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Algorithmic Self-Tracking for Health: User Perspectives on Risk Awareness and Coping Strategies
[journal article]
Abstract Self-tracking with wearable devices and mobile applications is a popular practice that relies on automated data collection and algorithm-driven analytics. Initially designed as a tool for personal use, a variety of public and corporate actors such as commercial organizations and insurance companies ... view more
Self-tracking with wearable devices and mobile applications is a popular practice that relies on automated data collection and algorithm-driven analytics. Initially designed as a tool for personal use, a variety of public and corporate actors such as commercial organizations and insurance companies now make use of self-tracking data. Associated social risks such as privacy violations or measurement inaccuracies have been theoretically derived, although empirical evidence remains sparse. This article conceptualizes self-tracking as algorithmic-selection applications and empirically examines users' risk awareness related to self-tracking applications as well as coping strategies as an option to deal with these risks. It draws on representative survey data collected in Switzerland. The results reveal that Swiss self-trackers' awareness of risks related to the applications they use is generally low and only a small number of those who self-track apply coping strategies. We further find only a weak association between risk awareness and the application of coping strategies. This points to a cost-benefit calculation when deciding how to respond to perceived risks, a behavior explained as a privacy calculus in extant literature. The widespread willingness to pass on personal data to insurance companies despite associated risks provides further evidence for this interpretation. The conclusions - made even more pertinent by the potential of wearables' track-and-trace systems and state-level health provision - raise questions about technical safeguarding, data and health literacies, and governance mechanisms that might be necessary considering the further popularization of self-tracking for health.... view less
Classification
Interactive, electronic Media
Medical Sociology
Technology Assessment
Free Keywords
algorithmic selection; coping strategies; mHealth; risk awareness; self-quantification; self-tracking apps; societal risks; user perception; wearables
Document language
English
Publication Year
2021
Page/Pages
p. 145-157
Journal
Media and Communication, 9 (2021) 4
Issue topic
Algorithmic Systems in the Digital Society
ISSN
2183-2439
Status
Published Version; peer reviewed