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

Institutions

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
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

A quantitative, multimodal wearable bioelectronic device for comprehensive stress assessment and sub-classification

Rahim Esfandyarpour, Shingirirai Chakoma, Anita Ghandehari, Jerome Rajendran et al.
1 citations
Nature Communications
Advanced Sensor and Energy Harvesting Materials
21.28
article

A quantitative, multimodal wearable bioelectronic device for comprehensive stress assessment and sub-classification

Rahim Esfandyarpour, Shingirirai Chakoma, Anita Ghandehari, Jerome Rajendran, Xiaochang Pei, Jorge Alfonso Tavares-Negrete
article en
1 citations

Abstract

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.

Nature CommunicationsVol. 17(1)
University of California, Irvine (US), Samueli Institute (US)
Life in Land
Openalex Percentile: Top 2%
Advanced Sensor and Energy Harvesting Materials
21.28
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

A quantitative, multimodal wearable bioelectronic device for comprehensive stress assessment and sub-classification — Rahim Esfandyarpour, Shingirirai Chakoma, et al. · Nature Communications (2026) | TGRS Research Map | TGRS