Mental health issues affect a significant portion of the world’s population and can result in debilitating and life-threatening outcomes. To address this increasingly pressing healthcare challenge, there is a need to research novel approaches for early detection and prevention. In particular, ubiquitous systems can play a central role in revealing and tracking clinically relevant behaviors, contexts, and symptoms. Further, such systems can passively detect relapse onset and enable the opportune delivery of effective intervention strategies.
However, despite their clear potential, the uptake of ubiquitous technologies into clinical mental healthcare is rare, and a number of challenges still face the overall efficacy of such technology-based solutions. The goal of this workshop is to bring together researchers interested in identifying, articulating, and addressing such issues and opportunities. Following the success of last year’s inaugural workshop, we aim to continue facilitating the UbiComp community in developing a holistic approach for sensing and intervention in the context of mental health.
We invite submissions in the areas and intersections of mental health, well-being, ubiquitous computing, and human-centered design, including but not limited to:
Design and implementation of computational platforms (e.g., mobile phones, instrumented homes, etc.) to collect health and well-being data.
Development of robust behavioral models that can handle data sparsity and mislabeling issues.
Integration of multimodal data from different sensor streams for personalized predictive modeling.
Automated inference from sensor data of high-level contexts (e.g., environmental, social, etc.) indicative of mental health status.
Design and implementation of feedback (e.g., reports, visualizations, proactive behavioral interventions, etc.) for both patients and caregivers.
Development of smartphone based automated behavioral interventions focusing on mental health and well-being.
Methods for sustaining user adherence and engagement over long periods of time.
Devising privacy-preserving strategies for data collection, analysis, and management.
Deployment in low-income communities and countries.
Identifying ways to better integrate ubiquitous technologies into existing healthcare infrastructures and government policy.
The deadline for submission is June 9th, 2017 (11:59 PDT)
Regular (up to 9 pages) or short (up to 5 pages) paper using SIGCHI Extended Abstract format. Papers should be in PDF format and not anonymized.
Submissions can be made at https://easychair.org/conferences/?conf=mhsi2017.
We encourage submissions of work-in-progress papers. Accepted papers will be considered for a future special issue in JMIR.
Laura E. Barnes, University of Virginia
Amy Bauer, University of Washington
Michael L. Birnbaum, Hofstra Northwell School of Medicine
Steven Chan, University of California, Davis
Orianna DeMasi, University of California, Berkeley
Stephen M Schueller, Northwestern University
John Torous, Harvard Medical School
Amir Muaremi, Stanford University
Steven Vannoy, University of Massachusetts Boston
Mi Zhang, Michigan State University
Arpita Bhattacharya, University of Washington
Philip Chow, University of Virginia
Scott Cambo, Northwestern University
Kevin Doherty, Trinity College Dublin
PhD Student in Information Science, Cornell University.
Postdoctoral Researcher in Information Science, Cornell University.
Reader in Data Science, UCL.