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University of Southern California
University of Southern California
USC Chan Division of Occupational Science and Occupational Therapy
USC Chan Division of Occupational Science and Occupational Therapy
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Research
Research

Detecting and mapping stress patterns across space and time: Multimodal modeling of individuals in real-world physical and social work environments

Principal Investigator: Shawn Roll
Co-Principal Investigator: Burcin Becerik-Gerber (USC Sonny Astani Department of Civil & Environmental Engineering)
Co-Principal Investigator: Gale Lucas (USC Institute for Creative Technologies)
Co-Principal Investigator: Shrikanth Narayanan (USC Electrical & Computer Engineering, Computer Science)

Project Period: September 1, 2022 – August 31, 2026
Funding Source: National Science Foundation; Division of Information and Intelligent Systems; Smart and Connected Health Program
Award Number: IIS-2204942
Anticipated Award: $1,099,995

Stress has been identified as the health epidemic of the 21st century, and office-related work is a significant driver of stress among Americans due to long hours, rapid deadlines, heavy workloads, and job insecurity. Yet, office workers are often entirely unaware of the impact of stress until they notice symptoms of declining physical or mental health or well-being, such as musculoskeletal discomfort, headaches, poor sleep, or lack of motivation. Even more problematic, most individuals do not know how their work activities and the physical and social work environments are related to stress and other health outcomes. While stress is almost always treated as unfavorable, stress can be positive. Opportunities exist to better understand how to promote eustress that is energizing and essential for productivity and minimize distress that leads to negative emotions, disturbed bodily states, strain, and burnout. The project aims to describe individualized experiences of stress and develop multimodal models using a wide range of bio-behavioral, environmental, and activity engagement sensing technologies to inform personalized, automated, or technology-supported intervention approaches to stress management as workers engage in their daily work.