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

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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.

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Understanding individual human mobility patterns

M. González, A L Barabási, C. Hidalgo (2008)

Despite their importance for urban planning, traffic forecasting, and the spread of biological and mobile viruses, our understanding of the basic laws governing human motion remains limited thanks to the lack of tools to monitor the time resolved location of individuals. Here we study the trajectory of 100,000 anonymized mobile phone users whose position is tracked for a six month period. We find that in contrast with the random trajectories predicted by the prevailing Levy flight and random walk models, human trajectories show a high degree of temporal and spatial regularity, each individual being characterized by a time independent characteristic length scale and a significant probability to return to a few highly frequented locations. After correcting for differences in travel distances and the inherent anisotropy of each trajectory, the individual travel patterns collapse into a single spatial probability distribution, indicating that despite the diversity of their travel history, humans follow simple reproducible patterns. This inherent similarity in travel patterns could impact all phenomena driven by human mobility, from epidemic prevention to emergency response, urban planning and agent based modeling.

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J. McGrath, S. Saha, D. CHANT … (2008)

Recent systematic reviews have encouraged the psychiatric research community to reevaluate the contours of schizophrenia epidemiology. This paper provides a concise overview of three related systematic reviews on the incidence, prevalence, and mortality associated with schizophrenia. The reviews shared key methodological features regarding search strategies, analysis of the distribution of the frequency estimates, and exploration of the influence of key variables (sex, migrant status, urbanicity, secular trend, economic status, and latitude). Contrary to previous interpretations, the incidence of schizophrenia shows prominent variation between sites. The median incidence of schizophrenia was 15.2/100,000 persons, and the central 80% of estimates varied over a fivefold range (7.7-43.0/100,000). The rate ratio for males:females was 1.4:1. Prevalence estimates also show prominent variation. The median lifetime morbid risk for schizophrenia was 7.2/1,000 persons. On the basis of the standardized mortality ratio, people with schizophrenia have a two- to threefold increased risk of dying (median standardized mortality ratio = 2.6 for all-cause mortality), and this differential gap in mortality has increased over recent decades. Compared with native-born individuals, migrants have an increased incidence and prevalence of schizophrenia. Exposures related to urbanicity, economic status, and latitude are also associated with various frequency measures. In conclusion, the epidemiology of schizophrenia is characterized by prominent variability and gradients that can help guide future research.

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Ecological momentary assessment.

Saul Shiffman, Arthur Stone, Michael R. Hufford (2008)

Assessment in clinical psychology typically relies on global retrospective self-reports collected at research or clinic visits, which are limited by recall bias and are not well suited to address how behavior changes over time and across contexts. Ecological momentary assessment (EMA) involves repeated sampling of subjects' current behaviors and experiences in real time, in subjects' natural environments. EMA aims to minimize recall bias, maximize ecological validity, and allow study of microprocesses that influence behavior in real-world contexts. EMA studies assess particular events in subjects' lives or assess subjects at periodic intervals, often by random time sampling, using technologies ranging from written diaries and telephones to electronic diaries and physiological sensors. We discuss the rationale for EMA, EMA designs, methodological and practical issues, and comparisons of EMA and recall data. EMA holds unique promise to advance the science and practice of clinical psychology by shedding light on the dynamics of behavior in real-world settings.

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Author and article information

Contributors

Jukka-Pekka Onnela:

Department of BiostatisticsHarvard TH Chan School of Public HealthHarvard UniversityBuilding 2, Room 423655 Huntington AvenueBoston, MA, MAUnited States1 617 495 10001 617 432 8077onnela@hsph.harvard.edu

ORCID: http://orcid.org/0000-0001-6613-8668

Journal

Journal ID (nlm-ta): JMIR Ment Health

Journal ID (iso-abbrev): JMIR Ment Health

Journal ID (publisher-id): JMH

Title: JMIR Mental Health

Publisher: JMIR Publications Inc. (Toronto, Canada )

ISSN (Electronic): 2368-7959

Publication date Collection: Apr-Jun 2016

Publication date (Electronic): 05 May 2016

Volume: 3

Issue: 2

Electronic Location Identifier: e16

Affiliations

[1] ¹Brigham and Women's Hospital Department of Psychiatry Harvard Medical School Boston, MAUnited States

[2] ²Beth Israel Deaconess Medical Center Department of Psychiatry Harvard Medical School Boston, MAUnited States

[3] ³Department of Social and Behavioral Sciences Harvard TH Chan School of Public Health Harvard University Boston, MAUnited States

[4] ⁴Department of Biostatistics Harvard TH Chan School of Public Health Harvard University Boston, MAUnited States

Author notes

Corresponding Author: Jukka-Pekka Onnela onnela@ 123456hsph.harvard.edu

Author information

John Torous http://orcid.org/0000-0002-5362-7937

Mathew V Kiang http://orcid.org/0000-0001-9198-150X

Jeanette Lorme http://orcid.org/0000-0003-3083-122X

Jukka-Pekka Onnela http://orcid.org/0000-0001-6613-8668

Article

Publisher ID: v3i2e16

DOI: 10.2196/mental.5165

PMC ID: 4873624

PubMed ID: 27150677

SO-VID: 3b98dd03-a3da-4c0d-bd1d-ecb2a7dfe811

License:

This is an open-access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/2.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Mental Health, is properly cited. The complete bibliographic information, a link to the original publication on http://mental.jmir.org/, as well as this copyright and license information must be included.

History

Date received : 24 September 2015

Date revision requested : 12 November 2015

Date revision received : 22 December 2015

Date accepted : 21 January 2016

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