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Büscher R, Winkler T, Mocellin J, Homan S, Josifovski N, Ciharova M, van Breda W, Kwon S, Larsen ME, Torous J, Firth J, Sander LB. A systematic review on passive sensing for the prediction of suicidal thoughts and behaviors. NPJ MENTAL HEALTH RESEARCH 2024; 3:42. [PMID: 39313519 PMCID: PMC11420362 DOI: 10.1038/s44184-024-00089-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/26/2023] [Accepted: 09/12/2024] [Indexed: 09/25/2024]
Abstract
Passive sensing data from smartphones and wearables may help improve the prediction of suicidal thoughts and behaviors (STB). In this systematic review, we explored the feasibility and predictive validity of passive sensing for STB. On June 24, 2024, we systematically searched Medline, Embase, Web of Science, PubMed, and PsycINFO. Studies were eligible if they investigated the association between STB and passive sensing, or the feasibility of passive sensing in this context. From 2107 unique records, we identified eleven prediction studies, ten feasibility studies, and seven protocols. Studies indicated generally lower model performance for passive compared to active data, with three out of four studies finding no incremental value. PROBAST ratings revealed major shortcomings in methodology and reporting. Studies suggested that passive sensing is feasible in high-risk populations. In conclusion, there is limited evidence on the predictive value of passive sensing for STB. We highlight important quality characteristics for future research.
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Affiliation(s)
- Rebekka Büscher
- Medical Psychology and Medical Sociology, Faculty of Medicine, University of Freiburg, Freiburg, Germany.
- Department of Clinical, Neuro and Developmental Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.
| | - Tanita Winkler
- Medical Psychology and Medical Sociology, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Jacopo Mocellin
- Experimental Psychopathology and Psychotherapy, Department of Psychology, University of Zurich, Zurich, Switzerland
- Department of Psychiatry, Psychotherapy and Psychosomatics, Psychiatric University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Stephanie Homan
- Experimental Psychopathology and Psychotherapy, Department of Psychology, University of Zurich, Zurich, Switzerland
- Department of Psychiatry, Psychotherapy and Psychosomatics, Psychiatric University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Natasha Josifovski
- Black Dog Institute, University of New South Wales, Sydney, NSW, Australia
| | - Marketa Ciharova
- Department of Clinical, Neuro and Developmental Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Black Dog Institute, University of New South Wales, Sydney, NSW, Australia
- Amsterdam Public Health Research Institute, Faculty of Behavioural and Movement Sciences, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Ward van Breda
- Department of Clinical, Neuro and Developmental Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Sam Kwon
- Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | - Mark E Larsen
- Black Dog Institute, University of New South Wales, Sydney, NSW, Australia
- Centre for Big Data Research in Health, University of New South Wales, Sydney, NSW, Australia
| | - John Torous
- Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | - Joseph Firth
- Division of Psychology and Mental Health, Manchester Academic Health Science Centre, University of Manchester, Manchester, UK
| | - Lasse B Sander
- Medical Psychology and Medical Sociology, Faculty of Medicine, University of Freiburg, Freiburg, Germany
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Quellec G, Berrouiguet S, Morgiève M, Dubois J, Leboyer M, Vaiva G, Azé J, Courtet P. Predicting suicidal ideation from irregular and incomplete time series of questionnaires in a smartphone-based suicide prevention platform: a pilot study. Sci Rep 2024; 14:20870. [PMID: 39242628 PMCID: PMC11379849 DOI: 10.1038/s41598-024-71760-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2023] [Accepted: 08/30/2024] [Indexed: 09/09/2024] Open
Abstract
Over 700,000 people die by suicide annually. Collecting longitudinal fine-grained data about at-risk individuals, as they occur in the real world, can enhance our understanding of the temporal dynamics of suicide risk, leading to better identification of those in need of immediate intervention. Self-assessment questionnaires were collected over time from 89 at-risk individuals using the EMMA smartphone application. An artificial intelligence (AI) model was trained to assess current level of suicidal ideation (SI), an early indicator of the suicide risk, and to predict its progression in the following days. A key challenge was the unevenly spaced and incomplete nature of the time series data. To address this, the AI was built on a missing value imputation algorithm. The AI successfully distinguished high SI levels from low SI levels both on the current day (AUC = 0.804, F1 = 0.625, MCC = 0.459) and three days in advance (AUC = 0.769, F1 = 0.576, MCC = 0.386). Besides past SI levels, the most significant questions were related to psychological pain, well-being, agitation, emotional tension, and protective factors such as contacts with relatives and leisure activities. This represents a promising step towards early AI-based suicide risk prediction using a smartphone application.
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Affiliation(s)
- Gwenolé Quellec
- Inserm, UMR 1101, LaTIM, IBRBS building, 22 avenue Camille Desmoulins, 29200, Brest, France.
| | - Sofian Berrouiguet
- Inserm, UMR 1101, LaTIM, IBRBS building, 22 avenue Camille Desmoulins, 29200, Brest, France
- Department of Psychiatry, CHU Brest, Brest, France
| | - Margot Morgiève
- Université Paris Cité, CNRS, Inserm, Cermes3, Paris, France
- Department of Emergency Psychiatry and Acute Care, Lapeyronie Hospital, CHU Montpellier, Montpellier, France
- ICM - Paris Brain Institute, Hôpital de la Pitié-Salpêtriére, Paris, France
- GEPS - Groupement d'Étude et de Prévention du Suicide, Paris, France
| | | | - Marion Leboyer
- Fondation Fondamental, Hôpital Albert-Chenevier, Créteil, France
- Faculté de Médicine, Institut National de la Santé et de la Recherche Médicale, Université Paris-Est Créteil, Créteil, France
- Assistance Publique Hôpitaux de Paris, Pôle de Psychiatrie et Addictologie, Hôpitaux Universitaires Henri Mondor, Créteil, France
| | - Guillaume Vaiva
- CHU Lille, Hôpital Fontan, Department of Psychiatry, Lille, France
- Centre National de Resources and Résilience pour les Psychotraumatisme, Université de Lille, Lille, France
- CNRS UMR-9193, SCALab - Sciences Cognitives et Sciences Affectives, Université de Lille, Lille, France
| | - Jérôme Azé
- LIRMM, CNRS, Univ Montpellier, Montpellier, France
| | - Philippe Courtet
- Department of Emergency Psychiatry and Acute Care, Lapeyronie Hospital, CHU Montpellier, Montpellier, France
- IGF, Univ. Montpellier, CNRS, INSERM, Montpellier, France
- Fondation Fondamental, Hôpital Albert-Chenevier, Créteil, France
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Barreras F, Watts DJ. The exciting potential and daunting challenge of using GPS human-mobility data for epidemic modeling. NATURE COMPUTATIONAL SCIENCE 2024; 4:398-411. [PMID: 38898315 DOI: 10.1038/s43588-024-00637-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/14/2023] [Accepted: 05/02/2024] [Indexed: 06/21/2024]
Abstract
Large-scale GPS location datasets hold immense potential for measuring human mobility and interpersonal contact, both of which are essential for data-driven epidemiology. However, despite their potential and widespread adoption during the COVID-19 pandemic, there are several challenges with these data that raise concerns regarding the validity and robustness of its applications. Here we outline two types of challenges-some related to accessing and processing these data, and some related to data quality-and propose several research directions to address them moving forward.
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Affiliation(s)
- Francisco Barreras
- Department of Computer and Information Science, University of Pennsylvania, Philadelphia, PA, USA.
| | - Duncan J Watts
- Department of Computer and Information Science, University of Pennsylvania, Philadelphia, PA, USA.
- Operations, Information and Decisions Department, Wharton School, University of Pennsylvania, Philadelphia, PA, USA.
- Annenberg School of Communication, University of Pennsylvania, Philadelphia, PA, USA.
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Dominiak M, Gędek A, Antosik AZ, Mierzejewski P. Mobile health for mental health support: a survey of attitudes and concerns among mental health professionals in Poland over the period 2020-2023. Front Psychiatry 2024; 15:1303878. [PMID: 38559395 PMCID: PMC10978719 DOI: 10.3389/fpsyt.2024.1303878] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/28/2023] [Accepted: 03/01/2024] [Indexed: 04/04/2024] Open
Abstract
Introduction Mobile health (mHealth) has emerged as a dynamic sector supported by technological advances and the COVID-19 pandemic and have become increasingly applied in the field of mental health. Aim The aim of this study was to assess the attitudes, expectations, and concerns of mental health professionals, including psychiatrists, psychologists, and psychotherapists, towards mHealth, in particular mobile health self-management tools and telepsychiatry in Poland. Material and methods This was a survey conducted between 2020 and 2023. A questionnaire was administered to 148 mental health professionals, covering aspects such as telepsychiatry, mobile mental health tools, and digital devices. Results The majority of professionals expressed readiness to use telepsychiatry, with a peak in interest during the COVID-19 pandemic, followed by a gradual decline from 2022. Concerns about telepsychiatry were reported by a quarter of respondents, mainly related to difficulties in correctly assessing the patient's condition, and technical issues. Mobile health tools were positively viewed by professionals, with 86% believing they could support patients in managing mental health and 74% declaring they would recommend patients to use them. Nevertheless, 29% expressed concerns about the effectiveness and data security of such tools. Notably, the study highlighted a growing readiness among mental health professionals to use new digital technologies, reaching 84% in 2023. Conclusion These findings emphasize the importance of addressing concerns and designing evidence-based mHealth solutions to ensure long-term acceptance and effectiveness in mental healthcare. Additionally, the study highlights the need for ongoing regulatory efforts to safeguard patient data and privacy in the evolving digital health landscape.
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Affiliation(s)
- Monika Dominiak
- Department of Pharmacology, Institute of Psychiatry and Neurology, Warsaw, Poland
| | - Adam Gędek
- Department of Pharmacology, Institute of Psychiatry and Neurology, Warsaw, Poland
- Praski Hospital, Warsaw, Poland
| | - Anna Z. Antosik
- Department of Psychiatry, Faculty of Medicine, Collegium Medicum, Cardinal Wyszynski University, Warsaw, Poland
| | - Paweł Mierzejewski
- Department of Pharmacology, Institute of Psychiatry and Neurology, Warsaw, Poland
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Schulte C, Sextl-Plötz T, Baumeister H, Titzler I, Sander LB, Sachser C, Steubl L, Zarski AC. What to do when the unwanted happens? Negative event management in studies on internet- and mobile-based interventions for youths and adults with two case reports. Internet Interv 2024; 35:100710. [PMID: 38283258 PMCID: PMC10818076 DOI: 10.1016/j.invent.2024.100710] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Revised: 01/08/2024] [Accepted: 01/10/2024] [Indexed: 01/30/2024] Open
Abstract
Background Despite severely burdened individuals, often being excluded from research studies on internet- and mobile-based interventions (IMIs), negative events (NEs) including suicidal thoughts and behaviors (STBs) can still occur during a trial. NEs require monitoring and adequate safety measures. However, study protocols frequently lack comprehensive descriptions of procedures for managing NEs. Aims This study aimed to illustrate the assessment, monitoring, and procedures for addressing NEs in two studies on IMIs in adults and youth using case reports, to identify strengths and weaknesses of the NE management approaches, and to derive key learnings and recommendations. Methods Two case reports were drawn from two distinct IMI studies. The first study, PSYCHOnlineTHERAPY, evaluates the combination of an IMI with on-site psychotherapy for anxiety and depressive disorders in adults (adult blended study). The second study evaluates a standalone, therapist-guided IMI for post-traumatic stress disorder (PTSD) in youth (youth standalone study). Potential NEs were predefined depending on the study sample. The case studies thoroughly document the systematic recording and ongoing monitoring of NEs through self-report and observer-based assessments during the interventions. The cases illustrate a variety of NE management strategies, including automated and personalized approaches, adapted to the specific nature and severity of the NEs. The NE management approaches are visualized using decision trees. Results In the adult blended case study, online questionnaires detected STBs and triggered automated support information. As on-site therapy had already ended, a telephone consultation session allowed for the identification and discussion of the heightened intensity of suicidal thoughts, along with the development of specific additional help options. In the youth standalone case study, heightened tension in an adolescent with PTSD during trauma processing could be addressed in a telephone therapeutic session focusing on resource activation and emotion regulation. The referral to on-site treatment was supported. Overall, advantages of the NE management included automated procedures, multimodal assessment of a wide range of NEs, and standardized procedures tailored to different severity levels. Weaknesses included the use of single-item assessments for STBs and lack of procedures in case of deterioration or nonresponse to treatment. Conclusion This study provides practical insights and derives key learnings and recommendations regarding the management of NEs in different IMI contexts for both adults and youth.
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Affiliation(s)
- Christina Schulte
- Technical University of Munich, Department of Sports and Health Sciences, Professorship Psychology and Digital Mental Health Care, Georg-Brauchle-Ring 60, 80992 Munich, Germany
| | - Theresa Sextl-Plötz
- Technical University of Munich, Department of Sports and Health Sciences, Professorship Psychology and Digital Mental Health Care, Georg-Brauchle-Ring 60, 80992 Munich, Germany
| | - Harald Baumeister
- Ulm University, Department of Clinical Psychology and Psychotherapy, Lise-Meitner-Str. 16, 89081 Ulm, Germany
| | - Ingrid Titzler
- Friedrich-Alexander-Universität Erlangen-Nürnberg, Department of Clinical Psychology and Psychotherapy, Nägelsbachstr. 25a, 91052 Erlangen, Germany
| | - Lasse B. Sander
- Medical Psychology and Medical Sociology, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Cedric Sachser
- Ulm University, Department of Child and Adolescent Psychiatry and Psychotherapy, Steinhövelstraße 1, 89075 Ulm, Germany
| | - Lena Steubl
- Ulm University, Department of Clinical Psychology and Psychotherapy, Lise-Meitner-Str. 16, 89081 Ulm, Germany
| | - Anna-Carlotta Zarski
- Technical University of Munich, Department of Sports and Health Sciences, Professorship Psychology and Digital Mental Health Care, Georg-Brauchle-Ring 60, 80992 Munich, Germany
- Friedrich-Alexander-Universität Erlangen-Nürnberg, Department of Clinical Psychology and Psychotherapy, Nägelsbachstr. 25a, 91052 Erlangen, Germany
- Philipps-University Marburg, Department of Clinical Psychology, Division of eHealth in Clinical Psychology, Schulstraße 12, 35032 Marburg, Germany
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Zainal NH. Is combined antidepressant medication (ADM) and psychotherapy better than either monotherapy at preventing suicide attempts and other psychiatric serious adverse events for depressed patients? A rare events meta-analysis. Psychol Med 2024; 54:457-472. [PMID: 37964436 DOI: 10.1017/s0033291723003306] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/16/2023]
Abstract
Antidepressant medication (ADM)-only, psychotherapy-only, and their combination are the first-line treatment options for major depressive disorder (MDD). Previous meta-analyses of randomized controlled trials (RCTs) established that psychotherapy and combined treatment were superior to ADM-only for MDD treatment remission or response. The current meta-analysis extended previous ones by determining the comparative efficacy of ADM-only, psychotherapy-only, and combined treatment on suicide attempts and other serious psychiatric adverse events (i.e. psychiatric emergency department [ED] visit, psychiatric hospitalization, and/or suicide death; SAEs). Peto odds ratios (ORs) and their 95% confidence intervals were computed from the present random-effects meta-analysis. Thirty-four relevant RCTs were included. Psychotherapy-only was stronger than combined treatment (1.9% v. 3.7%; OR 1.96 [1.20-3.20], p = 0.012) and ADM-only (3.0% v. 5.6%; OR 0.45 [0.30-0.67], p = 0.001) in decreasing the likelihood of SAEs in the primary and trim-and-fill sensitivity analyses. Combined treatment was better than ADM-only in reducing the probability of SAEs (6.0% v. 8.7%; OR 0.74 [0.56-0.96], p = 0.029), but this comparative efficacy finding was non-significant in the sensitivity analyses. Subgroup analyses revealed the advantage of psychotherapy-only over combined treatment and ADM-only for reducing SAE risk among children and adolescents and the benefit of combined treatment over ADM-only among adults. Overall, psychotherapy and combined treatment outperformed ADM-only in reducing the likelihood of SAEs, perhaps by conferring strategies to enhance reasons for living. Plausibly, psychotherapy should be prioritized for high-risk youths and combined treatment for high-risk adults with MDD.
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Affiliation(s)
- Nur Hani Zainal
- Department of Health Care Policy, Harvard Medical School, Boston, MA, USA
- Department of Psychology, National University of Singapore, Singapore
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7
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Dominiak M, Gędek A, Antosik AZ, Mierzejewski P. Prevalence, attitudes and concerns toward telepsychiatry and mobile health self-management tools among patients with mental disorders during and after the COVID-19 pandemic: a nationwide survey in Poland from 2020 to 2023. Front Psychiatry 2024; 14:1322695. [PMID: 38260801 PMCID: PMC10801431 DOI: 10.3389/fpsyt.2023.1322695] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/16/2023] [Accepted: 12/19/2023] [Indexed: 01/24/2024] Open
Abstract
Introduction Mobile Health (mHealth) is a rapidly growing field of medicine that has the potential to significantly change everyday clinical practice, including in psychiatry. The COVID-19 pandemic and technological developments have accelerated the adoption of telepsychiatry and mobile solutions, but patient perceptions and expectations of mHealth remain a key factor in its implementation. Aim The aim of this study was to assess (1) the prevalence, (2) attitudes, preferences and (3) concerns about mobile mental health, including telepsychiatry and self-management tools, among patients with mental disorders over the period 2020-2023, i.e., at the onset, peak and after the expiration of the COVID-19 pandemic. Materials and methods A semi-structured survey was administrated to 354 patients with mental disorders in Poland. The questions were categorized into three section, addressing prevalence, attitudes, and concerns about telepsychiatry and mobile health self-management tools. The survey was conducted continuously from May 2020 to the end of May 2023. Result As many as 95.7% of patients with mental disorders used mobile devices at least once a week. Over the course of 3 years (from 2020 to 2023), there was a significant increase in the readiness of patients to embrace new technologies, with the percentage rising from 20% to 40%. In particular, a remarkable growth in patient preferences for telepsychiatry was observed, with a significant increase from 47% in 2020 to a substantial 96% in 2023. Similarly, mHealth self-management tools were of high interest to patients. In 2020, 62% of patients like the idea of using mobile apps and other mobile health tools to support the care and treatment process. This percentage also increased during the pandemic, reaching 66% in 2023. At the same time, the percentage of patients who have concerns about using m-health solutions has gradually decreased, reaching 35% and 28% in 2023 for telepsychiatry and for the reliability and safety of m-health self-management tools, respectively. Conclusion This study highlights the growing acceptance of modern technologies in psychiatric care, with patients showing increased readiness to use telepsychiatry and mobile health self-management tools, in particular mobile applications, after the COVID-19 pandemic. This was triggered by the pandemic, but continues despite its expiry. In the face of patient readiness, the key issue now is to ensure the safety and efficacy of these tools, along with providing clear guidelines for clinicians. It is also necessary to draw the attention of health systems to the widespread implementation of these technologies to improve the care of patients with mental disorders.
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Affiliation(s)
- Monika Dominiak
- Department of Pharmacology, Institute of Psychiatry and Neurology, Warsaw, Poland
| | - Adam Gędek
- Department of Pharmacology, Institute of Psychiatry and Neurology, Warsaw, Poland
| | - Anna Z. Antosik
- Department of Psychiatry, Faculty of Medicine, Collegium Medicum, Cardinal Wyszynski University in Warsaw, Warsaw, Poland
| | - Paweł Mierzejewski
- Department of Pharmacology, Institute of Psychiatry and Neurology, Warsaw, Poland
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Buchman DZ, Imahori D, Lo C, Hui K, Walker C, Shaw J, Davis KD. The Influence of Using Novel Predictive Technologies on Judgments of Stigma, Empathy, and Compassion among Healthcare Professionals. AJOB Neurosci 2024; 15:32-45. [PMID: 37450417 DOI: 10.1080/21507740.2023.2225470] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/18/2023]
Abstract
BACKGROUND Our objective was to evaluate whether the description of a machine learning (ML) app or brain imaging technology to predict the onset of schizophrenia or alcohol use disorder (AUD) influences healthcare professionals' judgments of stigma, empathy, and compassion. METHODS We randomized healthcare professionals (N = 310) to one vignette about a person whose clinician seeks to predict schizophrenia or an AUD, using a ML app, brain imaging, or a psychosocial assessment. Participants used scales to measure their judgments of stigma, empathy, and compassion. RESULTS Participants randomized to the ML vignette endorsed less anger and more fear relative to the psychosocial vignette, and the brain imaging vignette elicited higher pity ratings. The brain imaging and ML vignettes evoked lower personal responsibility judgments compared to the psychosocial vignette. Physicians and nurses reported less empathy than clinical psychologists. CONCLUSIONS The use of predictive technologies may reinforce essentialist views about mental health and substance use that may increase specific aspects of stigma and reduce others.
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Affiliation(s)
- Daniel Z Buchman
- Centre for Addiction and Mental Health
- Dalla Lana School of Public Health, University of Toronto
- University of Toronto Joint Centre for Bioethics
| | | | - Christopher Lo
- Dalla Lana School of Public Health, University of Toronto
- Temerty Faculty of Medicine, University of Toronto
- College of Healthcare Sciences, James Cook University, Singapore
| | - Katrina Hui
- Centre for Addiction and Mental Health
- Temerty Faculty of Medicine, University of Toronto
| | | | - James Shaw
- University of Toronto Joint Centre for Bioethics
- Temerty Faculty of Medicine, University of Toronto
| | - Karen D Davis
- Temerty Faculty of Medicine, University of Toronto
- Krembil Brain Institute, University Health Network
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Singh V, Sarkar S, Gaur V, Grover S, Singh OP. Clinical Practice Guidelines on using artificial intelligence and gadgets for mental health and well-being. Indian J Psychiatry 2024; 66:S414-S419. [PMID: 38445270 PMCID: PMC10911327 DOI: 10.4103/indianjpsychiatry.indianjpsychiatry_926_23] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/06/2023] [Revised: 12/12/2023] [Accepted: 12/18/2023] [Indexed: 03/07/2024] Open
Affiliation(s)
- Vipul Singh
- Department of Psychiatry, Government Medical College, Kannauj, Uttar Pradesh, India
| | - Sharmila Sarkar
- Department of Psychiatry, Calcutta National Medical College, Kolkata, West Bengal, India
| | - Vikas Gaur
- Department of Psychiatry, Jaipur National University Institute for Medical Sciences and Research Centre, Jaipur, Rajasthan, India
| | - Sandeep Grover
- Department of Psychiatry, Post Graduate Institute of Medical Education and Research, Chandigarh, India
| | - Om Prakash Singh
- Department of Psychiatry, Midnapore Medical College, Midnapore, West Bengal, India E-mail:
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Spanakis P, Lorimer B, Newbronner E, Wadman R, Crosland S, Gilbody S, Johnston G, Walker L, Peckham E. Digital health literacy and digital engagement for people with severe mental ill health across the course of the COVID-19 pandemic in England. BMC Med Inform Decis Mak 2023; 23:193. [PMID: 37752460 PMCID: PMC10523616 DOI: 10.1186/s12911-023-02299-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2023] [Accepted: 09/16/2023] [Indexed: 09/28/2023] Open
Abstract
BACKGROUND An unprecedented acceleration in digital mental health services happened during the COVID-19 pandemic. However, people with severe mental ill health (SMI) might be at risk of digital exclusion, partly because of a lack of digital skills, such as digital health literacy. The study seeks to examine how the use of the Internet has changed during the pandemic for people with SMI, and explore digital exclusion, symptomatic/health related barriers to internet engagement, and digital health literacy. METHODS Over the period from July 2020 to February 2022, n = 177 people with an SMI diagnosis (psychosis-spectrum disorder or bipolar affective disorder) in England completed three surveys providing sociodemographic information and answering questions regarding their health, use of the Internet, and digital health literacy. RESULTS 42.5% of participants reported experiences of digital exclusion. Cochrane-Q analysis showed that there was significantly more use of the Internet at the last two assessments (80.8%, and 82.2%) compared to that at the beginning of the pandemic (65.8%; ps < 0.001). Although 34.2% of participants reported that their digital skills had improved during the pandemic, 54.4% still rated their Internet knowledge as being fair or worse than fair. Concentration difficulties (62.6%) and depression (56.1%) were among the most frequently reported symptomatic barriers to use the Internet. The sample was found to have generally moderate levels of digital health literacy (M = 26.0, SD = 9.6). Multiple regression analysis showed that higher literacy was associated with having outstanding/good self-reported knowledge of the Internet (ES = 6.00; 95% CI: 3.18-8.82; p < .001), a diagnosis of bipolar disorder (compared to psychosis spectrum disorder - ES = 5.14; 95% CI: 2.47-7.81; p < .001), and being female (ES = 3.18; 95% CI: 0.59-5.76; p = .016). CONCLUSIONS These findings underline the need for training and support among people with SMI to increase digital skills, facilitate digital engagement, and reduce digital engagement, as well as offering non-digital engagement options to service users with SMI.
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Affiliation(s)
- P Spanakis
- Department of Health Sciences, University of York, York, UK.
- Department of Psychology, University of Crete, Rethymnon, Greece.
| | - B Lorimer
- Department of Health Sciences, University of York, York, UK
| | - E Newbronner
- Department of Health Sciences, University of York, York, UK
| | - R Wadman
- Department of Health Sciences, University of York, York, UK
| | - S Crosland
- Department of Health Sciences, University of York, York, UK
| | - S Gilbody
- Department of Health Sciences, University of York, York, UK
| | - G Johnston
- Independent Peer Researcher, Clackmannan, UK
| | - L Walker
- School of Health and Psychological Sciences, City, University of London, London, UK
| | - E Peckham
- School of Medical and Health Sciences, Bangor University, Bangor, UK
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11
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Lei C, Qu D, Liu K, Chen R. Ecological Momentary Assessment and Machine Learning for Predicting Suicidal Ideation Among Sexual and Gender Minority Individuals. JAMA Netw Open 2023; 6:e2333164. [PMID: 37695580 PMCID: PMC10495869 DOI: 10.1001/jamanetworkopen.2023.33164] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/13/2023] [Accepted: 08/03/2023] [Indexed: 09/12/2023] Open
Abstract
Importance Suicidality poses a serious global health concern, particularly in the sexual and gender minority population. While various studies have focused on investigating chronic stressors, the precise prediction effect of daily experiences on suicide ideation remains uncertain. Objective To test the extent to which mood fluctuations and contextual stressful events experienced by sexual and gender minority individuals may predict later short- and long-term suicide ideation. Design, Setting, and Participants This diagnostic study collected twice-daily data on mood states and stressful events from sexual and gender minority individuals over 25 days throughout 3 waves of the Chinese Lunar New Year (before, during, and after), and follow-up surveys assessing suicidal ideation were conducted 1, 3, and 8 months later. Online recruitment advertisements were used to recruit young adults throughout China. Eligible participants were self-identified as sexual and gender minority individuals aged 18 to 29 years. Those who were diagnosed with psychotic disorders (eg, schizophrenia spectrum or schizotypal disorder) or prevented from objective factors (ie, not having a phone or having an irregular sleep rhythm) were excluded. Data were collected from January to October 2022. Main Outcomes and Measures To predict short-term (1 month) and longer-term (3 and 8 months) suicidal ideation, the study tested several approaches by using machine learning including chronic stress baseline data (baseline approach), dynamic patterns of mood states and stressful events (ecological momentary assessment [EMA] approach), and a combination of baseline data and dynamic patterns (EMA plus baseline approach). Results A total of 103 sexual and gender minority individuals participated in the study (mean [SD] age, 24.2 [2.5] years; 72 [70%] female). Of these, 19 (18.4%; 95% CI, 10.9%-25.9%), 25 (24.8%; 95% CI, 16.4%-33.2%), 30 (29.4%; 95% CI, 20.6%-38.2%), and 32 (31.1%; 95% CI, 22.2%-40.0%) reported suicidal ideation at baseline, 1, 3, and 8 months follow-up, respectively. The EMA approach showed better performance than the baseline and baseline plus EMA approaches at 1-month follow-up (area under the receiver operating characteristic curve [AUC], 0.80; 95% CI, 0.78-0.81) and slightly better performance on the prediction of suicidal ideation at 3 and 8 months' follow-up. In addition, the best approach predicting suicidal ideation was obtained during Lunar New Year period at 1-month follow-up, which had a mean AUC of 0.77 (95% CI, 0.74-0.79) and better performance at 3 and 8 months' follow-up (AUC, 0.74; 95% CI, 0.72-0.76 and AUC, 0.72; 95% CI, 0.69-0.74, respectively). Conclusions and Relevance The findings in this study emphasize the importance of contextual risk factors experienced by sexual and gender minority individuals at different stages. The use of machine learning may facilitate the identification of individuals who are at risk and aid in the development of personalized process-based early prevention programs to mitigate future suicide risk.
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Affiliation(s)
- Chang Lei
- Vanke School of Public Health, Tsinghua University, Beijing, China
- Institute for Healthy China, Tsinghua University, Beijing, China
| | - Diyang Qu
- Vanke School of Public Health, Tsinghua University, Beijing, China
- Institute for Healthy China, Tsinghua University, Beijing, China
| | - Kunxu Liu
- Vanke School of Public Health, Tsinghua University, Beijing, China
- Institute for Healthy China, Tsinghua University, Beijing, China
| | - Runsen Chen
- Vanke School of Public Health, Tsinghua University, Beijing, China
- Institute for Healthy China, Tsinghua University, Beijing, China
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Umapathy VR, Rajinikanth B S, Samuel Raj RD, Yadav S, Munavarah SA, Anandapandian PA, Mary AV, Padmavathy K, R A. Perspective of Artificial Intelligence in Disease Diagnosis: A Review of Current and Future Endeavours in the Medical Field. Cureus 2023; 15:e45684. [PMID: 37868519 PMCID: PMC10590060 DOI: 10.7759/cureus.45684] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/20/2023] [Indexed: 10/24/2023] Open
Abstract
Artificial intelligence (AI) has demonstrated significant promise for the present and future diagnosis of diseases. At the moment, AI-powered diagnostic technologies can help physicians decipher medical pictures like X-rays, magnetic resonance imaging, and computed tomography scans, resulting in quicker and more precise diagnoses. In order to make a prospective diagnosis, AI algorithms may also examine patient information, symptoms, and medical background. The application of AI in disease diagnosis is anticipated to grow as the field develops. In the future, AI may be used to find patterns in enormous volumes of medical data, aiding in disease prediction and prevention before symptoms appear. Additionally, by combining genetic data, lifestyle data, and environmental variables, AI may help in the diagnosis of complicated diseases. It is crucial to remember that while AI can be a powerful tool, it cannot take the place of qualified medical personnel. Instead, AI ought to support and improve diagnostic procedures, enhancing patient care and healthcare results. Future research and the use of AI for disease diagnosis must take ethical issues, data protection, and ongoing model validation into account.
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Affiliation(s)
- Vidhya Rekha Umapathy
- Public Health Dentistry, Thai Moogambigai Dental College and Hospital, Dr. MGR Educational and Research Institute, Chennai, IND
| | - Suba Rajinikanth B
- Paediatrics, Faculty of Medicine-Sri Lalithambigai Medical College and Hospital, Dr. MGR Educational and Research Institute, Chennai, IND
| | | | - Sankalp Yadav
- Medicine, Shri Madan Lal Khurana Chest Clinic, Moti Nagar, New Delhi, IND
| | - Sithy Athiya Munavarah
- Pathology, Sri Lalithambigai Medical College and Hospital, Dr. MGR Educational and Research Institute, Chennai, IND
| | | | - A Vinita Mary
- Public Health Dentistry, Thai Moogambigai Dental College and Hospital, Dr. MGR Educational and Research Institute, Chennai, IND
| | - Karthika Padmavathy
- Pathology, Sri Lalithambigai Medical College and Hospital, Dr. MGR Educational and Research Institute, Chennai, IND
| | - Akshay R
- Computer Science and Engineering, School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, IND
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Horwitz AG, Kentopp SD, Cleary J, Ross K, Wu Z, Sen S, Czyz EK. Using machine learning with intensive longitudinal data to predict depression and suicidal ideation among medical interns over time. Psychol Med 2023; 53:5778-5785. [PMID: 36177889 PMCID: PMC10060441 DOI: 10.1017/s0033291722003014] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/16/2022] [Revised: 08/04/2022] [Accepted: 09/05/2022] [Indexed: 11/05/2022]
Abstract
BACKGROUND Use of intensive longitudinal methods (e.g. ecological momentary assessment, passive sensing) and machine learning (ML) models to predict risk for depression and suicide has increased in recent years. However, these studies often vary considerably in length, ML methods used, and sources of data. The present study examined predictive accuracy for depression and suicidal ideation (SI) as a function of time, comparing different combinations of ML methods and data sources. METHODS Participants were 2459 first-year training physicians (55.1% female; 52.5% White) who were provided with Fitbit wearable devices and assessed daily for mood. Linear [elastic net regression (ENR)] and non-linear (random forest) ML algorithms were used to predict depression and SI at the first-quarter follow-up assessment, using two sets of variables (daily mood features only, daily mood features + passive-sensing features). To assess accuracy over time, models were estimated iteratively for each of the first 92 days of internship, using data available up to that point in time. RESULTS ENRs using only the daily mood features generally had the best accuracy for predicting mental health outcomes, and predictive accuracy within 1 standard error of the full 92 day models was attained by weeks 7-8. Depression at 92 days could be predicted accurately (area under the curve >0.70) after only 14 days of data collection. CONCLUSIONS Simpler ML methods may outperform more complex methods until passive-sensing features become better specified. For intensive longitudinal studies, there may be limited predictive value in collecting data for more than 2 months.
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Affiliation(s)
- Adam G. Horwitz
- Department of Psychiatry, University of Michigan, Ann Arbor, MI, USA
| | - Shane D. Kentopp
- Department of Psychiatry, University of Michigan, Ann Arbor, MI, USA
| | - Jennifer Cleary
- Department of Psychology, University of Michigan, Ann Arbor, MI, USA
| | - Katherine Ross
- Department of Psychology, University of Michigan, Ann Arbor, MI, USA
| | - Zhenke Wu
- Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA
| | - Srijan Sen
- Department of Psychiatry, University of Michigan, Ann Arbor, MI, USA
| | - Ewa K. Czyz
- Department of Psychiatry, University of Michigan, Ann Arbor, MI, USA
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Czyz EK, King CA, Al-Dajani N, Zimmermann L, Hong V, Nahum-Shani I. Ecological Momentary Assessments and Passive Sensing in the Prediction of Short-Term Suicidal Ideation in Young Adults. JAMA Netw Open 2023; 6:e2328005. [PMID: 37552477 PMCID: PMC10410485 DOI: 10.1001/jamanetworkopen.2023.28005] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/28/2023] [Accepted: 06/29/2023] [Indexed: 08/09/2023] Open
Abstract
Importance Advancements in technology, including mobile-based ecological momentary assessments (EMAs) and passive sensing, have immense potential to identify short-term suicide risk. However, the extent to which EMA and passive data, particularly in combination, have utility in detecting short-term risk in everyday life remains poorly understood. Objective To examine whether and what combinations of self-reported EMA and sensor-based assessments identify next-day suicidal ideation. Design, Setting, and Participants In this intensive longitudinal prognostic study, participants completed EMAs 4 times daily and wore a sensor wristband (Fitbit Charge 3) for 8 weeks. Multilevel machine learning methods, including penalized generalized estimating equations and classification and regression trees (CARTs) with repeated 5-fold cross-validation, were used to optimize prediction of next-day suicidal ideation based on time-varying features from EMAs (affective, cognitive, behavioral risk factors) and sensor data (sleep, activity, heart rate). Young adult patients who visited an emergency department with recent suicidal ideation and/or suicide attempt were recruited. Identified via electronic health record screening, eligible individuals were contacted remotely to complete enrollment procedures. Participants (aged 18 to 25 years) completed 14 708 EMA observations (64.4% adherence) and wore a sensor wristband approximately half the time (55.6% adherence). Data were collected between June 2020 and July 2021. Statistical analysis was performed from January to March 2023. Main Outcomes and Measures The outcome was presence of next-day suicidal ideation. Results Among 102 enrolled participants, 83 (81.4%) were female; 6 (5.9%) were Asian, 5 (4.9%) were Black or African American, 9 (8.8%) were more than 1 race, and 76 (74.5%) were White; mean (SD) age was 20.9 (2.1) years. The best-performing model incorporated features from EMAs and showed good predictive accuracy (mean [SE] cross-validated area under the receiver operating characteristic curve [AUC], 0.84 [0.02]), whereas the model that incorporated features from sensor data alone showed poor prediction (mean [SE] cross-validated AUC, 0.56 [0.02]). Sensor-based features did not improve prediction when combined with EMAs. Suicidal ideation-related features were the strongest predictors of next-day ideation. When suicidal ideation features were excluded, an alternative EMA model had acceptable predictive accuracy (mean [SE] cross-validated AUC, 0.76 [0.02]). Both EMA models included features at different timescales reflecting within-day, end-of-day, and time-varying cumulative effects. Conclusions and Relevance In this prognostic study, self-reported risk factors showed utility in identifying near-term suicidal thoughts. Best-performing models required self-reported information, derived from EMAs, whereas sensor-based data had negligible predictive accuracy. These results may have implications for developing decision algorithms identifying near-term suicidal thoughts to guide risk monitoring and intervention delivery in everyday life.
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Affiliation(s)
- Ewa K. Czyz
- Department of Psychiatry, University of Michigan, Ann Arbor
| | - Cheryl A. King
- Department of Psychiatry, University of Michigan, Ann Arbor
- Department of Psychology, University of Michigan, Ann Arbor
| | - Nadia Al-Dajani
- Department of Psychiatry, University of Michigan, Ann Arbor
- Now with Department of Psychological and Brain Sciences, University of Louisville, Louisville, Kentucky
| | - Lauren Zimmermann
- Department of Psychiatry, University of Michigan, Ann Arbor
- Institute for Social Research, University of Michigan, Ann Arbor
| | - Victor Hong
- Department of Psychiatry, University of Michigan, Ann Arbor
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Ammerman BA, Jacobucci R. The impact of social connection on near-term suicidal ideation. Psychiatry Res 2023; 326:115338. [PMID: 37453309 DOI: 10.1016/j.psychres.2023.115338] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/17/2023] [Revised: 07/05/2023] [Accepted: 07/06/2023] [Indexed: 07/18/2023]
Abstract
While predominant suicide theories emphasize the role of social connectedness in suicidal thinking, there is a need to better understand (a) how specific aspects of social connection relate to suicidal ideation and (b) the timeframe over which these relationships persist. The current study examined ecological momentary assessment data over a 30-day period from 35 participants with past-year suicidal thoughts or behaviors (mean age = 25.88; 62.9% women; 68.6% White) to address these questions. Results demonstrated that absence of social contact was associated with next timepoint suicidal ideation, even after considering the suicidal ideation autoregressive effect (i.e., concurrent), with effects strongest in the short-term. Findings provide preliminary evidence of the need to assess for the presence of social contact, and for assessments to occur in close proximity (i.e., a few hours), to capture the true dynamics of risk for suicidal ideation. Although needing replication, results suggest importance of just-in-time interventions targeting suicidal ideation.
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Affiliation(s)
- Brooke A Ammerman
- Department of Psychology, University of Notre Dame, 390 Corbett Family Hall, Notre Dame, IN 46656, United States.
| | - Ross Jacobucci
- Department of Psychology, University of Notre Dame, 390 Corbett Family Hall, Notre Dame, IN 46656, United States
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16
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Levis M, Levy J, Dufort V, Russ CJ, Shiner B. Dynamic suicide topic modelling: Deriving population-specific, psychosocial and time-sensitive suicide risk variables from Electronic Health Record psychotherapy notes. Clin Psychol Psychother 2023; 30:795-810. [PMID: 36797651 PMCID: PMC11172400 DOI: 10.1002/cpp.2842] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2023] [Accepted: 02/14/2023] [Indexed: 02/18/2023]
Abstract
In the machine learning subfield of natural language processing, a topic model is a type of unsupervised method that is used to uncover abstract topics within a corpus of text. Dynamic topic modelling (DTM) is used for capturing change in these topics over time. The study deploys DTM on corpus of electronic health record psychotherapy notes. This retrospective study examines whether DTM helps distinguish closely matched patients that did and did not die by suicide. Cohort consists of United States Department of Veterans Affairs (VA) patients diagnosed with Posttraumatic Stress Disorder (PTSD) between 2004 and 2013. Each case (those who died by suicide during the year following diagnosis) was matched with five controls (those who remained alive) that shared psychotherapists and had similar suicide risk based on VA's suicide prediction algorithm. Cohort was restricted to patients who received psychotherapy for 9+ months after initial PTSD diagnoses (cases = 77; controls = 362). For cases, psychotherapy notes from diagnosis until death were examined. For controls, psychotherapy notes from diagnosis until matched case's death date were examined. A Python-based DTM algorithm was utilized. Derived topics identified population-specific themes, including PTSD, psychotherapy, medication, communication and relationships. Control topics changed significantly more over time than case topics. Topic differences highlighted engagement, expressivity and therapeutic alliance. This study strengthens groundwork for deriving population-specific, psychosocial and time-sensitive suicide risk variables.
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Affiliation(s)
- Maxwell Levis
- White River Junction VA Medical Center, Hartford, Vermont, USA
- Geisel School of Medicine at Dartmouth, Hanover, New Hampshire, USA
| | - Joshua Levy
- Geisel School of Medicine at Dartmouth, Hanover, New Hampshire, USA
| | - Vincent Dufort
- White River Junction VA Medical Center, Hartford, Vermont, USA
| | - Carey J. Russ
- White River Junction VA Medical Center, Hartford, Vermont, USA
- Geisel School of Medicine at Dartmouth, Hanover, New Hampshire, USA
| | - Brian Shiner
- White River Junction VA Medical Center, Hartford, Vermont, USA
- Geisel School of Medicine at Dartmouth, Hanover, New Hampshire, USA
- National Center for PTSD Executive Division, Hartford, Vermont, USA
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17
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Charlton CE, Karvelis P, McIntyre RS, Diaconescu AO. Suicide prevention and ketamine: insights from computational modeling. Front Psychiatry 2023; 14:1214018. [PMID: 37457775 PMCID: PMC10342546 DOI: 10.3389/fpsyt.2023.1214018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/28/2023] [Accepted: 06/12/2023] [Indexed: 07/18/2023] Open
Abstract
Suicide is a pressing public health issue, with over 700,000 individuals dying each year. Ketamine has emerged as a promising treatment for suicidal thoughts and behaviors (STBs), yet the complex mechanisms underlying ketamine's anti-suicidal effect are not fully understood. Computational psychiatry provides a promising framework for exploring the dynamic interactions underlying suicidality and ketamine's therapeutic action, offering insight into potential biomarkers, treatment targets, and the underlying mechanisms of both. This paper provides an overview of current computational theories of suicidality and ketamine's mechanism of action, and discusses various computational modeling approaches that attempt to explain ketamine's anti-suicidal effect. More specifically, the therapeutic potential of ketamine is explored in the context of the mismatch negativity and the predictive coding framework, by considering neurocircuits involved in learning and decision-making, and investigating altered connectivity strengths and receptor densities targeted by ketamine. Theory-driven computational models offer a promising approach to integrate existing knowledge of suicidality and ketamine, and for the extraction of model-derived mechanistic parameters that can be used to identify patient subgroups and personalized treatment approaches. Future computational studies on ketamine's mechanism of action should optimize task design and modeling approaches to ensure parameter reliability, and external factors such as set and setting, as well as psychedelic-assisted therapy should be evaluated for their additional therapeutic value.
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Affiliation(s)
- Colleen E. Charlton
- Krembil Center for Neuroinformatics, Center for Addiction and Mental Health (CAMH), Toronto, ON, Canada
| | - Povilas Karvelis
- Krembil Center for Neuroinformatics, Center for Addiction and Mental Health (CAMH), Toronto, ON, Canada
| | - Roger S. McIntyre
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada
- Department of Pharmacology and Toxicology, University of Toronto, Toronto, ON, Canada
| | - Andreea O. Diaconescu
- Krembil Center for Neuroinformatics, Center for Addiction and Mental Health (CAMH), Toronto, ON, Canada
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada
- Institute of Medical Sciences, University of Toronto, Toronto, ON, Canada
- Department of Psychology, University of Toronto, Toronto, ON, Canada
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Kleiman EM, Glenn CR, Liu RT. The use of advanced technology and statistical methods to predict and prevent suicide. NATURE REVIEWS PSYCHOLOGY 2023; 2:347-359. [PMID: 37588775 PMCID: PMC10426769 DOI: 10.1038/s44159-023-00175-y] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 03/13/2023] [Indexed: 08/18/2023]
Abstract
In the past decade, two themes have emerged across suicide research. First, according to meta-analyses, the ability to predict and prevent suicidal thoughts and behaviours is weaker than would be expected for the size of the field. Second, review and commentary papers propose that technological and statistical methods (such as smartphones, wearables, digital phenotyping and machine learning) might become solutions to this problem. In this Review, we aim to strike a balance between the pessimistic picture presented by these meta-analyses and the optimistic picture presented by review and commentary papers about the promise of advanced technological and statistical methods to improve the ability to understand, predict and prevent suicide. We divide our discussion into two broad categories. First, we discuss the research aimed at assessment, with the goal of better understanding or more accurately predicting suicidal thoughts and behaviours. Second, we discuss the literature that focuses on prevention of suicidal thoughts and behaviours. Ecological momentary assessment, wearables and other technological and statistical advances hold great promise for predicting and preventing suicide, but there is much yet to do.
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Affiliation(s)
- Evan M. Kleiman
- Department of Psychology, Rutgers, The State University of New Jersey, Piscataway, NJ, USA
| | | | - Richard T. Liu
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
- Department of Psychiatry, Massachusetts General Hospital, Boston, MA, USA
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Czyz EK, Koo HJ, Al-Dajani N, King CA, Nahum-Shani I. Predicting short-term suicidal thoughts in adolescents using machine learning: developing decision tools to identify daily level risk after hospitalization. Psychol Med 2023; 53:2982-2991. [PMID: 34879890 PMCID: PMC9814182 DOI: 10.1017/s0033291721005006] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/06/2021] [Revised: 10/22/2021] [Accepted: 11/16/2021] [Indexed: 01/11/2023]
Abstract
BACKGROUND Mobile technology offers unique opportunities for monitoring short-term suicide risk in daily life. In this study of suicidal adolescent inpatients, theoretically informed risk factors were assessed daily following discharge to predict near-term suicidal ideation and inform decision algorithms for identifying elevations in daily level risk, with implications for real-time suicide-focused interventions. METHODS Adolescents (N = 78; 67.9% female) completed brief surveys texted daily for 4 weeks after discharge (n = 1621 observations). Using multi-level classification and regression trees (CARTSs) with repeated 5-fold cross-validation, we tested (a) a simple prediction model incorporating previous-day scores for each of 10 risk factors, and (b) a more complex model incorporating, for each of these factors, a time-varying person-specific mean over prior days together with deviation from that mean. Models also incorporated missingness and contextual (study week, day of the week) indicators. The outcome was the presence/absence of next-day suicidal ideation. RESULTS The best-performing model (cross-validated AUC = 0.86) was a complex model that included ideation duration, hopelessness, burdensomeness, and self-efficacy to refrain from suicidal action. An equivalent model that excluded ideation duration had acceptable overall performance (cross-validated AUC = 0.78). Models incorporating only previous-day scores, with and without ideation duration (cross-validated AUC of 0.82 and 0.75, respectively), showed relatively weaker performance. CONCLUSIONS Results suggest that specific combinations of dynamic risk factors assessed in adolescents' daily life have promising utility in predicting next-day suicidal thoughts. Findings represent an important step in the development of decision tools identifying short-term risk as well as guiding timely interventions sensitive to proximal elevations in suicide risk in daily life.
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Affiliation(s)
- E. K. Czyz
- Department of Psychiatry, University of Michigan, Ann Arbor, MI, USA
| | - H. J. Koo
- Department of Psychiatry, University of Michigan, Ann Arbor, MI, USA
| | - N. Al-Dajani
- Department of Psychiatry, University of Michigan, Ann Arbor, MI, USA
| | - C. A. King
- Department of Psychiatry, University of Michigan, Ann Arbor, MI, USA
| | - I. Nahum-Shani
- Institute for Social Research, University of Michigan, Ann Arbor, MI, USA
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20
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Alon N, Perret S, Segal R, Torous J. Clinical Considerations for Digital Resources in Care for Patients With Suicidal Ideation. FOCUS (AMERICAN PSYCHIATRIC PUBLISHING) 2023; 21:160-165. [PMID: 37201138 PMCID: PMC10172563 DOI: 10.1176/appi.focus.20220073] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/20/2023]
Abstract
Smartphone apps offer accessible new tools that may help prevent suicide and that offer support for individuals with active suicidal ideation. Numerous smartphone apps for mental health conditions exist; however, their functionality is limited, and evidence is nascent. A new generation of apps using smartphone sensors and integrating real-time data on evolving risk offers the potential of more personalized support, but these apps present ethical risks and currently remain more in the research domain than in the clinical domain. Nevertheless, clinicians can use apps to benefit patients. This article outlines practical strategies to select safe and effective apps for the creation of a digital toolkit that can augment suicide prevention and safety plans. By creating a unique digital toolkit for each patient, clinicians can help ensure that the apps selected will be most relevant, engaging, and effective.
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Affiliation(s)
- Noy Alon
- Division of Digital Psychiatry (Alon, Perret, Torous) and mental health services consultant (Segal), Beth Israel Deaconess Medical Center, Harvard Medical School, Boston
| | - Sarah Perret
- Division of Digital Psychiatry (Alon, Perret, Torous) and mental health services consultant (Segal), Beth Israel Deaconess Medical Center, Harvard Medical School, Boston
| | - Rebecca Segal
- Division of Digital Psychiatry (Alon, Perret, Torous) and mental health services consultant (Segal), Beth Israel Deaconess Medical Center, Harvard Medical School, Boston
| | - John Torous
- Division of Digital Psychiatry (Alon, Perret, Torous) and mental health services consultant (Segal), Beth Israel Deaconess Medical Center, Harvard Medical School, Boston
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21
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Mann JJ, Michel CA, Auerbach RP. Improving Suicide Prevention Through Evidence-Based Strategies: A Systematic Review. FOCUS (AMERICAN PSYCHIATRIC PUBLISHING) 2023; 21:182-196. [PMID: 37201140 PMCID: PMC10172556 DOI: 10.1176/appi.focus.23021004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/20/2023]
Abstract
Objective The authors sought to identify scalable evidence-based suicide prevention strategies. Methods A search of PubMed and Google Scholar identi- fied 20,234 articles published between September 2005 and December 2019, of which 97 were randomized controlled trials with suicidal behavior or ideation as primary outcomes or epidemiological studies of limiting access to lethal means, using educational approaches, and the impact of antidepressant treatment. Results Training primary care physicians in depression rec- ognition and treatment prevents suicide. Educating youths on depression and suicidal behavior, as well as active out- reach to psychiatric patients after discharge or a suicidal crisis, prevents suicidal behavior. Meta-analyses find that antidepressants prevent suicide attempts, but individual randomized controlled trials appear to be underpowered. Ketamine reduces suicidal ideation in hours but is untested for suicidal behavior prevention. Cognitive-behavioral therapy and dialectical behavior therapy prevent suicidal behavior. Active screening for suicidal ideation or behavior is not proven to be better than just screening for depression. Education of gatekeepers about youth suicidal behavior lacks effectiveness. No randomized trials have been reported for gatekeeper training for prevention of adult suicidal behavior. Algorithm-driven electronic health record screening, Internet-based screening, and smartphone passive monitoring to identify high-risk patients are under-studied. Means restriction, including of firearms, prevents suicide but is sporadically employed in the United States, even though firearms are used in half of all U.S. suicides. Conclusions Training general practitioners warrants wider implementation and testing in other nonpsychiatrist physi- cian settings. Active follow-up of patients after discharge or a suicide-related crisis should be routine, and restricting firearm access by at-risk individuals warrants wider use. Combination approaches in health care systems show promise in reducing suicide in several countries, but evaluating the benefit attributable to each component is essential. Further suicide rate reduction requires evaluating newer approaches, such as electronic health record-derived algorithms, Internet-based screening methods, ketamine's potential benefit for preventing attempts, and passive monitoring of acute suicide risk change.Reprinted from Am J Psychiatry 2021; 178:611-624, with permission from American Psychiatric Association Publishing. Copyright © 2021.
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Affiliation(s)
- J John Mann
- Division of Molecular Imaging and Neuropathology (Mann, Michel) and Division of Child and Adolescent Psychiatry (Auerbach), New York State Psychiatric Institute and Department of Psychiatry, Columbia University, New York (Mann, Auerbach); Division of Clinical Developmental Neuro- science, Sackler Institute for Developmental Psychobiology, Columbia University, New York (Auerbach)
| | - Christina A Michel
- Division of Molecular Imaging and Neuropathology (Mann, Michel) and Division of Child and Adolescent Psychiatry (Auerbach), New York State Psychiatric Institute and Department of Psychiatry, Columbia University, New York (Mann, Auerbach); Division of Clinical Developmental Neuro- science, Sackler Institute for Developmental Psychobiology, Columbia University, New York (Auerbach)
| | - Randy P Auerbach
- Division of Molecular Imaging and Neuropathology (Mann, Michel) and Division of Child and Adolescent Psychiatry (Auerbach), New York State Psychiatric Institute and Department of Psychiatry, Columbia University, New York (Mann, Auerbach); Division of Clinical Developmental Neuro- science, Sackler Institute for Developmental Psychobiology, Columbia University, New York (Auerbach)
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22
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Patchin JW, Hinduja S, Meldrum RC. Digital self-harm and suicidality among adolescents. Child Adolesc Ment Health 2023; 28:52-59. [PMID: 35811440 DOI: 10.1111/camh.12574] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 05/17/2022] [Indexed: 01/25/2023]
Abstract
BACKGROUND Research on digital self-harm - the anonymous online posting, sending, or otherwise sharing of hurtful content about oneself - is still in its infancy. Yet unexplored is whether digital self-harm is related to suicidal ideation or suicide attempts. METHODS In the current study, survey data were collected in 2019 from a national sample of 4972 American middle and high school students (Mage = 14.5; 50% female). Logistic regression analysis was used to assess whether lifetime engagement in two different indicators of digital self-harm was associated with suicidal thoughts and attempts within the past year. RESULTS Logistic regression analysis showed that engagement in digital self-harm was associated with a five- to sevenfold increase in the likelihood of reporting suicidal thoughts and a nine- to 15-fold increase in the likelihood of a suicide attempt. CONCLUSIONS Results suggest a connection between digital self-harm and suicidality. As such, health professionals must screen for digital self-harm to address underlying mental health problems among youth that may occur prior to or alongside suicidality, and parents/caregivers must convey to children that they are available to dialog, support, and assist with the root issues that may eventually manifest as digital self-harm.
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Affiliation(s)
- Justin W Patchin
- Department of Political Science, University of Wisconsin-Eau Claire, Eau Claire, WI, USA
| | - Sameer Hinduja
- School of Criminology and Criminal Justice, Florida Atlantic University, Boca Raton, FL, USA
| | - Ryan C Meldrum
- Department of Criminology and Criminal Justice, Florida International University, Miami, FL, USA
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23
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Gaffney MR, Adams KH, Syme KL, Hagen EH. Response to: "Are depression and suicidality evolved signals? Evidently, no". EVOL HUM BEHAV 2023. [DOI: 10.1016/j.evolhumbehav.2023.02.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/25/2023]
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24
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Sander LB, Spangenberg L, La Sala L, Van Ballegooijen W. Editorial: Digital suicide prevention. Front Digit Health 2023; 5:1148356. [PMID: 36937249 PMCID: PMC10020690 DOI: 10.3389/fdgth.2023.1148356] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2023] [Accepted: 02/03/2023] [Indexed: 03/06/2023] Open
Affiliation(s)
- Lasse Bosse Sander
- Medical Psychology and Medical Sociology, Faculty of Medicine, University of Freiburg, Freiburg, Germany
- Correspondence: Lasse Bosse Sander
| | - Lena Spangenberg
- Department of Medical Psychology and Medical Sociology, Leipzig University, Leipzig, Germany
| | - Louise La Sala
- Orygen, Centre for Youth Mental Health, University of Melbourne, Parkville, VIC, Australia
| | - Wouter Van Ballegooijen
- Department of Clinical, Neuro and Developmental Psychology & Department of Psychiatry, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
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25
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Srinivansan S, Harnett NG, Zhang L, Dahlgren MK, Jang J, Lu S, Nephew BC, Palermo CA, Pan X, Eltabakh MY, Frederick BB, Gruber SA, Kaufman ML, King J, Ressler KJ, Winternitz S, Korkin D, Lebois LAM. Unravelling psychiatric heterogeneity and predicting suicide attempts in women with trauma-related dissociation using artificial intelligence. Eur J Psychotraumatol 2022; 13:2143693. [PMID: 38872600 PMCID: PMC9677973 DOI: 10.1080/20008066.2022.2143693] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/01/2022] [Accepted: 10/20/2022] [Indexed: 11/19/2022] Open
Abstract
Background: Suicide is a leading cause of death, and rates of attempted suicide have increased during the COVID-19 pandemic. The under-diagnosed psychiatric phenotype of dissociation is associated with elevated suicidal self-injury; however, it has largely been left out of attempts to predict and prevent suicide.Objective: We designed an artificial intelligence approach to identify dissociative patients and predict prior suicide attempts in an unbiased, data-driven manner.Method: Participants were 30 controls and 93 treatment-seeking female patients with posttraumatic stress disorder (PTSD) and various levels of dissociation, including some with the PTSD dissociative subtype and some with dissociative identity disorder (DID).Results: Unsupervised learning models identified patients along a spectrum of dissociation. Moreover, supervised learning models accurately predicted prior suicide attempts with an F1 score up to 0.83. DID had the highest risk of prior suicide attempts, and distinct subtypes of dissociation predicted suicide attempts in PTSD and DID.Conclusions: These findings expand our understanding of the dissociative phenotype and underscore the urgent need to assess for dissociation to identify individuals at high-risk of suicidal self-injury.
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Affiliation(s)
- Suhas Srinivansan
- Data Science Program, Worcester Polytechnic Institute, Worcester, MA, USA
- Department of Dermatology, Stanford School of Medicine, Stanford, CA, USA
| | - Nathaniel G. Harnett
- McLean Hospital, Belmont, MA, USA
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
| | - Liang Zhang
- Data Science Program, Worcester Polytechnic Institute, Worcester, MA, USA
| | - M. Kathryn Dahlgren
- McLean Hospital, Belmont, MA, USA
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
| | - Junbong Jang
- Department of Computer Science, Worcester Polytechnic Institute, Worcester, MA, USA
| | - Senbao Lu
- Bioinformatics and Computational Biology Program, Worcester Polytechnic Institute, Worcester, MA, USA
| | - Benjamin C. Nephew
- Department of Biology and Biotechnology, Worcester Polytechnic Institute, Worcester, MA, USA
- Department of Neuroscience, Worcester Polytechnic Institute, Worcester, MA, USA
| | | | - Xi Pan
- McLean Hospital, Belmont, MA, USA
| | - Mohamed Y. Eltabakh
- Data Science Program, Worcester Polytechnic Institute, Worcester, MA, USA
- Department of Computer Science, Worcester Polytechnic Institute, Worcester, MA, USA
- Bioinformatics and Computational Biology Program, Worcester Polytechnic Institute, Worcester, MA, USA
| | - Blaise B. Frederick
- McLean Hospital, Belmont, MA, USA
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
| | - Staci A. Gruber
- McLean Hospital, Belmont, MA, USA
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
| | - Milissa L. Kaufman
- McLean Hospital, Belmont, MA, USA
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
| | - Jean King
- Department of Biology and Biotechnology, Worcester Polytechnic Institute, Worcester, MA, USA
- Department of Neuroscience, Worcester Polytechnic Institute, Worcester, MA, USA
| | - Kerry J. Ressler
- McLean Hospital, Belmont, MA, USA
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
| | - Sherry Winternitz
- McLean Hospital, Belmont, MA, USA
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
| | - Dmitry Korkin
- Data Science Program, Worcester Polytechnic Institute, Worcester, MA, USA
- Department of Computer Science, Worcester Polytechnic Institute, Worcester, MA, USA
- Bioinformatics and Computational Biology Program, Worcester Polytechnic Institute, Worcester, MA, USA
- Department of Biology and Biotechnology, Worcester Polytechnic Institute, Worcester, MA, USA
- Department of Neuroscience, Worcester Polytechnic Institute, Worcester, MA, USA
| | - Lauren A. M. Lebois
- McLean Hospital, Belmont, MA, USA
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
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26
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Winkler T, Büscher R, Larsen ME, Kwon S, Torous J, Firth J, Sander LB. Passive Sensing in the Prediction of Suicidal Thoughts and Behaviors: Protocol for a Systematic Review. JMIR Res Protoc 2022; 11:e42146. [PMID: 36445737 PMCID: PMC9748797 DOI: 10.2196/42146] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2022] [Revised: 10/19/2022] [Accepted: 10/25/2022] [Indexed: 11/05/2022] Open
Abstract
BACKGROUND Suicide is a severe public health problem, resulting in a high number of attempts and deaths each year. Early detection of suicidal thoughts and behaviors (STBs) is key to preventing attempts. We discuss passive sensing of digital and behavioral markers to enhance the detection and prediction of STBs. OBJECTIVE The paper presents the protocol for a systematic review that aims to summarize existing research on passive sensing of STBs and evaluate whether the STB prediction can be improved using passive sensing compared to prior prediction models. METHODS A systematic search will be conducted in the scientific databases MEDLINE, PubMed, Embase, PsycINFO, and Web of Science. Eligible studies need to investigate any passive sensor data from smartphones or wearables to predict STBs. The predictive value of passive sensing will be the primary outcome. The practical implications and feasibility of the studies will be considered as secondary outcomes. Study quality will be assessed using the Prediction Model Risk of Bias Assessment Tool (PROBAST). If studies are sufficiently homogenous, we will conduct a meta-analysis of the predictive value of passive sensing on STBs. RESULTS The review process started in July 2022 with data extraction in September 2022. Results are expected in December 2022. CONCLUSIONS Despite intensive research efforts, the ability to predict STBs is little better than chance. This systematic review will contribute to our understanding of the potential of passive sensing to improve STB prediction. Future research will be stimulated since gaps in the current literature will be identified and promising next steps toward clinical implementation will be outlined. TRIAL REGISTRATION OSF Registries osf-registrations-hzxua-v1; https://osf.io/hzxua. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID) DERR1-10.2196/42146.
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Affiliation(s)
- Tanita Winkler
- Institute of Psychology, University of Freiburg, Freiburg, Germany
| | - Rebekka Büscher
- Medical Psychology and Medical Sociology, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Mark Erik Larsen
- Black Dog Institute, University of New South Wales, Sydney, Australia
| | - Sam Kwon
- Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, United States
| | - John Torous
- Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, United States
| | - Joseph Firth
- Division of Psychology and Mental Health, Manchester Academic Health Science Centre, University of Manchester, Manchester, United Kingdom
| | - Lasse B Sander
- Medical Psychology and Medical Sociology, Faculty of Medicine, University of Freiburg, Freiburg, Germany
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27
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Liu X, Liu M, Li H, Mo L, Liu X. Transition from Depression to Suicidal Attempt in Young Adults: The Mediation Effect of Self-Esteem and Interpersonal Needs. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:14342. [PMID: 36361235 PMCID: PMC9656722 DOI: 10.3390/ijerph192114342] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/11/2022] [Revised: 10/26/2022] [Accepted: 11/01/2022] [Indexed: 06/16/2023]
Abstract
BACKGROUND Depression increases the risk of suicide. Depression and suicide attempts are significantly impacted by low self-esteem and interpersonal needs (i.e., thwarted belongingness (TB) and perceived burdensomeness (PB)). More research is required to clarify how these factors affected the change from depression to suicidal attempts, which would dramatically lower the suicide fatality rate. We sought to examine the mediating roles of self-esteem, TB, and PB in Chinese young adults, since previous research shows that self-esteem has a strong relationship with TB, while TB and PB have strong relationships with suicide attempts. METHODS Measures on depression, interpersonal needs, and attempted suicide were completed by a sample of 247 Chinese social media users who had stated suicidal ideation online. RESULTS The findings showed that people who attempted suicide had significantly higher levels of TB and PB. Suicidal attempts were also impacted by depression via the mediational chains, which included self-esteem, TB, and PB. CONCLUSIONS Our findings might contribute to the expansion of the interpersonal theory of suicide and have an impact on effective suicide prevention.
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Affiliation(s)
- Xingyun Liu
- Key Laboratory of Adolescent Cyberpsychology and Behavior, Ministry of Education, Key Laboratory of Human Development and Mental Health of Hubei Province, School of Psychology, Central China Normal University, Wuhan 430079, China
| | - Miao Liu
- Key Laboratory of Adolescent Cyberpsychology and Behavior, Ministry of Education, Key Laboratory of Human Development and Mental Health of Hubei Province, School of Psychology, Central China Normal University, Wuhan 430079, China
| | - He Li
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Chinese Academy of Sciences, Beijing 100101, China
| | - Liuling Mo
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Chinese Academy of Sciences, Beijing 100101, China
| | - Xiaoqian Liu
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Chinese Academy of Sciences, Beijing 100101, China
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28
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Holmgren JG, Morrow A, Coffee AK, Nahod PM, Santora SH, Schwartz B, Stiegmann RA, Zanetti CA. Utilizing digital predictive biomarkers to identify Veteran suicide risk. Front Digit Health 2022; 4:913590. [PMID: 36329831 PMCID: PMC9624222 DOI: 10.3389/fdgth.2022.913590] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2022] [Accepted: 09/12/2022] [Indexed: 12/02/2022] Open
Abstract
Veteran suicide is one of the most complex and pressing health issues in the United States. According to the 2020 National Veteran Suicide Prevention Annual Report, since 2018 an average of 17.2 Veterans died by suicide each day. Veteran suicide risk screening is currently limited to suicide hotlines, patient reporting, patient visits, and family or friend reporting. As a result of these limitations, innovative approaches in suicide screening are increasingly garnering attention. An essential feature of these innovative methods includes better incorporation of risk factors that might indicate higher risk for tracking suicidal ideation based on personal behavior. Digital technologies create a means through which measuring these risk factors more reliably, with higher fidelity, and more frequently throughout daily life is possible, with the capacity to identify potentially telling behavior patterns. In this review, digital predictive biomarkers are discussed as they pertain to suicide risk, such as sleep vital signs, sleep disturbance, sleep quality, and speech pattern recognition. Various digital predictive biomarkers are reviewed and evaluated as well as their potential utility in predicting and diagnosing Veteran suicidal ideation in real time. In the future, these digital biomarkers could be combined to generate further suicide screening for diagnosis and severity assessments, allowing healthcare providers and healthcare teams to intervene more optimally.
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Affiliation(s)
- Jackson G. Holmgren
- Rocky Vista University College of Osteopathic Medicine, Ivins, UT, United States,Correspondence: Jackson G. Holmgren
| | - Adelene Morrow
- Rocky Vista University College of Osteopathic Medicine, Ivins, UT, United States
| | - Ali K. Coffee
- Rocky Vista University College of Osteopathic Medicine, Ivins, UT, United States
| | - Paige M. Nahod
- Rocky Vista University College of Osteopathic Medicine, Parker, CO, United States
| | - Samantha H. Santora
- Rocky Vista University College of Osteopathic Medicine, Parker, CO, United States
| | - Brian Schwartz
- Department of Medical Humanities, Rocky Vista University College of Osteopathic Medicine, Parker, CO, United States
| | - Regan A. Stiegmann
- Department of Tracks and Special Programs, Rocky Vista University College of Osteopathic Medicine, Parker, CO, United States,Flight Medicine, US Air Force Academy, Colorado Springs, CO, United States
| | - Cole A. Zanetti
- Department of Tracks and Special Programs, Rocky Vista University College of Osteopathic Medicine, Parker, CO, United States,Chief Health Informatics Officer, Ralph H Johnson VA Health System, Charleston, SC, United States
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29
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Levis M, Levy J, Dufort V, Gobbel GT, Watts BV, Shiner B. Leveraging unstructured electronic medical record notes to derive population-specific suicide risk models. Psychiatry Res 2022; 315:114703. [PMID: 35841702 DOI: 10.1016/j.psychres.2022.114703] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/30/2022] [Revised: 06/17/2022] [Accepted: 06/29/2022] [Indexed: 01/11/2023]
Abstract
Electronic medical record (EMR)-based suicide risk prediction methods typically rely on analysis of structured variables such as demographics, visit history, and prescription data. Leveraging unstructured EMR notes may improve predictive accuracy by allowing access to nuanced clinical information. We utilized natural language processing (NLP) to analyze a large EMR note corpus to develop a data-driven suicide risk prediction model. We developed a matched case-control sample of U.S. Department of Veterans Affairs (VA) patients in 2015 and 2016. We randomly matched each case (all patients that died by suicide in that interval, n = 5029) with five controls (patients that remained alive). We processed note corpus using NLP methods and applied machine-learning classification algorithms to output. We calculated area under the curve (AUC) and risk tiers to determine predictive accuracy. NLP-derived models demonstrated strong predictive accuracy. Patients that scored within top 10% of risk model accounted for up to 29% of suicide decedents. NLP-derived model compares positively to other leading prediction methods. Our approach is highly implementable, only requiring access to text data and open-source software. Additional studies should evaluate ensemble models incorporating NLP-derived information alongside more typical structured variables.
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Affiliation(s)
- Maxwell Levis
- VAMC White River Junction, 163 Veterans Dr., White River Junction VT, 05009 United States; Department of Psychiatry, Geisel School of Medicine, 1 Rope Ferry Rd, Hanover NH, 03755 United States.
| | - Joshua Levy
- Departments of Pathology and Laboratory Medicine, Geisel School of Medicine, 1 Rope Ferry Rd, Hanover NH, 03755 United States
| | - Vincent Dufort
- VAMC White River Junction, 163 Veterans Dr., White River Junction VT, 05009 United States
| | - Glenn T Gobbel
- Department of Biomedical Informatics, 2201 West End Ave, Nashville TN, 37235 United States
| | - Bradley V Watts
- VAMC White River Junction, 163 Veterans Dr., White River Junction VT, 05009 United States; Department of Psychiatry, Geisel School of Medicine, 1 Rope Ferry Rd, Hanover NH, 03755 United States; VA Office of Systems Redesign and Improvement, 215 North Main Street, White River Junction VT, 05009, United States
| | - Brian Shiner
- VAMC White River Junction, 163 Veterans Dr., White River Junction VT, 05009 United States; Department of Psychiatry, Geisel School of Medicine, 1 Rope Ferry Rd, Hanover NH, 03755 United States; National Center for PTSD, White River Junction, VT, United States
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30
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Hopkins D, Rickwood DJ, Hallford DJ, Watsford C. Structured data vs. unstructured data in machine learning prediction models for suicidal behaviors: A systematic review and meta-analysis. Front Digit Health 2022; 4:945006. [PMID: 35983407 PMCID: PMC9378826 DOI: 10.3389/fdgth.2022.945006] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2022] [Accepted: 06/29/2022] [Indexed: 11/23/2022] Open
Abstract
Suicide remains a leading cause of preventable death worldwide, despite advances in research and decreases in mental health stigma through government health campaigns. Machine learning (ML), a type of artificial intelligence (AI), is the use of algorithms to simulate and imitate human cognition. Given the lack of improvement in clinician-based suicide prediction over time, advancements in technology have allowed for novel approaches to predicting suicide risk. This systematic review and meta-analysis aimed to synthesize current research regarding data sources in ML prediction of suicide risk, incorporating and comparing outcomes between structured data (human interpretable such as psychometric instruments) and unstructured data (only machine interpretable such as electronic health records). Online databases and gray literature were searched for studies relating to ML and suicide risk prediction. There were 31 eligible studies. The outcome for all studies combined was AUC = 0.860, structured data showed AUC = 0.873, and unstructured data was calculated at AUC = 0.866. There was substantial heterogeneity between the studies, the sources of which were unable to be defined. The studies showed good accuracy levels in the prediction of suicide risk behavior overall. Structured data and unstructured data also showed similar outcome accuracy according to meta-analysis, despite different volumes and types of input data.
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Affiliation(s)
- Danielle Hopkins
- Faculty of Health, University of Canberra, Canberra, ACT, Australia
- *Correspondence: Danielle Hopkins
| | | | | | - Clare Watsford
- Faculty of Health, University of Canberra, Canberra, ACT, Australia
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31
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Advancing Digital Medicine with Wearables in the Wild. SENSORS 2022; 22:s22124576. [PMID: 35746358 PMCID: PMC9227612 DOI: 10.3390/s22124576] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 06/09/2022] [Accepted: 06/15/2022] [Indexed: 02/04/2023]
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32
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Bhatt P, Liu J, Gong Y, Wang J, Guo Y. Emerging Artificial Intelligence–Empowered mHealth: Scoping Review. JMIR Mhealth Uhealth 2022; 10:e35053. [PMID: 35679107 PMCID: PMC9227797 DOI: 10.2196/35053] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2021] [Revised: 01/23/2022] [Accepted: 04/08/2022] [Indexed: 11/13/2022] Open
Abstract
Background
Artificial intelligence (AI) has revolutionized health care delivery in recent years. There is an increase in research for advanced AI techniques, such as deep learning, to build predictive models for the early detection of diseases. Such predictive models leverage mobile health (mHealth) data from wearable sensors and smartphones to discover novel ways for detecting and managing chronic diseases and mental health conditions.
Objective
Currently, little is known about the use of AI-powered mHealth (AIM) settings. Therefore, this scoping review aims to map current research on the emerging use of AIM for managing diseases and promoting health. Our objective is to synthesize research in AIM models that have increasingly been used for health care delivery in the last 2 years.
Methods
Using Arksey and O’Malley’s 5-point framework for conducting scoping reviews, we reviewed AIM literature from the past 2 years in the fields of biomedical technology, AI, and information systems. We searched 3 databases, PubsOnline at INFORMS, e-journal archive at MIS Quarterly, and Association for Computing Machinery (ACM) Digital Library using keywords such as “mobile healthcare,” “wearable medical sensors,” “smartphones”, and “AI.” We included AIM articles and excluded technical articles focused only on AI models. We also used the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) technique for identifying articles that represent a comprehensive view of current research in the AIM domain.
Results
We screened 108 articles focusing on developing AIM models for ensuring better health care delivery, detecting diseases early, and diagnosing chronic health conditions, and 37 articles were eligible for inclusion, with 31 of the 37 articles being published last year (76%). Of the included articles, 9 studied AI models to detect serious mental health issues, such as depression and suicidal tendencies, and chronic health conditions, such as sleep apnea and diabetes. Several articles discussed the application of AIM models for remote patient monitoring and disease management. The considered primary health concerns belonged to 3 categories: mental health, physical health, and health promotion and wellness. Moreover, 14 of the 37 articles used AIM applications to research physical health, representing 38% of the total studies. Finally, 28 out of the 37 (76%) studies used proprietary data sets rather than public data sets. We found a lack of research in addressing chronic mental health issues and a lack of publicly available data sets for AIM research.
Conclusions
The application of AIM models for disease detection and management is a growing research domain. These models provide accurate predictions for enabling preventive care on a broader scale in the health care domain. Given the ever-increasing need for remote disease management during the pandemic, recent AI techniques, such as federated learning and explainable AI, can act as a catalyst for increasing the adoption of AIM and enabling secure data sharing across the health care industry.
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Affiliation(s)
- Paras Bhatt
- Department of Electrical & Computer Engineering, The University of Texas at San Antonio, San Antonio, TX, United States
| | - Jia Liu
- The University of Texas Health Science Center at San Antonio, San Antonio, TX, United States
| | - Yanmin Gong
- Department of Electrical & Computer Engineering, The University of Texas at San Antonio, San Antonio, TX, United States
| | - Jing Wang
- Florida State University, Tallahassee, FL, United States
| | - Yuanxiong Guo
- Department of Electrical & Computer Engineering, The University of Texas at San Antonio, San Antonio, TX, United States
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33
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Kilshaw RE, Adamo C, Butner JE, Deboeck PR, Shi Q, Bulik CM, Flatt RE, Thornton LM, Argue S, Tregarthen J, Baucom BRW. Passive Sensor Data for Characterizing States of Increased Risk for Eating Disorder Behaviors in the Digital Phenotyping Arm of the Binge Eating Genetics Initiative: Protocol for an Observational Study. JMIR Res Protoc 2022; 11:e38294. [PMID: 35653175 PMCID: PMC9204566 DOI: 10.2196/38294] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2022] [Revised: 04/10/2022] [Accepted: 04/21/2022] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND Data that can be easily, efficiently, and safely collected via cell phones and other digital devices have great potential for clinical application. Here, we focus on how these data could be used to refine and augment intervention strategies for binge eating disorder (BED) and bulimia nervosa (BN), conditions that lack highly efficacious, enduring, and accessible treatments. These data are easy to collect digitally but are highly complex and present unique methodological challenges that invite innovative solutions. OBJECTIVE We describe the digital phenotyping component of the Binge Eating Genetics Initiative, which uses personal digital device data to capture dynamic patterns of risk for binge and purge episodes. Characteristic data signatures will ultimately be used to develop personalized models of eating disorder pathologies and just-in-time interventions to reduce risk for related behaviors. Here, we focus on the methods used to prepare the data for analysis and discuss how these approaches can be generalized beyond the current application. METHODS The University of North Carolina Biomedical Institutional Review Board approved all study procedures. Participants who met diagnostic criteria for BED or BN provided real time assessments of eating behaviors and feelings through the Recovery Record app delivered on iPhones and the Apple Watches. Continuous passive measures of physiological activation (heart rate) and physical activity (step count) were collected from Apple Watches over 30 days. Data were cleaned to account for user and device recording errors, including duplicate entries and unreliable heart rate and step values. Across participants, the proportion of data points removed during cleaning ranged from <0.1% to 2.4%, depending on the data source. To prepare the data for multivariate time series analysis, we used a novel data handling approach to address variable measurement frequency across data sources and devices. This involved mapping heart rate, step count, feeling ratings, and eating disorder behaviors onto simultaneous minute-level time series that will enable the characterization of individual- and group-level regulatory dynamics preceding and following binge and purge episodes. RESULTS Data collection and cleaning are complete. Between August 2017 and May 2021, 1019 participants provided an average of 25 days of data yielding 3,419,937 heart rate values, 1,635,993 step counts, 8274 binge or purge events, and 85,200 feeling observations. Analysis will begin in spring 2022. CONCLUSIONS We provide a detailed description of the methods used to collect, clean, and prepare personal digital device data from one component of a large, longitudinal eating disorder study. The results will identify digital signatures of increased risk for binge and purge events, which may ultimately be used to create digital interventions for BED and BN. Our goal is to contribute to increased transparency in the handling and analysis of personal digital device data. TRIAL REGISTRATION ClinicalTrials.gov NCT04162574; https://clinicaltrials.gov/ct2/show/NCT04162574. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID) DERR1-10.2196/38294.
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Affiliation(s)
- Robyn E Kilshaw
- Department of Psychology, University of Utah, Salt Lake City, UT, United States
| | - Colin Adamo
- Department of Psychology, University of Utah, Salt Lake City, UT, United States
| | - Jonathan E Butner
- Department of Psychology, University of Utah, Salt Lake City, UT, United States
| | - Pascal R Deboeck
- Department of Psychology, University of Utah, Salt Lake City, UT, United States
| | - Qinxin Shi
- Department of Psychology, University of Utah, Salt Lake City, UT, United States
| | - Cynthia M Bulik
- Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States.,Department of Nutrition, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States.,Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Rachael E Flatt
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.,Department of Psychology and Neuroscience, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Laura M Thornton
- Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Stuart Argue
- Recovery Record, San Francisco, CA, United States
| | | | - Brian R W Baucom
- Department of Psychology, University of Utah, Salt Lake City, UT, United States
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Abstract
BACKGROUND Digital phenotyping has been defined as the moment-by-moment assessment of an illness state through digital means, promising objective, quantifiable data on psychiatric patients' conditions, and could potentially improve diagnosis and management of mental illness. As it is a rapidly growing field, it is to be expected that new literature is being published frequently. OBJECTIVE We conducted this scoping review to assess the current state of literature on digital phenotyping and offer some discussion on the current trends and future direction of this area of research. METHODS We searched four databases, PubMed, Ovid MEDLINE, PsycINFO and Web of Science, from inception to August 25th, 2021. We included studies written in English that 1) investigated or applied their findings to diagnose psychiatric disorders and 2) utilized passive sensing for management or diagnosis. Protocols were excluded. A narrative synthesis approach was used, due to the heterogeneity and variability in outcomes and outcome types reported. RESULTS Of 10506 unique records identified, we included a total of 107 articles. The number of published studies has increased over tenfold from 2 in 2014 to 28 in 2020, illustrating the field's rapid growth. However, a significant proportion of these (49% of all studies and 87% of primary studies) were proof of concept, pilot or correlational studies examining digital phenotyping's potential. Most (62%) of the primary studies published evaluated individuals with depression (21%), BD (18%) and SZ (23%) (Appendix 1). CONCLUSION There is promise shown in certain domains of data and their clinical relevance, which have yet to be fully elucidated. A consensus has yet to be reached on the best methods of data collection and processing, and more multidisciplinary collaboration between physicians and other fields is needed to unlock the full potential of digital phenotyping and allow for statistically powerful clinical trials to prove clinical utility.
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Affiliation(s)
- Alex Z R Chia
- Lee Kong Chian School of Medicine, Nanyang Technological University Singapore, Singapore City, Singapore
| | - Melvyn W B Zhang
- Lee Kong Chian School of Medicine, Nanyang Technological University Singapore, Singapore City, Singapore.,National Addictions Management Service, Institute of Mental Health, Singapore City, Singapore
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35
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Kulkarni P, Kirkham R, McNaney R. Opportunities for Smartphone Sensing in E-Health Research: A Narrative Review. SENSORS (BASEL, SWITZERLAND) 2022; 22:3893. [PMID: 35632301 PMCID: PMC9147201 DOI: 10.3390/s22103893] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/01/2022] [Revised: 05/16/2022] [Accepted: 05/19/2022] [Indexed: 12/10/2022]
Abstract
Recent years have seen significant advances in the sensing capabilities of smartphones, enabling them to collect rich contextual information such as location, device usage, and human activity at a given point in time. Combined with widespread user adoption and the ability to gather user data remotely, smartphone-based sensing has become an appealing choice for health research. Numerous studies over the years have demonstrated the promise of using smartphone-based sensing to monitor a range of health conditions, particularly mental health conditions. However, as research is progressing to develop the predictive capabilities of smartphones, it becomes even more crucial to fully understand the capabilities and limitations of using this technology, given its potential impact on human health. To this end, this paper presents a narrative review of smartphone-sensing literature from the past 5 years, to highlight the opportunities and challenges of this approach in healthcare. It provides an overview of the type of health conditions studied, the types of data collected, tools used, and the challenges encountered in using smartphones for healthcare studies, which aims to serve as a guide for researchers wishing to embark on similar research in the future. Our findings highlight the predominance of mental health studies, discuss the opportunities of using standardized sensing approaches and machine-learning advancements, and present the trends of smartphone sensing in healthcare over the years.
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Affiliation(s)
- Pranav Kulkarni
- Department of Human Centered Computing, Faculty of IT, Monash University, Clayton, VIC 3168, Australia; (R.K.); (R.M.)
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36
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Gupta M, Ramar D, Vijayan R, Gupta N. Artificial Intelligence Tools for Suicide Prevention in Adolescents and Young Adults. ADOLESCENT PSYCHIATRY 2022. [DOI: 10.2174/2210676612666220408095913] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Abstract
Background:
Artificial Intelligence is making a significant transformation in human lives. Its application in the medical and healthcare field has been also observed making an impact and improving overall outcomes. There has been a quest for similar processes in mental health due to the lack of observable changes in the areas of suicide prevention. In the last five years, there has been an emerging body of empirical research applying the technology of artificial intelligence (AI) and machine learning (ML) in mental health.
Objective:
To review the clinical applicability of the AI/ML-based tools in suicide prevention.
Methods:
The compelling question of predicting suicidality has been the focus of this research.
We performed a broad literature search and then identified 36 articles relevant to meet the objectives of this review. We review the available evidence and provide a brief overview of the advances in this field.
Conclusion:
In the last five years, there has been more evidence supporting the implementation of these algorithms in clinical practice. Its current clinical utility is limited to using electronic health records and could be highly effective in conjunction with existing tools for suicide prevention. Other potential sources of relevant data include smart devices and social network sites. There are some serious questions about data privacy and ethics which need more attention while developing these new modalities in suicide research.
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Affiliation(s)
| | - Dhanvendran Ramar
- Bellin Health Psychiatric Clinical Services, & Medical College of Wisconsin Green Bay Wisconsin 54301
| | - Rekha Vijayan
- Bellin Health Psychiatric Clinical Services, & Medical College of Wisconsin Green Bay Wisconsin 54301
| | - Nihit Gupta
- University of West Virginia, Reynolds Memorial Hospital Glendale WV 26038
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37
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Chen M, Chan KL. Effectiveness of Digital Health Interventions on Unintentional Injury, Violence, and Suicide: Meta-Analysis. TRAUMA, VIOLENCE & ABUSE 2022; 23:605-619. [PMID: 33094703 DOI: 10.1177/1524838020967346] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Digital technologies are increasingly used in health-care delivery and are being introduced into work to prevent unintentional injury, violence, and suicide to reduce mortality. To understand the potential of digital health interventions (DHIs) to prevent and reduce these problems, we conduct a meta-analysis and provide an overview of their effectiveness and characteristics related to the effects. We searched electronic databases and reference lists of relevant reviews to identify randomized controlled trials (RCTs) published in or before March 2020 evaluating DHIs on injury, violence, or suicide reduction. Based on the 34 RCT studies included in the meta-analysis, the overall random effect size was 0.21, and the effect sizes for reducing suicidal ideation, interpersonal violence, and unintentional injury were 0.17, 0.24, and 0.31, respectively, which can be regarded as comparable to the effect sizes of traditional face-to-face interventions. However, there was considerable heterogeneity between the studies. In conclusion, DHIs have great potential to reduce unintentional injury, violence, and suicide. Future research should explore DHIs' successful components to facilitate future implementation and wider access.
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Affiliation(s)
- Mengtong Chen
- Department of Social Work, 26679Hong Kong Baptist University, Kowloon Tong, Hong Kong
| | - Ko Ling Chan
- Department of Applied Social Sciences, 26680The Hong Kong Polytechnic University, Hunghom, Hong Kong
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38
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Bentley KH, Zuromski KL, Fortgang RG, Madsen EM, Kessler D, Lee H, Nock MK, Reis BY, Castro VM, Smoller JW. Implementing Machine Learning Models for Suicide Risk Prediction in Clinical Practice: Focus Group Study With Hospital Providers. JMIR Form Res 2022; 6:e30946. [PMID: 35275075 PMCID: PMC8956996 DOI: 10.2196/30946] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2021] [Revised: 01/14/2022] [Accepted: 01/24/2022] [Indexed: 11/19/2022] Open
Abstract
Background Interest in developing machine learning models that use electronic health record data to predict patients’ risk of suicidal behavior has recently proliferated. However, whether and how such models might be implemented and useful in clinical practice remain unknown. To ultimately make automated suicide risk–prediction models useful in practice, and thus better prevent patient suicides, it is critical to partner with key stakeholders, including the frontline providers who will be using such tools, at each stage of the implementation process. Objective The aim of this focus group study is to inform ongoing and future efforts to deploy suicide risk–prediction models in clinical practice. The specific goals are to better understand hospital providers’ current practices for assessing and managing suicide risk; determine providers’ perspectives on using automated suicide risk–prediction models in practice; and identify barriers, facilitators, recommendations, and factors to consider. Methods We conducted 10 two-hour focus groups with a total of 40 providers from psychiatry, internal medicine and primary care, emergency medicine, and obstetrics and gynecology departments within an urban academic medical center. Audio recordings of open-ended group discussions were transcribed and coded for relevant and recurrent themes by 2 independent study staff members. All coded text was reviewed and discrepancies were resolved in consensus meetings with doctoral-level staff. Results Although most providers reported using standardized suicide risk assessment tools in their clinical practices, existing tools were commonly described as unhelpful and providers indicated dissatisfaction with current suicide risk assessment methods. Overall, providers’ general attitudes toward the practical use of automated suicide risk–prediction models and corresponding clinical decision support tools were positive. Providers were especially interested in the potential to identify high-risk patients who might be missed by traditional screening methods. Some expressed skepticism about the potential usefulness of these models in routine care; specific barriers included concerns about liability, alert fatigue, and increased demand on the health care system. Key facilitators included presenting specific patient-level features contributing to risk scores, emphasizing changes in risk over time, and developing systematic clinical workflows and provider training. Participants also recommended considering risk-prediction windows, timing of alerts, who will have access to model predictions, and variability across treatment settings. Conclusions Providers were dissatisfied with current suicide risk assessment methods and were open to the use of a machine learning–based risk-prediction system to inform clinical decision-making. They also raised multiple concerns about potential barriers to the usefulness of this approach and suggested several possible facilitators. Future efforts in this area will benefit from incorporating systematic qualitative feedback from providers, patients, administrators, and payers on the use of these new approaches in routine care, especially given the complex, sensitive, and unfortunately still stigmatized nature of suicide risk.
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Affiliation(s)
- Kate H Bentley
- Center for Precision Psychiatry, Department of Psychiatry, Massachusetts General Hospital, Boston, MA, United States.,Department of Psychology, Harvard University, Cambridge, MA, United States.,Harvard Medical School, Boston, MA, United States
| | - Kelly L Zuromski
- Department of Psychology, Harvard University, Cambridge, MA, United States
| | - Rebecca G Fortgang
- Department of Psychology, Harvard University, Cambridge, MA, United States
| | - Emily M Madsen
- Center for Precision Psychiatry, Department of Psychiatry, Massachusetts General Hospital, Boston, MA, United States.,Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, United States
| | - Daniel Kessler
- Department of Psychology, Harvard University, Cambridge, MA, United States
| | - Hyunjoon Lee
- Center for Precision Psychiatry, Department of Psychiatry, Massachusetts General Hospital, Boston, MA, United States.,Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, United States
| | - Matthew K Nock
- Department of Psychology, Harvard University, Cambridge, MA, United States
| | - Ben Y Reis
- Harvard Medical School, Boston, MA, United States.,Predictive Medicine Group, Computational Health Informatics Program, Boston Children's Hospital, Boston, MA, United States
| | - Victor M Castro
- Research Information Science and Computing, Mass General Brigham, Somerville, MA, United States
| | - Jordan W Smoller
- Center for Precision Psychiatry, Department of Psychiatry, Massachusetts General Hospital, Boston, MA, United States.,Harvard Medical School, Boston, MA, United States.,Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, United States
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39
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Machine learning for suicidal ideation identification: A systematic literature review. COMPUTERS IN HUMAN BEHAVIOR 2022. [DOI: 10.1016/j.chb.2021.107095] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
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40
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Agne NA, Tisott CG, Ballester P, Passos IC, Ferrão YA. Predictors of suicide attempt in patients with obsessive-compulsive disorder: an exploratory study with machine learning analysis. Psychol Med 2022; 52:715-725. [PMID: 32669156 DOI: 10.1017/s0033291720002329] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
BACKGROUND Patients with obsessive-compulsive disorder (OCD) are at increased risk for suicide attempt (SA) compared to the general population. However, the significant risk factors for SA in this population remains unclear - whether these factors are associated with the disorder itself or related to extrinsic factors, such as comorbidities and sociodemographic variables. This study aimed to identify predictors of SA in OCD patients using a machine learning algorithm. METHODS A total of 959 outpatients with OCD were included. An elastic net model was performed to recognize the predictors of SA among OCD patients, using clinical and sociodemographic variables. RESULTS The prevalence of SA in our sample was 10.8%. Relevant predictors of SA founded by the elastic net algorithm were the following: previous suicide planning, previous suicide thoughts, lifetime depressive episode, and intermittent explosive disorder. Our elastic net model had a good performance and found an area under the curve of 0.95. CONCLUSIONS This is the first study to evaluate risk factors for SA among OCD patients using machine learning algorithms. Our results demonstrate an accurate risk algorithm can be created using clinical and sociodemographic variables. All aspects of suicidal phenomena need to be carefully investigated by clinicians in every evaluation of OCD patients. Particular attention should be given to comorbidity with depressive symptoms.
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Affiliation(s)
- Neusa Aita Agne
- Federal University of Health Sciences of Porto Alegre (UFCSPA), Porto Alegre (RS), Brazil
| | - Caroline Gewehr Tisott
- Federal University of Health Sciences of Porto Alegre (UFCSPA), Porto Alegre (RS), Brazil
| | - Pedro Ballester
- Department of Psychiatry and Behavioural Neurosciences, McMaster University, Hamilton, ON, Canada
| | - Ives Cavalcante Passos
- Laboratory of Molecular Psychiatry, Centro de Pesquisa Experimental (CPE) and Centro de Pesquisa Clínica (CPC), Hospital de Clínicas de Porto Alegre (HCPA), Instituto Nacional de Ciência e Tecnologia Translacional em Medicina (INCT-TM), Porto Alegre (RS), Brazil
- Department of Psychiatry, Universidade Federal do Rio Grande do Sul, School of Medicine, Graduate Program in Psychiatry and Behavioral Sciences, Porto Alegre, Brazil
| | - Ygor Arzeno Ferrão
- Federal University of Health Sciences of Porto Alegre (UFCSPA), Porto Alegre (RS), Brazil
- Brazilian Research Consortium on Obsessive-Compulsive Spectrum Disorders (C-TOC), Porto Alegre, Brazil
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41
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Kirtley OJ, van Mens K, Hoogendoorn M, Kapur N, de Beurs D. Translating promise into practice: a review of machine learning in suicide research and prevention. Lancet Psychiatry 2022; 9:243-252. [PMID: 35183281 DOI: 10.1016/s2215-0366(21)00254-6] [Citation(s) in RCA: 21] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/10/2021] [Revised: 07/02/2021] [Accepted: 07/07/2021] [Indexed: 02/06/2023]
Abstract
In ever more pressured health-care systems, technological solutions offering scalability of care and better resource targeting are appealing. Research on machine learning as a technique for identifying individuals at risk of suicidal ideation, suicide attempts, and death has grown rapidly. This research often places great emphasis on the promise of machine learning for preventing suicide, but overlooks the practical, clinical implementation issues that might preclude delivering on such a promise. In this Review, we synthesise the broad empirical and review literature on electronic health record-based machine learning in suicide research, and focus on matters of crucial importance for implementation of machine learning in clinical practice. The challenge of preventing statistically rare outcomes is well known; progress requires tackling data quality, transparency, and ethical issues. In the future, machine learning models might be explored as methods to enable targeting of interventions to specific individuals depending upon their level of need-ie, for precision medicine. Primarily, however, the promise of machine learning for suicide prevention is limited by the scarcity of high-quality scalable interventions available to individuals identified by machine learning as being at risk of suicide.
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Affiliation(s)
| | | | - Mark Hoogendoorn
- Department of Computer Science, Vrij Universiteit Amsterdam, Amsterdam, Netherlands
| | - Navneet Kapur
- Centre for Mental Health and Safety and Greater Manchester National Institute for Health Research Patient Safety Translational Research Centre, University of Manchester, Manchester, UK; Greater Manchester Mental Health NHS Foundation Trust, Manchester, UK
| | - Derek de Beurs
- Department of Epidemiology, Trimbos Institute, Utrecht, Netherlands
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42
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Lejeune A, Le Glaz A, Perron PA, Sebti J, Baca-Garcia E, Walter M, Lemey C, Berrouiguet S. Artificial intelligence and suicide prevention: a systematic review. Eur Psychiatry 2022; 65:1-22. [PMID: 35166203 PMCID: PMC8988272 DOI: 10.1192/j.eurpsy.2022.8] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/14/2021] [Revised: 12/13/2021] [Accepted: 12/20/2021] [Indexed: 11/23/2022] Open
Abstract
Background Suicide is one of the main preventable causes of death. Artificial intelligence (AI) could improve methods for assessing suicide risk. The objective of this review is to assess the potential of AI in identifying patients who are at risk of attempting suicide. Methods A systematic review of the literature was conducted on PubMed, EMBASE, and SCOPUS databases, using relevant keywords. Results Thanks to this research, 296 studies were identified. Seventeen studies, published between 2014 and 2020 and matching inclusion criteria, were selected as relevant. Included studies aimed at predicting individual suicide risk or identifying at-risk individuals in a specific population. The AI performance was overall good, although variable across different algorithms and application settings. Conclusions AI appears to have a high potential for identifying patients at risk of suicide. The precise use of these algorithms in clinical situations, as well as the ethical issues it raises, remain to be clarified.
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Affiliation(s)
- Alban Lejeune
- URCI Mental Health Department, Brest Medical University Hospital, Brest, France
| | - Aziliz Le Glaz
- URCI Mental Health Department, Brest Medical University Hospital, Brest, France
| | | | - Johan Sebti
- Mental Health Department, French Polynesia Hospital, FFC3+H9G, Pirae, French Polynesia
| | | | - Michel Walter
- URCI Mental Health Department, Brest Medical University Hospital, Brest, France
- EA 7479 SPURBO, Université de Bretagne Occidentale, Brest, France
| | - Christophe Lemey
- URCI Mental Health Department, Brest Medical University Hospital, Brest, France
- EA 7479 SPURBO, Université de Bretagne Occidentale, Brest, France
- SPURBO, IMT Atlantique, Lab-STICC, UMR CNRS 6285, F-29238, Brest, France
| | - Sofian Berrouiguet
- URCI Mental Health Department, Brest Medical University Hospital, Brest, France
- LaTIM, INSERM, UMR 1101, Brest, France
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43
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Balcombe L, De Leo D. The Potential Impact of Adjunct Digital Tools and Technology to Help Distressed and Suicidal Men: An Integrative Review. Front Psychol 2022; 12:796371. [PMID: 35058855 PMCID: PMC8765720 DOI: 10.3389/fpsyg.2021.796371] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2021] [Accepted: 12/09/2021] [Indexed: 12/12/2022] Open
Abstract
Suicidal men feel the need to be self-reliant and that they cannot find another way out of relationship or socioeconomic issues. Suicide prevention is of crucial importance worldwide. The much higher rate of suicide in men engenders action. The prelude is a subjective experience that can be very isolating and severely distressing. Men may not realize a change in their thinking and behaviors, which makes it more difficult to seek and get help, thereby interrupting a "downward spiral". Stoicism often prevents men from admitting to their personal struggle. The lack of "quality" connections and "non-tailored" therapies has led to a high number of men "walking out" on traditional clinical approaches. But there are complicated relationships in motivations and formative behaviors of suicide with regards to emotional state, psychiatric disorders, interpersonal life events and suicidal behavior method selection. Middle-aged and older men have alternated as the most at-risk of suicide. There is no one solution that applies to all men, but digital tools may be of assistance (e.g., video conferences, social networks, telephone calls, and emails). Digital interventions require higher levels of effectiveness for distress and suicidality but self-guided approaches may be the most suitable for men especially where linked with an integrated online suicide prevention platform (e.g., quick response with online chats, phone calls, and emails). Furthermore, technology-enabled models of care offer promise to advance appropriate linking to mental health services through better and faster understanding of the specific needs of individuals (e.g., socio-cultural) and the type and level of suicidality experienced. Long-term evidence for suicidality and its evaluation may benefit from progressing human computer-interaction and providing impetus for an eminent integrated digital platform.
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Affiliation(s)
- Luke Balcombe
- Australian Institute for Suicide Research and Prevention, School of Applied Psychology, Griffith University, Brisbane, QLD, Australia
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44
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Chan EC, Wallace K, Yang EH, Roper L, Aryal G, Lodhi RJ, Isenberg R, Carnes P, Baskys A, Green B, Aitchison KJ. The feasibility and acceptability of mobile application-based assessment of suicidality using self-report components of a novel tool, the Suicide Ideation and Behavior Assessment Tool (SIBAT). Psychiatry Res 2022; 307:114316. [PMID: 34896843 DOI: 10.1016/j.psychres.2021.114316] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/17/2021] [Revised: 11/23/2021] [Accepted: 11/26/2021] [Indexed: 11/17/2022]
Abstract
The aim of this study was to assess the validity of a mobile application-based self-report questionnaire in the assessment of suicidality. We developed a program for the administration of self-report components of the Suicide Ideation and Behavior Assessment Tool (SIBAT). We invited university students and trainees enrolled in a study of addictions to complete this component of the SIBAT using the program on their mobile devices or personal computer. 196 participants completed all required modules of the SIBAT, with 97 using their mobile device and 99 using their personal computer. Rates of completed questionnaires between the two groups were compared, as were the responses to the items and the total scores. There was a significant difference between proportions of scale completion in both groups, with a greater number of participants who used a personal computer to complete the scale not responding to all questions compared to participants who used a mobile device to complete the scale. Data collected via mobile device showed good concurrent validity with data collected via personal computer. A trend toward greater disclosure of suicidality was observed in the mobile device group however, replication of these findings using larger sample sizes is needed.
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Affiliation(s)
- Eric C Chan
- Department of Psychiatry, University of Alberta, Edmonton, Alberta, Canada; Department of Psychiatry, University of Calgary, Calgary, Alberta, Canada.
| | - Keanna Wallace
- Department of Medical Genetics, University of Alberta, Edmonton, Alberta, Canada
| | - Esther H Yang
- Department of Medical Genetics, University of Alberta, Edmonton, Alberta, Canada
| | - Leslie Roper
- Department of Medical Genetics, University of Alberta, Edmonton, Alberta, Canada
| | - Garima Aryal
- Department of Medical Genetics, University of Alberta, Edmonton, Alberta, Canada
| | - Rohit J Lodhi
- Department of Psychiatry, University of Saskatchewan, Saskatoon, Saskatchewan, Canada
| | - Richard Isenberg
- American Foundation for Addiction Research, Psychological Counseling Services, Scottsdale, Arizona, USA
| | - Patrick Carnes
- Department of Psychiatry, University of Alberta, Edmonton, Alberta, Canada
| | - Andrius Baskys
- Department of Psychiatry, University of Alberta, Edmonton, Alberta, Canada; Graduate College of Biomedical Sciences and University Medical Center, Western University of Health Sciences, Pomona, California, USA; Memory Disorders and Genomic Medicine Clinic, Riverside, California, USA
| | - Bradley Green
- Department of Psychology, University of Texas at Tyler, Tyler, USA
| | - Katherine J Aitchison
- Department of Psychiatry, University of Alberta, Edmonton, Alberta, Canada; Department of Medical Genetics, University of Alberta, Edmonton, Alberta, Canada
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45
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Morgiève M, Yasri D, Genty C, Dubois J, Leboyer M, Vaiva G, Berrouiguet S, Azé J, Courtet P. Acceptability and satisfaction with emma, a smartphone application dedicated to suicide ecological assessment and prevention. Front Psychiatry 2022; 13:952865. [PMID: 36032223 PMCID: PMC9403788 DOI: 10.3389/fpsyt.2022.952865] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/25/2022] [Accepted: 07/19/2022] [Indexed: 11/30/2022] Open
Abstract
BACKGROUND As mHealth may contribute to suicide prevention, we developed emma, an application using Ecological Momentary Assessment and Intervention (EMA/EMI). OBJECTIVE This study evaluated emma usage rate and acceptability during the first month and satisfaction after 1 and 6 months of use. METHODS Ninety-nine patients at high risk of suicide used emma for 6 months. The acceptability and usage rate of the EMA and EMI modules were monitored during the first month. Satisfaction was assessed by questions in the monthly EMA (Likert scale from 0 to 10) and the Mobile App Rating Scale (MARS; score: 0-5) completed at month 6. After inclusion, three follow-up visits (months 1, 3, and 6) took place. RESULTS Seventy-five patients completed at least one of the proposed EMAs. Completion rates were lower for the daily than weekly EMAs (60 and 82%, respectively). The daily completion rates varied according to the question position in the questionnaire (lower for the last questions, LRT = 604.26, df = 1, p-value < 0.0001). Completion rates for the daily EMA were higher in patients with suicidal ideation and/or depression than in those without. The most used EMI was the emergency call module (n = 12). Many users said that they would recommend this application (mean satisfaction score of 6.92 ± 2.78) and the MARS score at month 6 was relatively high (overall rating: 3.3 ± 0.87). CONCLUSION Emma can target and involve patients at high risk of suicide. Given the promising users' satisfaction level, emma could rapidly evolve into a complementary tool for suicide prevention.
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Affiliation(s)
- Margot Morgiève
- Université Paris Cité, CNRS, Inserm, Cermes3, Paris, France.,Department of Emergency Psychiatry and Acute Care, Lapeyronie Hospital, CHU Montpellier, Montpellier, France.,ICM - Paris Brain Institute, Hôpital de la Pitié-Salpêtriére, Paris, France.,GEPS - Groupement d'Étude et de Prévention du Suicide, Paris, France
| | - Daniel Yasri
- Université Paris Cité, CNRS, Inserm, Cermes3, Paris, France.,Department of Emergency Psychiatry and Acute Care, Lapeyronie Hospital, CHU Montpellier, Montpellier, France
| | - Catherine Genty
- Université Paris Cité, CNRS, Inserm, Cermes3, Paris, France.,Department of Emergency Psychiatry and Acute Care, Lapeyronie Hospital, CHU Montpellier, Montpellier, France
| | - Jonathan Dubois
- Université Paris Cité, CNRS, Inserm, Cermes3, Paris, France.,Department of Emergency Psychiatry and Acute Care, Lapeyronie Hospital, CHU Montpellier, Montpellier, France
| | - Marion Leboyer
- Fondation Fondamental, Hôpital Albert-Chenevier, Créteil, France.,Faculté de Médicine, Institut National de la Santé et de la Recherche Médicale, Université Paris-Est Créteil, Créteil, France.,Assistance Publique Hôpitaux de Paris, Pôle de Psychiatrie et Addictologie, Hôpitaux Universitaires Henri Mondor, Créteil, France
| | - Guillaume Vaiva
- CHU Lille, Hôpital Fontan, Department of Psychiatry, Lille, France.,Centre National de Resources and Résilience pour les Psychotraumatisme, Université de Lille, Lille, France.,CNRS UMR-9193, SCALab - Sciences Cognitives et Sciences Affectives, Université de Lille, Lille, France
| | - Sofian Berrouiguet
- Laboratoire du Traitement de l'Information Médicale, INSERM UMR1101, CHRU Brest, Brest, France
| | - Jérôme Azé
- LIRMM, CNRS, Univ Montpellier, Montpellier, France
| | - Philippe Courtet
- Université Paris Cité, CNRS, Inserm, Cermes3, Paris, France.,Department of Emergency Psychiatry and Acute Care, Lapeyronie Hospital, CHU Montpellier, Montpellier, France.,Fondation Fondamental, Hôpital Albert-Chenevier, Créteil, France
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46
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Emden D, Goltermann J, Dannlowski U, Hahn T, Opel N. Technical feasibility and adherence of the Remote Monitoring Application in Psychiatry (ReMAP) for the assessment of affective symptoms. J Affect Disord 2021; 294:652-660. [PMID: 34333173 DOI: 10.1016/j.jad.2021.07.030] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/25/2020] [Revised: 06/14/2021] [Accepted: 07/11/2021] [Indexed: 10/20/2022]
Abstract
BACKGROUND Smartphone-based monitoring constitutes a cost-effective instrument to assess and predict affective symptom trajectories. Large-scale transdiagnostic studies utilizing this methodology are yet lacking in psychiatric research. Thus, we introduce the Remote Monitoring Application in Psychiatry (ReMAP) and evaluate its feasibility and adherence in a large transdiagnostic sample. METHODS The ReMAP app was distributed among n = 997 healthy control participants and psychiatric patients, including affective, anxiety, and psychotic disorders. Passive sensor data (acceleration, geolocation, walking distance, steps), optional standardized self-reports on mood and sleep, and voice samples were assessed. Feasibility and adherence were evaluated based on frequency of transferred data, and participation duration. Preliminary results are presented while data collection is ongoing. RESULTS Retention rates of 90.25% for the minimum study duration of two weeks and 33.09% for one year were achieved (median participation 135 days, IQR=111). Participants transferred an average of 51.83 passive events per day. An average of 34.59 self-report events were transferred per user, with a considerable range across participants (0-552 events). Clinical and non-clinical subgroups did not differ in participation duration or rate of data transfer. The mean rate of days with passive data was higher and less heterogeneous in iOS (91.85%, SD=21.25) as compared to Android users (63.04%, SD=35.09). LIMITATIONS Subjective user experience was not assessed limiting conclusions about app acceptance. CONCLUSIONS ReMAP is a technically feasible tool to assess affective symptoms with high temporal resolution in large-scale transdiagnostic samples with good adherence. Future studies should account for differences between operating systems.
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Affiliation(s)
- Daniel Emden
- Institute for Translational Psychiatry, University of Münster, Germany
| | - Janik Goltermann
- Institute for Translational Psychiatry, University of Münster, Germany
| | - Udo Dannlowski
- Institute for Translational Psychiatry, University of Münster, Germany
| | - Tim Hahn
- Institute for Translational Psychiatry, University of Münster, Germany
| | - Nils Opel
- Institute for Translational Psychiatry, University of Münster, Germany; Interdisciplinary Centre for Clinical Research (IZKF) Münster, University of Münster, Germany.
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47
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MacLeod L, Suruliraj B, Gall D, Bessenyei K, Hamm S, Romkey I, Bagnell A, Mattheisen M, Muthukumaraswamy V, Orji R, Meier S. A Mobile Sensing App to Monitor Youth Mental Health: Observational Pilot Study. JMIR Mhealth Uhealth 2021; 9:e20638. [PMID: 34698650 PMCID: PMC8579216 DOI: 10.2196/20638] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2020] [Revised: 02/02/2021] [Accepted: 07/27/2021] [Indexed: 01/19/2023] Open
Abstract
Background Internalizing disorders are the most common psychiatric problems observed among youth in Canada. Sadly, youth with internalizing disorders often avoid seeking clinical help and rarely receive adequate treatment. Current methods of assessing internalizing disorders usually rely on subjective symptom ratings, but internalizing symptoms are frequently underreported, which creates a barrier to the accurate assessment of these symptoms in youth. Therefore, novel assessment tools that use objective data need to be developed to meet the highest standards of reliability, feasibility, scalability, and affordability. Mobile sensing technologies, which unobtrusively record aspects of youth behaviors in their daily lives with the potential to make inferences about their mental health states, offer a possible method of addressing this assessment barrier. Objective This study aims to explore whether passively collected smartphone sensor data can be used to predict internalizing symptoms among youth in Canada. Methods In this study, the youth participants (N=122) completed self-report assessments of symptoms of anxiety, depression, and attention-deficit hyperactivity disorder. Next, the participants installed an app, which passively collected data about their mobility, screen time, sleep, and social interactions over 2 weeks. Then, we tested whether these passive sensor data could be used to predict internalizing symptoms among these youth participants. Results More severe depressive symptoms correlated with more time spent stationary (r=0.293; P=.003), less mobility (r=0.271; P=.006), higher light intensity during the night (r=0.227; P=.02), and fewer outgoing calls (r=−0.244; P=.03). In contrast, more severe anxiety symptoms correlated with less time spent stationary (r=−0.249; P=.01) and greater mobility (r=0.234; P=.02). In addition, youths with higher anxiety scores spent more time on the screen (r=0.203; P=.049). Finally, adding passively collected smartphone sensor data to the prediction models of internalizing symptoms significantly improved their fit. Conclusions Passively collected smartphone sensor data provide a useful way to monitor internalizing symptoms among youth. Although the results replicated findings from adult populations, to ensure clinical utility, they still need to be replicated in larger samples of youth. The work also highlights intervention opportunities via mobile technology to reduce the burden of internalizing symptoms early on.
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Affiliation(s)
- Lucy MacLeod
- Department of Psychiatry, Dalhousie University, Halifax, NS, Canada
| | | | - Dominik Gall
- Department of Psychology, University of Würzburg, Würzburg, Germany
| | - Kitti Bessenyei
- Department of Psychiatry, Dalhousie University, Halifax, NS, Canada
| | - Sara Hamm
- Department of Psychiatry, Dalhousie University, Halifax, NS, Canada
| | - Isaac Romkey
- Department of Psychiatry, Dalhousie University, Halifax, NS, Canada
| | - Alexa Bagnell
- Department of Psychiatry, Dalhousie University, Halifax, NS, Canada
| | | | | | - Rita Orji
- Faculty of Computer Science, Dalhousie University, Halifax, NS, Canada
| | - Sandra Meier
- Department of Psychiatry, Dalhousie University, Halifax, NS, Canada
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48
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Chan EC, Wallace K, Yang EH, Roper L, Aryal G, Lodhi RJ, Baskys A, Isenberg R, Carnes P, Green B, Aitchison KJ. Internal consistency and concurrent validity of self-report components of a new instrument for the assessment of suicidality, the Suicide Ideation and Behavior Assessment Tool (SIBAT). Psychiatry Res 2021; 304:114128. [PMID: 34343876 DOI: 10.1016/j.psychres.2021.114128] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/17/2021] [Revised: 07/14/2021] [Accepted: 07/15/2021] [Indexed: 10/20/2022]
Abstract
This study aimed to assess the internal consistency of self-report components of the Suicide Ideation and Behavior Assessment Tool (SIBAT) and validate it with relevant elements of the Mini International Neuropsychiatric Interview (MINI). The SIBAT is a newly developed instrument for the evaluation of suicidality. In this study, we invited university students and trainees participating in a study of addictions to complete the self-report component of the SIBAT as an add-on study. We evaluated the internal consistency of the self-report component of the SIBAT and validated it against the suicidality component of the MINI. Data were analysed using both complete case analysis and multiple imputation. SIBAT data were collected for 394 participants, 314 of whom had also completed the MINI. The internal consistency of modules 2, 3, and 5 of the SIBAT was high. Each item from module 5 had a statistically significant association with the corresponding item from the MINI. The sum of scores from modules 2 and 3 had a moderate correlation with the assessment of suicide risk determined by the MINI, and a strong correlation with the total score of SIBAT module 5. The completion median time of modules 2, 3 and 5 was 14.3 min.
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Affiliation(s)
- Eric C Chan
- Department of Psychiatry, University of Alberta, Edmonton, Alberta, Canada; Department of Psychiatry, University of Calgary, Calgary, Alberta, Canada.
| | - Keanna Wallace
- Department of Medical Genetics, University of Alberta, Edmonton, Alberta, Canada
| | - Esther H Yang
- Department of Medical Genetics, University of Alberta, Edmonton, Alberta, Canada
| | - Leslie Roper
- Department of Medical Genetics, University of Alberta, Edmonton, Alberta, Canada
| | - Garima Aryal
- Department of Medical Genetics, University of Alberta, Edmonton, Alberta, Canada
| | - Rohit J Lodhi
- Department of Psychiatry, University of Saskatchewan, Saskatoon, SK, Canada
| | - Andrius Baskys
- Department of Psychiatry, University of Alberta, Edmonton, Alberta, Canada; Graduate College of Biomedical Sciences and University Medical Center, Western University of Health Sciences, Pomona, California, United States; Memory Disorders and Genomic Medicine Clinic, Riverside, California, United States
| | - Richard Isenberg
- American Foundation for Addiction Research, Psychological Counseling Services, Scottsdale, Arizona, United States
| | - Patrick Carnes
- Department of Psychiatry, University of Alberta, Edmonton, Alberta, Canada
| | - Bradley Green
- Department of Psychology, University of Texas at Tyler, Tyler, United States
| | - Katherine J Aitchison
- Department of Psychiatry, University of Alberta, Edmonton, Alberta, Canada; Department of Medical Genetics, University of Alberta, Edmonton, Alberta, Canada.
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Icick R, Karsinti E, Brousse G, Chrétienneau C, Trabut JB, Belforte B, Coeuru P, Moisan D, Deschenau A, Cottencin O, Gay A, Lack P, Pelissier-Alicot AL, Dupuy G, Fortias M, Etain B, Lépine JP, Laplanche JL, Bellivier F, Vorspan F, Bloch V. Childhood trauma and the severity of past suicide attempts in outpatients with cocaine use disorders. Subst Abus 2021; 43:623-632. [PMID: 34597243 DOI: 10.1080/08897077.2021.1975875] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Abstract
INTRODUCTION Suicide attempts have been associated with both cocaine use disorder (CocUD) and childhood trauma. We investigated how childhood trauma is an independent risk factor for serious and recurrent suicide attempts in CocUD. Method: 298 outpatients (23% women) with CocUD underwent standardized assessments of substance dependence (Diagnostic and Statistical Manual-mental disorders, fourth edition, text revised), impulsiveness, resilience, and childhood trauma, using validated tools. Suicide attempts history was categorized as single vs. recurrent or non-serious vs. serious depending on the lifetime number of suicide attempts and the potential or actual lethality of the worst attempt reported, respectively. Bivariate and multinomial regression analyses were used to characterize which childhood trauma patterns were associated with the suicide attempts groups. Results: 58% of CocUD patients reported childhood trauma. Recurrent and serious suicide attempts clustered together and were thus combined into "severe SA." Severe suicide attempt risk increased proportionally to the number of childhood traumas (test for trend, p = 9 × 10-7). Non-severe suicide attempt risk increased with impulsiveness and decreased with resilience. In multinomial regression models, a higher number of traumas and emotional abuse were independently and only associated with severe vs. non-severe suicide attempts (effect size = 0.82, AUC = 0.7). The study was limited by its cross-sectional design. Conclusion: These preferential associations between childhood trauma and severe suicide attempts warrant specific monitoring of suicide attempts risk in CocUD, regardless of the severity of addiction profiles.
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Affiliation(s)
- Romain Icick
- Département de Psychiatrie et de Médecine Addictologique, Assistance Publique - Hôpitaux de Paris (AP-HP), Groupe Hospitalier Saint-Louis - Lariboisière - Fernand Widal, Paris, France.,INSERM U1144, "Therapeutic Optimization in Neuropsychopharmacology", Paris, France.,INSERM UMR-S1144, Université de Paris, Paris, France
| | - Emily Karsinti
- Département de Psychiatrie et de Médecine Addictologique, Assistance Publique - Hôpitaux de Paris (AP-HP), Groupe Hospitalier Saint-Louis - Lariboisière - Fernand Widal, Paris, France.,INSERM U1144, "Therapeutic Optimization in Neuropsychopharmacology", Paris, France.,ED139, Laboratoire CLIPSYD, Paris Nanterre University, Nanterre, France
| | - Georges Brousse
- INSERM UMR-1107, Neuro-Dol, Université Clermont-Auvergne, Clermont-Ferrand, France
| | - Clara Chrétienneau
- INSERM U1144, "Therapeutic Optimization in Neuropsychopharmacology", Paris, France
| | | | - Beatriz Belforte
- APHP, Hôpital Européen Georges Pompidou, CSAPA Monte-Cristo, Paris, France
| | | | | | | | - Olivier Cottencin
- Université de Lille, CHU Lille - Psychiaty and Addiction Medicine Department, INSERM U1172 - Lille Neuroscience & Cognition Centre (LiNC), Plasticity & SubjectivitY team, Lille, France
| | - Aurélia Gay
- Service d'Addictologie, CHU St Etienne, Saint Etienne, France
| | | | | | - Gaël Dupuy
- Département de Psychiatrie et de Médecine Addictologique, Assistance Publique - Hôpitaux de Paris (AP-HP), Groupe Hospitalier Saint-Louis - Lariboisière - Fernand Widal, Paris, France
| | - Maeva Fortias
- Département de Psychiatrie et de Médecine Addictologique, Assistance Publique - Hôpitaux de Paris (AP-HP), Groupe Hospitalier Saint-Louis - Lariboisière - Fernand Widal, Paris, France
| | - Bruno Etain
- Département de Psychiatrie et de Médecine Addictologique, Assistance Publique - Hôpitaux de Paris (AP-HP), Groupe Hospitalier Saint-Louis - Lariboisière - Fernand Widal, Paris, France.,INSERM U1144, "Therapeutic Optimization in Neuropsychopharmacology", Paris, France.,INSERM UMR-S1144, Université de Paris, Paris, France
| | - Jean-Pierre Lépine
- Département de Psychiatrie et de Médecine Addictologique, Assistance Publique - Hôpitaux de Paris (AP-HP), Groupe Hospitalier Saint-Louis - Lariboisière - Fernand Widal, Paris, France.,INSERM U1144, "Therapeutic Optimization in Neuropsychopharmacology", Paris, France.,INSERM UMR-S1144, Université de Paris, Paris, France
| | - Jean-Louis Laplanche
- Département de Psychiatrie et de Médecine Addictologique, Assistance Publique - Hôpitaux de Paris (AP-HP), Groupe Hospitalier Saint-Louis - Lariboisière - Fernand Widal, Paris, France.,INSERM U1144, "Therapeutic Optimization in Neuropsychopharmacology", Paris, France.,INSERM UMR-S1144, Université de Paris, Paris, France
| | - Frank Bellivier
- Département de Psychiatrie et de Médecine Addictologique, Assistance Publique - Hôpitaux de Paris (AP-HP), Groupe Hospitalier Saint-Louis - Lariboisière - Fernand Widal, Paris, France.,INSERM U1144, "Therapeutic Optimization in Neuropsychopharmacology", Paris, France.,INSERM UMR-S1144, Université de Paris, Paris, France
| | - Florence Vorspan
- Département de Psychiatrie et de Médecine Addictologique, Assistance Publique - Hôpitaux de Paris (AP-HP), Groupe Hospitalier Saint-Louis - Lariboisière - Fernand Widal, Paris, France.,INSERM U1144, "Therapeutic Optimization in Neuropsychopharmacology", Paris, France.,INSERM UMR-S1144, Université de Paris, Paris, France
| | - Vanessa Bloch
- Département de Psychiatrie et de Médecine Addictologique, Assistance Publique - Hôpitaux de Paris (AP-HP), Groupe Hospitalier Saint-Louis - Lariboisière - Fernand Widal, Paris, France.,INSERM U1144, "Therapeutic Optimization in Neuropsychopharmacology", Paris, France.,INSERM UMR-S1144, Université de Paris, Paris, France
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50
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Minen MT, Stieglitz EJ. Wearables for Neurologic Conditions: Considerations for Our Patients and Research Limitations. Neurol Clin Pract 2021; 11:e537-e543. [PMID: 34484952 DOI: 10.1212/cpj.0000000000000971] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2020] [Accepted: 07/07/2020] [Indexed: 12/24/2022]
Abstract
Purpose of Review In 2019, over 50 million Americans were expected to use wearables at least monthly. The technologies have varied capabilities, with many designed to monitor health conditions. We present a narrative review to raise awareness of wearable technologies that may be relevant to the field of neurology. We also discuss the implications of these wearables for our patients and briefly discuss issues related to researching new wearable technologies. Recent Findings There are a variety of wearables for neurologic conditions, e.g., stroke (for potential arrhythmia capture), epilepsy, Parkinson disease, and sleep. Research is being performed to capture the risk of neuropsychiatric relapse. However, data are limited and adherence to these wearables is often poorly studied. Summary The care of neurology patients may ultimately be improved with the use of wearable technologies. More research needs to examine efficacy and implementation strategies.
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Affiliation(s)
- Mia T Minen
- Division of Headache Medicine (MTM), NYU Langone Departments of Neurology and Population Health, New York, NY; and CIPPA/US (EJS), New York, NY
| | - Eric J Stieglitz
- Division of Headache Medicine (MTM), NYU Langone Departments of Neurology and Population Health, New York, NY; and CIPPA/US (EJS), New York, NY
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