Assessing OS-Level Automation for Digital Self-Control Interventions

Research on Digital Self-Control Tools (DSCTs) - mobile applications designed to support self-control over technology use - faces barriers related to technical complexity, operating-system constraints, and privacy. This paper investigates OS-level automation as a lightweight and privacy-centric approach to implementing and distributing digital self-control interventions without writing code. First, grounding our analysis in a state-of-the-art taxonomy of digital self-control strategies, we assess the extent to which iOS Shortcuts can function as a modular intervention toolkit. Our findings show that it can reproduce 75\% of intervention features found in contemporary DSCTs, while enabling novel capabilities such as feature-level, context-aware, and multi-device interventions. Second, we report a between-subject study (N = 193) investigating how intervention distribution strategies influence participant privacy perceptions in digital wellbeing experiments. We conclude the paper by discussing how emerging AI technologies could further amplify this approach - grounding our discussion in a concrete instance from our feasibility assessment in which an on-device AI model was already used to generate intervention content - and by outlining how AI-assisted authoring could lower barriers to creating personalized interventions.