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

  • Psychiatry in the Digital Age (J Shore, Section Editor)
  • Published:
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Abstract

Purpose of Review

As 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 Findings

Advances 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.

Summary

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.

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Author information

Authors and Affiliations

  1. Department of Psychiatry and Division of Digital Psychiatry, Beth Israel Deaconess Medical Center, Harvard Medical School, 330 Brookline Ave, Boston, MA, 02115, USA

    John Torous

  2. Black Dog Institute, University of New South Wales, Sydney, New South Wales, Australia

    Mark E. Larsen

  3. Department of Psychiatry, University of California San Diego, La Jolla, CA, USA

    Colin Depp

  4. Veterans Affairs San Diego Healthcare System, San Diego, CA, USA

    Colin Depp

  5. Sam and Rose Stein Institute for Research on Aging, University of California San Diego, La Jolla, CA, USA

    Colin Depp

  6. Oxford Institute of Population Ageing, University of Oxford, Oxford, UK

    Theodore D. Cosco

  7. Department of Biostatistics, University of Pennsylvania, Philadelphia, PA, USA

    Ian Barnett

  8. Department of Psychology, Harvard University, Cambridge, MA, USA

    Matthew K. Nock

  9. Department of Psychiatry, Harvard Medical School, Cambridge, MA, USA

    Matthew K. Nock

  10. NICM Health Research Institute, School of Science and Health, University of Western Sydney, Sydney, Australia

    Joe Firth

  11. Division of Psychology and Mental Health, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK

    Joe Firth

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  1. John Torous
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Corresponding author

Correspondence to John Torous.

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Conflict of Interest

John Torous, Colin Depp, Theodore D. Cosco, Ian Barnett, Matthew K. Nock, and Joe Firth declare that they have no conflict of interest.

Mark E. Larsen reports a grant from the National Health and Medical Research Council.

Human and Animal Rights and Informed Consent

This article does not contain any studies with human or animal subjects performed by any of the authors.

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This article is part of the Topical Collection on Psychiatry in the Digital Age

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Torous, J., Larsen, M.E., Depp, C. et al. Smartphones, Sensors, and Machine Learning to Advance Real-Time Prediction and Interventions for Suicide Prevention: a Review of Current Progress and Next Steps. Curr Psychiatry Rep 20, 51 (2018). https://doi.org/10.1007/s11920-018-0914-y

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  • DOI: https://doi.org/10.1007/s11920-018-0914-y

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