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Bounded control in digital health: Privacy discourse and algorithmic governance in Apple Health
Aybike Serttaş1, Sema Bulat Demir1, Gizem Uyanik Uğur1
1Faculty of Communication, İstanbul Aydın University, Türkiye.
Objective:
This study examines how user control is constructed, communicated, and experienced in digital health tracking, using Apple Health as a case study. Rather than treating control as either fully empowering or simply illusory, the study conceptualizes it as bounded, relational, and discursively produced within platform environments.
Methods:
The study uses a qualitative, triangulated design combining critical discourse analysis of Apple's official "Health App and Privacy" text with semi-structured interviews with Apple Watch and Apple Health users (n = 18). The analysis compares corporate privacy discourse with users' everyday experiences of metrics, goals, notifications, permissions, and health-related feedback.
Results:
Apple's privacy discourse frames users as active decision-makers who can manage privacy, sharing, and platform participation. At the same time, this discourse situates user choice within predefined platform architectures, permissions, and data-processing conditions. Interview findings show that users often understand the limits of platform options and engage with Apple Health in pragmatic and informed ways. However, their everyday practices are also shaped by goal-setting, notifications, quantified feedback, and affective responses such as motivation, reassurance, pressure, guilt, and dependence.
Conclusions:
The findings suggest that user control in digital health should be understood as bounded rather than simply false. Apple Health provides functional choices and privacy settings, but these operate within a wider system of platform-defined options, behavioral prompts, and datafication. The study contributes to digital health and health communication scholarship by showing how privacy discourse, interface design, and self-tracking practices jointly produce structured forms of autonomy under algorithmic governance.
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