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
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
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
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
Understanding Emotion Changes in Mobile Experience SamplingMobile experience sampling methods~(ESMs) are widely used to measure users' affective states by randomly sending self-report requests. However, this random probing can interrupt users and adversely influence users' emotional states by inducing disturbance and stress. This work aims to understand how ESMs themselves may compromise the validity of ESM responses and what contextual factors contribute to changes in emotions when users respond to ESMs. Towards this goal, we analyze 2,227 samples of the mobile ESM data collected from 78 participants. Our results show ESM interruptions positively or negatively affected users' emotional states in at least 38\% of ESMs, and the changes in emotions are closely related to the contexts users were in prior to ESMs. Finally, we discuss the implications of using the ESM and possible considerations for mitigating the variability in emotional responses in the context of mobile data collection for affective computing.2022SKSoowon Kang et al.KAIST, KAISTMental Health Apps & Online Support CommunitiesNotification & Interruption ManagementCHI