Tag: Publication

Software Reference Architecture for Real-Time Mobile Digital Phenotyping: Evaluation of System Designs

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...

Using Experience Sampling Methods to Study Identity Dynamics: A 5-Step Guide

This paper provides a five-step practical guide to ESM—full flow from theoretical specification to study design, data collection, data analysis, and results presentation and interpretation—that may be helpful for identity scholars seeking to integrate ESM in their work. In presenting each step, we outline the core goals and decisions involved and provide an illustrative example, describing how specific design decisions were made in a small empirical study of how ethnic-racial and gender identity change across social situations and how the resulting specifics and empirical results might be reported in a journal manuscript.

Beyond apps: a method to model full distributions of screen content over time to study media effects

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...

Time-Related Considerations for Modeling Event-Based Data Collected via Ecological Momentary Assessment

In this article, we provide an overview of survival analysis, describe several time-based considerations for modeling event-based EMA data using survival analysis, and provide several illustrative examples of the different time-based considerations...

Smartphone use among adolescents during school hours: High-intensity objective observations across different instructional contexts

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. This study uses high-intensity smartphone observations from 139 13–17-year-olds (Mage=15.52; 47.2% female)...

Risk Communication in Real Time: Examining Individuals’ Smartphone Use During the 2021 Texas Winter Disaster With Mobile Sensing

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...

The Screenome: Unlocking the New Science of Everyday Life

"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."

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

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)...

What Your Phone Knows Could Help Scientists Understand Your 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."

Time-Related Considerations for Modeling Event-Based Data Collected via Ecological Momentary Assessment

This article provides an overview of survival analysis, describes several time-based considerations for modeling event-based EMA data using survival analysis, and provides several illustrative examples of the different time-based considerations...