New Tools for New Research in Psychiatry: A Scalable and Customizable Platform to Empower Data Driven Smartphone Research

By John B. Torous, M. Kiang, Jeanette Lorme, Jukka-Pekka Onnela. JMIR Mental Health, 2016

671 citations recorded by the source corpus.

Background A longstanding barrier to progress in psychiatry, both in clinical settings and research trials, has been the persistent difficulty of accurately and reliably quantifying disease phenotypes. Mobile phone technology combined with data science has the potential to offer medicine a wealth of additional information on disease phenotypes, but the large majority of existing smartphone apps are not intended for use as biomedical research platforms and, as such, do not generate research-quality data. Objective Our aim is not the creation of yet another app per se but rather the establishment of a platform to collect research-quality smartphone raw sensor and usage pattern data. Our ultimate goal is to develop statistical, mathematical, and computational methodology to enable us and others to extract biomedical and clinical insights from smartphone data. Methods We report on the development and early testing of Beiwe, a research platform featuring a study portal, smartphone app, database, and data modeling and analysis tools designed and developed specifically for transparent, customizable, and reproducible biomedical research use, in particular for the study of psychiatric and neurological disorders. We also outline a proposed study using the platform for patients with schizophrenia. Results We demonstrate the passive data capabilities of the Beiwe platform and early results of its analytical capabilities. Conclusions Smartphone sensors and phone usage patterns, when coupled with appropriate statistical learning tools, are able to capture various social and behavioral manifestations of illnesses, in naturalistic settings, as lived and experienced by patients. The ubiquity of smartphones makes this type of moment-by-moment quantification of disease phenotypes highly scalable and, when integrated within a transparent research platform, presents tremendous opportunities for research, discovery, and patient health.

Subfields: Applied Psychology, General Health Professions, Health, Toxicology and Mutagenesis

View Paper: https://www.semanticscholar.org/paper/44996ae0e429c558c4b4f02afab52deadfbe753f

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In Applied Psychology

In General Health Professions

Citations in the corpus

Relapse prediction in schizophrenia through digital phenotyping: a pilot study (2018, methodology): “While still nascent, smartphone interventions for schizophrenia offer an immediate target for relapse prediction systems [19].”
Personal Sensing: Understanding Mental Health Using Ubiquitous Sensors and Machine Learning (2017, background): “a be as reality mining (Eagle & Pentland 2006), personal informatics (Li et al. 2010), digital phenotyping (Jain et al. 2015, Torous et al. 2016), and personal sensing (Klasnja et al. 2009).”