Visually Encoding the Lived Experience of Bipolar Disorder

Issues of social identity, attitudes towards self-disclosure, and potentially biased approaches to what is considered "typical" or "normal" are critical factors when designing visualizations for personal informatics systems. This is particularly true when working with vulnerable populations like those who self-track to manage serious mental illnesses like bipolar disorder (BD). We worked with individuals diagnosed with BD to 1) better understand sense-making challenges related to the representation and interpretation of personal data and 2) probe the benefits, risks, and limitations of participatory approaches to designing personal data visualizations that better reflect their lived experiences. We describe our co-design process, present a series of emergent visual encoding schemas resulting from these activities, and report on the assessment of these speculative designs by participants. We conclude by summarizing important considerations and implications for designing personal data visualizations for (and with) people who self-track to manage serious mental illness.