Jmir Mental Health最新文献

{"title":"Social Media Use in Adolescents: Bans, Benefits, and Emotion Regulation Behaviors.","authors":"Kelsey L McAlister, Clare C Beatty, Jacqueline E Smith-Caswell, Jacqlyn L Yourell, Jennifer L Huberty","doi":"10.2196/64626","DOIUrl":"10.2196/64626","url":null,"abstract":"<p><strong>Unlabelled: </strong>Social media is an integral part of adolescents' daily lives, but the significant time they invest in social media has raised concerns about the effect on their mental health. Bans and severe restrictions on social media use are quickly emerging as an attempt to regulate social media use; however, evidence supporting their effectiveness is limited. Adolescents experience several benefits from social media, including increased social connection, reduced loneliness, and a safe space for marginalized groups (eg, LGBTQ+) to interact. Rather than enforcing bans and severe restrictions, emotion regulation should be leveraged to help adolescents navigate the digital social environment. This viewpoint paper proposes a nuanced approach toward regulating adolescent social media use by (1) discontinuing the use of ineffective bans, (2) recognizing the benefits social media use can have, and (3) fostering emotion regulation skills in adolescents to encourage the development of self-regulation.</p>","PeriodicalId":48616,"journal":{"name":"Jmir Mental Health","volume":"11 ","pages":"e64626"},"PeriodicalIF":4.8,"publicationDate":"2024-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11554337/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142576612","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Automated Real-Time Tool for Promoting Crisis Resource Use for Suicide Risk (ResourceBot): Development and Usability Study.","authors":"Daniel Dl Coppersmith, Kate H Bentley, Evan M Kleiman, Adam C Jaroszewski, Merryn Daniel, Matthew K Nock","doi":"10.2196/58409","DOIUrl":"10.2196/58409","url":null,"abstract":"<p><strong>Background: </strong>Real-time monitoring captures information about suicidal thoughts and behaviors (STBs) as they occur and offers great promise to learn about STBs. However, this approach also introduces questions about how to monitor and respond to real-time information about STBs. Given the increasing use of real-time monitoring, there is a need for novel, effective, and scalable tools for responding to suicide risk in real time.</p><p><strong>Objective: </strong>The goal of this study was to develop and test an automated tool (ResourceBot) that promotes the use of crisis services (eg, 988) in real time through a rule-based (ie, if-then) brief barrier reduction intervention.</p><p><strong>Methods: </strong>ResourceBot was tested in a 2-week real-time monitoring study of 74 adults with recent suicidal thoughts.</p><p><strong>Results: </strong>ResourceBot was deployed 221 times to 36 participants. There was high engagement with ResourceBot (ie, 87% of the time ResourceBot was deployed, a participant opened the tool and submitted a response to it), but zero participants reported using crisis services after engaging with ResourceBot. The most reported reasons for not using crisis services were beliefs that the resources would not help, wanting to handle things on one's own, and the resources requiring too much time or effort. At the end of the study, participants rated ResourceBot with good usability (mean of 75.6 out of 100) and satisfaction (mean of 20.8 out of 32).</p><p><strong>Conclusions: </strong>This study highlights both the possibilities and challenges of developing effective real-time interventions for suicide risk and areas for refinement in future work.</p>","PeriodicalId":48616,"journal":{"name":"Jmir Mental Health","volume":"11 ","pages":"e58409"},"PeriodicalIF":4.8,"publicationDate":"2024-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11565085/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142559171","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Digital Phenotyping of Mental and Physical Conditions: Remote Monitoring of Patients Through RADAR-Base Platform.","authors":"Zulqarnain Rashid, Amos A Folarin, Yuezhou Zhang, Yatharth Ranjan, Pauline Conde, Heet Sankesara, Shaoxiong Sun, Callum Stewart, Petroula Laiou, Richard J B Dobson","doi":"10.2196/51259","DOIUrl":"10.2196/51259","url":null,"abstract":"<p><strong>Background: </strong>The use of digital biomarkers through remote patient monitoring offers valuable and timely insights into a patient's condition, including aspects such as disease progression and treatment response. This serves as a complementary resource to traditional health care settings leveraging mobile technology to improve scale and lower latency, cost, and burden.</p><p><strong>Objective: </strong>Smartphones with embedded and connected sensors have immense potential for improving health care through various apps and mobile health (mHealth) platforms. This capability could enable the development of reliable digital biomarkers from long-term longitudinal data collected remotely from patients.</p><p><strong>Methods: </strong>We built an open-source platform, RADAR-base, to support large-scale data collection in remote monitoring studies. RADAR-base is a modern remote data collection platform built around Confluent's Apache Kafka to support scalability, extensibility, security, privacy, and quality of data. It provides support for study design and setup and active (eg, patient-reported outcome measures) and passive (eg, phone sensors, wearable devices, and Internet of Things) remote data collection capabilities with feature generation (eg, behavioral, environmental, and physiological markers). The back end enables secure data transmission and scalable solutions for data storage, management, and data access.</p><p><strong>Results: </strong>The platform has been used to successfully collect longitudinal data for various cohorts in a number of disease areas including multiple sclerosis, depression, epilepsy, attention-deficit/hyperactivity disorder, Alzheimer disease, autism, and lung diseases. Digital biomarkers developed through collected data are providing useful insights into different diseases.</p><p><strong>Conclusions: </strong>RADAR-base offers a contemporary, open-source solution driven by the community for remotely monitoring, collecting data, and digitally characterizing both physical and mental health conditions. Clinicians have the ability to enhance their insight through the use of digital biomarkers, enabling improved prevention, personalization, and early intervention in the context of disease management.</p>","PeriodicalId":48616,"journal":{"name":"Jmir Mental Health","volume":"11 ","pages":"e51259"},"PeriodicalIF":4.8,"publicationDate":"2024-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11524428/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142510891","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Outcomes of Providing Children Aged 7-12 Years With Access to Evidence-Based Anxiety Treatment Via a Standalone Digital Intervention Using Immersive Gaming Technology: Real-World Evaluation.","authors":"Brioney Gee, Bonnie Teague, Andrew Laphan, Tim Clarke, Georgianna Coote, Jessica Garner, Jon Wilson","doi":"10.2196/52866","DOIUrl":"10.2196/52866","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Anxiety disorders are among the most common mental health conditions in childhood, but most children with anxiety disorders do not access evidence-based interventions. The delivery of therapeutic interventions via digital technologies has been proposed to significantly increase timely access to evidence-based treatment. Lumi Nova (BfB Labs Limited) is a digital therapeutic intervention designed to deliver evidence-based anxiety treatment for those aged 7-12 years through a mobile app incorporating immersive gaming technology.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;We aimed to evaluate the real-world impact of providing access to Lumi Nova through UK National Health Service-funded mental health services.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;We analyzed precollected anonymized data routinely captured through the implementation of Lumi Nova from children aged 7-12 years, who lived in the United Kingdom and had the opportunity to use the intervention for at least 1 week over an 18-month period. Engagement indices included whether the game key was activated, number of unique sessions, time spent engaging, and number of \"challenges\" completed. Clinical outcomes were assessed using the Goal-Based Outcomes measure and Child Outcome Rating Scale. Demographic data were analyzed to assess the health equality implications of Lumi Nova.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;Of 1029 eligible families invited to use Lumi Nova, 644 (62.5%) activated their game key, of whom 374 (58.1%) completed at least one in-game graded exposure challenge. The median number of unique sessions was 6 (IQR 3-12) and the median time spent engaging with the intervention was 42 (IQR 15-79) minutes. For the subset of young people with paired outcomes, there were statistically significant small to medium improvements in goal-based outcome scores (n=224; t223=5.78, P&lt;.001; d=0.37, 95% CI 0.25-0.52) and Child Outcome Rating Scale scores (n=123; t122=5.10, P&lt;.001; d=0.46, 95% CI 0.27-0.65) between the first and last data points. Two in 5 young people's scores reflected a change that would be considered reliable. Analysis of demographic characteristics tentatively suggested that children from ethnic minority backgrounds and those living in the most deprived neighbourhoods may be less likely to access Lumi Nova, but children from socioeconomically deprived areas were more likely to successfully complete a challenge once they accessed the intervention (P=.02). However, the level of missing data and small number of children in some demographic groups limited meaningful statistical comparisons.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;This study provides initial evidence that Lumi Nova may be associated with improved outcomes for those aged 7-12 years seeking anxiety treatment in real-world settings. However, the lack of a control comparator group and information about concurrent treatments accessed by the young people, in addition to substantial attrition, limited the analysis tha","PeriodicalId":48616,"journal":{"name":"Jmir Mental Health","volume":"11 ","pages":"e52866"},"PeriodicalIF":4.8,"publicationDate":"2024-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11526415/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142510901","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Correction: Digital Psychotherapies for Adults Experiencing Depressive Symptoms: Systematic Review and Meta-Analysis.","authors":"Joanna Omylinska-Thurston, Supritha Aithal, Shaun Liverpool, Rebecca Clark, Zoe Moula, January Wood, Laura Viliardos, Edgar Rodríguez-Dorans, Fleur Farish-Edwards, Ailsa Parsons, Mia Eisenstadt, Marcus Bull, Linda Dubrow-Marshall, Scott Thurston, Vicky Karkou","doi":"10.2196/67439","DOIUrl":"10.2196/67439","url":null,"abstract":"<p><p>[This corrects the article DOI: 10.2196/55500.].</p>","PeriodicalId":48616,"journal":{"name":"Jmir Mental Health","volume":"11 ","pages":"e67439"},"PeriodicalIF":4.8,"publicationDate":"2024-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11535777/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142478190","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Large Language Models for Mental Health Applications: Systematic Review.","authors":"Zhijun Guo, Alvina Lai, Johan H Thygesen, Joseph Farrington, Thomas Keen, Kezhi Li","doi":"10.2196/57400","DOIUrl":"10.2196/57400","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Large language models (LLMs) are advanced artificial neural networks trained on extensive datasets to accurately understand and generate natural language. While they have received much attention and demonstrated potential in digital health, their application in mental health, particularly in clinical settings, has generated considerable debate.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;This systematic review aims to critically assess the use of LLMs in mental health, specifically focusing on their applicability and efficacy in early screening, digital interventions, and clinical settings. By systematically collating and assessing the evidence from current studies, our work analyzes models, methodologies, data sources, and outcomes, thereby highlighting the potential of LLMs in mental health, the challenges they present, and the prospects for their clinical use.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;Adhering to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, this review searched 5 open-access databases: MEDLINE (accessed by PubMed), IEEE Xplore, Scopus, JMIR, and ACM Digital Library. Keywords used were (mental health OR mental illness OR mental disorder OR psychiatry) AND (large language models). This study included articles published between January 1, 2017, and April 30, 2024, and excluded articles published in languages other than English.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;In total, 40 articles were evaluated, including 15 (38%) articles on mental health conditions and suicidal ideation detection through text analysis, 7 (18%) on the use of LLMs as mental health conversational agents, and 18 (45%) on other applications and evaluations of LLMs in mental health. LLMs show good effectiveness in detecting mental health issues and providing accessible, destigmatized eHealth services. However, assessments also indicate that the current risks associated with clinical use might surpass their benefits. These risks include inconsistencies in generated text; the production of hallucinations; and the absence of a comprehensive, benchmarked ethical framework.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;This systematic review examines the clinical applications of LLMs in mental health, highlighting their potential and inherent risks. The study identifies several issues: the lack of multilingual datasets annotated by experts, concerns regarding the accuracy and reliability of generated content, challenges in interpretability due to the \"black box\" nature of LLMs, and ongoing ethical dilemmas. These ethical concerns include the absence of a clear, benchmarked ethical framework; data privacy issues; and the potential for overreliance on LLMs by both physicians and patients, which could compromise traditional medical practices. As a result, LLMs should not be considered substitutes for professional mental health services. However, the rapid development of LLMs underscores their potential as valuable clinical ai","PeriodicalId":48616,"journal":{"name":"Jmir Mental Health","volume":"11 ","pages":"e57400"},"PeriodicalIF":4.8,"publicationDate":"2024-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11530718/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142478192","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Empowering Mental Health Monitoring Using a Macro-Micro Personalization Framework for Multimodal-Multitask Learning: Descriptive Study.","authors":"Meishu Song, Zijiang Yang, Andreas Triantafyllopoulos, Zixing Zhang, Zhe Nan, Muxuan Tang, Hiroki Takeuchi, Toru Nakamura, Akifumi Kishi, Tetsuro Ishizawa, Kazuhiro Yoshiuchi, Björn Schuller, Yoshiharu Yamamoto","doi":"10.2196/59512","DOIUrl":"10.2196/59512","url":null,"abstract":"<p><strong>Background: </strong>The field of mental health technology presently has significant gaps that need addressing, particularly in the domain of daily monitoring and personalized assessments. Current noninvasive devices such as wristbands and smartphones are capable of collecting a wide range of data, which has not yet been fully used for mental health monitoring.</p><p><strong>Objective: </strong>This study aims to introduce a novel dataset for personalized daily mental health monitoring and a new macro-micro framework. This framework is designed to use multimodal and multitask learning strategies for improved personalization and prediction of emotional states in individuals.</p><p><strong>Methods: </strong>Data were collected from 298 individuals using wristbands and smartphones, capturing physiological signals, speech data, and self-annotated emotional states. The proposed framework combines macro-level emotion transformer embeddings with micro-level personalization layers specific to each user. It also introduces a Dynamic Restrained Uncertainty Weighting method to effectively integrate various data types for a balanced representation of emotional states. Several fusion techniques, personalization strategies, and multitask learning approaches were explored.</p><p><strong>Results: </strong>The proposed framework was evaluated using the concordance correlation coefficient, resulting in a score of 0.503. This result demonstrates the framework's efficacy in predicting emotional states.</p><p><strong>Conclusions: </strong>The study concludes that the proposed multimodal and multitask learning framework, which leverages transformer-based techniques and dynamic task weighting strategies, is superior for the personalized monitoring of mental health. The study indicates the potential of transforming daily mental health monitoring into a more personalized app, opening up new avenues for technology-based mental health interventions.</p>","PeriodicalId":48616,"journal":{"name":"Jmir Mental Health","volume":"11 ","pages":"e59512"},"PeriodicalIF":4.8,"publicationDate":"2024-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11530727/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142478191","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Correction: Data Integrity Issues With Web-Based Studies: An Institutional Example of a Widespread Challenge.","authors":"Blandine French, Camilla Babbage, Katherine Bird, Lauren Marsh, Mirabel Pelton, Shireen Patel, Sarah Cassidy, Stefan Rennick-Egglestone","doi":"10.2196/67286","DOIUrl":"10.2196/67286","url":null,"abstract":"<p><p>[This corrects the article DOI: 10.2196/58432.].</p>","PeriodicalId":48616,"journal":{"name":"Jmir Mental Health","volume":"11 ","pages":"e67286"},"PeriodicalIF":4.8,"publicationDate":"2024-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11528159/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142478188","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An Ethical Perspective on the Democratization of Mental Health With Generative AI.","authors":"Zohar Elyoseph, Tamar Gur, Yuval Haber, Tomer Simon, Tal Angert, Yuval Navon, Amir Tal, Oren Asman","doi":"10.2196/58011","DOIUrl":"10.2196/58011","url":null,"abstract":"<p><strong>Unlabelled: </strong>Knowledge has become more open and accessible to a large audience with the \"democratization of information\" facilitated by technology. This paper provides a sociohistorical perspective for the theme issue \"Responsible Design, Integration, and Use of Generative AI in Mental Health.\" It evaluates ethical considerations in using generative artificial intelligence (GenAI) for the democratization of mental health knowledge and practice. It explores the historical context of democratizing information, transitioning from restricted access to widespread availability due to the internet, open-source movements, and most recently, GenAI technologies such as large language models. The paper highlights why GenAI technologies represent a new phase in the democratization movement, offering unparalleled access to highly advanced technology as well as information. In the realm of mental health, this requires delicate and nuanced ethical deliberation. Including GenAI in mental health may allow, among other things, improved accessibility to mental health care, personalized responses, and conceptual flexibility, and could facilitate a flattening of traditional hierarchies between health care providers and patients. At the same time, it also entails significant risks and challenges that must be carefully addressed. To navigate these complexities, the paper proposes a strategic questionnaire for assessing artificial intelligence-based mental health applications. This tool evaluates both the benefits and the risks, emphasizing the need for a balanced and ethical approach to GenAI integration in mental health. The paper calls for a cautious yet positive approach to GenAI in mental health, advocating for the active engagement of mental health professionals in guiding GenAI development. It emphasizes the importance of ensuring that GenAI advancements are not only technologically sound but also ethically grounded and patient-centered.</p>","PeriodicalId":48616,"journal":{"name":"Jmir Mental Health","volume":"11 ","pages":"e58011"},"PeriodicalIF":4.8,"publicationDate":"2024-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11500620/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142478187","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Correction: Digital Mental Health Interventions for Alleviating Depression and Anxiety During Psychotherapy Waiting Lists: Systematic Review.","authors":"Sijia Huang, Yiyue Wang, Gen Li, Brian J Hall, Thomas J Nyman","doi":"10.2196/67281","DOIUrl":"10.2196/67281","url":null,"abstract":"<p><p>[This corrects the article DOI: 10.2196/56650.].</p>","PeriodicalId":48616,"journal":{"name":"Jmir Mental Health","volume":"11 ","pages":"e67281"},"PeriodicalIF":4.8,"publicationDate":"2024-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11525075/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142478189","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}