Transforming Digital Phenotyping Raw Data Into Actionable Biomarkers, Quality Metrics, and Data Visualizations Using Cortex Software Package: Tutorial

As digital phenotyping, the capture of active and passive data from consumer devices such as smartphones, becomes morecommon, the need to properly process the data and derive replicable features from it has become paramount. Cortex is anopen-source data processing pipeline for digital phenotyping data, optimized for use with the mindLAMP apps, which is usedby nearly 100 research teams across the world. Cortex is designed to help teams (1) assess digital phenotyping data quality inreal time, (2) derive replicable clinical features from the data, and (3) enable easy-to-share data visualizations. Cortex offers manyoptions to work with digital phenotyping data, although some common approaches are likely of value to all teams using it. Thispaper highlights the reasoning, code, and example steps necessary to fully work with digital phenotyping data in a streamlinedmanner. Covering how to work with the data, assess its quality, derive features, and visualize findings, this paper is designed tooffer the reader the knowledge and skills to apply toward analyzing any digital phenotyping data set. More specifically, the paperwill teach the reader the ins and outs of the Cortex Python package. This includes background information on its interaction withthe mindLAMP platform, some basic commands to learn what data can be pulled and how, and more advanced use of the packagemixed with basic Python with the goal of creating a correlation matrix. After the tutorial, different use cases of Cortex are discussed,along with limitations. Toward highlighting clinical applications, this paper also provides 3 easy ways to implement examplesof Cortex use in real-world settings. By understanding how to work with digital phenotyping data and providing ready-to-deploycode with Cortex, the paper aims to show how the new field of digital phenotyping can be both accessible to all and rigorous inmethodology.