Exploring Data-Driven Approaches to Stress Management: A Systematic Review of Stress Tracking, Intervention, and System Evaluation MethodsAdvances 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.2026YKYoungji Koh et al.KAISTSleep & Stress MonitoringHealth Self-TrackingBehavior Change & Reflection TechnologyCHI
Mind the SIM: Awareness and Mental Models in a South Korean Case StudyMobile phone numbers function as single keys to banking, government, and commerce, making the Subscriber Identity Module (SIM) a critical element of security. In April 2025, South Korea’s largest carrier experienced a SIM breach that compromised authentication keys and exposed nearly 27 million subscriber identifiers. We conducted semi-structured interviews with mental-model elicitation (N=33) to examine user awareness, responses, and understanding of SIM-based authentication. Results reveal a pronounced awareness–action gap: participants recognized the breach yet held incomplete mental models, perceived little personal risk, and rarely acted protectively, even when affected. Learned helplessness, reliance on carriers, and the invisibility of SIM shaped these passive responses. Brief educational interventions improved conceptual understanding but seldom produced lasting behavioral change. Our findings demonstrate how technical opacity and psychological factors jointly inhibit protective action and offer design implications for usable security, emphasizing interventions that realign users’ mental models with system risks to foster sustainable practices.2026HLHyunsoo Lee et al.KAISTPrivacy by Design & User ControlPrivacy Perception & Decision-MakingExplainable AI (XAI)CHI
Understanding Behind the Smile of Emotion Workers: Detecting After-Call Stress in Call AgentsCall 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.2026DLHeejeong Lim et al.Korea Advanced Institute of Science & Technology (KAIST)Health Self-TrackingBehavior Change & Reflection TechnologySleep & Stress MonitoringCHI
Why stressed, Mom?: Exploring Family Reflection on Social and Emotional Sensor Data through Family InformaticsWhile family informatics has been developed for monitoring and tracking family-centered health data, there remains a gap in understanding how family informatics can support families in reflecting on their social behaviors and emotional dynamics. We address this gap with SELaD, a system that captures and visualizes social-emotional data from daily family interactions using audio, video, and physiological sensors. In a semi-naturalistic study with 17 families ($n=51$), we investigated how this data facilitates reflection. Our findings reveal a process we term \emph{relational reflection}, where families collaboratively interpret multimodal data to deepen their understanding of conversational dynamics and emotional influences by recalling their shared history and expectation of good communication. This process was particularly enriched by emotional data from multiple sources that families could cross-reference and reconcile. This work presents SELaD as a technology probe and empirically grounds the concept of relational reflection, positioning it as a foundation for designing future reflective technologies.2026HPHyesoo Park et al.Georgia Institute of TechnologyBehavior Change & Reflection TechnologyContext-Aware ComputingEmotion-Sensing WearablesCHI
‘In That Small Space with Just the Two of Us’: User Experiences with Cumpa in a Robotic Counseling CenterThe growing demand for mental health support has highlighted the limitations of traditional counseling accessibility, increasing the usage of digital mental health interventions. There has been a rising interest in using robots to support mental health due to their benefits in engagement and rapport. Capitalizing on the opportunity of placemaking for designing a feasible robotic digital mental health intervention, our study explores the Robot Counseling Center (RCC) and its robotic counselor, Cumpa, designed to improve mental health accessibility and user engagement. A two-week field study with 20 participants evaluated RCC’s impact on their mental health, engagement, and sense of place within a counseling environment. Results indicate that RCC positively influences emotional awareness and engagement. Our findings provide insights into the role of social robots in mental health interventions and offer design implications for developing robotic counseling centers as supportive, effective spaces, contributing to building better places and interactive systems.2025CLChanhee Lee et al.Designing for Mental Health SupportCSCW
HateBuffer: Safeguarding Content Moderators' Mental Well-Being through Hate Speech Content ModificationHate speech remains a persistent and unresolved challenge in online platforms. Content moderators, working on the front lines to review user-generated content and shield viewers from hate speech, often find themselves unprotected from the mental burden as they continuously engage with offensive language. To safeguard moderators' mental well-being, we designed HateBuffer, which anonymizes targets of hate speech, paraphrases offensive expressions into less offensive forms, and shows the original expressions when moderators opt to see them. Our user study with 80 participants consisted of a simulated hate speech moderation task set on a fictional news platform followed by semi-structured interviews. Although participants rated the hate severity of comments lower while using HateBuffer, contrary to our expectations, they did not experience improved emotion or reduced fatigue compared with the control group. In interviews, however, participants described HateBuffer as an effective buffer against emotional contagion and the normalization of biased opinions in hate speech. Notably, HateBuffer did not compromise moderation accuracy and even contributed to a slight increase in recall. We explore possible explanations for the discrepancy between the perceived benefits of HateBuffer and its measured impact on mental well-being. We also underscore the promise of text-based content modification techniques as tools for a healthier content moderation environment.2025SPSubin Park et al.Hate SpeechCSCW
Exploring Modular Prompt Design for Emotion and Mental Health RecognitionRecent advances in large language models (LLM) offered human-like capabilities for comprehending emotion and mental states. Prior studies explored diverse prompt engineering techniques for improving classification performance, but there is a lack of analysis of prompt design space and the impact of each component. To bridge this gap, we conduct a qualitative thematic analysis of existing prompts for emotion and mental health classification tasks to define the key components for prompt design space. We then evaluate the impact of major prompt components, such as persona and task instruction, on classification performance by using four LLM models and five datasets. Modular prompt design offers new insights into examining performance variability as well as promoting transparency and reproducibility in LLM-based tasks within health and well-being intervention systems.2025MKMinseo Kim et al.Hankuk University of Foreign StudiesHuman-LLM CollaborationMental Health Apps & Online Support CommunitiesSleep & Stress MonitoringCHI
CounterStress: Enhancing Stress Coping Planning through Counterfactual Explanations in Personal InformaticsPersonal informatics (PI) systems have been utilized to help individuals manage health issues such as stress by leveraging insights from self-tracking data. However, PI users may struggle to develop effective coping strategies because factors influencing stress are often difficult to change in practice, and multiple factors can contribute to stress simultaneously. In this study, we introduce CounterStress, a PI system designed to assist users in identifying contextual changes needed to address high-stress situations. CounterStress employs counterfactual explanations to identify and suggest alternative contextual changes, offering users actionable strategies to achieve a desired state. We conducted both lab-based and field user studies with 12 participants to evaluate the system's usability and applicability, focusing on the benefits of counterfactual-based coping strategies, how users select viable strategies, and their real-world applications. Based on our findings, we discuss design implications for effectively leveraging counterfactuals in PI systems to support users' stress-coping planning.2025GJGyuwon Jung et al.KAIST, School of ComputingMental Health Apps & Online Support CommunitiesSleep & Stress MonitoringCHI
DataSentry: Building Missing Data Management System for In-the-Wild Mobile Sensor Data Collection through Multi-Year Iterative Design ApproachMobile sensor data collection in people’s daily lives is essential for understanding fine-grained human behaviors. However, in-the-wild data collection often results in missing data due to participant and system-related issues. While existing monitoring systems in the mobile sensing field provide an opportunity to detect missing data, they fall short in monitoring data across many participants and sensors and diagnosing the root causes of missing data, accounting for heterogeneous sensing characteristics of mobile sensor data. To address these limitations, we undertook a multi-year iterative design process to develop a system for monitoring missing data in mobile sensor data collection. Our final prototype, DataSentry, enables the detection, diagnosis, and addressing of missing data issues across many participants and sensors, considering both within- and between-person variability. Based on the iterative design process, we share our experiences, lessons learned, and design implications for developing advanced missing data management systems.2025YJYugyeong Jung et al.KAIST, School of ComputingUbiquitous ComputingField StudiesComputational Methods in HCICHI
I Was Told to Install the Antivirus App, but I'm Not Sure I Need It: Understanding Smartphone Antivirus Software Adoption and User PerceptionsThe rising threat of mobile malware has prompted security vendors to recommend antivirus software for smartphones, yet user misconceptions, regulatory requirements, and improper use undermine its effectiveness. Our mixed-method study, consisting of in-depth interviews with 23 participants and a survey of 250 participants, examines smartphone antivirus software adoption in South Korea, where mandatory installation for banking and other financial apps is common. Many users confuse antivirus software with general security tools and remain unaware of its limited scope. Adoption is significantly influenced by perceived vulnerability, response efficacy, self-efficacy, social norms, and awareness, while concerns about system performance and skepticism about necessity lead to discontinuation or non-use. Mandatory installations for financial apps in South Korea contribute to user misconceptions, negative perceptions, and a false sense of security. These findings highlight the need for targeted user education, clearer communication about mobile-specific threats, and efforts to promote informed and effective engagement with antivirus software.2025SJSeyoung Jin et al.Sungkyunkwan UniversityPrivacy by Design & User ControlPasswords & AuthenticationCHI
Like Adding a Small Weight to a Scale About to Tip: Personalizing Micro-Financial Incentives for Digital WellbeingPersonalized behavior change interventions can be effective as they dynamically adapt to an individual’s context. Financial incentives, a commonly used intervention in commercial applications and policy-making, offer a mechanism for creating personalized micro-interventions that are both quantifiable and amenable to systematic evaluation. However, the effectiveness of such personalized micro-financial incentives in real-world settings remains largely unexplored. In this study, we propose a personalization strategy that dynamically adjusts the amount of micro-financial incentives to promote smartphone use regulation and explore its efficacy and user experience through a four-week, in-the-wild user study. The results demonstrate that the proposed method is highly cost-effective without compromising intervention effectiveness. Based on these findings, we discuss the role of micro-financial incentives in enhancing awareness, design considerations for personalized micro-financial incentive systems, and their potential benefits and limitations concerning motivation change.2025SJSueun Jang et al.KAIST, School of ComputingAlgorithmic Transparency & AuditabilityMental Health Apps & Online Support CommunitiesCHI
FamilyScope: Visualizing Affective Aspects of Family Social Interactions using Passive Sensor DataThis work presents FamilyScope, a sensor-based family informatics system that enables reflection upon family data collected from family activity scenarios (e.g., game playing and movie watching) that include affective aspects of a family's social interactions. We conducted a user study with ten families (n=30) in a smart home testbed to observe how our system supports data reflection of the affective and behavioral states among family members. Our findings showed that FamilyScope facilitated family data reflection on the affective and behavioral aspects of family interactions. Overall, families reported that the system well reflected family members' general tendencies in terms of affective and behavioral responses and even helped them gain new insights about each other. Based on the findings, we provide practical design approaches for co-reflection in family informatics systems.2024HLHyunsoo Lee et al.Session 2a: Navigating Family Dynamics and Youth Health JourneysCSCW
A Reproducible Stress Prediction Pipeline with Mobile Sensor DataZhang 等人设计了一个可重复的压力预测管道，利用移动传感器数据实时评估用户压力水平。2024PZPanyu Zhang et al.Sleep & Stress MonitoringBiosensors & Physiological MonitoringUbiComp
Hide-and-seek: Detecting Workers' Emotional Workload in Emotional Labor Contexts Using Multimodal SensingPark 等人提出基于多模态传感的方法，检测情绪劳动情境中工作者的情绪负担，为职业心理健康评估提供技术支持。2024EPEunji Park et al.Notification & Interruption ManagementWorkplace Wellbeing & Work StressUbiComp
Interrupting for Microlearning: Understanding Perceptions and Interruptibility of Proactive Conversational Microlearning ServicesSignificant investment of time and effort for language learning has prompted a growing interest in microlearning. While microlearning requires frequent participation in 3-to-10-minute learning sessions, the recent widespread of smart speakers in homes presents an opportunity to expand learning opportunities by proactively providing microlearning in daily life. However, such proactive provision can distract users. Despite the extensive research on proactive smart speakers and their opportune moments for proactive interactions, our understanding of opportune moments for more-than-one-minute interactions remains limited. This study aims to understand user perceptions and opportune moments for more-than-one-minute microlearning using proactive smart speakers at home. We first developed a proactive microlearning service through six pilot studies (n=29), and then conducted a three-week field study (n=28). We identified the key contextual factors relevant to opportune moments for microlearning of various durations, and discussed the design implications for proactive conversational microlearning services at home.2024MKMinyeong Kim et al.Kangwon National UniversityVoice User Interface (VUI) DesignSmart Home Interaction DesignCHI
PriviAware: Exploring Data Visualization and Dynamic Privacy Control Support for Data Collection in Mobile Sensing ResearchWith increased interest in leveraging personal data collected from 24/7 mobile sensing for digital healthcare research, supporting user-friendly consent to data collection for user privacy has also become important. This work proposes \emph{PriviAware}, a mobile app that promotes flexible user consent to data collection with data exploration and contextual filters that enable users to turn off data collection based on time and places that are considered privacy-sensitive. We conducted a user study (N = 58) to explore how users leverage data exploration and contextual filter functions to explore and manage their data and whether our system design helped users mitigate their privacy concerns. Our findings indicate that offering fine-grained control is a promising approach to raising users’ privacy awareness under the dynamic nature of the pervasive sensing context. We provide practical privacy-by-design guidelines for mobile sensing research.2024HLHyunsoo Lee et al.KAIST, KAISTPrivacy by Design & User ControlPrivacy Perception & Decision-MakingContext-Aware ComputingCHI
Exploring Context-Aware Mental Health Self-Tracking Using Multimodal Smart Speakers in Home EnvironmentsPeople with mental health issues often stay indoors, reducing their outdoor activities. This situation emphasizes the need for self-tracking technology in homes for mental health research, offering insights into their daily lives and potentially improving care. This study leverages a multimodal smart speaker to design a proactive self-tracking research system that delivers mental health surveys using an experience sampling method (ESM). Our system determines ESM delivery timing by detecting user context transitions and allowing users to answer surveys through voice dialogues or touch interactions. Furthermore, we explored the user experience of a proactive self-tracking system by conducting a four-week field study (n=20). Our results show that context transition-based ESM delivery can increase user compliance. Participants preferred touch interactions to voice commands, and the modality selection varied depending on the user's immediate activity context. We explored the design implications for home-based, context-aware self-tracking with multimodal speakers, focusing on practical applications.2024JLJieun Lim et al.KAISTSleep & Stress MonitoringContext-Aware ComputingCHI
Navigating User-System Gaps: Understanding User-Interactions in User-Centric Context-Aware Systems for Digital Well-being InterventionIn this paper, we investigate the challenges users face with a user-centric context-aware intervention system. Users often face gaps when the system's responses do not align with their goals and intentions. We explore these gaps through a prototype system that enables users to specify context-action intervention rules as they desire. We conducted a lab study to understand how users perceive and cope with gaps while translating their intentions as rules, revealing that users experience context-mapping and context-recognition uncertainties (instant evaluation cycle). We also performed a field study to explore how users perceive gaps and make adaptations of rules when the operation of specified rules in real-world settings (delayed evaluation cycle). This research highlights the dynamic nature of user interaction with context-aware systems and suggests the potential of such systems in supporting digital well-being. It provides insights into user adaptation processes and offers guidance for designing user-centric context-aware applications.2024IKInyeop Kim et al.KAIST, KAISTUniversal & Inclusive DesignPrivacy by Design & User ControlContext-Aware ComputingCHI
DeepStress: Supporting Stressful Context Sensemaking in Personal Informatics Systems Using a Quasi-experimental ApproachPersonal informatics (PI) systems are widely used in various domains such as mental health to provide insights from self-tracking data for behavior change. Users are highly interested in examining relationships from the self-tracking data, but identifying causality is still considered challenging. In this study, we design DeepStress, a PI system that helps users analyze contextual factors causally related to stress. DeepStress leverages a quasi-experimental approach to address potential biases related to confounding factors. To explore the user experience of DeepStress, we conducted a user study and a follow-up diary study using participants' own self-tracking data collected for 6 weeks. Our results show that DeepStress helps users consider multiple contexts when investigating causalities and use the results to manage their stress in everyday life. We discuss design implications for causality support in PI systems.2024GJGyuwon Jung et al.KAISTMental Health Apps & Online Support CommunitiesSleep & Stress MonitoringCHI
S-ADL: Exploring Smartphone-based Activities of Daily Living to Detect Blood Alcohol Concentration in a Controlled EnvironmentIn public health and safety, precise detection of blood alcohol concentration (BAC) plays a critical role in implementing responsive interventions that can save lives. While previous research has primarily focused on computer-based or neuropsychological tests for BAC identification, the potential use of daily smartphone activities for BAC detection in real-life scenarios remains largely unexplored. Drawing inspiration from Instrumental Activities of Daily Living (I-ADL), our hypothesis suggests that Smartphone-based Activities of Daily Living (S-ADL) can serve as a viable method for identifying BAC. In our proof-of-concept study, we propose, design, and assess the feasibility of using S-ADLs to detect BAC in a scenario-based controlled laboratory experiment involving 40 young adults. In this study, we identify key S-ADL metrics, such as delayed texting in SMS, site searching, and finance management, that significantly contribute to BAC detection (with an AUC-ROC and accuracy of 81%). We further discuss potential real-life applications of the proposed BAC model.2024HLHansoo Lee et al.Korea Advanced Institute of Science and TechnologyHuman Pose & Activity RecognitionMental Health Apps & Online Support CommunitiesBiosensors & Physiological MonitoringCHI