This project is investigating the similarities and relationships between two growing forms of self-quantification. The first, the Quantified Self movement, brings together individuals who advocate for self-tracking as a way to understand oneself: “self knowledge through numbers.” QS practitioners track their own activities (e.g., diet and exercise), performance measures (mental and physical testing), mood states, and medical diagnostics as a way to monitor and make sense of their health and happiness. The second revolves around the availability of personal genomic sequence data. Consumers can provide DNA samples that are analyzed for genetic markers that correspond to everything from eye color to breast cancer risk. Similarly, metagenomic analysis (genomic testing of populations of microorganisms) is being offered as a way to understand the ecosystem of non-human organisms to which our bodies play host.
We are exploring how these forms of quantification enable algorithmic understandings of the human body. Advocates of self-quantificationpromote a view that collecting data about inputs (food consumption, environmental factors, stress levels, etc.), outputs (disease, moods, activity levels, bowel movements, etc.), and processing instructions (the genetic code) will empower individuals to comprehend and intervene in their personal predictable human system. We will probe how this algorithmic rhetoric is understood and experienced, and how these participants understand the potential social and scientific benefits and risks of sharing personal health data.
Personal Genomics & QS Project Team:
- Judith Gregory
- Matthew Bietz
- Scout Calvert
- Geoffrey C. Bowker
- Cory Knobel