Evidence map›Paper›PMID 29511333›Full record

ArticleNeuropsychopharmacology : official publication of the American College of Neuropsychopharmacology2018

Relapse prediction in schizophrenia through digital phenotyping: a pilot study.

Ian Barnett, John Torous, Patrick Staples, Luis Sandoval, Matcheri Keshavan, Jukka-Pekka Onnela

2 registry-linked trialsAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to 2 registered trials, which are not on this map. Cited by 197 papers, 13 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
197citing papers in PubMed, 13 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.

NCT04279483 nacompletednot on this mapstarted 2019, after this paper: background citation

Geo Smoking Study and Neural and Geospatial Examination of Smokers' Exposure to Retail Environments

TypeinterventionalSponsorAbramson Cancer Center at Penn MedicineRan2019 to 2024Enrolled343ConditionsSmoking, Tobacco, Smoking, Smoking, CigaretteArmsTobacco retailer, Non-tobacco retailer
NCT06676059 recruitingnot on this mapstarted 2026, after this paper: background citation

SMART-r: Substance Monitoring and Active Relapse Tracking Repository

TypeobservationalSponsorNational Institute on Drug Abuse (NIDA)Ran2026 to 2044Enrolled10,000ConditionsAlcoholism, Substance-Related Disorders
3 · Its place in the literature

Who cites it

197 citing papers in PubMed, 13 syntheses or guidelines pooled it.

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  13. Digital biomarkers from geolocation data in bipolar disorder and schizophrenia: a systematic review.Journal of the American Medical Informatics Association : JAMIA · 2019
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137 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

6 authors.

Ian BarnettDepartment of Biostatistics, University of Pennsylvania, Philadelphia, PA, USA. ibarnett@pennmedicine.upenn.edu.
John TorousDepartment of Psychiatry, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA.
Patrick StaplesDepartment of Biostatistics, Harvard TH Chan School of Public Health, Boston, MA, USA.
Luis SandovalDepartment of Psychiatry, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA.
Matcheri KeshavanDepartment of Psychiatry, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA.
Jukka-Pekka OnnelaDepartment of Biostatistics, Harvard TH Chan School of Public Health, Boston, MA, USA.

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

Among individuals diagnosed, hospitalized, and treated for schizophrenia, up to 40% of those discharged may relapse within 1 year even with appropriate treatment. Passively collected smartphone behavioral data present a scalable and at present underutilized opportunity to monitor patients in order to identify possible warning signs of relapse. Seventeen patients with schizophrenia in active treatment at a state mental health clinic in Boston used the Beiwe app on their personal smartphone for up to 3 months. By testing for changes in mobility patterns and social behavior over time as measured through smartphone use, we were able to identify statistically significant anomalies in patient behavior in the days prior to relapse. We found that the rate of behavioral anomalies detected in the 2 weeks prior to relapse was 71% higher than the rate of anomalies during other time periods. Our findings show how passive smartphone data, data collected in the background during regular phone use without active input from the subjects, can provide an unprecedented and detailed view into patient behavior outside the clinic. Real-time detection of behavioral anomalies could signal the need for an intervention before an escalation of symptoms and relapse occur, therefore reducing patient suffering and reducing the cost of care.

Indexed as

Diagnosis, Computer-AssistedMobile ApplicationsSmartphoneTelemedicineHumansMotor ActivityPatient Reported Outcome MeasuresPilot ProjectsPrognosisRecurrenceSchizophreniaSocial Behavior

Identifiers

PMID29511333
PMCPMC6006347

What OpenQuestion holds

Textmetadata
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.