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Over the past twenty years, research in psychiatry has focused primarily on the early detection of schizophrenia. The objective has been to engage the patient with prodromal symptoms in a trajectory of care. It has also been a question of being able to offer treatment as soon as the patient "at risk" of schizophrenia triggered a possible first psychotic episode. Standardized clinical tools were developed and now allow identification of subjects at risk of developing psychotic disorders. However, the reliability of predictions of the psychotic transition, which is between 15 and 25%, remains insufficient. In order to improve care, it is now necessary to highlight markers to refine the prediction of the risk of developing schizophrenia. Some teams are trying to identify linguistic anomalies in UHR subjects (disorganized speech, illogical thoughts, poor speech, altered semantic verbal fluencies…). Some of these abnormalities could be specific to the transition to psychosis. The severity of these markers could be proportional to the progressive stage of the disorder, consistent with the hypothesis of a continuum from normal to pathological in schizophrenia. In addition, automated speech analysis techniques in UHR subjects allow identification of subtle semantic and syntactic anomalies (a decrease in semantic coherence, but also the use of possessive pronouns and a poverty of speech) predictive in 79% of cases of psychotic transition. Some authors demonstrate the value of using linguistic markers and automated speech analysis methods to improve the predictive model of the transition to schizophrenia. However, from reification of language to desubjectification of the individual, this transformation in clinical practice raises ethical and epistemological challenges.

BACKGROUND Schizophrenia is a disease associated with a high burden. Improvement in care is necessary. OBJECTIVE The objective of our study is to analyse the current capabilities of artificial intelligence (AI) to use social media data as a diagnostic tool for schizophrenia. METHODS A systematic review of the literature was conducted on several databases, using relevant keywords. RESULTS 93 studies were identified, among which 7 studies were included. The included studies have shown encouraging results. Social media data could be used in several ways in care of patients with schizophrenia, including the monitoring of patients after a first episode of psychosis. Several studies had major limitations. The studies were heterogeneous in quality and in their objective. Most studies did not have access to health data for the training of the algorithm. We tried to identify several limitations in the methodology of the included studies. We made recommendation for future studies. CONCLUSIONS AI could be used to analyse social media data in order to bring new tools in the care of patients with schizophrenia.

Background Machine learning systems are part of the field of artificial intelligence that automatically learn models from data to make better decisions. Natural language processing (NLP), by using corpora and learning approaches, provides good performance in statistical tasks, such as text classification or sentiment mining. Objective The primary aim of this systematic review was to summarize and characterize, in methodological and technical terms, studies that used machine learning and NLP techniques for mental health. The secondary aim was to consider the potential use of these methods in mental health clinical practice Methods This systematic review follows the PRISMA (Preferred Reporting Items for Systematic Review and Meta-analysis) guidelines and is registered with PROSPERO (Prospective Register of Systematic Reviews; number CRD42019107376). The search was conducted using 4 medical databases (PubMed, Scopus, ScienceDirect, and PsycINFO) with the following keywords: machine learning, data mining, psychiatry, mental health, and mental disorder. The exclusion criteria were as follows: languages other than English, anonymization process, case studies, conference papers, and reviews. No limitations on publication dates were imposed. Results A total of 327 articles were identified, of which 269 (82.3%) were excluded and 58 (17.7%) were included in the review. The results were organized through a qualitative perspective. Although studies had heterogeneous topics and methods, some themes emerged. Population studies could be grouped into 3 categories: patients included in medical databases, patients who came to the emergency room, and social media users. The main objectives were to extract symptoms, classify severity of illness, compare therapy effectiveness, provide psychopathological clues, and challenge the current nosography. Medical records and social media were the 2 major data sources. With regard to the methods used, preprocessing used the standard methods of NLP and unique identifier extraction dedicated to medical texts. Efficient classifiers were preferred rather than transparent functioning classifiers. Python was the most frequently used platform. Conclusions Machine learning and NLP models have been highly topical issues in medicine in recent years and may be considered a new paradigm in medical research. However, these processes tend to confirm clinical hypotheses rather than developing entirely new information, and only one major category of the population (ie, social media users) is an imprecise cohort. Moreover, some language-specific features can improve the performance of NLP methods, and their extension to other languages should be more closely investigated. However, machine learning and NLP techniques provide useful information from unexplored data (ie, patients’ daily habits that are usually inaccessible to care providers). Before considering It as an additional tool of mental health care, ethical issues remain and should be discussed in a timely manner. Machine learning and NLP methods may offer multiple perspectives in mental health research but should also be considered as tools to support clinical practice.

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.

Resume Objectifs La pandemie de COVID-19 touche a ce jour plus de 3 000 000 de personnes a travers le monde et les mesures de prevention ont conduit au confinement de plus de la moitie de l’humanite. La communaute scientifique et les autorites politiques redoutent une epidemie de suicides secondaires a cette crise. L’objectif de cette revue de la litterature est d’analyser l’impact de la pandemie de COVID-19 sur les dimensions du processus suicidaire, son interaction avec les differents facteurs de risque. Nous proposons egalement des solutions innovantes pour y remedier. Methodes Nous avons realise une revue narrative des articles internationaux traitant des grandes pandemies (COVID-19, SRAS) et de leur influence sur la vulnerabilite suicidaire. Resultats De nombreux facteurs sont susceptibles de majorer l’emergence d’idees de suicide et le passage a l’acte suicidaire au cours de cette crise. La distanciation et le confinement pourraient favoriser un sentiment de deconnexion et la perception d’une douleur sociale chez les individus vulnerables. Certaines populations deja « a risque suicidaire eleve » pourraient etre encore fragilisees par la pandemie actuelle : personnes âgees, personnel medical et individus exposes a une forte precarite economique. Plusieurs solutions innovantes adaptees aux contraintes de la distanciation et du confinement permettront de prevenir le risque suicidaire : outils de sante connectee, protocole VigilanS, therapie comportementale et cognitive de troisieme vague et pratique artistique. Conclusions Il est possible que cette crise inedite exacerbe certaines dimensions du processus suicidaire. Cependant, a l’heure de l’innovation, plusieurs outils de prevention du suicide ont toute leur place dans les nouveaux modes de soins et devront etre experimentes a grande echelle.

Resume Si le suicide reste « le seul probleme philosophique vraiment serieux » (Camus), un certain nombre de philosophes l’ont legitime alors que d’autres l’ont condamne tout au long de l’histoire. Plus recemment, l’un d’entre eux, Michel Cornu (2000), a forge a son sujet l’expression « ethique de l’inquietude ». Cette absence de repos, au sens etymologique, peut s’entendre a la fois a un niveau epistemologique (absence de repos sur un seul savoir constitue, donc necessite d’une approche resolument interdisciplinaire de l’objet « suicide »), mais aussi comme une posture relationnelle, a savoir une absence de repos vis-a-vis de l’autre, autrement dit le souci de l’autre. Nous evoquerons ensuite les sources philosophiques du souci de l’autre depuis la cura de Saint Augustin jusqu’a la responsabilite de Levinas et la sollicitude de Ricœur, en passant par l’inquietude de Kierkegaard et la sollicitude ontologique de Heidegger. Nous terminerons en envisageant les tensions ethiques inherentes a une telle notion, oscillant entre bienveillance et surveillance, et entre justice et equite.

L’évaluation ponctuelle du risque suicidaire habituellement conduite aux urgences, après un geste suicidaire, ne rend pas compte de son évolution après la sortie des soins, alors même que le risque de récidive reste important plusieurs mois après. Dans ces conditions, les possibilités d’identification, et donc de prise en charge, des patients à risque suicidaire sont limitées. Le développement de la santé connectée (eHealth) donne désormais accès en temps réel à des informations sur l’état de santé d’un patient entre deux séjours en centre de soins. Cette extension de l’évaluation clinique à l’environnement du patient permet de développer des outils d’aide à la décision face à la gestion du risque suicidaire.

In Europe, euthanasia for mental suffering related to a psychiatric disease is allowed in different ways in three countries (the Netherlands, Belgium, Switzerland). In Belgium, the law decriminaliz...

Background and objectives Technology based assessments are being used for screening and monitoring in a wide scope of medical specialties, including mental health field. Depression and anxiety are common disorders in which e-health tools can be useful. We aimed to compare clinician assessment of illness severity in patients with depression and anxiety diagnosis with computer-based self-assessment within 24 h of clinician evaluation via MEmind ( www.memind.net ), a novel web-tool. Methods From May 2014, adult patients attended in outpatient settings in Fundacion Jimenez Diaz Psychiatry Department were registered in MEmind, a web tool designed for psychiatric assessment. During the recruitment, clinicians use CGI-S for patient assessment via MEmind and provide patients a code and password to use the web-tool. We selected those patients diagnosed with depression and/or anxiety who connected within 24 h of the clinical visit and complete in the web page GHQ and WHO-5 scales. We calculated a bivariate correlation for CGI-S, WHO-5 and GHQ-12. Results Of the 231 participants, 157 (68%) were diagnosed with anxiety disorders and 74 (32%) with depression. Using the Spearman Rho test for correlation, we found a low correlation between CGI-S and total WHO-5 (r = −0.192; p = 0.006) and between CGI-S and total GHQ-12 (r = 0.211; p = 0.002) and a good correlation between total WHO-5 and total GQH-12 (r = −0.606; p = 0.000). Conclusions We found a low correlation between clinician assessment and patients’ self-reports within 24 h of clinician evaluation. Factors that potentially influenced the degree of correlation related with patients, clinicians, measurements and technology are discussed.

Resume Objectif Comparer la qualite de vie (QoL), la depression, l’anxiete et la fatigue chez les patients inclus en prospectif pour un syndrome de Gougerot-Sjogren primitif (SSp) ou pour un syndrome sec non associe au syndrome de Gougerot-Sjogren. Methodes Les patients ayant eu une evaluation multidisciplinaire dans le centre universitaire de Brest, France, pour suspicion de SSp et ayant un syndrome sec ont ete inclus de maniere prospective entre novembre 2006 et decembre 2013. Les memes examens standardises ont ete pratiques chez tous les patients. Le diagnostic de SSp et de syndrome sec non associe au SSp etait base sur l’opinion du medecin evaluateur. Chaque patient remplissait trois questionnaires valides sur la qualite de vie (SF-36), la fatigue (MFI), la depression et l’anxiete (HADS). Resultats Des 95 patients inclus, 55 (57,9 %) avaient un SSp et 40 (42,1 %) avaient un syndrome sec non associe au SSp. La repartition par sexe, âge, duree de la maladie, etait similaire dans les deux groupes. Le groupe SSp avait une proportion significativement plus elevee de patients ayant des examens anormaux objectivant une secheresse (test de Schirmer et flux salivaire). Les scores du SF-36, HADS et MFI etaient alteres de facon identique dans les deux groupes. L’anxiete etait plus frequente que la depression dans les deux groupes. Le domaine le plus atteint etait la vitalite dans le SF-36 et la fatigue generale/physique dans le MFI. L’atteinte systemique extraglandulaire n’etait pas un facteur determinant majeur de l’alteration de la qualite de vie chez les patients ayant un SSp. Conclusion Le syndrome sec est associe a une perturbation significative des scores SF-36, HADS et MFI independamment des anomalies des examens objectifs et du diagnostic de SSp. L’anxiete est plus frequente que la depression et devrait etre prise en compte lors de la prise en charge de tous les patients atteints de syndrome sec.

Suicide is a leading cause of death globally, and certain locations experience clusters of increased frequencies of suicidal behaviours. Prevention efforts are warranted in these locations to prevent contagion effects, and there is increasing interest in technology-supported suicide prevention interventions. Crisis support services are also implementing online and mobile health support. This study investigated the relationship between geospatial suicide clusters in the US and service use, and emergency responses initiated by, a text message-based crisis support service. 103,570 conversations involving 64,391 unique users over a two-year period were de-identified, analysed, and mapped to the state and county level. Moderate correlations were observed between service user rate and suicide mortality (ρ=0.53), and active rescues and suicide mortality (ρ=0.46). Suicide clusters were associated with a non-significant increase in service use (p=0.06) and active rescues (p=0.48). While it was not possible to observe significant cluster effects within this dataset, future analysis involving data collected through mobile health platforms may lead to better identification of risk at an individual level.

BACKGROUND Many suicide risk factors have been identified, but traditional clinical methods do not allow for the accurate prediction of suicide behaviors. To face this challenge, emma, an app for ecological momentary assessment (EMA), ecological momentary intervention (EMI), and prediction of suicide risk in high-risk patients, was developed. OBJECTIVE The aim of this case report study was to describe how subjects at high risk of suicide use the emma app in real-world conditions. METHODS The Ecological Mental Momentary Assessment (EMMA) study is an ongoing, longitudinal, interventional, multicenter trial in which patients at high risk for suicide are recruited to test emma, an app designed to be used as a self-help tool for suicidal crisis management. Participants undergo clinical assessment at months 0, 1, 3, and 6 after inclusion, mainly to assess and characterize the presence of mental disorders and suicidal thoughts and behaviors. Patient recruitment is still ongoing. Some data from the first 14 participants who already completed the 6-month follow-up were selected for this case report study, which evaluated the following: (1) data collected by emma (ie, responses to EMAs), (2) metadata on emma use, (3) clinical data, and (4) qualitative assessment of the participants' experiences. RESULTS EMA completion rates were extremely heterogeneous with a sharp decrease over time. The completion rates of the weekly EMAs (25%-87%) were higher than those of the daily EMAs (0%-53%). Most patients (10/14, 71%) answered the EMA questionnaires spontaneously. Similarly, the use of the Safety Plan Modules was very heterogeneous (2-75 times). Specifically, 11 patients out of 14 (79%) used the Call Module (1-29 times), which was designed by our team to help them get in touch with health care professionals and/or relatives during a crisis. The diversity of patient profiles and use of the EMA and EMI modules proposed by emma were highlighted by three case reports. CONCLUSIONS These preliminary results indicate that patients have different clinical and digital profiles and needs that require a highly scalable, interactive, and customizable app. They also suggest that it is possible and acceptable to collect longitudinal, fine-grained, contextualized data (ie, EMA) and to offer personalized intervention (ie, EMI) in real time to people at high risk of suicide. To become a complementary tool for suicide prevention, emma should be integrated into existing emergency procedures. CLINICALTRIAL ClinicalTrials.gov NCT03410381; https://clinicaltrials.gov/ct2/show/NCT03410381

BACKGROUND Machine learning systems are part of the field of artificial intelligence that automatically learn models from data to make better decisions. Natural language processing (NLP), by using corpora and learning approaches, provides good performance in statistical tasks, such as text classification or sentiment mining. OBJECTIVE The primary aim of this systematic review was to summarize and characterize, in methodological and technical terms, studies that used machine learning and NLP techniques for mental health. The secondary aim was to consider the potential use of these methods in mental health clinical practice METHODS This systematic review follows the PRISMA (Preferred Reporting Items for Systematic Review and Meta-analysis) guidelines and is registered with PROSPERO (Prospective Register of Systematic Reviews; number CRD42019107376). The search was conducted using 4 medical databases (PubMed, Scopus, ScienceDirect, and PsycINFO) with the following keywords: machine learning, data mining, psychiatry, mental health, and mental disorder. The exclusion criteria were as follows: languages other than English, anonymization process, case studies, conference papers, and reviews. No limitations on publication dates were imposed. RESULTS A total of 327 articles were identified, of which 269 (82.3%) were excluded and 58 (17.7%) were included in the review. The results were organized through a qualitative perspective. Although studies had heterogeneous topics and methods, some themes emerged. Population studies could be grouped into 3 categories: patients included in medical databases, patients who came to the emergency room, and social media users. The main objectives were to extract symptoms, classify severity of illness, compare therapy effectiveness, provide psychopathological clues, and challenge the current nosography. Medical records and social media were the 2 major data sources. With regard to the methods used, preprocessing used the standard methods of NLP and unique identifier extraction dedicated to medical texts. Efficient classifiers were preferred rather than transparent functioning classifiers. Python was the most frequently used platform. CONCLUSIONS Machine learning and NLP models have been highly topical issues in medicine in recent years and may be considered a new paradigm in medical research. However, these processes tend to confirm clinical hypotheses rather than developing entirely new information, and only one major category of the population (ie, social media users) is an imprecise cohort. Moreover, some language-specific features can improve the performance of NLP methods, and their extension to other languages should be more closely investigated. However, machine learning and NLP techniques provide useful information from unexplored data (ie, patients’ daily habits that are usually inaccessible to care providers). Before considering It as an additional tool of mental health care, ethical issues remain and should be discussed in a timely manner. Machine learning and NLP methods may offer multiple perspectives in mental health research but should also be considered as tools to support clinical practice.

Background: In an electronic health context, combining traditional structured clinical assessment methods and routine electronic health-based data capture may be a reliable method to build a dynamic clinical decision-support system (CDSS) for suicide prevention. Objective: The aim of this study was to describe the data mining module of a Web-based CDSS and to identify suicide repetition risk in a sample of suicide attempters. Methods: We analyzed a database of 2802 suicide attempters. Clustering methods were used to identify groups of similar patients, and regression trees were applied to estimate the number of suicide attempts among these patients. Results: We identified 3 groups of patients using clustering methods. In addition, relevant risk factors explaining the number of suicide attempts were highlighted by regression trees. Conclusions: Data mining techniques can help to identify different groups of patients at risk of suicide reattempt. The findings of this study can be combined with Web-based and smartphone-based data to improve dynamic decision making for clinicians., This study received a Hospital Clinical Research Grant (PHRC 2009) from the French Health Ministry. None of the funding sources had any involvement in the study design; collection, analysis, or interpretation of data; writing of the report; or the decision to submit the paper for publication. This study was funded partially by Instituto de Salud Carlos III (ISCIII PI13/02200; PI16/01852), Delegación del Gobierno para el Plan Nacional de Drogas (20151073), and the American Foundation for Suicide Prevention (LSRG-1-005-16).

International audience; Background: Many mental disorders are preceded by a prodromal phase consisting of various attenuated and unspecific symptoms and functional impairment. Electronic health records are generally used to capture these symptoms during medical consultation. Internet and mobile technologies provide the opportunity to monitor symptoms emerging in patients' environments using ecological momentary assessment techniques to support preventive therapeutic decision making.Objective: The objective of this study was to assess the acceptability of a Web-based app designed to collect medical data during appointments and provide ecological momentary assessment features.Methods: We recruited clinicians at 4 community psychiatry departments in France to participate. They used the app to assess patients and to collect data after viewing a video of a young patient’s emerging psychiatric consultation. We then asked them to answer a short anonymous self-administered questionnaire that evaluated their experience, the acceptability of the app, and their habit of using new technologies.Results: Of 24 practitioners invited, 21 (88%) agreed to participate. Most of them were between 25 and 45 years old, and greater age was not associated with poorer acceptability. Most of the practitioners regularly used new technologies, and 95% (20/21) connected daily to the internet, with 70% (15/21) connecting 3 times a day or more. However, only 57% (12/21) reported feeling comfortable with computers. Of the clinicians, 86% (18/21) would recommend the tool to their colleagues and 67% (14/21) stated that they would be interested in daily use of the app. Most of the clinicians (16/21, 76%) found the interface easy to use and useful. However, several clinicians noted the lack of readability (8/21, 38%) and the need to improve ergonometric features (4/21, 19%), in particular to facilitate browsing through various subsections. Some participants (5/21, 24%) were concerned about the storage of medical data and most of them (11/21, 52%) seemed to be uncomfortable with this.Conclusions: We describe the first step of the development of a Web app combining an electronic health record and ecological momentary assessment features. This online tool offers the possibility to assess patients and to integrate medical data easily into face-to-face conditions. The acceptability of this app supports the feasibility of its broader implementation. This app could help to standardize assessment and to build up a strong database. Used in conjunction with robust data mining analytic techniques, such a database would allow exploration of risk factors, patterns of symptom evolution, and identification of distinct risk subgroups.

International audience; We reviewed how scholars recently addressed the complex relationship that binds distress, affective disorders, and suicidal behaviors on the one hand and social networking on the other. We considered the latest machine learning performances in detecting affective-related outcomes from social media data, and reviewed understandings of how, why, and with what consequences distressed individuals use social network sites. Finally, we examined how these insights may concretely instantiate on the individual level with a qualitative case series. Recent Findings Machine learning classifiers are progressively stabilizing with moderate to high performances in detecting affective-related diagnosis, symptoms, and risks from social media linguistic markers. Qualitatively, such markers appear to translate ambivalent and socially constrained motivations such as self-disclosure, passive support seeking, and connectedness reinforcement. Summary Binding data science and psychosocial research appears as the unique condition to ground a translational web-clinic for treating and preventing affective-related issues on social media.

BACKGROUND Sleep disorders are a major public health issue. Nearly one in two people will experience sleep disturbances during lifetime with a potential harmful impact on well-being, physical and mental health. The rise of connected objects is bringing new opportunities in sleep monitoring. OBJECTIVE To better understand the clinical value wearables-based sleep monitoring, we conducted a review of the literature, including feasibility studies and clinical trials on this topic. METHODS We searched PubMed, PsycINFO, ScienceDirect, the Cochrane Library, Scopus, and Web of Science up to June 2017. We created the list of keywords based on two domains: wearables and sleep. We used the Preferred Reporting Items for Systematic Reviews and Meta-Analyses to identify, select, and critically appraise relevant research while minimizing bias. RESULTS The initial research collected 255 articles. 18 articles meeting the inclusion criteria were included in the final analysis. Out of the selected articles, four categories appeared. Feasibility studies propose testing new connected tools during sleep, on small samples of subjects. Population comparison studies propose to evaluate the sleep of patients compared to that of healthy subjects. Several studies evaluated connected objects in comparison with polysomnography, a reference test in sleep assessment. Finally, an article evaluates the impact of sleep disorders in the clinic. CONCLUSIONS We conducted a broad analysis of studies on the clinical and technical aspects of the use of wearables for sleep monitoring. This review of the literature showed that wearables are acceptable and promising monitoring tools in a wide range of clinical applications for sleep monitoring.

International audience; INTRODUCTION:The early postattempt period is considered to be one of the most at-risk time windows for suicide reattempt or completion. Among the postcrisis prevention programmes developed to compensate for this risk, brief contact interventions (BCIs) have been proven to be efficient but not equally for each subpopulation of attempters. VigilanS is a region-wide programme that relies on an algorithmic system to tailor surveillance and BCI provisions to individuals discharged from the hospital after a suicide attempt.AIM:VigilanS' main objective is to reduce suicide and suicide reattempt rates both at the individual level (patients included in VigilanS) and at the populational level (inhabitants of the Nord-Pas-de-Calais region).METHODS AND ANALYSIS:At discharge, every attempter coming from a participating centre is given a crisis card with an emergency number to contact in case of distress. Patients are then systematically recontacted 6 months later. An additional 10-day call is also given if the index suicide attempt is not the first one. Depending on the clinical evaluation during the phone call, the call team may carry out proportionated crisis interventions. Personalised postcards are sent whenever patients are unreachable by phone or in distress. On the populational level, mean suicide and suicide attempt rates in Nord-Pas-de-Calais will be compared before and after the implementation of the programme. Here/there cross-sectional comparisons with a control region will test the spatial specificity of the observed fluctuations, while time-series analyses will be performed to corroborate the temporal plausibility of imputing these fluctuations to the implementation of the programme. On the individual level, patients entered in VigilanS will be prospectively compared with a matched control cohort by means of survival analyses (survival curve comparisons and Cox models).DISCUSSION:VigilanS interventional components fall under the ordinary law care regime, and the individuals' general rights as patients apply with no addendums or restrictions for their participation in the programme. The research section received authorisation from the Ethical Committee of Lille Nord-Ouest under the caption 'Study aimed at evaluating routine care' and is registered in 'Clinical Trials'. The French Ministry of Health plans to extend the experimentation to other regions and probe the relevance of this type of 'bottom-up' territorial prevention policy at the national level.TRIAL REGISTRATION NUMBER:NCT03134885.

BACKGROUND: There is growing evidence in the literature that brief contact interventions (BCIs) might be reliable suicide prevention strategies. OBJECTIVE: To assess the effectiveness of a decision-making algorithm for suicide prevention (ALGOS) combining existing BCIs in reducing suicide reattempts in patients discharged after a suicide attempt. METHODS: A randomized, multicenter, controlled, parallel trial was conducted in 23 hospitals. The study was conducted from January 26, 2010, to February 28, 2013. People who had made a suicide attempt were randomly assigned to either the intervention group (ALGOS) or the control group. The primary outcome was the rate of participants who reattempted suicide (fatal or not) within the 6-month study period. RESULTS: 1,040 patients were recruited. After 6 months, 58 participants in the intervention group (12.8%) reattempted suicide compared with 77 (17.2%) in the control group. The difference between groups (4.4%; 95% CI, -0.7% to 9.0%) was not significant (complete-case analysis, P = .059). CONCLUSIONS: These results may help researchers better integrate BCIs into routine health care and provide new insights concerning personalized suicide prevention strategies. TRIAL REGISTRATION: ClinicalTrials.gov identifier: NCT01123174.

Frontiers in Psychiatry, 9, ISSN:1664-0640

International audience; Background: Patients with eating disorders are characterized by pathological eating habits and a tendency to overestimate their weight and body shape. Virtual reality shows promise for the evaluation and management of patients with eating disorders. This technology, when accepted by this population, allows immersion in virtual environments, assessment, and therapeutic approaches, by exposing users to high-calorie foods or changes in body shape. Objective: To better understand the value of virtual reality, we conducted a review of the literature, including clinical studies proposing the use of virtual reality for the evaluation and management of patients with eating disorders. Methods: We searched PubMed, PsycINFO, ScienceDirect, the Cochrane Library, Scopus, and Web of Science up to April 2017. We created the list of keywords based on two domains: virtual reality and eating disorders. We used the Preferred Reporting Items for Systematic Reviews and Meta-Analyses to identify, select, and critically appraise relevant research while minimizing bias. Results: The initial database searches identified 311 articles, 149 of which we removed as duplicates. We analyzed the resulting set of 26 unique studies that met the inclusion criteria. Of these, 8 studies were randomized controlled trials, 13 were nonrandomized studies, and 5 were clinical trials with only 1 participant. Most articles focused on clinical populations (19/26, 73%), with the remainder reporting case-control studies (7/26, 27%). Most of the studies used visual immersive equipment (16/26, 62%) with a head-mounted display (15/16, 94%). Two main areas of interest emerged from these studies: virtual work on patients' body image (7/26, 27%) and exposure to virtual food stimuli (10/26, 38%). Conclusions: We conducted a broad analysis of studies on the use of virtual reality in patients with eating disorders. This review of the literature showed that virtual reality is an acceptable and promising therapeutic tool for patients with eating disorders.

Introduction Les travaux recents ont mis en avant le role de la neurotransmission glutamatergique dans la schizophrenie. Objectifs Cette revue narrative analyse les connaissances actuelles de l’alteration de la neurotransmission glutamatergique dans la schizophrenie. Methode Nous avons utilise les mots cles : « schizophrenia » AND « glutamate » OR « ketamine » OR « NMDA » dans PubMed. Parmi les 5600 resultats, nous avons selectionne les articles publies entre 2010 et 2019. Les articles ont ete selectionnes en fonction de leur pertinence. Resultats Les 16 articles selectionnes apportent des preuves solides du modele glutamatergique dans la schizophrenie. Ce modele explique les symptomes negatifs et cognitifs par un exces d’inhibition de la voie mesocorticale par les interneurones GABA qui sont directement stimules par les voies glutamatergiques. Les antagonistes NMDA induisent une symptomatologie similaire a celle de la schizophrenie chez des sujets sains. Des preuves de ce modele ont ete apporte par l’imagerie cerebrale, la genetique, les etudes post-mortem, et les etudes de Mismatch Negativity (MMN). Les antipsychotiques actuellement utilises ont seulement un effet limite sur la neurotransmission glutamatergique. La clozapine semble etre la molecule ayant l’effet le plus important sur les voies glutamatergique. Les co-agonistes des recepteurs NMDA, comme la sarcosine et la D-serine, pourraient avoir un interet dans le traitement de la symptomatologie negative. Conclusions Ces etudes renforcent le lien entre l’hypofonction des recepteurs NDMDA et la schizophrenie. Ce modele apporte de nouvelles perspectives therapeutiques. Le potentiel des molecules ciblant les voies glutamatergiques doit cependant etre etudie a plus grande echelle. D’autres molecules ciblant d’autres voies glutamatergiques pour egalement etre interessantes dans le traitement de la schizophrenie.