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Stress AI Technology Commercialized by CuesHub and Piloted in New Studies

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Stress AI Technology Commercialized by CuesHub and Piloted in New Studies

The mDOT Center’s innovative stress AI technology, originally developed through the Mobile Open Observation of Daily Stressors (MOODS) study, has been successfully commercialized and licensed to CuesHub, a newly formed startup. CuesHub has launched a suite of applications compatible with smartphones and smartwatches, alongside a dashboard designed for coaches, employers, and researchers to monitor and interpret stress-related data.

 

The technology is now being piloted in two new Service Projects (SP9 and SP10), which are evaluating its effectiveness in assessing stress and identifying daily stressors. These pilot studies are paving the way for larger-scale research, with several grant submissions planned to further investigate the clinical utility of AI-driven stress monitoring.

 

In parallel, a joint publication with CP11 was featured at ACM CHI 2024, highlighting the technology’s capability to provide real-time, actionable insights into stress and related behaviors. Additional manuscripts and grant applications are in progress, reflecting ongoing efforts to expand the use and evaluation of this tool across diverse populations and settings.

 

The technology was also presented at the Tennessee Society for Human Resources (TN-SHRM) Conference & Expo in August, demonstrating its potential for real-world applications in workplace wellness, research, and behavioral health. By bridging mHealth research and commercialization, the mDOT Center continues to advance practical solutions for stress management.

 

Paper citation
Neupane, S., Saha, M., Ali, N., Hnat, T., Samiei, S. A., Nandugudi, A., Almeida, D. M., & Kumar, S. (2024). Momentary stressor logging and reflective visualizations: Implications for stress management with wearables. In Proceedings of the 2024 CHI Conference on Human Factors in Computing Systems (Article 809, 19 pages). Association for Computing Machinery. https://doi.org/10.1145/3613904.3642662

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