**Published** : 13/03/2025 **Last edited** : 13/03/2025 _____ ### **Definition** Digital phenotyping is the moment-by-moment quantification of an individual's human phenotype in situ using data collected from personal digital devices[^1][^2]. This multidisciplinary field of science utilizes both active and passive data collection methods, with passive data being gathered without requiring user participation[^3]. Digital phenotyping primarily relies on smartphones and other wearable devices to capture a wide range of behavioral patterns, social interactions, physical mobility, and physiological markers[^4][^5]. The collected data can include GPS location, accelerometer readings, call and messaging logs, screen behavior, voice modulation, and various other digital biomarkers[^4]. This approach aims to provide objective measurements of human behavior and function in both health and disease states, offering new insights particularly valuable in fields like psychiatry where traditional diagnostic methods often rely on subjective self-reports[^5][^6]. Digital phenotyping has the potential to revolutionize healthcare by enabling early detection of disease onset, monitoring treatment responses, and predicting relapses, especially in mental health conditions[^6][^7]. However, as the field continues to evolve, it faces challenges related to data privacy, security, and the need for standardized methodologies to ensure reliable and clinically relevant outcomes[^2][^5]. ### **Illustration** ```mermaid graph TD D[Digital Phenotyping] D --> E[Mobility Data] D --> F[Social Interactions] D --> G[Typing Patterns] D --> H[Voice Analysis] D --> I[Mental State Inference] I --> J[Cognitive States] I --> K[Affective States] I --> L[Conative States] ``` ## Sources [^1]:Torous, J., Kiang, M. V., Lorme, J., & Onnela, J. P. (2016). New tools for new research in psychiatry: A scalable and customizable platform to empower data driven smartphone research. JMIR Mental Health, 3(2), e16. [^2]:Jain, S. H., Powers, B. W., Hawkins, J. B., & Brownstein, J. S. (2015). The digital phenotype. Nature Biotechnology, 33(5), 462-463. [^3.]:Torous, J., Onnela, J. P., & Keshavan, M. (2017). New dimensions and new tools to realize the potential of RDoC: Digital phenotyping via smartphones and connected devices. Translational Psychiatry, 7(3), e1053. [^4]: Insel, T. R. (2017). Digital phenotyping: Technology for a new science of behavior. JAMA, 318(13), 1215-1216. [^5]:Huckvale, K., Venkatesh, S., & Christensen, H. (2019). Toward clinical digital phenotyping: A timely opportunity to consider purpose, quality, and safety. NPJ Digital Medicine, 2(1), 1-11. [^6]: Raballo, A. (2018). Digital phenotyping: An overarching framework to capture our extended mental states. The Lancet Psychiatry, 5(3), 194-195. [^7]:Jongs, N. (2021). Passive digital phenotyping: Objective quantification of human behaviour through smartphones. University of Groningen.