Evidence map›Paper›PMID 29956120›Full record

ReviewCurrent psychiatry reports2018

Smartphones, Sensors, and Machine Learning to Advance Real-Time Prediction and Interventions for Suicide Prevention: a Review of Current Progress and Next Steps.

John Torous, Mark E Larsen, Colin Depp, Theodore D Cosco, Ian Barnett, Matthew K Nock, Joe Firth

Abstract readReview
PubMed Publisher
In one paragraph

Review in Current psychiatry reports, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 93 papers, 6 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
93citing papers in PubMed, 6 pooled it
17.5field-weighted citation impact, top 1% of its field
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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

93 citing papers in PubMed, 6 syntheses or guidelines pooled it, 213 citations in OpenAlex.

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  11. Digital doppelgangers in psychiatry.Discover mental health · 2026
    Review
  12. Article
  13. Innovations to improve outcomes and uptake of psychotherapies for mental disorders: a state-of-the-art review.World psychiatry : official journal of the World Psychiatric Association (WPA) · 2026
    Article
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33 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

7 authors at 7 institutions in 3 countries.

John TorousDepartment of Psychiatry and Division of Digital Psychiatry, Beth Israel Deaconess Medical Center, Harvard Medical School, 330 Brookline Ave, Boston, MA, 02115, USA. jtorous@bidmc.harvard.edu.
Mark E LarsenBlack Dog Institute, University of New South Wales, Sydney, New South Wales, Australia.
Colin DeppDepartment of Psychiatry, University of California San Diego, La Jolla, CA, USA.
Theodore D CoscoOxford Institute of Population Ageing, University of Oxford, Oxford, UK.
Ian BarnettDepartment of Biostatistics, University of Pennsylvania, Philadelphia, PA, USA.
Matthew K NockDepartment of Psychology, Harvard University, Cambridge, MA, USA.
Joe FirthNICM Health Research Institute, School of Science and Health, University of Western Sydney, Sydney, Australia.
Beth Israel Deaconess Medical Center · USHarvard University · USInstitute on Aging · USUniversity of Oxford · GBUniversity of Pennsylvania · USUNSW Sydney · AUWestern Sydney University · AU

Funding

Digital Phenotyping for Computational Models of Relapse Prediction in Early Course PsychosisK23MH116130 · NIMH · BETH ISRAEL DEACONESS MEDICAL CENTER · PI TOROUS, JOHN · 2018 to 2021
$872k
6 · The paper itself

Abstract

purpose of reviewAs rates of suicide continue to rise, there is urgent need for innovative approaches to better understand, predict, and care for those at high risk of suicide. Numerous mobile and sensor technology solutions have already been proposed, are in development, or are already available today. This review seeks to assess their clinical evidence and help the reader understand the current state of the field. RECENT

findingsAdvances in smartphone sensing, machine learning methods, and mobile apps directed towards reducing suicide offer promising evidence; however, most of these innovative approaches are still nascent. Further replication and validation of preliminary results is needed. Whereas numerous promising mobile and sensor technology based solutions for real time understanding, predicting, and caring for those at highest risk of suicide are being studied today, their clinical utility remains largely unproven. However, given both the rapid pace and vast scale of current research efforts, we expect clinicians will soon see useful and impactful digital tools for this space within the next 2 to 5 years.

Indexed as

SmartphoneSuicide PreventionHumansMachine LearningMobile ApplicationsAlgorithmsAppsBig dataMachine learningMental healthMobile healthSmartphonesSuicide

Identifiers

PMID29956120
OpenAlexW2810500942

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.