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Franco, Z., Baker, N., R. Okusanya, T., Haque, M. R., Gresser, J., Rubya, S., et al. (2023). Customizing the BattlePeer App: Connecting First Responders with Peer Support to Manage Mental Health Crises. In Jaziar Radianti, Ioannis Dokas, Nicolas Lalone, & Deepak Khazanchi (Eds.), Proceedings of the 20th International ISCRAM Conference (pp. 272–283). Omaha, USA: University of Nebraska at Omaha.
Abstract: The prevalence and severity of mental health disorders are high among first responders. Routine exposure to trauma, unique work patterns and the social stigma of seeking care exacerbate their challenges. While there are many mHealth applications for effective interventions, they primarily focus on support, education, and symptom identification and management. Our research uses empirical data to inform the customization of the BattlePeer application, previously tested among US veterans. Through focus groups with first responders, we identify specific barriers to help in this population. Our work highlights the potential benefits of adapting an app to create effective peer support strategies. We suggest the modification of BattlePeer to help first responders meet their mental health needs through peer support with tailored feedback and notifications. This will help negotiate the pervasive social isolation and hesitance in articulating emotions described in focus groups that lend to negative mental health outcomes.
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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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