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.

中文翻译：

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健康的算法自我跟踪：用户对风险意识和应对策略的看法

使用可穿戴设备和移动应用程序进行自我跟踪是一种流行的做法，它依赖于自动数据收集和算法驱动的分析。最初设计为个人使用的工具，各种公共和企业参与者，如商业组织和保险公司，现在都在使用自我跟踪数据。尽管经验证据仍然很少，但理论上已经得出了相关的社会风险，例如隐私侵犯或测量不准确。本文将自我跟踪概念化为算法选择应用程序，并实证检验用户与自我跟踪应用程序相关的风险意识，以及作为应对这些风险的一种选择的应对策略。它利用了在瑞士收集的具有代表性的调查数据。结果表明，瑞士自我追踪者对与他们使用的应用程序相关的风险意识普遍较低，只有少数自我追踪者采用了应对策略。我们进一步发现风险意识和应对策略的应用之间只有微弱的关联。这指向了在决定如何应对感知风险时进行成本效益计算，这种行为在现有文献中被解释为隐私计算。尽管存在相关风险，但将个人数据传递给保险公司的广泛意愿为这种解释提供了进一步的证据。这些结论——由于可穿戴设备的追踪系统和州级健康服务的潜力变得更加相关——提出了关于技术保障、数据和健康素养的问题，