Alejandro Porras-Segovia1, Aurora Cobo2, Isaac Díaz-Oliván3, Antonio Artés-Rodríguez2, Sofian Berrouiguet4, Jorge Lopez-Castroman5, Philippe Courtet6, Maria Luisa Barrigón7, María A Oquendo8, Enrique Baca-García9. 1. Instituto de Investigación Sanitaria Fundación Jiménez Díaz, Madrid, Spain. 2. Department of Signal Theory, Universidad Carlos III de Madrid, Leganés, Spain; Instituto de Investigación Sanitaria Gregorio Marañón, Madrid, Spain. 3. Instituto de Investigación Sanitaria Fundación Jiménez Díaz, Madrid, Spain; Universidad Autónoma de Madrid. 4. Department of Psychiatry, Centre Hospitalier Universitaire De Brest, Brest, France. 5. University of Montpellier & INSERM u1061, Montpellier, France; Nimes University Hospital, Nimes, France; CIBERSAM, Spain. 6. University of Montpellier & INSERM u1061, Montpellier, France. 7. Instituto de Investigación Sanitaria Fundación Jiménez Díaz, Madrid, Spain; Universidad Autónoma de Madrid; Department of Psychiatry, Hospital Universitario Fundación Jiménez Díaz, Madrid, Spain. 8. Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, USA. 9. Instituto de Investigación Sanitaria Fundación Jiménez Díaz, Madrid, Spain; Department of Signal Theory, Universidad Carlos III de Madrid, Leganés, Spain; Instituto de Investigación Sanitaria Gregorio Marañón, Madrid, Spain.; Universidad Autónoma de Madrid; Department of Psychiatry, Hospital Universitario Fundación Jiménez Díaz, Madrid, Spain; Department of Psychiatry, Hospital Universitario Central de Villalba, Madrid; Department of Psychiatry, Hospital Universitario Infanta Elena, Valdemoro, Madrid; Department of Psychiatry, Hospital Universitario Rey Juan Carlos, Móstoles, Madrid; Universidad Católica del Maule, Talca, Chile. Electronic address: Madrid.ebacgar2@yahoo.es.
Abstract
BACKGROUND: Smartphone monitoring could contribute to the elucidation of the correlates of suicidal thoughts and behaviors (STB). In this study, we employ smartphone monitoring and machine learning techniques to explore the association of wish to die (passive suicidal ideation) with disturbed sleep, altered appetite and negative feelings. METHODS: This is a prospective cohort study carried out among adult psychiatric outpatients with a history of STB. A daily questionnaire was administered through the MEmind smartphone application. Participants were followed-up for a median of 89.8 days, resulting in 9,878 person-days. Data analysis employed a machine learning technique called Indian Buffet Process. RESULTS: 165 patients were recruited, 139 had the MEmind mobile application installed on their smartphone, and 110 answered questions regularly enough to be included in the final analysis. We found that the combination of wish to die and sleep problems was one of the most relevant latent features found across the sample, showing that these variables tend to be present during the same time frame (96 hours). CONCLUSIONS: Disturbed sleep emerges as a potential clinical marker for passive suicidal ideation. Our findings stress the importance of evaluating sleep as part of the screening for suicidal behavior. Compared to previous smartphone monitoring studies on suicidal behavior, this study includes a long follow-up period and a large sample.
BACKGROUND: Smartphone monitoring could contribute to the elucidation of the correlates of suicidal thoughts and behaviors (STB). In this study, we employ smartphone monitoring and machine learning techniques to explore the association of wish to die (passive suicidal ideation) with disturbed sleep, altered appetite and negative feelings. METHODS: This is a prospective cohort study carried out among adult psychiatric outpatients with a history of STB. A daily questionnaire was administered through the MEmind smartphone application. Participants were followed-up for a median of 89.8 days, resulting in 9,878 person-days. Data analysis employed a machine learning technique called Indian Buffet Process. RESULTS: 165 patients were recruited, 139 had the MEmind mobile application installed on their smartphone, and 110 answered questions regularly enough to be included in the final analysis. We found that the combination of wish to die and sleep problems was one of the most relevant latent features found across the sample, showing that these variables tend to be present during the same time frame (96 hours). CONCLUSIONS: Disturbed sleep emerges as a potential clinical marker for passive suicidal ideation. Our findings stress the importance of evaluating sleep as part of the screening for suicidal behavior. Compared to previous smartphone monitoring studies on suicidal behavior, this study includes a long follow-up period and a large sample.