One-Week Suicide Risk Prediction Using Real-Time Smartphone Monitoring: Prospective Cohort Study
BackgroundSuicide 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...
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| Format: | Article |
| Language: | English |
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JMIR Publications
2023-09-01
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| Series: | Journal of Medical Internet Research |
| Online Access: | https://www.jmir.org/2023/1/e43719 |
| _version_ | 1797729062240649216 |
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| author | Maria Luisa Barrigon Lorena Romero-Medrano Pablo Moreno-Muñoz Alejandro Porras-Segovia Jorge Lopez-Castroman Philippe Courtet Antonio Artés-Rodríguez Enrique Baca-Garcia |
| author_facet | Maria Luisa Barrigon Lorena Romero-Medrano Pablo Moreno-Muñoz Alejandro Porras-Segovia Jorge Lopez-Castroman Philippe Courtet Antonio Artés-Rodríguez Enrique Baca-Garcia |
| author_sort | Maria Luisa Barrigon |
| collection | DOAJ |
| description |
BackgroundSuicide 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.
ObjectiveWe 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.
MethodsWe 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.
ResultsDuring 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.
ConclusionsWe 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. |
| first_indexed | 2024-03-12T11:23:31Z |
| format | Article |
| id | doaj.art-d439d378709b4f869a02e030983c280e |
| institution | Directory Open Access Journal |
| issn | 1438-8871 |
| language | English |
| last_indexed | 2024-03-12T11:23:31Z |
| publishDate | 2023-09-01 |
| publisher | JMIR Publications |
| record_format | Article |
| series | Journal of Medical Internet Research |
| spelling | doaj.art-d439d378709b4f869a02e030983c280e2023-09-01T14:46:30ZengJMIR PublicationsJournal of Medical Internet Research1438-88712023-09-0125e4371910.2196/43719One-Week Suicide Risk Prediction Using Real-Time Smartphone Monitoring: Prospective Cohort StudyMaria Luisa Barrigonhttps://orcid.org/0000-0002-2497-6353Lorena Romero-Medranohttps://orcid.org/0000-0003-1453-0160Pablo Moreno-Muñozhttps://orcid.org/0000-0002-7249-2986Alejandro Porras-Segoviahttps://orcid.org/0000-0003-2019-9099Jorge Lopez-Castromanhttps://orcid.org/0000-0003-0618-0690Philippe Courtethttps://orcid.org/0000-0002-6519-8586Antonio Artés-Rodríguezhttps://orcid.org/0000-0001-6540-7109Enrique Baca-Garciahttps://orcid.org/0000-0002-6963-6555 BackgroundSuicide 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. ObjectiveWe 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. MethodsWe 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. ResultsDuring 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. ConclusionsWe 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.https://www.jmir.org/2023/1/e43719 |
| spellingShingle | Maria Luisa Barrigon Lorena Romero-Medrano Pablo Moreno-Muñoz Alejandro Porras-Segovia Jorge Lopez-Castroman Philippe Courtet Antonio Artés-Rodríguez Enrique Baca-Garcia One-Week Suicide Risk Prediction Using Real-Time Smartphone Monitoring: Prospective Cohort Study Journal of Medical Internet Research |
| title | One-Week Suicide Risk Prediction Using Real-Time Smartphone Monitoring: Prospective Cohort Study |
| title_full | One-Week Suicide Risk Prediction Using Real-Time Smartphone Monitoring: Prospective Cohort Study |
| title_fullStr | One-Week Suicide Risk Prediction Using Real-Time Smartphone Monitoring: Prospective Cohort Study |
| title_full_unstemmed | One-Week Suicide Risk Prediction Using Real-Time Smartphone Monitoring: Prospective Cohort Study |
| title_short | One-Week Suicide Risk Prediction Using Real-Time Smartphone Monitoring: Prospective Cohort Study |
| title_sort | one week suicide risk prediction using real time smartphone monitoring prospective cohort study |
| url | https://www.jmir.org/2023/1/e43719 |
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