We propose the Stanford Screenomics platform as a software reference architecture that uses a modular design to integrate parallel processing and edge computing, enabling scalable, real-time digital phenotyping on smartphones...

We introduce smartphone screen content patterns (content patterns) as a new construct and propose an analytical framework that represents smartphone use as distributions of experienced screen content rather than summaries of time spent on devices or in apps. We operationalize content patterns using deep learning embeddings of moment-by-moment screenshots and formalize use periods as probability distributions over embedding space...

Adolescent smartphone use in school and how it shapes learning and development have attracted substantial attention. Yet, objective data on digital behavior during school hours are limited...

In this study, we provide a detailed description of how individuals encounter risk communication during extreme weather events by analyzing comprehensive records of all the content that 11 adults viewed on their smartphones (n = 162,418 screenshots collected via mobile sensing) over 5 days of the winter disaster that struck Texas in February 2021. Participants viewed substantial amounts of risk communication, which comprised, on average, 21% of their total smartphone use during the winter disaster...

"Health science has mapped our genome, microbiome, and environment; but everyday behavior has remained largely invisible. This post explores the "Screenome," a new frontier that captures the digital traces of daily life and opens a powerful lens on how behavior shapes health."

"Stanford scientists have released an open-source platform that lets health researchers study the “screenome” – the digital traces of our daily lives – while protecting participants’ privacy."

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)...

"In this episode of School’s In, Professor Nilam Ram discusses the Human Screenome Project, a research study that explores cell phone usage and what it says about the way we process information."

The Stanford Screenomics Project introduces an open-source platform for Screenomics
research, enabling in-situ, real-time capture of multimodal digital traces from users’ smartphones as they go about their everyday lives.

Media are critically important in areas of wellbeing, democracy, poverty, violence, education, climate action and more.
But in spite of big data promises, no one really knows what people actually see and do on their screens in an increasingly complex digital world.