Visualizing Personal Rhythms: A Critical Visual Analysis of Mental Health in Flux

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Visualizations of personal data in self-tracking systems can make even subtle shifts in mental and physical states observable, greatly influencing how health and wellness goals are set, pursued, and achieved. At the same time, recent work in data ethics cautions that standardized models can have unintended negative consequences for some user groups. Through collaborative design and critical visual analysis, this study contrasts conventional visualizations of personal data with the ways that vulnerable populations represent their lived experiences. Participants self-tracked to manage bipolar disorder, a mental illness characterized by severe and unpredictable mood changes. During design sessions, each created a series of timeline drawings depicting their experiences with mental health. Examples of adaptive and vernacular design, these images use both normative standards and individualized graphic modifications. Analysis shows that conventional visual encodings can support facets of self-assessment while also imposing problematic normative standards onto deeply personal experiences.