Citation

Torous J, Larsen ME, Depp C, Cosco TD, Barnett I, Nock MK, Firth J. Curr. Psychiatry Rep. 2018; 20(7): e51.

Affiliation

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

Copyright

(Copyright © 2018, Holtzbrinck Springer Nature Publishing Group)

DOI

10.1007/s11920-018-0914-y

PMID

29956120

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.

Language: en

Keywords

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