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

arXiv: https://arxiv.org/abs/2502.17650 · HTML: https://mlsanity.com/paper/2502.17650/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 45 references that are in the map.
- First posted on arXiv 24 Feb 2025.

## Builds on (most cited first, 8 of 8)

- [A Prompt Pattern Catalog to Enhance Prompt Engineering with ChatGPT](https://mlsanity.com/paper/2302.11382/) (2023)
- [A Computational Framework for Behavioral Assessment of LLM Therapists](https://mlsanity.com/paper/2401.00820/) (2024)
- [MindScape Study: Integrating LLM and Behavioral Sensing for Personalized AI-Driven Journaling Experiences](https://mlsanity.com/paper/2409.09570/) (2024)
- [LLM-based Conversational AI Therapist for Daily Functioning Screening and Psychotherapeutic Intervention via Everyday Smart Devices](https://mlsanity.com/paper/2403.10779/) (2024)
- [Momentary Stressor Logging and Reflective Visualizations: Implications for Stress Management with Wearables](https://mlsanity.com/paper/2401.16307/) (2024)
- [MindGuard: Towards Accessible and Sitgma-free Mental Health First Aid via Edge LLM](https://mlsanity.com/paper/2409.10064/) (2024)
- [Integrating Physiological Data with Large Language Models for Empathic Human-AI Interaction](https://mlsanity.com/paper/2404.15351/) (2024)
- [Chatbot-supported Thesis Writing: An Autoethnographic Report](https://mlsanity.com/paper/2311.10729/) (2023)

## Built on by (most cited first, 1 of 1)

- [Guiding Language Models to Be More Empathetic: Culturally Sensitive Mental Health Advice Generation Through Human-LLM Collaboration](https://mlsanity.com/paper/2607.23538/) (2026)

## Abstract (arXiv)

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.