![]() However, the majority of STA cannot distinguish if it is the user themselves snoring or rolling around in bed-or, for example, the user’s significant other who did not consent to be tracked. Other than for tracking steps, heart rates or alike, most currently available STA operate without body contact to the user. This information can concern the STA user themselves, but also another person sleeping in the same room. Hence, STA provide access to completely new personal information. However, other than tracking steps or food intake, the activities and sensations tracked while sleeping are usually not conscious to oneself. Given their significant market share, the raising awareness for sleep-hygiene (Irish et al., 2015) and the growing phenomenon of self-tracking (Lupton, 2016), it is not far-fetched that sleep tracking might become the new ‘step tracking’. Although such consumer STA still come with lack in accuracy, for example, regarding wake and deep sleep detection (Fino et al., 2020), they have increasingly proliferated. Assisted by built-in sensors, additional external sensing devices and scoring algorithms, such applications promise to analyze and score sleep data without human assistance, and generate assessment reports for the user with real-time feedback on their quality of sleep and specific sleep parameters (Van de Water et al., 2011). Consumer-targeted sleep tracking applications (STA) to monitor, measure and/or influence sleep duration and/or quality are among the most downloaded apps for iOS and Android (Choi et al., 2018 SimilarWeb, 2022). In recent years, the topic of sleep has also attracted the interest of technology companies and developers. Sleep as ‘the other third of life’ (Doug et al., 2013) has been subject to many areas: from art (Kryger, 2019) to psychology, philosophy and history (Espie & Morin, 2012), from medicine (Stuck et al., 2020) to management (Rosekind et al., 2010 Spreitzer & Barne, 2015). In addition, the analysis helps to evaluate the use of STA, in terms of different risks for specific user groups, the sensitive contexts of usage, and potential interference with the intimacy of third parties. As the three dimensions medicalization, vulnerability, and relationality play a distinguishing role in the use of STA, they should be especially addressed in responsible development and implementation of STA. The analysis indicates that these three normative dimensions are decisive for assessing STA and that the ethical profile of this technology varies greatly depending on the respective user group. Secondly, an ethical analysis was conducted focusing on the three ethical dimensions: (1) medicalization, (2) vulnerability, and (3) relationality. Firstly, the field of mobile health applications was screened for STA to explore their typical functions and target user groups. The aim of the current analysis is to identify the characteristics of sleep tracking apps and to explore the specific ethical aspects associated with this form of self-tracking. Nevertheless, a specific ethical analysis of the use of these technologies is still missing so far. However, STA also raise ethical questions, for example, on the autonomy of the sleeping person, or potential effects on third parties. Assisted by built-in and/or external sensors, these apps can analyze sleep data and generate assessment reports for the user on their sleep duration and quality. Consumer-targeted sleep tracking applications (STA) that run on mobile devices (e.g., smartphones) promise to be useful tools for the individual user. ![]()
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