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<p>Smartphone-based digital trace data can offer powerful insights for identifying behavioural patterns and health risks. However, existing tools for comprehensive data collection lack scalability, customizability, transparency and accessibility. To address these gaps, we developed an open-source platform that enables in situ capture of multimodal digital traces from smartphones (for example, moment-by-moment capture of screenshots, application usage logs, interaction histories and phone sensor readings). The Stanford Screenomics Data Collection application allows researchers to tailor data types and quality, data transfer methods and upload cadence. The Dashboard application supports real-time monitoring of participants’ data provision, identification of data issues and automated reactive communications to participants. The platform’s back end uses a NoSQL database for secure, and Health Insurance Portability and Accountability Act-compliant storage. Using illustrative 24-h digital trace data, we demonstrate how the platform expands the range of possible digital phenotyping studies.</p>

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An open-source platform for multimodal digital trace data collection from smartphones

  • 期刊论文

  • 发表日期：

    2026年3月4日

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  • Ian Kim,
  • Jack Boffa,
  • Mujung Cho,
  • David E. Conroy,
  • Nathan Kline,
  • Nick Haber,
  • Thomas N. Robinson,
  • Byron Reeves,
  • Nilàm Ram

摘要

Smartphone-based digital trace data can offer powerful insights for identifying behavioural patterns and health risks. However, existing tools for comprehensive data collection lack scalability, customizability, transparency and accessibility. To address these gaps, we developed an open-source platform that enables in situ capture of multimodal digital traces from smartphones (for example, moment-by-moment capture of screenshots, application usage logs, interaction histories and phone sensor readings). The Stanford Screenomics Data Collection application allows researchers to tailor data types and quality, data transfer methods and upload cadence. The Dashboard application supports real-time monitoring of participants’ data provision, identification of data issues and automated reactive communications to participants. The platform’s back end uses a NoSQL database for secure, and Health Insurance Portability and Accountability Act-compliant storage. Using illustrative 24-h digital trace data, we demonstrate how the platform expands the range of possible digital phenotyping studies.