Bipolar Tech

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.


  • Saeed Abdullah
  • Liam Albright
  • Tanzeem Choudhury
  • Gavin Doherty
  • Ellen Frank
  • Geri Gay
  • Caitie Lustig
  • Mark Matthews
  • Elizabeth Murnane
  • Justin Petelka
  • Jaime Snyder
  • Beck Tench
  • Lucy Van Kleunen
  • Tara Walker