A quantitative, multimodal wearable bioelectronic device for comprehensive stress assessment and sub-classification
Stress is a universal experience impacting mental and physical health. However, no precise, objective wearable tool exists for continuous, long-term stress monitoring, which is essential for understanding stress-related health outcomes. To address this gap, we introduce SQC-SAS, a multimodal wearable device that simultaneously and continuously measures multiple physiological and molecular stress biomarkers for quantitative stress assessment and sub-classification. This device features exceptional environmental stability, reusability, and fully wireless data and power operation. Machine learning enables data-driven stress assessment and classification across multiple stress states, allowing biomarker profiles to be correlated with each state. Its wristband-like design enables continuous stress monitoring and real-time visualization. We envision our wearable will greatly advance precise, objective stress assessment and monitoring, offering unprecedented capabilities and laying the foundation for personalized interventions and a deeper understanding of stress-related outcomes.
Authors
- Rahim Esfandyarpour (ORCID: https://orcid.org/0000-0002-4528-3601)
- Shingirirai Chakoma (ORCID: https://orcid.org/0000-0002-6959-9141)
- Anita Ghandehari
- Jerome Rajendran
- Xiaochang Pei
- Jorge Alfonso Tavares-Negrete
Institutions
- University of California, Irvine (US)
- Samueli Institute (US)
Publication Details
- Journal
- Nature Communications
- Published
- 2026-01-29
- DOI
- https://doi.org/10.1038/s41467-025-67747-9
- Citations
- 1
- Primary Topic
- Advanced Sensor and Energy Harvesting Materials
- Type
- article
- Field-Weighted Citation Impact
- 21.28