect and are evaluated in real-world use. To address this gap, we conducted a systematic review of 2,152 papers and selected 52 empirical studies where stress tracking informed interventions. Using a framework based on three stress constructs (subjective stress, psycho-physiological stress, and exposure stress), we analyzed how definitions of stress shape detection indicators, intervention design and timing, and evaluation methods. We show that stress conceptualization strongly influences system design, and we propose a conceptual framework linking detection, intervention, and evaluation to guide future user-centered stress management technologies.
Emotion regulation (ER) is a dynamic process that often unfolds in social contexts. However, current digital ER tools predominantly rely on single-agent systems that lack the complexity of social dynamics. Swarm user interfaces present unique affordances for ER through their collective adaptability and expressive group behaviour. However, their potential in supporting ER remains underexplored. To investigate how swarm user interfaces can be designed to support ER, we conducted a series of speculative participatory design workshops with 15 participants through the Magic Machine Workshop method, where participants created and enacted interactive swarm-based artifacts with craft materials. The analysis led to diverse contexts of use, envisioned swarm framing, and interaction modes. Based on these findings, we synthesize eight interaction patterns that translate abstract user metaphors into robotic behaviors. We conclude by articulating design opportunities and challenges, positioning swarm interfaces as a novel medium for ER support.
Call agents, a representative group of emotion workers, must manage emotions under constrained autonomy, yet workplace stress-sensing has primarily centered on knowledge work. We ask how task‑aligned cycle of emotional labor, alternating customer interaction (CI) and non‑customer interaction (nCI), shapes stress and how it manifests in data. We conducted a month-long in-the-wild formative mixed-methods study with professional call agents, collecting structured task logs, environmental and behavioral signals, and per-call stress self-reports, followed by semi-structured interviews. Task logs, used as a new sensor modality, were incorporated as primary sensing signals, and task-related features were extracted by respecting CI boundaries for modeling. Our results showed that a short 5-minute windowing approach was comparable to task-aligned windowing using multimodal sensors, with task-related features being considered the most important across all generalized models. Personalized models improved further and shifted importance toward diverse data sources, revealing individual differences in preparation patterns. Interviews support those findings, reveal key modelling challenges, and highlight potential benefits of semi-automated self-tracking. We discuss implications for timing interventions at breakpoints suited for work patterns, and ethically deploying stress support for emotion workers.
Psychotherapy is crucial for managing cue-induced cravings. However, most research has focused on explicit drug cues that elicit intense cravings, and recreating such high-risk scenarios in practice can inadvertently heighten cravings afterward, making these approaches impractical or ethically problematic in real-world settings. To address this, we developed a cue-exposure technology probe system, VirtualCravingProbe, which integrates VR simulations with real-time biofeedback to enhance self-awareness in clinical drug psychotherapy. We conducted an exploratory study with twelve patients recovering from methamphetamine addiction using the VirtualCravingProbe system, generating design guidelines for future iterations of an integrated VR and biofeedback-assisted therapy tool. Results revealed qualitative evidence that real-time heart-rate monitoring in VR heightened patients’ awareness of triggers and their craving responses. These findings align with the CBT cognitive-triangle framework, which emphasizes the interplay of thoughts, emotions, and behaviors. Moreover, the system demonstrated potential to enrich patient–therapist dialogue and support the adoption of effective coping strategies.
Millions of people now use non-clinical Large Language Model (LLM) tools like ChatGPT for mental well-being support. This paper investigates what it means to design such tools responsibly, and how to operationalize that responsibility in their design and evaluation. By interviewing experts and analyzing related regulations, we found that designing an LLM tool responsibly involves: (1) Articulating the specific benefits it guarantees and for whom. Does it guarantee specific, proven relief, like an over-the-counter drug, or offer minimal guarantees, like a nutritional supplement? (2) Specifying the LLM tool's ``\textit{active ingredients}'' for improving well-being and whether it guarantees their effective delivery (like a primary care provider) or not (like a yoga instructor). These specifications outline an LLM tool's pertinent risks, appropriate evaluation metrics, and the respective responsibilities of LLM developers, tool designers, and users. These analogies—\textit{LLM tools as supplements, drugs, yoga instructors, and primary care providers}—can scaffold further conversations about their responsible design.