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.
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.
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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Papers of particular interest, published recently, have been highlighted as: • Of importance •• Of major importance
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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.
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
- Mobile health
- Big data
- Machine learning
- Mental health