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Barrigon, Maria Luisa; Romero-Medrano, Lorena; Moreno-Muñoz, Pablo; Porras-Segovia, Alejandro; Lopez-Castroman, Jorge; Courtet, Philippe; Artés-Rodríguez, Antonio; Baca-Garcia, Enrique
One-Week Suicide Risk Prediction Using Real-Time Smartphone Monitoring: Prospective Cohort Study Artículo de revista
En: J Med Internet Res, vol. 25, pp. e43719, 0000, ISSN: 1438-8871.
Resumen | Enlaces | BibTeX | Etiquetas: e-health; m-health; Ecological Mometary Asssessment; risk prediction; sensor monitoring; suicidal; suicide attempt; suicide
@article{info:doi/10.2196/43719,
title = {One-Week Suicide Risk Prediction Using Real-Time Smartphone Monitoring: Prospective Cohort Study},
author = {Maria Luisa Barrigon and Lorena Romero-Medrano and Pablo Moreno-Mu\~{n}oz and Alejandro Porras-Segovia and Jorge Lopez-Castroman and Philippe Courtet and Antonio Art\'{e}s-Rodr\'{i}guez and Enrique Baca-Garcia},
url = {http://www.ncbi.nlm.nih.gov/pubmed/37656498},
doi = {10.2196/43719},
issn = {1438-8871},
journal = {J Med Internet Res},
volume = {25},
pages = {e43719},
abstract = {Background: Suicide is a major global public health issue that is becoming increasingly common despite preventive efforts. Though current methods for predicting suicide risk are not sufficiently accurate, technological advances provide invaluable tools with which we may evolve toward a personalized, predictive approach. Objective: We aim to predict the short-term (1-week) risk of suicide by identifying changes in behavioral patterns characterized through real-time smartphone monitoring in a cohort of patients with suicidal ideation. Methods: We recruited 225 patients between February 2018 and March 2020 with a history of suicidal thoughts and behavior as part of the multicenter SmartCrisis study. Throughout 6 months of follow-up, we collected information on the risk of suicide or mental health crises. All participants underwent voluntary passive monitoring using data generated by their own smartphones, including distance walked and steps taken, time spent at home, and app usage. The algorithm constructs daily activity profiles for each patient according to these data and detects changes in the distribution of these profiles over time. Such changes are considered critical periods, and their relationship with suicide-risk events was tested. Results: During follow-up, 18 (8%) participants attempted suicide, and 14 (6.2%) presented to the emergency department for psychiatric care. The behavioral changes identified by the algorithm predicted suicide risk in a time frame of 1 week with an area under the curve of 0.78, indicating good accuracy. Conclusions: We describe an innovative method to identify mental health crises based on passively collected information from patients' smartphones. This technology could be applied to homogeneous groups of patients to identify different types of crises.},
keywords = {e-health; m-health; Ecological Mometary Asssessment; risk prediction; sensor monitoring; suicidal; suicide attempt; suicide},
pubstate = {published},
tppubtype = {article}
}
Background: Suicide is a major global public health issue that is becoming increasingly common despite preventive efforts. Though current methods for predicting suicide risk are not sufficiently accurate, technological advances provide invaluable tools with which we may evolve toward a personalized, predictive approach. Objective: We aim to predict the short-term (1-week) risk of suicide by identifying changes in behavioral patterns characterized through real-time smartphone monitoring in a cohort of patients with suicidal ideation. Methods: We recruited 225 patients between February 2018 and March 2020 with a history of suicidal thoughts and behavior as part of the multicenter SmartCrisis study. Throughout 6 months of follow-up, we collected information on the risk of suicide or mental health crises. All participants underwent voluntary passive monitoring using data generated by their own smartphones, including distance walked and steps taken, time spent at home, and app usage. The algorithm constructs daily activity profiles for each patient according to these data and detects changes in the distribution of these profiles over time. Such changes are considered critical periods, and their relationship with suicide-risk events was tested. Results: During follow-up, 18 (8%) participants attempted suicide, and 14 (6.2%) presented to the emergency department for psychiatric care. The behavioral changes identified by the algorithm predicted suicide risk in a time frame of 1 week with an area under the curve of 0.78, indicating good accuracy. Conclusions: We describe an innovative method to identify mental health crises based on passively collected information from patients' smartphones. This technology could be applied to homogeneous groups of patients to identify different types of crises.