Serious mental illnesses, including schizophrenia and major mood disorders such as bipolar disorder (BD), account for three of the six leading causes of long-term disability worldwide. These are some of the most challenging illnesses to treat, and they are associated with considerable personal and societal costs (e.g., higher rates of death,chronic disease complications, increased health care costs, and burdens to families and communities).

Although there have been a recent increase in the use of mobile technologies to detect changes in mental health, there is also an acknowledgement that merely deploying these technologies may not be enough, and that the design of the patient-facing element is crucial to patient engagement. Previous work in mental healthcare technologies have mostly looked at deploying “off the shelf” technologies like SMS, psycho-educational websites and online forums in mental health care settings, providing evidence that these approaches can be effective).

Obtaining ecologically valid data at the early stages of application development is challenging. The stigma associated with mental illness and the difficulties that designers face in understanding the lived experience of mental illness are considerable. Researchers traditionally rely upon focus groups with patients which center on identifying needs and eliciting feedback on early designs. More recent approaches in the HCI community have included patients and therapists in the design process through participatory workshops. These efforts, while valuable, tend to focus on high-level user needs and do not get at the lived experience and context of people living with mental illness.

Currently, we are provide evidence of the value of in situ design: incorporating ongoing real-world use of a system into existing participatory design practice. This approach yields important insights about the practical use of everyday technologies in ways that we would not have uncovered by asking clinicians and patients to engage with the design process using abstract scenarios and hypothetical thinking; we believe this is an important methodological advance that can be applied to other mHealth contexts. Second, we present the design of MoodRhythm, a support system for patients with BD that reflects intentional design choices informed by low-level cognitive and physiological understandings of the disease and grounded in a clinically validated, evidence-based social therapy treatment from the field of clinical psychology. In addition, while many approaches have focused predominantly on how patient--collecteddata could be used in clinical settings, we explore how to provide valuable, meaningful and privacy-sensitive feedback back to the patient, thereby closing the loop and potentially increasing the incentive for recording data.

Publications

• Xu, T., Yu, J., Doyle, D.T., & Voida, S. (2023, October). Technology-mediated strategies for coping with mental health challenges: Insights from people with bipolar disorder. Journal of the ACM on Human-Computer Interaction 7(CSCW2).
• Hoefer, M.J.D., Schumacher, B.E., Szafir, D.A., & Voida, S. (2022). Visualizing uncertainty in multi-source mental health data. In Extended Abstracts of the 2022 ACM CHI Conference on Human Factors in Computing Systems (CHI EA ’22), New Orleans, LA. ACM Press.
• Hoefer, M.J.D., Van Kleunen, L., Goodby, C., Blackburn, L.B., Panati, P., & Voida, S. (2021). The multiplicative patient and the clinical workflow: Clinician perspectives on social interfaces for self-tracking and managing bipolar disorder. In Proceedings of the ACM Conference on Designing Interactive Systems (DIS ‘21, pp. 907–925), Virtual Event. ACM Press.
• Petelka, J., Kleunen, L.V., Murnane, E., Albright, L., Voida, S., & Snyder, J. (2020). Becoming (in)visible: Privacy, transparency, and disclosure in the self-management of bipolar disorder. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, Honolulu, Hawai’i. ACM.
• Snyder, J., Murnane, E., Lustig, C., & Voida, S. (2019, May). Visually encoding the lived experience of bipolar disorder. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems (pp. 133:1–133:14), Glasgow, Scotland, UK. ACM.
• Kleunen, L.V., & Voida, S. (2019). Challenges in supporting social practices around personal data for long-term mental health management. Position paper for the Fourth International Workshop on Mental Health and Well-being: Sensing and Intervention, held in conjunction with the ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp 2019), London, UK, September 10.
• Murnane, E.L., Walker, T.G., Tench, B., Voida, S., & Snyder, J. (2018, November). Personal informatics in interpersonal contexts: Towards the design of technology that supports the social ecologies of long-term mental health management. Proceedings of the ACM on Human-Computer Interaction—CSCW 2(CSCW), 127:1–127:27. CSCW 2018 Best Paper Honorable Mention (among the top 5% of all submissions).
• Matthews, M., Abdullah, S., Murnane, E., Voida, S., Choudhury, T., Gay, G., & Frank, E. (2016, August). Development and evaluation of a smartphone-based measure of social rhythms for bipolar disorder. Assessment 23(4), 472–483.
• Matthews, M., Voida, S., Abdullah, S., Doherty, G., Choudhury, T., Im, S., & Gay, G. (2015). In situ design for mental illness: Considering the pathology of bipolar disorder in mHealth design. In Proceedings of the 17th International Conference on Human–Computer Interaction with Mobile Devices and Services (MobileHCI 2015, pp. 86–97), Copenhagen, Denmark, August 24–7. ACM Press.

Research Sponsors