Momentary Stressor Logging and Reflective Visualizations: Implications for Stress Management with Wearables
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Abstract (arXiv)
Commercial wearables from Fitbit, Garmin, and Whoop have recently introduced real-time notifications based on detecting changes in physiological responses indicating potential stress. In this paper, we investigate how these new capabilities can be leveraged to improve stress management. We developed a smartwatch app, a smartphone app, and a cloud service, and conducted a 100-day field study with 122 participants who received prompts triggered by physiological responses several times a day. They were asked whether they were stressed, and if so, to log the most likely stressor. Each week, participants received new visualizations of their data to self-reflect on patterns and trends. Participants reported better awareness of their stressors, and self-initiating fourteen kinds of behavioral changes to reduce stress in their daily lives. Repeated self-reports over 14 weeks showed reductions in both stress intensity (in 26,521 momentary ratings) and stress frequency (in 1,057 weekly surveys).
Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). First posted on arXiv 29 Jan 2024.
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Citing papers per year: 2025: 3; 2026: 3.
6 papers.
- 2025Pulse-PPG: An Open-Source Field-Trained PPG Foundation Model for Wearable Applications Across Lab and Field Settingscited by 35
- 2025Wearable Meets LLM for Stress Management: A Duoethnographic Study Integrating Wearable-Triggered Stressors and LLM Chatbots for Personalized Interventionscited by 1
- 2026A foundation model for electrodermal activity datacited by 1
- 2025Prototype-based Self-Supervised Multimodal Learning for PPG and Accelerometry Signalscited by 0
- 2026CounselReflect: Opportunities and Challenges for Designing Tools to Support Self-Reflection on Mental Health and Well-Being Conversations with AIcited by 0
- 2026Personalized and Context-Aware Transformer Models for Predicting Post-Intervention Physiological Responses from Wearable Sensor Datacited by 0
- 2026Personalized and Context-Aware Transformer Models for Predicting Post-Intervention Physiological Responses from Wearable Sensor Data16 Apr 2026
- 2026CounselReflect: Opportunities and Challenges for Designing Tools to Support Self-Reflection on Mental Health and Well-Being Conversations with AI31 Mar 2026
- 2026A foundation model for electrodermal activity data20 Feb 2026
- 2025Prototype-based Self-Supervised Multimodal Learning for PPG and Accelerometry Signals10 Oct 2025
- 2025Wearable Meets LLM for Stress Management: A Duoethnographic Study Integrating Wearable-Triggered Stressors and LLM Chatbots for Personalized Interventions24 Feb 2025
- 2025Pulse-PPG: An Open-Source Field-Trained PPG Foundation Model for Wearable Applications Across Lab and Field Settings3 Feb 2025