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.
Submission deadline: June 16th, 2017 (11:59 PM PDT)
Decisions to authors: July 1, 2017
Camera-ready deadline: July 8, 2017
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.
All accepted papers will be included in the ACM Digital Library as part of UbiComp conference supplemental proceedings. Papers will be reviewed by the workshop's technical program committee according to criteria regarding a submission's quality, relevance to the workshop's topics, and, foremost, its potential to spark discussions about directions, insights, and solutions in the context of mental health, sensing, and intervention. Research papers, case studies, and position papers are all welcome.
In particular, we encourage authors to keep the following options in mind when preparing submissions:
Works-In-Progress: To facilitate sharing of thought-provoking ideas and high-potential though preliminary research, authors are welcome to make submissions describing early-stage, in-progress, and/or exploratory work in order to elicit feedback, discover collaboration opportunities, and generally spark discussion.
Special Issue on Computing and Mental Health in the Journal of Medical Internet Research (JMIR): When submitting to the workshop, authors will have the option to specify if they would like an expanded version of their paper to be considered for inclusion in an upcoming special issue in JMIR dedicated to the topic of computing and mental health. Authors selected for inclusion would then be invited to expand their workshop submission to a full length manuscript of no more than 7,500 words, due in mid-Fall 2017. This JMIR special issue will also include manuscripts produced through a similar solitication from the related workshop on Computing and Mental Health held annually at CHI.
Laura E. Barnes, University of Virginia
Amy Bauer, University of Washington
Arpita Bhattacharya, University of Washington
Michael L. Birnbaum, Hofstra Northwell School of Medicine
Scott Cambo, Northwestern University
Steven Chan, University of California, Davis
Philip Chow, University of Virginia
Orianna DeMasi, University of California, Berkeley
Kevin Doherty, Trinity College Dublin
Amir Muaremi, Stanford University
Katie O’Leary, University of Washington
Stephen M Schueller, Northwestern University
John Torous, Harvard Medical School
Steven Vannoy, University of Massachusetts Boston
Mi Zhang, Michigan State University
PhD Student in Information Science, Cornell University.
Postdoctoral Researcher in Information Science, Cornell University.
Reader in Data Science, UCL.