Jmir Mental Health最新文献

{"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":"<p><strong>Background: </strong>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.</p><p><strong>Objective: </strong>To examine the feasibility, safety, and efficacy of trainer-led, at-home, telehealth-based AE in individuals with schizophrenia.</p><p><strong>Methods: </strong>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).</p><p><strong>Results: </strong>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<.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>.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).</p><p><strong>Conclusions: </strong>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":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;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.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;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.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;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.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;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.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;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":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;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.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;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.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;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.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;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.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;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}

{"title":"Does the Digital Therapeutic Alliance Exist? Integrative Review.","authors":"Amylie Malouin-Lachance, Julien Capolupo, Chloé Laplante, Alexandre Hudon","doi":"10.2196/69294","DOIUrl":"10.2196/69294","url":null,"abstract":"<p><strong>Background: </strong>Mental health disorders significantly impact global populations, prompting the rise of digital mental health interventions, such as artificial intelligence (AI)-powered chatbots, to address gaps in access to care. This review explores the potential for a \"digital therapeutic alliance (DTA),\" emphasizing empathy, engagement, and alignment with traditional therapeutic principles to enhance user outcomes.</p><p><strong>Objective: </strong>The primary objective of this review was to identify key concepts underlying the DTA in AI-driven psychotherapeutic interventions for mental health. The secondary objective was to propose an initial definition of the DTA based on these identified concepts.</p><p><strong>Methods: </strong>The PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) for scoping reviews and Tavares de Souza's integrative review methodology were followed, encompassing systematic literature searches in Medline, Web of Science, PsycNet, and Google Scholar. Data from eligible studies were extracted and analyzed using Horvath et al's conceptual framework on a therapeutic alliance, focusing on goal alignment, task agreement, and the therapeutic bond, with quality assessed using the Newcastle-Ottawa Scale and Cochrane Risk of Bias Tool.</p><p><strong>Results: </strong>A total of 28 studies were identified from an initial pool of 1294 articles after excluding duplicates and ineligible studies. These studies informed the development of a conceptual framework for a DTA, encompassing key elements such as goal alignment, task agreement, therapeutic bond, user engagement, and the facilitators and barriers affecting therapeutic outcomes. The interventions primarily focused on AI-powered chatbots, digital psychotherapy, and other digital tools.</p><p><strong>Conclusions: </strong>The findings of this integrative review provide a foundational framework for the concept of a DTA and report its potential to replicate key therapeutic mechanisms such as empathy, trust, and collaboration in AI-driven psychotherapeutic tools. While the DTA shows promise in enhancing accessibility and engagement in mental health care, further research and innovation are needed to address challenges such as personalization, ethical concerns, and long-term impact.</p>","PeriodicalId":48616,"journal":{"name":"Jmir Mental Health","volume":"12 ","pages":"e69294"},"PeriodicalIF":4.8,"publicationDate":"2025-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11830484/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143383653","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":"Evaluation of a Guided Chatbot Intervention for Young People in Jordan: Feasibility Randomized Controlled Trial.","authors":"Anne Marijn de Graaff, Rand Habashneh, Sarah Fanatseh, Dharani Keyan, Aemal Akhtar, Adnan Abualhaija, Muhannad Faroun, Ibrahim Said Aqel, Latefa Dardas, Chiara Servili, Mark van Ommeren, Richard Bryant, Kenneth Carswell","doi":"10.2196/63515","DOIUrl":"10.2196/63515","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Depression and anxiety are a leading cause of disability worldwide and often start during adolescence and young adulthood. The majority of young people live in low- and middle-income countries where there is a lack of mental health services. The World Health Organization (WHO) developed a guided, nonartificial intelligence chatbot intervention called Scalable Technology for Adolescents and youth to Reduce Stress (STARS) to reduce symptoms of depression and anxiety among young people affected by adversity.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;The objective of this study was to evaluate the feasibility of the STARS intervention and study procedures among young people in Jordan.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;A 2-arm, single-blind, feasibility randomized controlled trial was conducted among 60 young people aged 18 years to 21 years living in Jordan with self-reported elevated levels of psychological distress. Immediately after baseline, participants were randomized 1:1 into the STARS intervention or enhanced care as usual (ECAU). STARS consisted of 10 lessons in which participants interacted with a chatbot and learned several cognitive behavioral therapy strategies, with optional guidance by a trained e-helper through 5 weekly phone calls. ECAU consisted of a static web page providing basic psychoeducation. Online questionnaires were administered at baseline (week 0) and postassessment (week 8) to assess depression (Hopkins Symptom Checklist-25 [HSCL-25]), anxiety (HSCL-25), functional impairment (WHO Disability Assessment Schedule [WHODAS] 2.0), psychological well-being (WHO-Five Well-Being Index [WHO-5]), and agency (State Hope Scale). Process evaluation interviews with stakeholders were conducted after the postassessment.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;Participants were recruited in December 2022 and January 2023. Of 700 screening website visits, 160 participants were eligible, and 60 participants (mean age 19.7, SD 1.16 years; 49/60, 82% female) continued to baseline and were randomized into STARS (n=30) or ECAU (n=30). Of those who received STARS, 37% (11/30) completed at least 8 chatbot lessons, and 13% (4/30) completed all 5 support calls. The research protocol functioned well in terms of balanced randomization, high retention at postassessment (48/60, 80%), and good psychometric properties of the online questionnaires. Process evaluation interviews with STARS participants, ECAU participants, e-helpers, and the clinical supervisor indicated the acceptability of the study procedures and the STARS and ECAU conditions and highlighted several aspects that could be improved, including the e-helper support and features of the STARS chatbot.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;This study demonstrated the feasibility and acceptability of the STARS intervention and research procedures. A fully powered, definitive randomized controlled trial will be conducted to evaluate the effectiveness of STARS.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Trial reg","PeriodicalId":48616,"journal":{"name":"Jmir Mental Health","volume":"12 ","pages":"e63515"},"PeriodicalIF":4.8,"publicationDate":"2025-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11840361/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143257036","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":"Evaluating the Effectiveness of InsightApp for Anxiety, Valued Action, and Psychological Resilience: Longitudinal Randomized Controlled Trial.","authors":"Victoria Amo, Falk Lieder","doi":"10.2196/57201","DOIUrl":"10.2196/57201","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Anxiety disorders are among the most prevalent mental disorders, and stress plays a significant role in their development. Ecological momentary interventions (EMIs) hold great potential to help people manage stress and anxiety by training emotion regulation and coping skills in real-life settings. InsightApp is a gamified EMI and research tool that incorporates elements from evidence-based therapeutic approaches. It is designed to strengthen people's metacognitive skills for coping with challenging real-life situations and embracing anxiety and other emotions.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;This randomized controlled trial aims to examine the effectiveness of InsightApp in (1) improving individuals' metacognitive strategies for coping with stress and anxiety and (2) promoting value-congruent action. It also evaluates how long these effects are retained. This experiment advances our understanding of the role of metacognition in emotional and behavioral reactivity to stress.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;We conducted a randomized controlled trial with 228 participants (completion rate: n=197, 86.4%; mean age 38, SD 11.50 years; age range 20-80 years; female: n=101, 52.6%; and White: n=175, 91.1%), who were randomly assigned to either the treatment or the active placebo control group. During the 1-week intervention phase, the treatment group engaged with InsightApp, while participants in the control group interacted with a placebo version of the app that delivered executive function training. We assessed the differences between the 2 groups in posttest and follow-up assessments of mental health and well-being while controlling for preexisting differences. Moreover, we used a multilevel model to analyze the longitudinal data, focusing on the within-participant causal effects of the intervention on emotional and behavioral reactivity to daily stressors. Specifically, we measured daily anxiety, struggle with anxiety, and value-congruent action.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;The intervention delivered by InsightApp yielded mixed results. On one hand, we found no significant posttest scores on mental health and well-being measures directly after the intervention or 7 days later (all P&gt;.22). In contrast, when confronted with real-life stress, the treatment group experienced a 15% lower increase in anxiety (1-tailed t test, t&lt;sub&gt;197&lt;/sub&gt;=-2.4; P=.009) and a 12% lower increase in the struggle with anxiety (t&lt;sub&gt;197&lt;/sub&gt;=-1.87; P=.031) than the control group. Furthermore, individuals in the treatment group demonstrated a 7% higher tendency to align their actions with their values compared to the control group (t&lt;sub&gt;197&lt;/sub&gt;=3.23; P=.002). After the intervention period, InsightApp's positive effects on the struggle with anxiety in reaction to stress were sustained, and increased to an 18% lower reactivity to stress (t&lt;sub&gt;197&lt;/sub&gt;=-2.84; P=.002).&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;As our study yielded mixed resul","PeriodicalId":48616,"journal":{"name":"Jmir Mental Health","volume":"12 ","pages":"e57201"},"PeriodicalIF":4.8,"publicationDate":"2025-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11836588/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143189954","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}