One-Week Suicide Risk Prediction Using Real-Time Smartphone Monitoring: Prospective Cohort Study.

Autor: Barrigon ML; Department of Psychiatry, Jimenez Diaz Foundation University Hospital, Madrid, Spain.; Institute of Psychiatry and Mental Health, Hospital General Universitario Gregorio Marañón, Madrid, Spain., Romero-Medrano L; Department of Signal Theory and Communications, Universidad Carlos III de Madrid, Madrid, Spain.; Evidence-Based Behavior (eB2), Madrid, Spain., Moreno-Muñoz P; Department of Signal Theory and Communications, Universidad Carlos III de Madrid, Madrid, Spain.; Cognitive Systems Section, Technical University of Denmark, Lyngby, Denmark., Porras-Segovia A; Department of Psychiatry, Jimenez Diaz Foundation University Hospital, Madrid, Spain., Lopez-Castroman J; Department of Signal Theory and Communications, Universidad Carlos III de Madrid, Madrid, Spain.; Department of Psychiatry, Centre Hospitalier Universitaire Nîmes, Nîmes, France.; Institut de Génomique Fonctionnelle, CNRS-INSERM, University of Montpellier, Montpellier, France., Courtet P; Institut de Génomique Fonctionnelle, CNRS-INSERM, University of Montpellier, Montpellier, France.; Department of Emergency Psychiatry and Acute Care, Centre Hospitalier Universitaire, Montpellier, France., Artés-Rodríguez A; Department of Signal Theory and Communications, Universidad Carlos III de Madrid, Madrid, Spain.; Evidence-Based Behavior (eB2), Madrid, Spain.; Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Carlos III Institute of Health, Madrid, Spain.; Instituto de Investigacion Sanitaria Gregorio Marañón, Madrid, Spain., Baca-Garcia E; Department of Psychiatry, Jimenez Diaz Foundation University Hospital, Madrid, Spain.; Evidence-Based Behavior (eB2), Madrid, Spain.; Department of Psychiatry, Centre Hospitalier Universitaire Nîmes, Nîmes, France.; Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Carlos III Institute of Health, Madrid, Spain.; Department of Psychiatry, Autonomous University of Madrid, Madrid, Spain.; Department of Psychiatry, Rey Juan Carlos University Hospital, Móstoles, Madrid, Spain.; Department of Psychiatry, General Hospital of Villalba, Madrid, Spain.; Department of Psychiatry, Infanta Elena University Hospital, Valdemoro, Madrid, Spain.; Department of Psychology, Universidad Catolica del Maule, Talca, Chile.
Jazyk: angličtina
Zdroj: Journal of medical Internet research [J Med Internet Res] 2023 Sep 01; Vol. 25, pp. e43719. Date of Electronic Publication: 2023 Sep 01.
DOI: 10.2196/43719
Abstrakt: 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.
(©Maria Luisa Barrigon, Lorena Romero-Medrano, Pablo Moreno-Muñoz, Alejandro Porras-Segovia, Jorge Lopez-Castroman, Philippe Courtet, Antonio Artés-Rodríguez, Enrique Baca-Garcia. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 01.09.2023.)
Databáze: MEDLINE
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