ArticleJournal of medical Internet research2024
Transforming Digital Phenotyping Raw Data Into Actionable Biomarkers, Quality Metrics, and Data Visualizations Using Cortex Software Package: Tutorial.
Article in Journal of medical Internet research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
13 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Interindividual Sleep Variability Across the Psychosis Spectrum: A Systematic Review and Meta-Analysis.JAMA network open · 2026Pooled it
- Building the foundations for global data banking in digital phenotyping for mental health.NPP - digital psychiatry and neuroscience · 2026Review
- Real-Time Smartphone Monitoring Assessments as a Cognitive Biomarker of Alzheimer Disease: Protocol for a Development Study.JMIR research protocols · 2026Article
- Comprehensive Model for Mental health Access and service use (CoMMA): A process model for technology-enhanced mental healthcare.Internet interventions · 2026Review
- Spezi Data Pipeline: Streamlining FHIR-based interoperable digital health data workflows.NPJ digital medicine · 2026Article
- Integrating Data Visualizations Into Digital Mental Health Care for Adults With Anxiety and Depression: Participatory Design and Case Study.Journal of participatory medicine · 2026Article
- Barriers and facilitators to usability of a smartphone-based digital mental health tool in older adults: Insights from a secondary analysis of mindLAMP.International psychogeriatrics · 2026Article
- LINC: a framework for maintaining high-quality passive data in digital phenotyping studies.Scientific reports · 2026Article
- AI-enabled passive digital phenotyping of experiential negative symptoms in schizophrenia-spectrum disorders: a narrative review on the path from sensing to clinical endpoints.Frontiers in psychiatry · 2026Review
- Social Media Detox and Youth Mental Health.JAMA network open · 2025Observational
- Interpreting psychiatric digital phenotyping data with large language models: a preliminary analysis.BMJ mental health · 2025Article
- Integrating Artificial Intelligence and Smartphone Technology to Enhance Personalized Assessment and Treatment for Eating Disorders.The International journal of eating disorders · 2025Review
- Prioritizing clinical data for psychiatric inpatient dashboards: insights from a nationwide survey of German university centers.Frontiers in digital health · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
As digital phenotyping, the capture of active and passive data from consumer devices such as smartphones, becomes more common, the need to properly process the data and derive replicable features from it has become paramount. Cortex is an open-source data processing pipeline for digital phenotyping data, optimized for use with the mindLAMP apps, which is used by nearly 100 research teams across the world. Cortex is designed to help teams (1) assess digital phenotyping data quality in real time, (2) derive replicable clinical features from the data, and (3) enable easy-to-share data visualizations. Cortex offers many options to work with digital phenotyping data, although some common approaches are likely of value to all teams using it. This paper highlights the reasoning, code, and example steps necessary to fully work with digital phenotyping data in a streamlined manner. Covering how to work with the data, assess its quality, derive features, and visualize findings, this paper is designed to offer the reader the knowledge and skills to apply toward analyzing any digital phenotyping data set. More specifically, the paper will teach the reader the ins and outs of the Cortex Python package. This includes background information on its interaction with the mindLAMP platform, some basic commands to learn what data can be pulled and how, and more advanced use of the package mixed 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 examples of Cortex use in real-world settings. By understanding how to work with digital phenotyping data and providing ready-to-deploy code with Cortex, the paper aims to show how the new field of digital phenotyping can be both accessible to all and rigorous in methodology.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.