Evidence map›Paper›PMID 27150677›Full record

ArticleJMIR mental health2016

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

Registry-linked trialAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in JMIR mental health, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT05681065 (Affective Based Integrated Care for Better Quality of Life), which is not on this map. Cited by 369 papers, 9 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
369citing papers in PubMed, 9 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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.

NCT05681065 nacompletednot on this mapstarted 2021, after this paper: background citation

Affective Based Integrated Care for Better Quality of Life (TeNDER).Pilot Implementation. Site 1: Primary Care Madrid, Spain.

TypeinterventionalSponsorGerencia de Atención Primaria, MadridRan2021 to 2023Enrolled534ConditionsChronic DiseaseArmsTeNDER tool
3 · Its place in the literature

Who cites it

369 citing papers in PubMed, 9 syntheses or guidelines pooled it.

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309 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

John TorousBrigham and Women's Hospital, Department of Psychiatry, Harvard Medical School, Boston, MA, United States.ORCID http://orcid.org/0000-0002-5362-7937
Mathew V KiangORCID http://orcid.org/0000-0001-9198-150X
Jeanette LormeORCID http://orcid.org/0000-0003-3083-122X
Jukka-Pekka OnnelaORCID http://orcid.org/0000-0001-6613-8668

Funding

Using mobile phones for social and behavioral sensing of mood disorder patientsDP2MH103909 · NIMH · HARVARD SCHOOL OF PUBLIC HEALTH · PI ONNELA, JUKKA-PEKKA · 2013 to 2013
$2.4M
NIMH NIH HHS DP2 MH103909
6 · The paper itself

Abstract

backgroundA 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.

objectiveOur 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.

methodsWe 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.

resultsWe demonstrate the passive data capabilities of the Beiwe platform and early results of its analytical capabilities.

conclusionsSmartphone 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.

Indexed as

evaluationinformaticsmental healthschizophreniasmartphone

Identifiers

PMID27150677
PMCPMC4873624

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.