Jmir Mental Health最新文献

{"title":"Patterns of Skills Review in Smartphone Cognitive Behavioral Therapy for Depression: Observational Study of Intervention Content Use.","authors":"Emily E Bernstein, Katharine E Daniel, Peyton E Miyares, Susanne S Hoeppner, Kate H Bentley, Ivar Snorrason, Lauren B Fisher, Jennifer L Greenberg, Hilary Weingarden, Oliver Harrison, Sabine Wilhelm","doi":"10.2196/63497","DOIUrl":"https://doi.org/10.2196/63497","url":null,"abstract":"<p><strong>Background: </strong>Smartphones could enhance access to effective cognitive behavioral therapy (CBT). Users may frequently and flexibly access bite-size CBT content on personal devices, review and practice skills, and thereby achieve better outcomes.</p><p><strong>Objective: </strong>We explored the distribution of actual interactions participants had with therapeutic content in a trial of smartphone CBT for depression and whether interactions were within assigned treatment modules or revisits to prior module content (ie, between-module interactions).</p><p><strong>Methods: </strong>We examined the association between the number of within- and between-module interactions and baseline and end-of-treatment symptom severity during an 8-week, single-arm open trial of a therapist-guided CBT for depression mobile health app.</p><p><strong>Results: </strong>Interactions were more frequent early in treatment and modestly declined in later stages. Within modules, most participants consistently made more interactions than required to progress to the next module and tended to return to all types of content rather than focus on 1 skill. By contrast, only 15 of 26 participants ever revisited prior module content (median number of revisits=1, mode=0, IQR 0-4). More revisits were associated with more severe end-of-treatment symptom severity after controlling for pretreatment symptom severity (P<.05).</p><p><strong>Conclusions: </strong>The results suggest that the frequency of use is an insufficient metric of engagement, lacking the nuance of what users are engaging with and when during treatment. This lens is essential for developing personalized recommendations and yielding better treatment outcomes.</p><p><strong>Trial registration: </strong>ClinicalTrials.gov NCT05386329; https://clinicaltrials.gov/study/NCT05386329?term=NCT05386329.</p>","PeriodicalId":48616,"journal":{"name":"Jmir Mental Health","volume":"12 ","pages":"e63497"},"PeriodicalIF":4.8,"publicationDate":"2025-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143494385","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":"Multimodal Digital Phenotyping Study in Patients With Major Depressive Episodes and Healthy Controls (Mobile Monitoring of Mood): Observational Longitudinal Study.","authors":"Talayeh Aledavood, Nguyen Luong, Ilya Baryshnikov, Richard Darst, Roope Heikkilä, Joel Holmén, Arsi Ikäheimonen, Annasofia Martikkala, Kirsi Riihimäki, Outi Saleva, Ana Maria Triana, Erkki Isometsä","doi":"10.2196/63622","DOIUrl":"https://doi.org/10.2196/63622","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Mood disorders are among the most common mental health conditions worldwide. Wearables and consumer-grade personal digital devices create digital traces that can be collected, processed, and analyzed, offering a unique opportunity to quantify and monitor individuals with mental disorders in their natural living environments.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;This study comprised (1) 3 subcohorts of patients with a major depressive episode, either with major depressive disorder, bipolar disorder, or concurrent borderline personality disorder, and (2) a healthy control group. We investigated whether differences in behavioral patterns could be observed at the group level, that is, patients versus healthy controls. We studied the volume and temporal patterns of smartphone screen and app use, communication, sleep, mobility, and physical activity. We investigated whether patients or controls exhibited more homogenous temporal patterns of activity when compared with other individuals in the same group. We examined which variables were associated with the severity of depression.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;In total, 188 participants were recruited to complete a 2-phase study. In the first 2 weeks, data from bed sensors, actigraphy, smartphones, and 5 sets of daily questions were collected. In the second phase, which lasted up to 1 year, only passive smartphone data and biweekly 9-item Patient Health Questionnaire data were collected. Survival analysis, statistical tests, and linear mixed models were performed.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;Survival analysis showed no statistically significant difference in adherence. Most participants did not stay in the study for 1 year. Weekday location variance showed lower values for patients (control: mean -10.04, SD 2.73; patient: mean -11.91, SD 2.50; Mann-Whitney U [MWU] test P=.004). Normalized entropy of location was lower among patients (control: mean 2.10, SD 1.38; patient: mean 1.57, SD 1.10; MWU test P=.05). The temporal communication patterns of controls were more diverse compared to those of patients (MWU test P&lt;.001). In contrast, patients exhibited more varied temporal patterns of smartphone use compared to the controls. We found that the duration of incoming calls (β=-0.08, 95% CI -0.12 to -0.04; P&lt;.001) and the SD of activity magnitude (β=-2.05, 95% CI -4.18 to -0.20; P=.02) over the 14 days before the 9-item Patient Health Questionnaire records were negatively associated with depression severity. Conversely, the duration of outgoing calls showed a positive association with depression severity (β=0.05, 95% CI 0.00-0.09; P=.02).&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;Our work shows the important features for future analyses of behavioral markers of mood disorders. However, among outpatients with mild to moderate depressive disorders, the group-level differences from healthy controls in any single modality remain relatively modest. Therefore, future studies need to com","PeriodicalId":48616,"journal":{"name":"Jmir Mental Health","volume":"12 ","pages":"e63622"},"PeriodicalIF":4.8,"publicationDate":"2025-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143472968","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":"Exploring the Ethical Challenges of Conversational AI in Mental Health Care: Scoping Review.","authors":"Mehrdad Rahsepar Meadi, Tomas Sillekens, Suzanne Metselaar, Anton van Balkom, Justin Bernstein, Neeltje Batelaan","doi":"10.2196/60432","DOIUrl":"https://doi.org/10.2196/60432","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Conversational artificial intelligence (CAI) is emerging as a promising digital technology for mental health care. CAI apps, such as psychotherapeutic chatbots, are available in app stores, but their use raises ethical concerns.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;We aimed to provide a comprehensive overview of ethical considerations surrounding CAI as a therapist for individuals with mental health issues.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;We conducted a systematic search across PubMed, Embase, APA PsycINFO, Web of Science, Scopus, the Philosopher's Index, and ACM Digital Library databases. Our search comprised 3 elements: embodied artificial intelligence, ethics, and mental health. We defined CAI as a conversational agent that interacts with a person and uses artificial intelligence to formulate output. We included articles discussing the ethical challenges of CAI functioning in the role of a therapist for individuals with mental health issues. We added additional articles through snowball searching. We included articles in English or Dutch. All types of articles were considered except abstracts of symposia. Screening for eligibility was done by 2 independent researchers (MRM and TS or AvB). An initial charting form was created based on the expected considerations and revised and complemented during the charting process. The ethical challenges were divided into themes. When a concern occurred in more than 2 articles, we identified it as a distinct theme.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;We included 101 articles, of which 95% (n=96) were published in 2018 or later. Most were reviews (n=22, 21.8%) followed by commentaries (n=17, 16.8%). The following 10 themes were distinguished: (1) safety and harm (discussed in 52/101, 51.5% of articles); the most common topics within this theme were suicidality and crisis management, harmful or wrong suggestions, and the risk of dependency on CAI; (2) explicability, transparency, and trust (n=26, 25.7%), including topics such as the effects of \"black box\" algorithms on trust; (3) responsibility and accountability (n=31, 30.7%); (4) empathy and humanness (n=29, 28.7%); (5) justice (n=41, 40.6%), including themes such as health inequalities due to differences in digital literacy; (6) anthropomorphization and deception (n=24, 23.8%); (7) autonomy (n=12, 11.9%); (8) effectiveness (n=38, 37.6%); (9) privacy and confidentiality (n=62, 61.4%); and (10) concerns for health care workers' jobs (n=16, 15.8%). Other themes were discussed in 9.9% (n=10) of the identified articles.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;Our scoping review has comprehensively covered ethical aspects of CAI in mental health care. While certain themes remain underexplored and stakeholders' perspectives are insufficiently represented, this study highlights critical areas for further research. These include evaluating the risks and benefits of CAI in comparison to human therapists, determining its appropriate roles in ther","PeriodicalId":48616,"journal":{"name":"Jmir Mental Health","volume":"12 ","pages":"e60432"},"PeriodicalIF":4.8,"publicationDate":"2025-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143472962","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":"Digital Migration of the Loewenstein Acevedo Scales for Semantic Interference and Learning (LASSI-L): Development and Validation Study in Older Participants.","authors":"Philip Harvey, Rosie Curiel-Cid, Peter Kallestrup, Annalee Mueller, Andrea Rivera-Molina, Sara Czaja, Elizabeth Crocco, David Loewenstein","doi":"10.2196/64716","DOIUrl":"10.2196/64716","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;The early detection of mild cognitive impairment is crucial for providing treatment before further decline. Cognitive challenge tests such as the Loewenstein-Acevedo Scales for Semantic Interference and Learning (LASSI-L) can identify individuals at highest risk for cognitive deterioration. Performance on elements of the LASSI-L, particularly proactive interference, correlate with the presence of critical Alzheimer disease biomarkers. However, in-person paper tests require skilled testers and are not practical in many community settings or for large-scale screening in prevention.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;This study reports on the development and initial validation of a self-administered computerized version of the Loewenstein-Acevedo Scales for Semantic Interference (LASSI), the digital LASSI (LASSI-D). A self-administered digital version, with an artificial intelligence-generated avatar assistant, was the migrated assessment.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;Cloud-based software was developed, using voice recognition technology, for English and Spanish versions of the LASSI-D. Participants were assessed with either the LASSI-L or LASSI-D first, in a sequential assessment study. Participants with amnestic mild cognitive impairment (aMCI; n=54) or normal cognition (NC; n=58) were also tested with traditional measures such as the Alzheimer Disease Assessment Scale-Cognition. We examined group differences in performance across the legacy and digital versions of the LASSI, as well as correlations between LASSI performance and other measures across the versions.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;Differences on recall and intrusion variables between aMCI and NC samples on both versions were all statistically significant (all P&lt;.001), with at least medium effect sizes (d&gt;0.68). There were no statistically significant performance differences in these variables between legacy and digital administration in either sample (all P&lt;.13). There were no language differences in any variables (P&gt;.10), and correlations between LASSI variables and other cognitive variables were statistically significant (all P&lt;.01). The most predictive legacy variables, proactive interference and failure to recover from proactive interference, were identical across legacy and migrated versions within groups and were identical to results of previous studies with the legacy LASSI-L. Classification accuracy was 88% for NC and 78% for aMCI participants.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;The results for the digital migration of the LASSI-D were highly convergent with the legacy LASSI-L. Across all indices of similarity, including sensitivity, criterion validity, classification accuracy, and performance, the versions converged across languages. Future studies will present additional validation data, including correlations with blood-based Alzheimer disease biomarkers and alternative forms. The current data provide convincing evidence of the use of a ","PeriodicalId":48616,"journal":{"name":"Jmir Mental Health","volume":"12 ","pages":"e64716"},"PeriodicalIF":4.8,"publicationDate":"2025-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11864698/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143460217","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":"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}