Maintaining High Passive Data Quality in Digital Phenotyping Studies: Insights From A Large Observational Study

Achieving high-quality passive data in smartphone-based digital phenotyping studies remain a significant challenge, particularly in real-world settings where technical barriers and inconsistent user engagement limit continuous data collection. In response to the field’s overreliance on imputation and model-based approaches which can bias derived features and distort associations with clinical outcomes, we introduce the LINC Framework. LINC, which stands for Launch, Interact, Notify, and Correct, is a practical, tool-driven, and easily implementable approach for improving passive data quality in digital phenotyping research. The framework is organized around four central domains: (a) device and app configuration, (b) participant engagement, (c) real-time data monitoring, and (d) troubleshooting disruptions in passive data collection. Each component is supported by tangible resources and strategies that can be readily adopted by other research teams, effectively linking researchers and industry partners to actionable tools. Applied to a two- to three-week observational study, the framework yielded encouraging results with nearly half the cohort achieving a GPS-based passive data quality of 80%, and over a third exceeding 90%. These findings demonstrate the feasibility of attaining high-quality passive data using a structured, framework-based approach and offer a scalable solution for improving passive data collection in digital phenotyping studies.