Jmir Mental Health最新文献

{"title":"Exploring the Effects of Variety and Amount of Mindfulness Practices on Depression, Anxiety, and Stress Symptoms: Longitudinal Study on a Mental Health-Focused eHealth System for Patients With Breast or Prostate Cancer.","authors":"Francesca Malandrone, Sara Urru, Paola Berchialla, Pierre Gilbert Rossini, Francesco Oliva, Silvia Bianchi, Manuel Ottaviano, Sergio Gonzalez-Martinez, Vladimir Carli, Gaetano Valenza, Enzo Pasquale Scilingo, Sara Carletto, Luca Ostacoli","doi":"10.2196/57415","DOIUrl":"https://doi.org/10.2196/57415","url":null,"abstract":"<p><strong>Background: </strong>Patients with cancer often face depression and anxiety, and mindfulness-based interventions, including internet-based versions, can effectively reduce these symptoms and improve their quality of life. This study aims to investigate the impact of internet-based mindfulness-based interventions (e-MBIs) on anxiety, depression, and stress symptoms in patients with prostate or breast cancer.</p><p><strong>Objective: </strong>The primary aims are to assess the association between the amount and variety of e-MBI practices and symptom reduction. Second, this study aims to examine how baseline information such as sociodemographic characteristics, dispositional mindfulness (DM), and dispositional self-compassion (DSC) correlate with both app usage and symptom reduction.</p><p><strong>Methods: </strong>Participants included 107 patients with cancer (68 women with breast cancer and 38 men with prostate cancer) enrolled in a hospital setting. They were assigned to the intervention group of the NEVERMIND project, using the e-BMI module via the NEVERMIND app. A longitudinal design involved Pearson correlation analysis to determine the relationship between the amount and duration of e-MBI practices. Linear regression analysis was conducted to gauge the dose-response effect, evaluating the impact of DM and DSC on depression, anxiety, and stress. Negative binomial regression was conudcted to study sociodemographic factors' influence on the amount of practice in e-MBIs.</p><p><strong>Results: </strong>The participants with more diverse and sustained mindfulness practices experienced significant reductions in depression, anxiety, and stress. A high correlation (0.94) between e-MBI practices and symptom reduction was also highlighted. Male, married, and highly educated patients were more likely to engage in mindfulness. Even if DM and DSC did not impact the amount or variety of practices correlated, they were correlated with symptom reduction, showing that higher levels were associated with significant reductions in depression, anxiety, and stress.</p><p><strong>Conclusions: </strong>While more e-MBI practice is linked to reduced anxiety, depression, and stress, this study emphasizes the crucial role of variety of practice over amount. DM and DSC are key in shaping intervention effectiveness and may act as protectors against psychological distress. Using app log data, our research provides a unique perspective on e-MBI impact, contributing to cancer care understanding and guiding future studies.</p>","PeriodicalId":48616,"journal":{"name":"Jmir Mental Health","volume":"11 ","pages":"e57415"},"PeriodicalIF":4.8,"publicationDate":"2024-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142688909","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Building Mutually Beneficial Collaborations Between Digital Navigators, Mental Health Professionals, and Clients: Naturalistic Observational Case Study.","authors":"Carla Gorban, Sarah McKenna, Min K Chong, William Capon, Robert Battisti, Alison Crowley, Bradley Whitwell, Antonia Ottavio, Elizabeth M Scott, Ian B Hickie, Frank Iorfino","doi":"10.2196/58068","DOIUrl":"10.2196/58068","url":null,"abstract":"<p><strong>Unlabelled: </strong>Despite the efficacy of digital mental health technologies (DMHTs) in clinical trials, low uptake and poor engagement are common in real-world settings. Accordingly, digital technology experts or \"digital navigators\" are increasingly being used to enhance engagement and shared decision-making between health professionals and clients. However, this area is relatively underexplored and there is a lack of data from naturalistic settings. In this paper, we report observational findings from the implementation of a digital navigator in a multidisciplinary mental health clinic in Sydney, Australia. The digital navigator supported clients and health professionals to use a measurement-based DMHT (the Innowell platform) for improved multidimensional outcome assessment and to guide personalized decision-making. Observational data are reported from implementation logs, platform usage statistics, and response rates to digital navigator emails and phone calls. Ultimately, support from the digital navigator led to improved data collection and clearer communications about goals for using the DMHT to track client outcomes; however, this required strong partnerships between health professionals, the digital navigator, and clients. The digital navigator helped to facilitate the integration of DMHT into care, rather than providing a stand-alone service. Thus, collaborations between health professionals and digital navigators are mutually beneficial and empower clients to be more engaged in their own care.</p>","PeriodicalId":48616,"journal":{"name":"Jmir Mental Health","volume":"11 ","pages":"e58068"},"PeriodicalIF":4.8,"publicationDate":"2024-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11559438/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142590973","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":"Virtual Reality Exposure Therapy for Reducing School Anxiety in Adolescents: Pilot Study.","authors":"Gesa Beele, Paula Liesong, Sabine Bojanowski, Kristian Hildebrand, Malte Weingart, Julia Asbrand, Christoph U Correll, Nexhmedin Morina, Peter J Uhlhaas","doi":"10.2196/56235","DOIUrl":"10.2196/56235","url":null,"abstract":"<p><strong>Background: </strong>Virtual reality exposure therapy (VRET) is a promising treatment approach for anxiety disorders. However, while its efficacy has been demonstrated in adults, research on the efficacy of VRET in the treatment of adolescents with anxiety disorders is largely lacking.</p><p><strong>Objective: </strong>A pilot study was carried out to test whether exposure to a virtual reality (VR) school environment elicits state anxiety and autonomic arousal in adolescents with school anxiety (diagnoses covering social anxiety disorder or specific phobia involving school contexts). In addition, we examined whether repeated VR exposure led to a reduction in this fear response, trait school anxiety, and social anxiety symptoms. Moreover, the relationship of presence, the subjective sense of \"being there,\" during VR exposure with anxiety measures and treatment response was examined.</p><p><strong>Methods: </strong>In a pilot study, 10 adolescents with school anxiety (age range 14 to 17 years) participated in five VRET sessions. Self-reported state anxiety, heart rate, and presence during exposure, as well as trait school anxiety and social anxiety before and after treatment, were measured.</p><p><strong>Results: </strong>The VR scenario induced state anxiety and autonomic arousal. After VRET, a significant reduction in state anxiety (η²=0.74) and social anxiety symptoms (d=0.82) as well as a trend toward a decrease in trait school anxiety were observed, while autonomic arousal did not change. In addition, presence during VR exposure was associated with state anxiety and treatment response.</p><p><strong>Conclusions: </strong>Our findings indicate the feasibility and potential effectiveness of VRET as a treatment method for symptoms of school and social anxiety in adolescents.</p>","PeriodicalId":48616,"journal":{"name":"Jmir Mental Health","volume":"11 ","pages":"e56235"},"PeriodicalIF":4.8,"publicationDate":"2024-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11576610/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142584475","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":"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":5.4,"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}