Societies 2014, 4(1), 68-84; doi:10.3390/soc4010068
Article

Visualized and Interacted Life: Personal Analytics and Engagements with Data Doubles

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Received: 4 December 2013; in revised form: 14 January 2014 / Accepted: 6 February 2014 / Published: 18 February 2014
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Abstract: A field of personal analytics has emerged around self-monitoring practices, which includes the visualization and interpretation of the data produced. This paper explores personal analytics from the perspective of self-optimization, arguing that the ways in which people confront and engage with visualized personal data are as significant as the technology itself. The paper leans on the concept of the “data double”: the conversion of human bodies and minds into data flows that can be figuratively reassembled for the purposes of personal reflection and interaction. Based on an empirical study focusing on heart-rate variability measurement, the discussion underlines that a distanced theorizing of personal analytics is not sufficient if one wants to capture affective encounters between humans and their data doubles. Research outcomes suggest that these explanations can produce permanence and stability while also profoundly changing ways in which people reflect on themselves, on others and on their daily lives.
Keywords: personal analytics; visibility; self-optimization; Quantified Self; data double; data visualizations; participatory research
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MDPI and ACS Style

Ruckenstein, M. Visualized and Interacted Life: Personal Analytics and Engagements with Data Doubles. Societies 2014, 4, 68-84.

AMA Style

Ruckenstein M. Visualized and Interacted Life: Personal Analytics and Engagements with Data Doubles. Societies. 2014; 4(1):68-84.

Chicago/Turabian Style

Ruckenstein, Minna. 2014. "Visualized and Interacted Life: Personal Analytics and Engagements with Data Doubles." Societies 4, no. 1: 68-84.

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