Format

Send to

Choose Destination
Curr Psychiatry Rep. 2018 Jun 28;20(7):51. doi: 10.1007/s11920-018-0914-y.

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

Author information

1
Department 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.
2
Black Dog Institute, University of New South Wales, Sydney, New South Wales, Australia.
3
Department of Psychiatry, University of California San Diego, La Jolla, CA, USA.
4
Veterans Affairs San Diego Healthcare System, San Diego, CA, USA.
5
Sam and Rose Stein Institute for Research on Aging, University of California San Diego, La Jolla, CA, USA.
6
Oxford Institute of Population Ageing, University of Oxford, Oxford, UK.
7
Department of Biostatistics, University of Pennsylvania, Philadelphia, PA, USA.
8
Department of Psychology, Harvard University, Cambridge, MA, USA.
9
Department of Psychiatry, Harvard Medical School, Cambridge, MA, USA.
10
NICM Health Research Institute, School of Science and Health, University of Western Sydney, Sydney, Australia.
11
Division of Psychology and Mental Health, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK.

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. 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.

KEYWORDS:

Algorithms; Apps; Big data; Machine learning; Mental health; Mobile health; Smartphones; Suicide

PMID:
29956120
DOI:
10.1007/s11920-018-0914-y

Supplemental Content

Full text links

Icon for Springer
Loading ...
Support Center