SwipeWell: A Multi-Agent System for Short-Video-Based Mobile Psychological Self-Assessment among Older Adults

Psychological self-assessment is fundamental to monitoring the mental well-being of the aging population. However, conventional paper-based methods and emerging LLM-driven conversational interfaces often suffer from low user engagement, high cognitive load, and usability barriers. This paper presents SwipeWell, a novel mobile psychological self-assessment system that leverages a human-in-the-loop multi-agent workflow to translate validated scales into psychometrically grounded and content-faithful animations, allowing users to intuitively log their status via sidebar interactions. We evaluated SwipeWell through a within-subjects study ( N = 27) with older adults, comparing it against traditional paper-and-pencil and LLM-based conversational assessments using three validated psychological scales covering emotion, cognition, and somatization. Psychometrically, SwipeWell maintained reliable and valid measurement performance. It established rank-order consistency for emotion and cognition, while facilitating somatic symptom interpretation through multimodal representations. Our empirical results demonstrate that SwipeWell is significantly more engaging, enjoyable, and time-efficient. Furthermore, participants experienced a significantly lower cognitive load and reported higher learnability compared to conversational systems. These findings highlight how embedding psychological self-assessment tasks into familiar, low-friction mobile interactions can foster accessible mobile health tools, providing design implications for future pervasive health technologies for older adults.