Jmir Mental Health最新文献

{"title":"AI Applications in Depression Detection and Diagnosis: Bibliometric and Visual Analysis of Trends and Future Directions.","authors":"Wenbo Ren, Xiali Xue, Lu Liu, Jiahuan Huang","doi":"10.2196/79293","DOIUrl":"10.2196/79293","url":null,"abstract":"<p><strong>Background: </strong>Depression is a highly prevalent and debilitating mental disorder, but its diagnosis largely relies on subjective assessments, creating challenges in accuracy and consistency. Advances in artificial intelligence (AI) offer promising avenues for more objective and efficient diagnostic approaches. Understanding the evolving landscape of AI applications in depression diagnosis is essential for guiding future research and clinical translation.</p><p><strong>Objective: </strong>This study aims to provide a comprehensive bibliometric and visual analysis of the global research trends, intellectual structure, and emerging frontiers in the application of AI for depression detection and diagnosis from 2015 to 2024.</p><p><strong>Methods: </strong>We conducted a systematic literature search in the Web of Science Core Collection database to identify publications on AI applications in depression diagnosis from January 1, 2015, to December 31, 2024. A total of 2304 articles were retrieved and analyzed using bibliometric software CiteSpace. The analysis encompassed temporal trends, keyword dynamics, author collaboration networks, institutional influence, country contributions, and intellectual foundations through co-citation analysis of journals and references.</p><p><strong>Results: </strong>The field exhibited exponential growth in publications and citations, particularly after 2018, reflecting increasing academic and clinical interest. Key thematic shifts were observed from traditional machine learning to advanced deep learning, multimodal fusion, and the integration of objective biomarkers (eg, electroencephalography, facial expressions). Leading contributors included institutions from China and the United States, with collaborative links also forming with countries such as Canada and Singapore. The intellectual base is highly interdisciplinary, drawing heavily from computer science, neuroscience, and psychiatry, with a notable surge in engineering and translational research.</p><p><strong>Conclusions: </strong>The integration of AI in depression diagnosis is a rapidly maturing and diversifying field, transitioning from theoretical exploration to clinically relevant applications that emphasize objective, data-driven approaches. The identified trends underscore the need for enhanced interdisciplinary and international collaboration, the development of ethical frameworks, and a focus on translating technological innovations into accessible and equitable mental health solutions. These findings offer valuable insights for researchers, clinicians, and policy makers to strategically advance AI-assisted depression diagnostics globally.</p>","PeriodicalId":48616,"journal":{"name":"Jmir Mental Health","volume":" ","pages":"e79293"},"PeriodicalIF":5.8,"publicationDate":"2025-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145193556","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":"Performance of Automatic Speech Analysis in Detecting Depression: Systematic Review and Meta-Analysis.","authors":"Patricia Laura Maran, María Dolores Braquehais, Alexandra Vlaic, María Teresa Alonzo-Castillo, Júlia Vendrell-Serres, Josep Antoni Ramos-Quiroga, Amanda Rodríguez-Urrutia","doi":"10.2196/67802","DOIUrl":"https://doi.org/10.2196/67802","url":null,"abstract":"<p><strong>Background: </strong>Despite the high prevalence and significant burden of depression, underdiagnosis remains a persistent challenge. Automatic speech analysis (ASA) has emerged as a promising method for depression assessment. However, a comprehensive quantitative synthesis evaluating its diagnostic accuracy is still lacking.</p><p><strong>Objective: </strong>This systematic review and meta-analysis aimed to assess the diagnostic performance of ASA in detecting depression, considering both machine learning and deep learning approaches.</p><p><strong>Methods: </strong>We conducted a systematic search across 8 databases, including MEDLINE, PsycInfo, Embase, CINAHL, IEEE Xplore, ACM Digital Library, Scopus, and Google Scholar from January 2013 to April 1, 2025. We included studies published in English that evaluated the accuracy of ASA for detecting depression, and reported performance metrics such as accuracy, sensitivity, specificity, precision, or confusion matrices. Study quality was assessed using a modified version of the Quality Assessment of Studies of Diagnostic Accuracy-Revised. A 3-level meta-analysis was performed to estimate the pooled highest and lowest accuracy, sensitivity, specificity, and precision. Meta-regressions and subgroup analyses were performed to explore heterogeneity across various factors, including type of publication, artificial intelligence algorithms, speech features, speech-eliciting tasks, ground truth assessment, validation approach, dataset, dataset language, participants' mean age, and sample size.</p><p><strong>Results: </strong>Of the 1345 records identified, 105 studies met the inclusion criteria. The pooled mean of the highest accuracy, sensitivity, specificity, and precision were 0.81 (95% CI 0.79 to 0.83), 0.84 (95% CI 0.81 to 0.86), 0.83 (95% CI 0.79 to 0.86), and 0.81 (95% CI 0.77 to 0.84), respectively, whereas the pooled mean of the lowest accuracy, sensitivity, specificity, and precision were 0.66 (95% CI 0.63 to 0.69), 0.63 (95% CI 0.58 to 0.68), 0.60 (95% CI 0.55 to 0.66), and 0.64 (95% CI 0.58 to 0.70), respectively.</p><p><strong>Conclusions: </strong>ASA shows promise as a method for detecting depression, though its readiness for clinical application as a standalone tool remains limited. At present, it should be regarded as a complementary method, with potential applications across diverse contexts. Further high-quality, peer-reviewed studies are needed to support the development of robust, generalizable models and to advance this emerging field.</p><p><strong>Trial registration: </strong>PROSPERO CRD42023444431; https://www.crd.york.ac.uk/PROSPERO/view/CRD42023444431.</p>","PeriodicalId":48616,"journal":{"name":"Jmir Mental Health","volume":"12 ","pages":"e67802"},"PeriodicalIF":5.8,"publicationDate":"2025-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145349326","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":"Effectiveness of text messages, and text messages plus peer support, on psychiatric readmission and length of stay: Outcomes from a stepped-wedge cluster randomized trial.","authors":"Vincent Agyapong, Reham Abdel Hameed Shalaby, Belinda Agyapong, Wanying Mao, Ernest Owusu, Hossam Eldin Elgendy, Ejemai Eboreime, Peter H Silverstone, Pierre Chue, Xin-Min Li, Wesley Vuong, Arto Ohinmaa, Frank MacMaster, Andrew J Greenshaw","doi":"10.2196/81760","DOIUrl":"https://doi.org/10.2196/81760","url":null,"abstract":"<p><strong>Background: </strong>Mental health recovery typically continues after patients leaves the hospital. However, hospital readmission in the 12 months after discharge is common and costly.</p><p><strong>Objective: </strong>To examine the effectiveness of supportive text messaging (SMS) and SMS with or without peer support service (SMS+/-PSS) on hospital readmission and length of stay after discharge from inpatient psychiatric care.</p><p><strong>Methods: </strong>A stepped-wedge cluster randomized trial was used to examine differences in the changes in the mean number of admissions and the mean duration of total length of stay in days, for patients discharged from psychiatric inpatient care, at six- and 12-months pre- and post index admissions, for two intervention periods compared to a control period of treatment as usual Trial registration: Clinicaltrials.gov, NCT05133726. Registered on the 24th of November 2021.</p><p><strong>Results: </strong>Overall, 1,070 participants were assigned to one of three study arms: SMS (n = 302), SMS+/-PS (n = 342), or treatment as usual (TAU, n = 426). The SMS+/-PS reduced readmissions and inpatient length of stay. Compared to TAU, SMS+/-PS reduced hospital readmissions six months pre and post index admission by an average of 0.26 admissions and SMS alone reduced inpatient length of stays six months pre and post index admission by an average of 7.87 days.</p><p><strong>Conclusions: </strong>Our results demonstrate that simple, low-cost digital tool-either by themselves or paired with peer support-can help close gaps in post-discharge care. We anticipate that these findings may inform future service delivery models and policy development aimed at enhancing post-discharge mental health support. By supporting smoother transitions and reducing future hospital use, such approaches may offer a scalable way to build more sustainable and person-centred mental health systems.</p><p><strong>Clinicaltrial: </strong>Clinicaltrials.gov, NCT05133726.</p>","PeriodicalId":48616,"journal":{"name":"Jmir Mental Health","volume":" ","pages":""},"PeriodicalIF":5.8,"publicationDate":"2025-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145313984","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":"Comparing Generative Artificial Intelligence and Mental Health Professionals for Clinical Decision-Making With Trauma-Exposed Populations: Vignette-Based Experimental Study.","authors":"Katherine E Wislocki, Sabahat Sami, Gahl Liberzon, Alyson K Zalta","doi":"10.2196/80801","DOIUrl":"10.2196/80801","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Trauma exposure is highly prevalent and associated with various health issues. However, health care professionals can exhibit trauma-related diagnostic overshadowing bias, leading to misdiagnosis and inadequate treatment of trauma-exposed populations. Generative artificial intelligence (GAI) models are increasingly used in health care contexts. No research has examined whether GAI demonstrates this bias in decision-making and how rates of this bias may compare to mental health professionals (MHPs).&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;This study aimed to assess trauma-related diagnostic overshadowing among frontier GAI models and compare evidence of trauma-related diagnostic overshadowing between frontier GAI models and MHPs.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;MHPs (N=232; mean [SD] age 43.7 [15.95] years) completed an experimental paradigm consisting of 2 vignettes describing adults presenting with obsessive-compulsive symptoms or substance abuse symptoms. One vignette included a trauma exposure history (ie, sexual trauma or physical trauma), and one vignette did not include a trauma exposure history. Participants answered questions about their preferences for diagnosis and treatment options for clients within the vignettes. GAI models (eg, Gemini 1.5 Flash, ChatGPT-4o mini, Claude Sonnet, and Meta Llama 3) completed the same experimental paradigm, with each block being reviewed by each GAI model 20 times. Mann-Whitney U tests and chi-square analyses were used to assess diagnostic and treatment decision-making across vignette factors and respondents.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;GAI models, similar to MHPs, demonstrated some evidence of trauma-related diagnostic overshadowing bias, particularly in Likert-based ratings of posttraumatic stress disorder diagnosis and treatment when sexual trauma was present (P&lt;.001). However, GAI models generally exhibited significantly less bias than MHPs across both Likert and forced-choice clinical decision tasks. Compared to MHPs, GAI models assigned higher ratings for the target diagnosis and treatment in obsessive-compulsive disorder vignettes (rb=0.43-0.63; P&lt;.001) and for the target treatment in substance use disorder vignettes (rb=0.57; P&lt;.001) when trauma was present. In forced-choice tasks, GAI models were significantly more accurate than MHPs in selecting the correct diagnosis and treatment for obsessive-compulsive disorder vignettes (χ²1=48.84-61.07; P&lt;.001) and for substance use disorder vignettes involving sexual trauma (χ²1=15.17-101.61; P&lt;.001).&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;GAI models demonstrate some evidence of trauma-related diagnostic overshadowing bias, yet the degree of bias varied by task and model. Moreover, GAI models generally demonstrated less bias than MHPs in this experimental paradigm. These findings highlight the importance of understanding GAI biases in mental health care. More research into bias reduction strategies and responsible implementation","PeriodicalId":48616,"journal":{"name":"Jmir Mental Health","volume":"12 ","pages":"e80801"},"PeriodicalIF":5.8,"publicationDate":"2025-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12527320/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145294204","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":"The Digital Therapeutic Alliance With Mental Health Chatbots: Diary Study and Thematic Analysis.","authors":"Zian Xu, Yi-Chieh Lee, Karolina Stasiak, Jim Warren, Danielle Lottridge","doi":"10.2196/76642","DOIUrl":"https://doi.org/10.2196/76642","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Mental health chatbots are increasingly used to address the global mental health treatment gap by offering scalable, accessible, and anonymous support. While prior research suggests that users may develop relationships with these chatbots, the mechanisms and individual differences underlying such relational experiences remain underexplored. As the concept of the digital therapeutic alliance (DTA) gains traction, a deeper understanding of subjective relationship-building processes is essential to inform the design of more effective digital mental health interventions.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;This study aimed to investigate how people subjectively perceive and develop relationships with mental health chatbots over time. We sought to identify key experiential dimensions and interactional dynamics that facilitate or hinder the formation of such bonds, contributing to the evolving conceptualization of the DTA.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;We conducted a 4-week short-term longitudinal diary study with 26 adult participants who interacted with two widely available mental health chatbots (Woebot and Wysa). Data were collected through weekly surveys, conversation screenshots, and semistructured interviews. A reflexive thematic analysis was used to identify recurring themes and interpret the emotional, communicative, and contextual factors shaping participants' relational experiences with the chatbots.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;A total of 18 participants reported forming a bond or light bond with at least one chatbot. Interview narratives revealed three relational categories: Bond (clear emotional connection), Light Bond (tentative or partial connection), and No Bond (absence of connection). Both participants with lower and higher psychological well-being (based on the World Health Organization-Five Well-Being Index scores) reported forming such relationships, suggesting that bonding capacity is not strictly dependent on mental health status. Thematic analysis identified six key themes that explain why people did or did not form bonds: the desire to lead or be led in conversation, alignment between preferred style of self-expression and accepted inputs, expectations for caring and nurturing from the chatbot, perceived effectiveness of the chatbot's advice and proposed activities, appreciation for colloquial communication, and valuing a private and nonjudgmental conversation.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;Our findings provide empirical insight into how people interpret and engage in relational processes with mental health chatbots, advancing the theoretical foundation of the DTA. Rather than favoring one design style, our analysis highlights the importance of alignment between preferences and the chatbot's interaction style and conversational role. Participants' initial expectations around empathy and trust also shaped how relationships developed. Drawing on these insights, we suggest that chatbots ma","PeriodicalId":48616,"journal":{"name":"Jmir Mental Health","volume":"12 ","pages":"e76642"},"PeriodicalIF":5.8,"publicationDate":"2025-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145276413","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":"A Personalized and Smartphone-Based Serious Gaming App Targeting Cognitive Impairments in Alcohol Use Disorder: Double-Blinded, Randomized Controlled Efficacy Trial Among Outpatients.","authors":"Nicolaj Mistarz, Laust Vind Knudsen, Anna Mejldal, Kjeld Andersen, Anneke Goudriaan, Lotte Skøt, Tanja Maria Michel, Angelina Isabella Mellentin","doi":"10.2196/67167","DOIUrl":"https://doi.org/10.2196/67167","url":null,"abstract":"<p><strong>Background: </strong>Alcohol use disorder (AUD) is associated with cognitive impairments that are known to affect the outcomes of conventional treatment. Digital cognitive training programs have been examined as a possible way of addressing these overlooked challenges. Existing findings regarding the efficacy of such training programs are divergent, and further studies are warranted to examine more engaging cognitive training programs using the latest technology. Smartphone-based training built upon the principles of serious gaming would not only increase the accessibility of the program, but it could also increase the motivation of the patients, potentially maximizing adherence to the training program.</p><p><strong>Objective: </strong>The aim of the present feasibility and efficacy study was to examine the feasibility and acceptability of the Brain+ Alco-Recover app (Brain+ A/S) with gamified elements among patients with AUD when delivered as an add-on to treatment-as-usual (TAU) and with minimal guidance from health care practitioners. In addition, the effects on cognitive and alcohol-related outcomes were examined.</p><p><strong>Methods: </strong>A total of 72 outpatients were randomized into either group A, experimental + TAU (n=36), or group B, sham + TAU (n=36), and they had to complete a 1-month training program in addition to primary treatment. Self-reported experience at the 6-month follow-up as well as actual game usage was used to determine the feasibility of the training program. Cognitive performance and alcohol consumption were assessed as well.</p><p><strong>Results: </strong>The patients in both groups reported a high level of acceptability, and up to 83% of the patients in the experimental group met the minimum requirements for the usage of the app. The experimental group also demonstrated significant improvements in working memory (P<.001). Although no significant differences were found between the 2 groups regarding clinical outcomes, a greater reduction in alcohol consumption was evident at the 6-month follow-up in the experimental group.</p><p><strong>Conclusions: </strong>The acceptability and adherence to the minimum training requirements deems the gamified Brain+ app as a feasible tool for cognitive training when delivered as an add-on to TAU. Furthermore, the potential improvements in cognitive functions should be further replicated in a larger-scale trial to assess whether these could be used to improve the treatment of AUD in the future.</p><p><strong>International registered report identifier (irrid): </strong>RR2-10.3389/fpsyt.2021.727001.</p>","PeriodicalId":48616,"journal":{"name":"Jmir Mental Health","volume":"12 ","pages":"e67167"},"PeriodicalIF":5.8,"publicationDate":"2025-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145245655","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":"Speech Emotion Recognition in Mental Health: Systematic Review of Voice-Based Applications.","authors":"Eric Jordan, Raphaël Terrisse, Valeria Lucarini, Motasem Alrahabi, Marie-Odile Krebs, Julien Desclés, Christophe Lemey","doi":"10.2196/74260","DOIUrl":"10.2196/74260","url":null,"abstract":"<p><strong>Background: </strong>The field of speech emotion recognition (SER) encompasses a wide variety of approaches, with artificial intelligence technologies providing improvements in recent years. In the domain of mental health, the links between individuals' emotional states and pathological diagnoses are of particular interest.</p><p><strong>Objective: </strong>This study aimed to investigate the performance of tools combining SER and artificial intelligence approaches with a view to their use within clinical contexts and to determine the extent to which SER technologies have already been applied within clinical contexts.</p><p><strong>Methods: </strong>The review includes studies applied to speech (audio) signals for a select set of pathologies or disorders and only includes those studies that evaluate diagnostic performance using machine learning performance metrics or statistical correlation measures. The PubMed, IEEE Xplore, arXiv, and ScienceDirect databases were queried as recently as February 2025. The Quality Assessment of Diagnostic Accuracy Studies tool was used to measure the risk of bias.</p><p><strong>Results: </strong>A total of 14 articles were included in the final review. The included papers addressed suicide risk (3/14, 21%), depression (8/14, 57%), and psychotic disorders (3/14, 21%).</p><p><strong>Conclusions: </strong>SER technologies are mostly used indirectly in mental health research and in a wide variety of ways, including different architectures, datasets, and pathologies. This diversity makes a direct assessment of the technology challenging. Nonetheless, promising results are obtained in various studies that attempt to diagnose patients based on either indirect or direct results from SER models. These results highlight the potential for this technology to be used within a clinical setting. Future work should focus on how clinicians can use these technologies collaboratively.</p><p><strong>Trial registration: </strong>PROSPERO CRD420251006669; https://www.crd.york.ac.uk/PROSPERO/view/CRD420251006669.</p>","PeriodicalId":48616,"journal":{"name":"Jmir Mental Health","volume":"12 ","pages":"e74260"},"PeriodicalIF":5.8,"publicationDate":"2025-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12521853/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145201665","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":"Smartphone-Based Mindfulness and Mentalization Ecological Momentary Interventions for Common Mental Health Problems: Pilot Randomized Controlled Trial.","authors":"Ciarán O'Driscoll, Sarah O'Reilly, Agata Julita Jaremba, Tobias Nolte, Madiha Shaikh","doi":"10.2196/79296","DOIUrl":"10.2196/79296","url":null,"abstract":"<p><strong>Background: </strong>Accessible ecological momentary interventions deliver brief, real-time support integrated into daily routines. Interpersonal dynamics and maladaptive coping mechanisms can contribute to an individual's anxiety and depression. Both mindfulness and mentalization represent psychological constructs with the potential to mitigate the negative impact of interpersonal stressors.</p><p><strong>Objective: </strong>This study aims to assess the feasibility and acceptability of an automated mindfulness- and mentalization-based ecological momentary intervention for common mental health problems as delivered via a mobile phone app.</p><p><strong>Methods: </strong>The design was a parallel-group pilot randomized controlled trial with 1:1 allocation ratio and exploratory framework. Recruitment of participants experiencing common mental health issues was internet-based from a university setting. Eligible participants were randomly allocated to fully automated mindfulness- or mentalization-based ecological momentary interventions via computer-generated randomization. Participants were blind to the alternative intervention options. Outcomes were self-assessed through questionnaires after 4 weeks. Primary outcomes were feasibility (recruitment, retention, and adherence) and acceptability (satisfaction ratings and qualitative feedback). Secondary outcomes included changes in depression (Patient Health Questionnaire-9 [PHQ-9]) and anxiety (Generalized Anxiety Disorder Questionnaire-7 [GAD-7]) scores.</p><p><strong>Results: </strong>A total of 84 participants were randomized (42 to each group). The interventions demonstrated good feasibility with an 89.2% retention rate and a mean adherence of 87.69% (SD 11.3%) across both groups. Acceptability ratings were positive, with favorable scores for ease of engagement (mean 5.20, SD 1.6), overall enjoyment (mean 5.15, SD 1.2), and likelihood of recommending the app (mean 5.11, SD 1.6) on a 7-point scale. For primary outcomes, both groups showed significant within-group reductions in PHQ-9 and GAD-7 scores, with moderate to large effect sizes (Cohen d=-0.68 to -0.81), with no significant difference between groups. Both treatments demonstrated clinically significant change, with 33 (44%) participants in both groups no longer meeting caseness criteria for anxiety and depression. Mindfulness performed better on improving assertiveness and perceived support compared to mentalization in the ecological momentary assessment data. One unintended harm was reported in the mindfulness arm, whereas none was reported in the mentalization arm.</p><p><strong>Conclusions: </strong>This pilot trial suggests that both mindfulness- and mentalization-based ecological momentary interventions are feasible and acceptable for individuals with common mental health problems and warrant further evaluation.</p>","PeriodicalId":48616,"journal":{"name":"Jmir Mental Health","volume":"12 ","pages":"e79296"},"PeriodicalIF":5.8,"publicationDate":"2025-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12483342/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145201651","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":"Dissemination of Information on Selective Serotonin Reuptake Inhibitors on TikTok: Analytical Mixed Methods Study of Creator Types, Content Tone, and User Engagement.","authors":"Brittany Quinn, Lindsey Nichols, Jennifer Frazee, Mark Payton, Rachel M A Linger","doi":"10.2196/77383","DOIUrl":"10.2196/77383","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;TikTok [ByteDance] is a significant source of mental health-related content, including discussions on selective serotonin reuptake inhibitors (SSRIs). While the app fosters community building, its algorithm also amplifies misinformation as influencers without relevant expertise often dominate conversations about SSRIs. These videos frequently highlight personal experiences, potentially overshadowing evidence-based information from health care professionals. Despite these concerns, TikTok holds potential as a tool for improving mental health literacy when used by professionals to provide credible information.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;This study aimed to examine TikTok videos on SSRIs, hypothesizing that content will predominantly emphasize negative experiences and that videos by nonmedical professionals will attract higher engagement. By analyzing creators, engagement metrics, content tone, and video tone, this study aimed to shed light on social media's role in shaping perceptions of SSRIs and mental health literacy.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;A sample of 99 TikTok videos was collected on December 8, 2024. Apify, a web scraper, compiled pertinent engagement metrics (URLs, likes, comments, and shares). Views were manually recorded. In total, 3 researchers evaluated video and content tones and documented findings in Qualtrics. User profiles were analyzed to classify creators as a \"medical professional\" or \"nonmedical professional\" based on verification of their credentials. Statistical analyses evaluated the hypotheses.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;The number of videos created by both nonmedical and medical professionals was roughly even. Approximately one-third (35/99, 35%) mentioned a specific SSRI (ie, fluoxetine, fluvoxamine, vilazodone, sertraline, paroxetine, citalopram, or escitalopram). Compared to medical professionals, nonmedical creators produced significantly more videos with a positive video tone (P&lt;.001). TikToks made by both groups of creators, however, had negative content tones (P=.78). Nonmedical professionals received significantly greater overall views (P=.01), likes (P=.01), and comments (P=.03), but overall shares were not significantly different (P=.18). Daily interaction metrics revealed that nonmedical professionals received more daily interaction, but these differences were not significant in terms of views (P=.09), likes (P=.06), comments (P=.15), or shares (P=.28).&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;Results showed that while both creator groups focused on negative SSRI side effects and experiences (content tone), the way they presented this information (video tone) differed. Medical professionals generally maintained a neutral video tone, whereas nonmedical professionals were more likely to adopt a positive video tone. This may explain why nonmedical professionals' videos had significantly more cumulative views, likes, and comments than medical professionals' videos. Thes","PeriodicalId":48616,"journal":{"name":"Jmir Mental Health","volume":"12 ","pages":"e77383"},"PeriodicalIF":5.8,"publicationDate":"2025-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12483472/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145201546","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":"Understanding the Needs of Moderators in Online Mental Health Forums: Realist Synthesis and Recommendations for Support.","authors":"Heather Robinson, Millissa Booth, Lauren Fothergill, Claire Friedrich, Zoe Glossop, Jade Haines, Andrew Harding, Rose Johnston, Steven Jones, Karen Machin, Rachel Meacock, Kristi Nielson, Paul Marshall, Jo-Anne Puddephatt, Tamara Rakić, Paul Rayson, Jo Rycroft-Malone, Nick Shryane, Zoe Swithenbank, Sara Wise, Fiona Lobban","doi":"10.2196/58891","DOIUrl":"10.2196/58891","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;There has been an increase in the use of online mental health forums to support mental health. These forums are often moderated by trained moderators to ensure a safe, therapeutic environment. While the moderator role is rewarding, it can also be challenging. There is a need to understand the impact of the role on moderators and how they can best be supported to maintain psychological well-being.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;This study aimed to understand how, why, and in what contexts moderator well-being is affected by the moderator role and produce actionable recommendations for how moderators can best be supported to maintain workplace psychological well-being.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;We conducted realist synthesis of (1) published and gray literature from 2019 to 2023, (2) stakeholder interviews with forum moderators and hosts, and (3) moderator training manuals developed by organizations that host online mental health forums. Self-determination theory was used as the theoretical basis for this synthesis.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;We developed 24 context-mechanism-outcome configurations from our realist analysis of 9 published papers, 18 interviews, and 5 training manuals. The findings highlight the specific ways in which moderator well-being can be supported through meeting the psychological needs for autonomy, competence, and relatedness. Forums that allow moderators to work in alignment with their personal motivations can increase moderator well-being. Forum organizations should support moderator competence through initial expectation setting, especially around moderator responsibility for user well-being, and ongoing support, such as meaningful supervision and peer support. Co-designed training, reflective practice, and experiential learning are all key to increasing moderator competence and satisfaction in the workplace. Working within a diverse team with access to innovative forum design can increase moderator psychological well-being. Organizational support for moderators' well-being through monitoring and encouraging self-care is vital to ensure moderators can effectively carry out their role. Making and supporting meaningful relationships in the forum can boost psychological well-being and the therapeutic value of the moderator role. Key challenges for moderators were dealing with conflicts between supporting open discussion and ensuring a safe community environment, sharing lived experiences in positive ways for both moderator and user, and supporting people within the limitations of an anonymous forum.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;This realist synthesis is the first to examine the impacts on well-being of being a moderator of an online mental health forum. Recommendations to support moderator psychological well-being are proposed, targeted at specific stakeholder groups to aid implementation. Organizational-level endorsement and facilitation of support are particularly impo","PeriodicalId":48616,"journal":{"name":"Jmir Mental Health","volume":"12 ","pages":"e58891"},"PeriodicalIF":5.8,"publicationDate":"2025-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12514405/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145179377","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}