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

John Torous, Mathew V Kiang, Jeanette Lorme, Jukka-Pekka Onnela, John Torous, Mathew V Kiang, Jeanette Lorme, Jukka-Pekka Onnela

Abstract

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.

Keywords: evaluation; informatics; mental health; schizophrenia; smartphone.

Conflict of interest statement

Conflicts of Interest: None declared.

Figures

Figure 1
Figure 1
Workflow on the Beiwe platform.
Figure 2
Figure 2
The Beiwe research administrator panel allows researchers to add new patients to a study (top) and create surveys and customize survey deployment (bottom).
Figure 3
Figure 3
Sample accelerometer data collected by Beiwe (24 hours).
Figure 4
Figure 4
Sample GPS data collected by Beiwe over a 5-minute interval.
Figure 5
Figure 5
Sample data showing a record of incoming and outgoing text messages and phone calls recorded by Beiwe (duration of phone calls is noted by length of the corresponding line, and text messages are noted by the + symbol).
Figure 6
Figure 6
Sample Bluetooth data collected by Beiwe demonstrate its ability to detect and log nearby signals over the course of a day.
Figure 7
Figure 7
Beiwe scans for nearby Wi-Fi signals throughout the day and records their hashed MAC addresses and signal strengths.
Figure 8
Figure 8
Voice samples are captured in MP4 file format or as raw uncompressed audio data depending on the intended use case.
Figure 9
Figure 9
Sample screenshots of customizable surveys that Beiwe is programmed to present to subjects.
Figure 10
Figure 10
A schematic of the proposed pilot study for patients with schizophrenia using the Beiwe platform.

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