Search

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., Trial Registration: ClinicalTrials.gov NCT03410381; https://clinicaltrials.gov/ct2/show/NCT03410381., (©Margot Morgiève, Catherine Genty, Jérôme Azé, Jonathan Dubois, Marion Leboyer, Guillaume Vaiva, Sofian Berrouiguet, Philippe Courtet. Originally published in JMIR mHealth and uHealth (http://mhealth.jmir.org), 09.10.2020.)

Background: Sleep disorders are a major public health issue. Nearly 1 in 2 people experience sleep disturbances during their lifetime, with a potential harmful impact on well-being and physical and mental health., Objective: The aim of this study was to better understand the clinical applications of wearable-based sleep monitoring; therefore, 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 the Web of Science through June 2019. We created the list of keywords based on 2 domains: wearables and sleep. The primary selection criterion was the reporting of clinical trials using wearable devices for sleep recording in adults., Results: The initial search identified 645 articles; 19 articles meeting the inclusion criteria were included in the final analysis. In all, 4 categories of the selected articles appeared. Of the 19 studies in this review, 58 % (11/19) were comparison studies with the gold standard, 21% (4/19) were feasibility studies, 15% (3/19) were population comparison studies, and 5% (1/19) assessed the impact of sleep disorders in the clinic. The samples were heterogeneous in size, ranging from 1 to 15,839 patients. Our review shows that mobile-health (mHealth) wearable-based sleep monitoring is feasible. However, we identified some major limitations to the reliability of wearable-based monitoring methods compared with polysomnography., Conclusions: This review showed that wearables provide acceptable sleep monitoring but with poor reliability. However, wearable mHealth devices appear to be promising tools for ecological monitoring., (©Elise Guillodo, Christophe Lemey, Mathieu Simonnet, Michel Walter, Enrique Baca-García, Vincent Masetti, Sorin Moga, Mark Larsen, HUGOPSY Network, Juliette Ropars, Sofian Berrouiguet. Originally published in JMIR mHealth and uHealth (http://mhealth.jmir.org), 01.04.2020.)

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., (©Sofian Berrouiguet, Romain Billot, Mark Erik Larsen, Jorge Lopez-Castroman, Isabelle Jaussent, Michel Walter, Philippe Lenca, Enrique Baca-García, Philippe Courtet. Originally published in JMIR Mental Health (http://mental.jmir.org), 07.05.2019.)

Background: The emergence of smartphones, wearable sensor technologies, and smart homes allows the nonintrusive collection of activity data. Thus, health-related events, such as activities of daily living (ADLs; eg, mobility patterns, feeding, sleeping, ...) can be captured without patients' active participation. We designed a system to detect changes in the mobility patterns based on the smartphone's native sensors and advanced machine learning and signal processing techniques., Objective: The principal objective of this work is to assess the feasibility of detecting mobility pattern changes in a sample of outpatients with depression using the smartphone's sensors. The proposed method processed the data acquired by the smartphone using an unsupervised detection technique., Methods: In this study, 38 outpatients from the Hospital Fundación Jiménez Díaz Psychiatry Department (Madrid, Spain) participated. The Evidence-Based Behavior (eB 2 ) app was downloaded by patients on the day of recruitment and configured with the assistance of a physician. The app captured the following data: inertial sensors, physical activity, phone calls and message logs, app usage, nearby Bluetooth and Wi-Fi connections, and location. We applied a change-point detection technique to location data on a sample of 9 outpatients recruited between April 6, 2017 and December 14, 2017. The change-point detection was based only on location information, but the eB 2 platform allowed for an easy integration of additional data. The app remained running in the background on patients' smartphone during the study participation., Results: The principal outcome measure was the identification of mobility pattern changes based on an unsupervised detection technique applied to the smartphone's native sensors data. Here, results from 5 patients' records are presented as a case series. The eB 2 system detected specific mobility pattern changes according to the patients' activity, which may be used as indicators of behavioral and clinical state changes., Conclusions: The proposed technique could automatically detect changes in the mobility patterns of outpatients who took part in this study. Assuming these mobility pattern changes correlated with behavioral changes, we have developed a technique that may identify possible relapses or clinical changes. Nevertheless, it is important to point out that the detected changes are not always related to relapses and that some clinical changes cannot be detected by the proposed method., (©Sofian Berrouiguet, David Ramírez, María Luisa Barrigón, Pablo Moreno-Muñoz, Rodrigo Carmona Camacho, Enrique Baca-García, Antonio Artés-Rodríguez. Originally published in JMIR Mhealth and Uhealth (http://mhealth.jmir.org), 10.12.2018.)

Background: Research indicates that maintaining contact either via letter or postcard with at-risk adults following discharge from care services after a suicide attempt (SA) can reduce reattempt risk. Pilot studies have demonstrated that interventions using mobile health (mHealth) technologies are feasible in a suicide prevention setting., Objective: The aim of this study was to report three cases of patients recruited in the Suicide Intervention Assisted by Messages (SIAM) study to describe how a mobile intervention may influence follow-up., Methods: SIAM is a 2-year, multicenter randomized controlled trial conducted by the Brest University Hospital, France. Participants in the intervention group receive SIAM text messages 48 hours after discharge, then at day 8 and day 15, and months 1, 2, 3, 4, 5, and 6. The study includes participants aged 18 years or older, who have attended a participating hospital for an SA, and have been discharged from the emergency department (ED) or a psychiatric unit (PU) for a stay of less than 7 days. Eligible participants are randomized between the SIAM intervention messages and a control group. In this study, we present three cases from the ongoing SIAM study that demonstrate the capability of a mobile-based brief contact intervention for triggering patient-initiated contact with a crisis support team at various time points throughout the mobile-based follow-up period., Results: Out of the 244 patients recruited in the SIAM randomized controlled trial, three cases were selected to illustrate the impact of mHealth on suicide risk management. Participants initiated contact with the emergency crisis support service after receiving text messages up to 6 months following discharge from the hospital. Contact was initiated immediately following receipt of a text message or up to 6 days following a message., Conclusions: This text message-based brief contact intervention has demonstrated the potential to reconnect suicidal individuals with crisis support services while they are experiencing suicidal ideation as well as in a period after receiving messages. As follow-up phone calls over an extended period of time may not be feasible, this intervention has the potential to offer simple technological support for individuals following discharge from the ED., Trial Registration: ClinicalTrials.gov NCT02106949; https://clinicaltrials.gov/ct2/show/NCT02106949 (Archived by WebCite at http://www.webcitation.org/6wMtAFL49)., (©Sofian Berrouiguet, Mark Erik Larsen, Catherine Mesmeur, Michel Gravey, Romain Billot, Michel Walter, HUGOPSY Network, Christophe Lemey, Philippe Lenca. Originally published in JMIR Mhealth and Uhealth (http://mhealth.jmir.org), 10.01.2018.)

Background: Suicide is a leading cause of death, particularly among young people. Continuity of care following discharge from hospital is critical, yet this is a time when individuals often lose contact with health care services. Offline brief contact interventions following a suicide attempt can reduce the number of repeat attempts, and text message (short message service, SMS) interventions are currently being evaluated., Objective: The aim of this study was to extend postattempt caring contacts by designing a brief Web-based intervention targeting proximal risk factors and the needs of this population during the postattempt period. This paper details the development process and describes the realized system., Methods: To inform the design of the intervention, a lived experience design group was established. Participants were asked about their experiences of support following their suicide attempt, their needs during this time, and how these could be addressed in a brief contact eHealth intervention. The intervention design was also informed by consultation with lived experience panels external to the project and a clinical design group., Results: Prompt outreach following discharge, initial distraction activities with low cognitive demands, and ongoing support over an extended period were identified as structural requirements of the intervention. Key content areas identified included coping with distressing feelings, safety planning, emotional regulation and acceptance, coping with suicidal thoughts, connecting with others and interpersonal relationships, and managing alcohol consumption., Conclusions: The RAFT (Reconnecting AFTer a suicide attempt) text message brief contact intervention combines SMS contacts with additional Web-based brief therapeutic content targeting key risk factors. It has the potential to reduce the number of repeat suicidal episodes and to provide accessible, acceptable, and cost-effective support for individuals who may not otherwise seek face-to-face treatment. A pilot study to test the feasibility and acceptability of the RAFT intervention is underway., (©Mark Erik Larsen, Fiona Shand, Kirsten Morley, Philip J. Batterham, Katherine Petrie, Bill Reda, Sofian Berrouiguet, Paul S. Haber, Gregory Carter, Helen Christensen. Originally published in JMIR Mental Health (http://mental.jmir.org), 13.12.2017.)