Smartphones, Sensors, and Machine Learning to Advance Real-Time Prediction and Interventions for Suicide Prevention: a Review of Current Progress and Next Steps
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 t...
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| Published in | Current psychiatry reports Vol. 20; no. 7; p. 51 |
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| Main Authors | , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
New York
Springer US
01.07.2018
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| Subjects | |
| Online Access | Get full text |
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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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| AbstractList | 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. 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. 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. |
| ArticleNumber | 51 |
| Author | Torous, John Nock, Matthew K. Firth, Joe Larsen, Mark E. Cosco, Theodore D. Depp, Colin Barnett, Ian |
| Author_xml | – sequence: 1 givenname: John surname: Torous fullname: Torous, John email: jtorous@bidmc.harvard.edu organization: Department of Psychiatry and Division of Digital Psychiatry, Beth Israel Deaconess Medical Center, Harvard Medical School – sequence: 2 givenname: Mark E. surname: Larsen fullname: Larsen, Mark E. organization: Black Dog Institute, University of New South Wales – sequence: 3 givenname: Colin surname: Depp fullname: Depp, Colin organization: Department of Psychiatry, University of California San Diego, Veterans Affairs San Diego Healthcare System, Sam and Rose Stein Institute for Research on Aging, University of California San Diego – sequence: 4 givenname: Theodore D. surname: Cosco fullname: Cosco, Theodore D. organization: Oxford Institute of Population Ageing, University of Oxford – sequence: 5 givenname: Ian surname: Barnett fullname: Barnett, Ian organization: Department of Biostatistics, University of Pennsylvania – sequence: 6 givenname: Matthew K. surname: Nock fullname: Nock, Matthew K. organization: Department of Psychology, Harvard University, Department of Psychiatry, Harvard Medical School – sequence: 7 givenname: Joe surname: Firth fullname: Firth, Joe organization: NICM Health Research Institute, School of Science and Health, University of Western Sydney, Division of Psychology and Mental Health, Faculty of Biology, Medicine and Health, University of Manchester |
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| Keywords | Smartphones Algorithms Mobile health Machine learning Mental health Big data Suicide Apps |
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| Title | Smartphones, Sensors, and Machine Learning to Advance Real-Time Prediction and Interventions for Suicide Prevention: a Review of Current Progress and Next Steps |
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