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<p>This paper describes ASTER, a novel procedure for collecting Screen Time data from iOS, iPadOS, watchOS, and devices using their built-in Apple Screen Time features. Traditional methods of studying digital behavior often rely on self-reported data, which are prone to inaccuracies (i.e., recall bias), limiting their validity and granularity. While Android devices have long facilitated real-time and granular behavior tracking through third-party applications, similar tools are not available on Apple devices due to Apple’s restrictions. This is problematic because there are significant differences between the user populations of iOS and Android. To address this gap, this study developed a data donation procedure that leverages the synchronization of screentime, enabling the extraction of comprehensive usage data of iPhones, iPads, Apple Watches, and Macs linked to the same Apple ID. The process involves donating system-level files used to generate Screen Time metrics on Mac, containing anonymized use data of all linked devices. We developed a tool that enables researchers to process these files into a usable dataset (e.g., JSON). This dataset provides granular insights into app usage without requiring substantial technical expertise or financial investment. While this approach enables the integration of Apple cross-device behavior into digital media research, it is limited to users with a Mac and can only capture data from the previous 4&#xa0;weeks. Additionally, the method is vulnerable to changes in Apple’s software structure, echoing the moving target problem. Nonetheless, this method marks an important step forward in current approaches to the passive sensing of smartphone behavior.</p>

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Making the impossible possible: Leveraging built-in features for non-intrusive and accurate Apple Screen Time tracking through ASTER

  • 期刊论文

  • 发表日期：

    2026年5月29日

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  • Marijn Martens,
  • Kyle Van Gaeveren

摘要

This paper describes ASTER, a novel procedure for collecting Screen Time data from iOS, iPadOS, watchOS, and devices using their built-in Apple Screen Time features. Traditional methods of studying digital behavior often rely on self-reported data, which are prone to inaccuracies (i.e., recall bias), limiting their validity and granularity. While Android devices have long facilitated real-time and granular behavior tracking through third-party applications, similar tools are not available on Apple devices due to Apple’s restrictions. This is problematic because there are significant differences between the user populations of iOS and Android. To address this gap, this study developed a data donation procedure that leverages the synchronization of screentime, enabling the extraction of comprehensive usage data of iPhones, iPads, Apple Watches, and Macs linked to the same Apple ID. The process involves donating system-level files used to generate Screen Time metrics on Mac, containing anonymized use data of all linked devices. We developed a tool that enables researchers to process these files into a usable dataset (e.g., JSON). This dataset provides granular insights into app usage without requiring substantial technical expertise or financial investment. While this approach enables the integration of Apple cross-device behavior into digital media research, it is limited to users with a Mac and can only capture data from the previous 4 weeks. Additionally, the method is vulnerable to changes in Apple’s software structure, echoing the moving target problem. Nonetheless, this method marks an important step forward in current approaches to the passive sensing of smartphone behavior.