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Olawunmi George, Rizwana Rizia, MD Fitrat Hossain, Nadiyah Johnson, Carla Echeveste, Jose Lizarraga Mazaba, et al. (2019). Visualizing Early Warning Signs of Behavioral Crisis in Military Veterans: Empowering Peer Decision Support. In Z. Franco, J. J. González, & J. H. Canós (Eds.), Proceedings of the 16th International Conference on Information Systems for Crisis Response And Management. Valencia, Spain: Iscram.
Abstract: Several attempts have been made at creating mobile solutions for patients with mental disorders. A preemptive approach would definitely outdo a reactive one. This project seeks to ensure better crisis detection, by assigning patients (veterans) to caregivers (mentors). This is called the mentor-mentee approach. Enhanced with the use of mobile technology, veterans can stay connected in their daily lives to mentors, who have gone through the same traumatic experiences and have overcome them. A mobile application for communication between veterans and their mentors has been developed, which helps mentors get constant feedback from their mentees about their state of well-being. However, being able to make good deductions from the data given as feedback is of great importance. Under-represent ing or over-representing the data could be dangerously misleading. This paper presents the design process in this project and the key things to note when designing a data visualization for
timely crisis detection and decision-making.
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Zeno Franco, Katinka Hooyer, Rizwana Rizia, A B M Kowser Patwary, Mathew Armstrong, Bryan Semaan, et al. (2016). Dryhootch Quick Reaction Force: Collaborative Information Design to Prevent Crisis in Military Veterans. In A. Tapia, P. Antunes, V.A. Bañuls, K. Moore, & J. Porto (Eds.), ISCRAM 2016 Conference Proceedings ? 13th International Conference on Information Systems for Crisis Response and Management. Rio de Janeiro, Brasil: Federal University of Rio de Janeiro.
Abstract: Crises range from global catastrophes to personal disasters. However, systematic inquiry on crises rarely employs a comparative approach to examine commonalities between these seemingly very different events. We argue here that individual psychosocial disasters can inform a broader discussion on crises. Our approach applies general crisis theory to a smartphone based psychosocial support system for US military veterans. We engaged in a process designed to explore how veteran peer-to-peer mentorship can be augmented with IS support to display potential early warning signs as first step toward preventative intervention for high risk behaviors. To gain a better understanding of how military veterans might benefit from such a system, this article focuses on a community collaborative design process. The co-design process used the Small Stories method, allowing important cultural characteristics of to emerge, illuminating considerations in IS design with military veterans, and highlighting how humans think about crisis events at the individual level.
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Zeno Franco, Katinka Hooyer, Tanvir Roushan, Casey O'Brien, Nadiyah Johnson, Bill Watson, et al. (2018). Detecting & Visualizing Crisis Events in Human Systems: an mHealth Approach with High Risk Veterans. In Kees Boersma, & Brian Tomaszeski (Eds.), ISCRAM 2018 Conference Proceedings – 15th International Conference on Information Systems for Crisis Response and Management (pp. 874–885). Rochester, NY (USA): Rochester Institute of Technology.
Abstract: Designing mHealth applications for mental health interventions has largely focused on education and patient self-management. Next generation applications must take on more complex tasks, including sensor-based detection of crisis events, search for individualized early warning signs, and support for crisis intervention. This project examines approaches to integrating multiple worn sensors to detect mental health crisis events in US military veterans. Our work has highlighted several practical and theoretical problems with applying technology to evaluation crises in human system, which are often subtle and difficult to detect, as compared to technological or natural crisis events. Humans often do not recognize when they are in crisis and under-report crises to prevent reputational damage. The current project explores preliminary use of the E4 Empatica wristband to characterize acute aggression using a combination of veteran self-report data on anger, professional actors simulating aggressive events, and preliminary efforts to discriminate between crisis data and early warning sign data.
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