Jmir Mental Health最新文献

{"title":"Toward a New Conceptual Framework for Digital Mental Health Technologies: Scoping Review.","authors":"Gareth Hopkin, Holly Coole, Francesca Edelmann, Lynda Ayiku, Richard Branson, Paul Campbell, Sophie Cooper, Mark Salmon","doi":"10.2196/63484","DOIUrl":"10.2196/63484","url":null,"abstract":"<p><strong>Background: </strong>Digital mental health technologies (DMHTs) are becoming more widely available and are seen as having the potential to improve the quality of mental health care. However, conversations around the potential impact of DMHTs can be impacted by a lack of focus on the types of technologies that are available. Several frameworks that could apply to DMHTs are available, but they have not been developed with comprehensive methods and have limitations.</p><p><strong>Objective: </strong>To address limitations with current frameworks, we aimed to identify existing literature on the categorization of DMHTs, to explore challenges with categorizing DMHTs for specific purposes, and to develop a new conceptual framework.</p><p><strong>Methods: </strong>We used an iterative approach to develop the framework. First, we completed a rapid review of the literature to identify studies that provided domains that could be used to categorize DMHTs. Second, findings from this review and associated issues were discussed by an expert working group, including professionals from a wide range of relevant settings. Third, we synthesized findings to develop a new conceptual framework.</p><p><strong>Results: </strong>The rapid review identified 3603 unique results, and hand searching identified another 3 potentially relevant papers. Of these, 24 papers were eligible for inclusion, which provided 10 domains to categorize DMHTs. The expert working group proposed a broad framework and based on the findings of the review and group discussions, we developed a new conceptual framework with 8 domains that represent important characteristics of DMHTs. These 8 domains are population, setting, platform or system, purpose, type of approach, human interaction, human responsiveness, and functionality.</p><p><strong>Conclusions: </strong>This conceptual framework provides a structure for various stakeholders to define the key characteristics of DMHTs. It has been developed with more comprehensive methods than previous attempts with similar aims. The framework can facilitate communication within the field and could undergo further iteration to ensure it is appropriate for specific purposes.</p>","PeriodicalId":48616,"journal":{"name":"Jmir Mental Health","volume":"12 ","pages":"e63484"},"PeriodicalIF":4.8,"publicationDate":"2025-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11864090/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143450570","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":"Mental Health Screening Using the Heart Rate Variability and Frontal Electroencephalography Features: A Machine Learning-Based Approach.","authors":"Je-Yeon Yun, Goomin Kwon, Miseon Shim, Seon-Min Kim, Seung-Hwan Lee, Sangshin Park","doi":"10.2196/72803","DOIUrl":"https://doi.org/10.2196/72803","url":null,"abstract":"<p><strong>Background: </strong>Heart rate variability (HRV) is a physiological marker of the cardiac autonomic modulation and related emotional regulation. Electroencephalography (EEG) is reflective of brain cortical activities and related psychopathology. The HRV and EEG have been employed in machine learning- and deep learning-based algorithms either alone or with other wearable device-based features to classify patients with psychiatric disorder (PT) and healthy controls (HC). Little study examined the utility of wearable device-based physiological markers to discern PT with various psychiatric diagnosis versus HC.</p><p><strong>Objective: </strong>This study examined the HRV and prefrontal EEG features most frequently selected in the support vector machine (SVM) having the highest classification accuracy of PT versus HC, contributing to the individual-level initial screening of PT and minimized duration of untreated psychiatric illness.</p><p><strong>Methods: </strong>A simultaneous acquisition of 5 minute-length PPG (measured on right ear lobe) and resting-state EEG (with eye-closed; using two left/right forehead-located electrodes) of 182 participants [87 PT (including major depressive disorder (70.1%) and panic disorder (12.6%)) and 95 HC] were performed. The PPG-based HRV features were quantified for both time- and frequency-domains. The time-varying EEG signals were converted into frequency-domain signals of the power spectral density. In the feature selection of the Gaussian radial basis function kernel-based support vector machine (SVM) models, estimators were comprised of top N (1£N£22) highest scored HRV/EEG features based on the one-way ANOVA F-value. Classification performance of SVM model (PT vs. HC) having N estimators was assessed using the Leave-one-out cross-validation (LOOCV; N = 182), to confirm those showing the highest balanced accuracy and area under the receiver operating characteristic curve (AUROC) as final classification model.</p><p><strong>Results: </strong>The final SVM model having 13 estimators showed balanced accuracy of 0.76 and AUROC of 0.78. Power spectral density of HRV in the high frequency, very low frequency, low frequency (LF) bands, and total power, a product of the mean of the 5-minute standard deviation of all NN intervals (SDNN) and normalized LF power of HRV, power spectral density of frontal EEG in the high alpha and alpha peak frequency comprised the top 13-scored classification features in > 90% of the LOOCV.</p><p><strong>Conclusions: </strong>This study showed a possible synergic effect of combining the HRV and prefrontal EEG features in machine learning-based mental health screening. Future studies to predict the treatment response and to propose the preferred treatment regimen based on the baseline physiological markers are required.</p><p><strong>Clinicaltrial: </strong>N/A.</p>","PeriodicalId":48616,"journal":{"name":"Jmir Mental Health","volume":" ","pages":""},"PeriodicalIF":4.8,"publicationDate":"2025-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143460219","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":"Promises and Pitfalls of Internet Search Data in Mental Health: Critical Review.","authors":"Alexandre Andrade Loch, Roman Kotov","doi":"10.2196/60754","DOIUrl":"10.2196/60754","url":null,"abstract":"<p><strong>Unlabelled: </strong>The internet is now integral to everyday life, and users' web-based search data could be of strategic importance in mental health care. As shown by previous studies, internet searches may provide valuable insights into an individual's mental state and could be of great value in early identification and helping in pathways to care. Internet search data can potentially provide real-time identification (eg, alert mechanisms for timely interventions). In this paper, we discuss the various problems related to the use of these data in research and clinical practice, including privacy concerns, integration with clinical information, and technical limitations. We also propose solutions to address these issues and provide possible future directions.</p>","PeriodicalId":48616,"journal":{"name":"Jmir Mental Health","volume":"12 ","pages":"e60754"},"PeriodicalIF":4.8,"publicationDate":"2025-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11855165/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143450565","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":"Telehealth-Based vs In-Person Aerobic Exercise in Individuals With Schizophrenia: Comparative Analysis of Feasibility, Safety, and Efficacy.","authors":"David Kimhy, Luz H Ospina, Melanie Wall, Daniel M Alschuler, Lars F Jarskog, Jacob S Ballon, Joseph McEvoy, Matthew N Bartels, Richard Buchsbaum, Marianne Goodman, Sloane A Miller, T Scott Stroup","doi":"10.2196/68251","DOIUrl":"10.2196/68251","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Aerobic exercise (AE) training has been shown to enhance aerobic fitness in people with schizophrenia. Traditionally, such training has been administered in person at gyms or other communal exercise spaces. However, following the advent of the COVID-19 pandemic, many clinics transitioned their services to telehealth-based delivery. Yet, at present, there is scarce information about the feasibility, safety, and efficacy of telehealth-based AE in this population.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;To examine the feasibility, safety, and efficacy of trainer-led, at-home, telehealth-based AE in individuals with schizophrenia.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;We analyzed data from the AE arm (n=37) of a single-blind, randomized clinical trial examining the impact of a 12-week AE intervention in people with schizophrenia. Following the onset of the COVID-19 pandemic, the AE trial intervention transitioned from in-person to at-home, telehealth-based delivery of AE, with the training frequency and duration remaining identical. We compared the feasibility, safety, and efficacy of the delivery of trainer-led AE training among participants undergoing in-person (pre-COVID-19; n=23) versus at-home telehealth AE (post-COVID-19; n=14).&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;The telehealth and in-person participants attended a similar number of exercise sessions across the 12-week interventions (26.8, SD 10.2 vs 26.1, SD 9.7, respectively; P=.84) and had similar number of weeks with at least 1 exercise session (10.4, SD 3.4 vs 10.6, SD 3.1, respectively; P=.79). The telehealth-based AE was associated with a significantly lower drop-out rate (telehealth: 0/14, 0%; in-person: 7/23, 30.4%; P=.04). There were no significant group differences in total time spent exercising (telehealth: 1246, SD 686 min; in-person: 1494, SD 580 min; P=.28); however, over the 12-week intervention, the telehealth group had a significantly lower proportion of session-time exercising at or above target intensity (telehealth: 33.3%, SD 21.4%; in-person: 63.5%, SD 16.3%; P&lt;.001). There were no AE-related serious adverse events associated with either AE delivery format. Similarly, there were no significant differences in the percentage of participants experiencing minor or moderate adverse events, such as muscle soreness, joint pain, blisters, or dyspnea (telehealth: 3/14, 21%; in-person: 5/19, 26%; P&gt;.99) or in the percentage of weeks per participant with at least 1 exercise-related adverse event (telehealth: 31%, SD 33%; in-person: 40%, SD 33%; P=.44). There were no significant differences between the telehealth versus in-person groups regarding changes in aerobic fitness as indexed by maximum oxygen consumption (VO2max; P=.27).&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;Our findings provide preliminary support for the delivery of telehealth-based AE for individuals with schizophrenia. Our results indicate that in-home telehealth-based AE is feasible and safe in this popula","PeriodicalId":48616,"journal":{"name":"Jmir Mental Health","volume":"12 ","pages":"e68251"},"PeriodicalIF":4.8,"publicationDate":"2025-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11844875/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143417119","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":"Leveraging Large Language Models and Agent-Based Systems for Scientific Data Analysis: Validation Study.","authors":"Dale Peasley, Rayus Kuplicki, Sandip Sen, Martin Paulus","doi":"10.2196/68135","DOIUrl":"10.2196/68135","url":null,"abstract":"<p><strong>Background: </strong>Large language models have shown promise in transforming how complex scientific data are analyzed and communicated, yet their application to scientific domains remains challenged by issues of factual accuracy and domain-specific precision. The Laureate Institute for Brain Research-Tulsa University (LIBR-TU) Research Agent (LITURAt) leverages a sophisticated agent-based architecture to mitigate these limitations, using external data retrieval and analysis tools to ensure reliable, context-aware outputs that make scientific information accessible to both experts and nonexperts.</p><p><strong>Objective: </strong>The objective of this study was to develop and evaluate LITURAt to enable efficient analysis and contextualization of complex scientific datasets for diverse user expertise levels.</p><p><strong>Methods: </strong>An agent-based system based on large language models was designed to analyze and contextualize complex scientific datasets using a \"plan-and-solve\" framework. The system dynamically retrieves local data and relevant PubMed literature, performs statistical analyses, and generates comprehensive, context-aware summaries to answer user queries with high accuracy and consistency.</p><p><strong>Results: </strong>Our experiments demonstrated that LITURAt achieved an internal consistency rate of 94.8% and an external consistency rate of 91.9% across repeated and rephrased queries. Additionally, GPT-4 evaluations rated 80.3% (171/213) of the system's answers as accurate and comprehensive, with 23.5% (50/213) receiving the highest rating of 5 for completeness and precision.</p><p><strong>Conclusions: </strong>These findings highlight the potential of LITURAt to significantly enhance the accessibility and accuracy of scientific data analysis, achieving high consistency and strong performance in complex query resolution. Despite existing limitations, such as model stability for highly variable queries, LITURAt demonstrates promise as a robust tool for democratizing data-driven insights across diverse scientific domains.</p>","PeriodicalId":48616,"journal":{"name":"Jmir Mental Health","volume":"12 ","pages":"e68135"},"PeriodicalIF":4.8,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11841814/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143415898","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":"Identifying Adolescent Depression and Anxiety Through Real-World Data and Social Determinants of Health: Machine Learning Model Development and Validation.","authors":"Mamoun T Mardini, Georges E Khalil, Chen Bai, Aparna Menon DivaKaran, Jessica M Ray","doi":"10.2196/66665","DOIUrl":"10.2196/66665","url":null,"abstract":"<p><strong>Background: </strong>The prevalence of adolescent mental health conditions such as depression and anxiety has significantly increased. Despite the potential of machine learning (ML), there is a shortage of models that use real-world data (RWD) to enhance early detection and intervention for these conditions.</p><p><strong>Objective: </strong>This study aimed to identify depression and anxiety in adolescents using ML techniques on RWD and social determinants of health (SDoH).</p><p><strong>Methods: </strong>We analyzed RWD of adolescents aged 10-17 years, considering various factors such as demographics, prior diagnoses, prescribed medications, medical procedures, and laboratory measurements recorded before the onset of anxiety or depression. Clinical data were linked with SDoH at the block-level. Three separate models were developed to predict anxiety, depression, and both conditions. Our ML model of choice was Extreme Gradient Boosting (XGBoost) and we evaluated its performance using the nested cross-validation technique. To interpret the model predictions, we used the Shapley additive explanation method.</p><p><strong>Results: </strong>Our cohort included 52,054 adolescents, identifying 12,572 with anxiety, 7812 with depression, and 14,019 with either condition. The models achieved area under the curve values of 0.80 for anxiety, 0.81 for depression, and 0.78 for both combined. Excluding SDoH data had a minimal impact on model performance. Shapley additive explanation analysis identified gender, race, educational attainment, and various medical factors as key predictors of anxiety and depression.</p><p><strong>Conclusions: </strong>This study highlights the potential of ML in early identification of depression and anxiety in adolescents using RWD. By leveraging RWD, health care providers may more precisely identify at-risk adolescents and intervene earlier, potentially leading to improved mental health outcomes.</p>","PeriodicalId":48616,"journal":{"name":"Jmir Mental Health","volume":"12 ","pages":"e66665"},"PeriodicalIF":4.8,"publicationDate":"2025-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11838812/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143411273","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":"Harnessing Internet Search Data as a Potential Tool for Medical Diagnosis: Literature Review.","authors":"Gregory J Downing, Lucas M Tramontozzi, Jackson Garcia, Emma Villanueva","doi":"10.2196/63149","DOIUrl":"10.2196/63149","url":null,"abstract":"<p><strong>Background: </strong>The integration of information technology into health care has created opportunities to address diagnostic challenges. Internet searches, representing a vast source of health-related data, hold promise for improving early disease detection. Studies suggest that patterns in search behavior can reveal symptoms before clinical diagnosis, offering potential for innovative diagnostic tools. Leveraging advancements in machine learning, researchers have explored linking search data with health records to enhance screening and outcomes. However, challenges like privacy, bias, and scalability remain critical to its widespread adoption.</p><p><strong>Objective: </strong>We aimed to explore the potential and challenges of using internet search data in medical diagnosis, with a specific focus on diseases and conditions such as cancer, cardiovascular disease, mental and behavioral health, neurodegenerative disorders, and nutritional and metabolic diseases. We examined ethical, technical, and policy considerations while assessing the current state of research, identifying gaps and limitations, and proposing future research directions to advance this emerging field.</p><p><strong>Methods: </strong>We conducted a comprehensive analysis of peer-reviewed literature and informational interviews with subject matter experts to examine the landscape of internet search data use in medical research. We searched for published peer-reviewed literature on the PubMed database between October and December 2023.</p><p><strong>Results: </strong>Systematic selection based on predefined criteria included 40 articles from the 2499 identified articles. The analysis revealed a nascent domain of internet search data research in medical diagnosis, marked by advancements in analytics and data integration. Despite challenges such as bias, privacy, and infrastructure limitations, emerging initiatives could reshape data collection and privacy safeguards.</p><p><strong>Conclusions: </strong>We identified signals correlating with diagnostic considerations in certain diseases and conditions, indicating the potential for such data to enhance clinical diagnostic capabilities. However, leveraging internet search data for improved early diagnosis and health care outcomes requires effectively addressing ethical, technical, and policy challenges. By fostering interdisciplinary collaboration, advancing infrastructure development, and prioritizing patient engagement and consent, researchers can unlock the transformative potential of internet search data in medical diagnosis to ultimately enhance patient care and advance health care practice and policy.</p>","PeriodicalId":48616,"journal":{"name":"Jmir Mental Health","volume":" ","pages":"e63149"},"PeriodicalIF":4.8,"publicationDate":"2025-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11862766/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143014496","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":"Physician Perspectives on the Potential Benefits and Risks of Applying Artificial Intelligence in Psychiatric Medicine: Qualitative Study.","authors":"Austin M Stroud, Susan H Curtis, Isabel B Weir, Jeremiah J Stout, Barbara A Barry, William V Bobo, Arjun P Athreya, Richard R Sharp","doi":"10.2196/64414","DOIUrl":"10.2196/64414","url":null,"abstract":"<p><strong>Background: </strong>As artificial intelligence (AI) tools are integrated more widely in psychiatric medicine, it is important to consider the impact these tools will have on clinical practice.</p><p><strong>Objective: </strong>This study aimed to characterize physician perspectives on the potential impact AI tools will have in psychiatric medicine.</p><p><strong>Methods: </strong>We interviewed 42 physicians (21 psychiatrists and 21 family medicine practitioners). These interviews used detailed clinical case scenarios involving the use of AI technologies in the evaluation, diagnosis, and treatment of psychiatric conditions. Interviews were transcribed and subsequently analyzed using qualitative analysis methods.</p><p><strong>Results: </strong>Physicians highlighted multiple potential benefits of AI tools, including potential support for optimizing pharmaceutical efficacy, reducing administrative burden, aiding shared decision-making, and increasing access to health services, and were optimistic about the long-term impact of these technologies. This optimism was tempered by concerns about potential near-term risks to both patients and themselves including misguiding clinical judgment, increasing clinical burden, introducing patient harms, and creating legal liability.</p><p><strong>Conclusions: </strong>Our results highlight the importance of considering specialist perspectives when deploying AI tools in psychiatric medicine.</p>","PeriodicalId":48616,"journal":{"name":"Jmir Mental Health","volume":"12 ","pages":"e64414"},"PeriodicalIF":4.8,"publicationDate":"2025-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11851033/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143383678","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":"Use of Digital Health Technologies for Dementia Care: Bibliometric Analysis and Report.","authors":"Hebatullah Abdulazeem, Israel Júnior Borges do Nascimento, Ishanka Weerasekara, Amin Sharifan, Victor Grandi Bianco, Ciara Cunningham, Indunil Kularathne, Genevieve Deeken, Jerome de Barros, Brijesh Sathian, Lasse Østengaard, Frederique Lamontagne-Godwin, Joost van Hoof, Ledia Lazeri, Cassie Redlich, Hannah R Marston, Ryan Alistair Dos Santos, Natasha Azzopardi-Muscat, Yongjie Yon, David Novillo-Ortiz","doi":"10.2196/64445","DOIUrl":"10.2196/64445","url":null,"abstract":"<p><strong>Background: </strong>Dementia is a syndrome that compromises neurocognitive functions of the individual and that is affecting 55 million individuals globally, as well as global health care systems, national economic systems, and family members.</p><p><strong>Objective: </strong>This study aimed to determine the status quo of scientific production on use of digital health technologies (DHTs) to support (older) people living with dementia, their families, and care partners. In addition, our study aimed to map the current landscape of global research initiatives on DHTs on the prevention, diagnosis, treatment, and support of people living with dementia and their caregivers.</p><p><strong>Methods: </strong>A bibliometric analysis was performed as part of a systematic review protocol using MEDLINE, Embase, Scopus, Epistemonikos, the Cochrane Database of Systematic Reviews, and Google Scholar for systematic and scoping reviews on DHTs and dementia up to February 21, 2024. Search terms included various forms of dementia and DHTs. Two independent reviewers conducted a 2-stage screening process with disagreements resolved by a third reviewer. Eligible reviews were then subjected to a bibliometric analysis using VOSviewer to evaluate document types, authorship, countries, institutions, journal sources, references, and keywords, creating social network maps to visualize emergent research trends.</p><p><strong>Results: </strong>A total of 704 records met the inclusion criteria for bibliometric analysis. Most reviews were systematic, with a substantial number covering mobile health, telehealth, and computer-based cognitive interventions. Bibliometric analysis revealed that the Journal of Medical Internet Research had the highest number of reviews and citations. Researchers from 66 countries contributed, with the United Kingdom and the United States as the most prolific. Overall, the number of publications covering the intersection of DHTs and dementia has increased steadily over time. However, the diversity of reviews conducted on a single topic has resulted in duplicated scientific efforts. Our assessment of contributions from countries, institutions, and key stakeholders reveals significant trends and knowledge gaps, particularly highlighting the dominance of high-income countries in this research domain. Furthermore, our findings emphasize the critical importance of interdisciplinary, collaborative teams and offer clear directions for future research, especially in underrepresented regions.</p><p><strong>Conclusions: </strong>Our study shows a steady increase in dementia- and DHT-related publications, particularly in areas such as mobile health, virtual reality, artificial intelligence, and sensor-based technologies interventions. This increase underscores the importance of systematic approaches and interdisciplinary collaborations, while identifying knowledge gaps, especially in lower-income regions. It is crucial that researchers worldwide adh","PeriodicalId":48616,"journal":{"name":"Jmir Mental Health","volume":"12 ","pages":"e64445"},"PeriodicalIF":4.8,"publicationDate":"2025-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11851039/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143392191","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 Efficacy of Conversational AI in Rectifying the Theory-of-Mind and Autonomy Biases: Comparative Analysis.","authors":"Marcin Rządeczka, Anna Sterna, Julia Stolińska, Paulina Kaczyńska, Marcin Moskalewicz","doi":"10.2196/64396","DOIUrl":"10.2196/64396","url":null,"abstract":"<p><strong>Background: </strong>The increasing deployment of conversational artificial intelligence (AI) in mental health interventions necessitates an evaluation of their efficacy in rectifying cognitive biases and recognizing affect in human-AI interactions. These biases are particularly relevant in mental health contexts as they can exacerbate conditions such as depression and anxiety by reinforcing maladaptive thought patterns or unrealistic expectations in human-AI interactions.</p><p><strong>Objective: </strong>This study aimed to assess the effectiveness of therapeutic chatbots (Wysa and Youper) versus general-purpose language models (GPT-3.5, GPT-4, and Gemini Pro) in identifying and rectifying cognitive biases and recognizing affect in user interactions.</p><p><strong>Methods: </strong>This study used constructed case scenarios simulating typical user-bot interactions to examine how effectively chatbots address selected cognitive biases. The cognitive biases assessed included theory-of-mind biases (anthropomorphism, overtrust, and attribution) and autonomy biases (illusion of control, fundamental attribution error, and just-world hypothesis). Each chatbot response was evaluated based on accuracy, therapeutic quality, and adherence to cognitive behavioral therapy principles using an ordinal scale to ensure consistency in scoring. To enhance reliability, responses underwent a double review process by 2 cognitive scientists, followed by a secondary review by a clinical psychologist specializing in cognitive behavioral therapy, ensuring a robust assessment across interdisciplinary perspectives.</p><p><strong>Results: </strong>This study revealed that general-purpose chatbots outperformed therapeutic chatbots in rectifying cognitive biases, particularly in overtrust bias, fundamental attribution error, and just-world hypothesis. GPT-4 achieved the highest scores across all biases, whereas the therapeutic bot Wysa scored the lowest. Notably, general-purpose bots showed more consistent accuracy and adaptability in recognizing and addressing bias-related cues across different contexts, suggesting a broader flexibility in handling complex cognitive patterns. In addition, in affect recognition tasks, general-purpose chatbots not only excelled but also demonstrated quicker adaptation to subtle emotional nuances, outperforming therapeutic bots in 67% (4/6) of the tested biases.</p><p><strong>Conclusions: </strong>This study shows that, while therapeutic chatbots hold promise for mental health support and cognitive bias intervention, their current capabilities are limited. Addressing cognitive biases in AI-human interactions requires systems that can both rectify and analyze biases as integral to human cognition, promoting precision and simulating empathy. The findings reveal the need for improved simulated emotional intelligence in chatbot design to provide adaptive, personalized responses that reduce overreliance and encourage independent coping sk","PeriodicalId":48616,"journal":{"name":"Jmir Mental Health","volume":"12 ","pages":"e64396"},"PeriodicalIF":4.8,"publicationDate":"2025-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11845887/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143371123","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}