Wearable Meets LLM for Stress Management: A Duoethnographic Study Integrating Wearable-Triggered Stressors and LLM Chatbots for Personalized Interventions

TL;DR

This study explores how wearable-triggered physiological data combined with LLM chatbots can provide personalized, real-time stress management interventions, highlighting the importance of tailored responses for effectiveness.

Contribution

It introduces a duoethnographic method to evaluate wearable and LLM integration for personalized stress interventions, emphasizing the value of brief event descriptions.

Findings

Most wearable-triggered events were meaningful but rarely required intervention.

Tailored interventions based on brief event descriptions were more effective.

The approach advances user-centric mental health support tools.

Abstract

We use a duoethnographic approach to study how wearable-integrated LLM chatbots can assist with personalized stress management, addressing the growing need for immediacy and tailored interventions. Two researchers interacted with custom chatbots over 22 days, responding to wearable-detected physiological prompts, recording stressor phrases, and using them to seek tailored interventions from their LLM-powered chatbots. They recorded their experiences in autoethnographic diaries and analyzed them during weekly discussions, focusing on the relevance, clarity, and impact of chatbot-generated interventions. Results showed that even though most events triggered by the wearable were meaningful, only one in five warranted an intervention. It also showed that interventions tailored with brief event descriptions were more effective than generic ones. By examining the intersection of wearables and LLM, this research contributes to developing more effective, user-centric mental health tools for real-time stress relief and behavior change.