Algorithmic Self-Tracking for Health: User Perspectives on Risk Awareness and Coping Strategies

• Published in: Media and communication (Lisboa) 2021-09, Vol.9 (4S1), p.145-157
• Main Authors: Festic, Noemi, Latzer, Michael, Smirnova, Svetlana
• Format: Article
• Language: English
• Subjects: Actors; Actresses; algorithmic selection; Algorithms; Application; Applications programs; Automation; Behavior; Communication; Coping; Coping strategies; Data collection; Data entry; Electronic devices; Empirical analysis; Governance; Health insurance; Insurance; Insurance companies; Internet; Measurement; mhealth; Mobile applications; Mobile computing; Privacy; Privacy, Right of; Risk; risk awareness; self-quantification; self-tracking apps; Social aspects; societal risks; Tracking; Tracking devices; user perception; Violations; Wearable computers; Wearable technology; wearables; Wireless telephone software
• ISSN: 2183-2439; 2183-2439
• DOI: 10.17645/mac.v9i4.4162

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.