ecological momentary assessment

Existing mobile sensing data collection methods neglect subjective feedback (e.g., questionnaires, self-reports), leading to fragmented contextual understanding and inaccurate behavioral modeling. To address this, we propose a human-in-the-loop collaborative sensing framework built upon the iLog platform. Our approach introduces three key innovations: (1) a dual-dimensional “context–time” modeling paradigm; (2) a calendar-style real-time monitoring dashboard; and (3) a dynamic acquisition plan revision mechanism. Leveraging context-aware modeling, real-time visual analytics, an adaptive experimental workflow engine, and purposeful human–system interaction design, the framework enhances controllability for researchers, participants, and the system itself. Evaluated with 350 university students, our method significantly improves semantic richness, contextual completeness, and overall data quality—enabling more accurate behavioral modeling and fine-grained personalized analysis.

This study addresses the challenge of designing lightweight digital interventions that can prompt action among low-motivation users within one minute, without requiring registration or sensing. Grounded in the Fogg Behavior Model and four design principles, the work proposes just-in-time micro-interventions targeting physical activity, healthy eating, and mental well-being. A novel mechanism enables users to collaboratively rewrite intervention prompts, facilitating “intentional personalization”—enhancing relevance while maintaining minimal friction. A 14-day field study (N=22) demonstrates that user-rewritten prompts significantly increase both acceptance and willingness to act, thereby validating co-creation as a viable lightweight strategy for effective personalization in behavioral interventions.

This study addresses participation discontinuity in digital mental health (DMH) tools caused by socially embedded disruptions (SEDs)—such as caregiving responsibilities, occupational stress, and acute health events. Through an 8-week SMS-based intervention and participatory design workshops, we conducted longitudinal textual interaction analysis, qualitative thematic coding, and contextual modeling to systematically identify and conceptualize SEDs for the first time. The research yields three actionable design principles: (1) structured self-care goal setting, (2) nonjudgmental offline frameworks, and (3) integration of external support resources. Empirical findings demonstrate that this context-sensitive framework significantly enhances users’ situational adaptability and long-term engagement. It advances DMH design from a technology-centric paradigm toward one deeply embedded in users’ social contexts, thereby providing both theoretical grounding and practical guidance for sustainable digital health interventions. (149 words)

Physiological emotion data collection suffers from misalignment between subjective labels and objective physiological responses, primarily due to human participant dependency and associated cognitive biases. Method: We conducted a VR-based emotion elicitation study with 37 participants, complemented by semi-structured interviews, to investigate participant-centered factors affecting labeling fidelity. Contribution/Results: We identify three critical human-induced interference factors—perceptual bias, experimental design mismatch, and environmental mismatch—and provide the first systematic characterization of the decoupling mechanism between subjective cognition and physiological response. Based on these findings, we propose a participant-centered experimental design paradigm and a context-enhanced annotation framework, yielding seven actionable guidelines for physiological emotion data collection. This work establishes a human-factor foundation for reliable affective labeling and advances AI-driven affective modeling by bridging cognitive and physiological domains.

This study addresses the lack of user-centered, clinically informed, and reusable design resources for augmented reality (AR)-based emotion regulation interventions, which currently hinders seamless support for real-time emotional management in daily life. Through a two-phase participatory design process, the research systematically integrates end-user needs with clinical feasibility by first eliciting design ideas from individuals with anxiety tendencies using the Nominal Group Technique, followed by expert-driven clustering and evaluation of these concepts. The resulting structured repository comprises 106 reusable AR intervention design concepts organized into eight thematic clusters. This resource lays a foundational framework for developing practical, scalable AR tools that effectively support everyday emotion regulation grounded in both user experience and clinical knowledge.

This work addresses the challenge that individuals often struggle to accurately recall the triggers and contextual details of stressful events, which limits the efficacy of psychological interventions. To overcome this, the authors propose HeartbeatCam, a novel system that integrates physiological stress signals from consumer-grade smartwatches with open-source augmented reality (AR) glasses to create a physiology-driven, self-triggered mechanism. Upon detecting elevated stress levels, the system automatically captures sparse audiovisual contextual data. This approach enables an action-oriented method for mental health awareness, facilitating precise retrospective analysis and timely therapeutic intervention during clinical sessions. The study demonstrates the feasibility and clinical value of physiology-informed contextual logging as an auxiliary tool in mental health care.

This study addresses the insufficient attention to participant engagement, ethical practice, and power dynamics in existing ecological momentary assessment (EMA) platforms used in adolescent research. To redress this gap, the authors developed and evaluated a youth-centered EMA platform through a longitudinal twin-case study complemented by in-depth interviews. The work proposes interaction design principles grounded in adolescent agency, ethical considerations, and research objectives. The platform employs a hybrid architecture comprising a gamified mobile application for participants and a researcher-facing web dashboard, which collectively enhance engagement and streamline study management. However, technical instability and rigid data structures raised privacy concerns and constrained meta-analytic potential. Emphasizing participatory design as a means to rebalance power relations, this research offers a novel paradigm for adolescent-oriented EMA studies.

This study addresses the critical gap in timely psychological intervention for cancer survivors, who often miss opportunities due to insufficient proactive help-seeking. To overcome this, we propose a novel approach leveraging a large language model (LLM) agent that autonomously queries passively sensed smartphone data. By integrating personalized baselines with population-level retrieval-augmented reasoning, the agent emulates clinical hypothesis testing to dynamically identify emotional regulation needs and optimal intervention windows. Our method transcends the limitations of conventional fixed-feature pipelines, achieving a prediction accuracy of 0.713 for intervention availability using passive data alone. When augmented with diary entries, it attains a balanced accuracy of 0.743 in predicting willingness to engage in emotion regulation. This work pioneers the application of agent-driven, hypothesis-testing-style reasoning in passive sensing, substantially enhancing the timeliness and personalization of mental health interventions.

This work addresses a fundamental limitation in traditional behavioral measurement, which often relies on passive observation under static or weakly controlled conditions and struggles to disentangle distinct internal mechanisms that produce similar overt behaviors. Treating human behavior as the observable output of a dynamic system, this study introduces— for the first time—the principles of system identification into behavioral science. It proposes a closed-loop experimental framework based on structured perturbations: precise, programmable disturbances are delivered via immersive environments while multimodal behavioral trajectories are simultaneously recorded. These data are integrated with dynamic computational models to enable mechanism-driven, real-time inference. By synergistically combining psychometrics, experimental design, and generative modeling, the approach advances behavioral science from descriptive analysis toward an identifiable, reproducible paradigm centered on generative mechanisms, substantially enhancing both theoretical rigor and causal interpretability in behavioral inference.

This study addresses the persistent gap between users’ willingness and actual behavior in data donation practices, focusing on how the presentation of personal data influences donation decisions—a dimension underexplored from a design-oriented perspective. Through a real-world experiment (N=24), the research evaluates three pre-donation data exploration frameworks: “self-focused,” “social comparison,” and “collective uniqueness.” Findings reveal that the “social comparison” frame significantly increases donation rates to 87.5%, outperforming the “self-focused” condition (62.5%), whereas the “collective uniqueness” frame reduces donations to 37.5% due to induced cognitive confusion and heightened privacy concerns. This work pioneers the integration of behavioral design into public-sector data donation, uncovering a pronounced framing effect in data selection and underscoring the critical role of interface design in fostering meaningful user participation.