Exploring Expert Perspectives on Wearable-Triggered LLM Conversational Support for Daily Stress Management
This work addresses the underexplored problem of designing meaningful systems for daily stress management, targeting mental health experts and users, but it is incremental as it focuses on early design exploration rather than deployment or validation.
The study tackled the design challenge of integrating wearable-triggered stress detection with LLM-based conversational support by developing EmBot, a mobile app used in interviews with 15 mental health experts to surface early design tensions and considerations.
Wearable devices increasingly support stress detection, while LLMs enable conversational mental health support. However, designing systems that meaningfully connect wearable-triggered stress events with generative dialogue remains underexplored, particularly from a design perspective. We present EmBot, a functional mobile application that combines wearable-triggered stress detection with LLM-based conversational support for daily stress management. We used EmBot as a design probe in semi-structured interviews with 15 mental health experts to examine their perspectives and surface early design tensions and considerations that arise from wearable-triggered conversational support, informing the future design of such systems for daily stress management and mental health support.