Jmir Mental Health最新文献

{"title":"Using the PowerMom Digital Health Platform to Support Prenatal Mental Health and Maternal Health Outcomes: Observational Cohort Study.","authors":"Giulia Milan, Victoria Lee, Matteo Gadaleta, Lauren Ariniello, Arij Faksh, Giorgio Quer, Toluwalase Ajayi","doi":"10.2196/70151","DOIUrl":"10.2196/70151","url":null,"abstract":"<p><strong>Background: </strong>Mental health disorders such as anxiety and depression are common among individuals of childbearing age. Such disorders can affect pregnancy and postpartum well-being. This study aims to study the impact of prenatal mental health on the pregnancy journey and highlights the use of mobile health technologies such as PowerMom for symptom tracking and screening.</p><p><strong>Objectives: </strong>We collected data in a decentralized digital trial using the PowerMom platform to investigate the impact of maternal mental health throughout pregnancy. The goal was to understand how anxiety and depression influence pregnancy-related symptoms, pregnancy outcomes, and postpartum well-being.</p><p><strong>Methods: </strong>Survey data were collected via PowerMom, a bilingual mobile research platform that integrates patient-reported outcomes, wearable data, and electronic health records. Participants were divided into 2 cohorts: those who reported receiving treatment for anxiety or depression during pregnancy (n=571) and those who reported not receiving treatment (n=1505). We compared self-reported symptoms, prepregnancy conditions, complications from past pregnancies, delivery outcomes, and postpartum mental health between cohorts, using the Fisher exact test and the Kruskal-Wallis test for statistical analysis.</p><p><strong>Results: </strong>Participants receiving treatment for anxiety or depression reported higher instances of physical symptoms than those untreated, with significant differences for 13 symptoms including fatigue (80.2% vs 65.4%; adjusted P<.001), nausea and vomiting (69.7% vs 52.7%; adjusted P<.001), and stomach cramping and abdominal pain (64.0% vs 50.4%; adjusted P<.001). Participants receiving treatment also had a higher prevalence of several conditions prior to pregnancy than those not receiving treatment, with significant differences noted in 4 out of 10 conditions: endometriosis (14.0% vs 8.8%; adjusted P=.007), hypertension (10.9% vs 3.9%; adjusted P<.001), eating disorder (7.7% vs 3.1%; adjusted P<.001), and heart disease (2.8% vs 0.5%; adjusted P<.001). Participants receiving treatment also reported a higher prevalence of complications in past pregnancies than those not receiving treatment, with significant differences noted in 2 out of 7 complications: high blood pressure (9.9% vs 5.8%; adjusted P=.016) and preeclampsia (9.2% vs 5.5%; adjusted P=.021). No significant differences were observed in mode of delivery, epidural use, stillbirth, and miscarriage rates between the 2 cohorts. Postpartum, treated participants reported significantly higher mental health composite scores, indicating more severe mental health symptoms. A higher percentage of treated participants were at high risk for having perinatal mood disorder (38/83, 45.8%) than untreated participants (36/196, 18.4%; P<.001).</p><p><strong>Conclusions: </strong>The PowerMom platform demonstrated its value in facilitating remote, scalable dat","PeriodicalId":48616,"journal":{"name":"Jmir Mental Health","volume":"12 ","pages":"e70151"},"PeriodicalIF":4.8,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144121074","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":"Effect of a Tailored eHealth Physical Activity Intervention on Physical Activity and Depression During Postpartum: Randomized Controlled Trial (The Postpartum Wellness Study).","authors":"Sylvia E Badon, Nina Oberman, Maya Ramsey, Charles P Quesenberry, Elaine Kurtovich, Lizeth Gomez Chavez, Susan D Brown, Cheryl L Albright, Mibhali Bhalala, Lyndsay A Avalos","doi":"10.2196/64507","DOIUrl":"https://doi.org/10.2196/64507","url":null,"abstract":"<p><strong>Background: </strong>Strong evidence suggests physical activity (PA) can ameliorate postpartum depression (PPD) symptoms; however, many postpartum individuals do not meet PA guidelines. Electronic health (eHealth) interventions are a promising approach to address common barriers to PA during postpartum.</p><p><strong>Objective: </strong>To test the effectiveness of a tailored eHealth PA intervention for increasing PA and decreasing depressive symptoms in individuals at high risk for PPD.</p><p><strong>Methods: </strong>We conducted a randomized controlled trial within the Kaiser Permanente Northern California integrated health care delivery system. From November 2020 to September 2022, individuals 2-6 months postpartum at high risk for PPD were randomized to an eHealth PA intervention (n=50) or usual care (n=49). The eHealth PA intervention group received access to an online library of 98 ten-minute workout videos developed for postpartum individuals, including interaction with their infants. At baseline, 3 months, and 6 months postrandomization, surveys were used to assess depressive symptoms, PA, sleep quality, anxiety symptoms, perceived stress, and mother-infant bonding. PA was also measured using a wrist-worn accelerometer for 7 days at each timepoint. Primary outcomes were depressive symptoms and device-measured moderate-to-vigorous intensity PA (dm-MVPA) at 3 months postrandomization. Secondary outcomes were self-reported MVPA (sr-MVPA) at 3 and 6 months postrandomization and depressive symptoms and dm-MVPA at 6 months postrandomization. Intent-to-treat and modified intent-to-treat (excluding participants in the intervention group who did not watch at least 1 video) analyses were conducted using linear regression adjusted for variables used in the randomization procedure and using multiple imputation to account for missing data.</p><p><strong>Results: </strong>Participants were 4 months postpartum at baseline with moderately severe depressive symptoms (mean PHQ-8 [Patient Health Questionnaire-8] score=12.6), on average. Intent-to-treat analyses indicated no association between the intervention and change in depressive symptoms (mean difference=-0.9; 95% CI -3.3 to 1.5), dm-MVPA per day (mean difference=-4.5 minutes; 95% CI -23.5 to 14.5), or sr-MVPA per week (mean difference=3.8; 95% CI -1.9 to 9.5) at 3 months postrandomization or 6 months postrandomization (depressive symptoms: mean difference=-1.3; 95% CI -3.7 to 1.1; dm-MVPA: mean difference=1.3 minutes; 95% CI -18.9 to 21.5; sr-MVPA: mean difference=-1.8 MET-hours; 95% CI -7.7 to 4.2). Engagement with the intervention was suboptimal; although 52% (n=26) of participants allocated to the intervention group logged on to the intervention website and watched at least 1 video, the median minutes watched per week peaked at 10 minutes 2 weeks postrandomization, then fell to zero for the rest of the follow-up period. Results from modified intent-to-treat analyses were similar to tho","PeriodicalId":48616,"journal":{"name":"Jmir Mental Health","volume":"12 ","pages":"e64507"},"PeriodicalIF":4.8,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144129255","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Comparison of Responses from Human Therapists and Large Language Model-Based Chatbots to Assess Therapeutic Communication: Mixed Methods Study.","authors":"Till Scholich, Maya Barr, Shannon Wiltsey Stirman, Shriti Raj","doi":"10.2196/69709","DOIUrl":"10.2196/69709","url":null,"abstract":"<p><strong>Background: </strong>Consumers are increasingly using large language model-based chatbots to seek mental health advice or intervention due to ease of access and limited availability of mental health professionals. However, their suitability and safety for mental health applications remain underexplored, particularly in comparison to professional therapeutic practices.</p><p><strong>Objective: </strong>This study aimed to evaluate how general-purpose chatbots respond to mental health scenarios and compare their responses to those provided by licensed therapists. Specifically, we sought to identify chatbots' strengths and limitations, as well as the ethical and practical considerations necessary for their use in mental health care.</p><p><strong>Methods: </strong>We conducted a mixed methods study to compare responses from chatbots and licensed therapists to scripted mental health scenarios. We created 2 fictional scenarios and prompted 3 chatbots to create 6 interaction logs. We recruited 17 therapists and conducted study sessions that consisted of 3 activities. First, therapists responded to the 2 scenarios using a Qualtrics form. Second, therapists went through the 6 interaction logs using a think-aloud procedure to highlight their thoughts about the chatbots' responses. Finally, we conducted a semistructured interview to explore subjective opinions on the use of chatbots for supporting mental health. The study sessions were analyzed using thematic analysis. The interaction logs from chatbot and therapist responses were coded using the Multitheoretical List of Therapeutic Interventions codes and then compared to each other.</p><p><strong>Results: </strong>We identified 7 themes describing the strengths and limitations of the chatbots as compared to therapists. These include elements of good therapy in chatbot responses, conversational style of chatbots, insufficient inquiry and feedback seeking by chatbots, chatbot interventions, client engagement, chatbots' responses to crisis situations, and considerations for chatbot-based therapy. In the use of Multitheoretical List of Therapeutic Interventions codes, we found that therapists evoked more elaboration (Mann-Whitney U=9; P=.001) and used more self-disclosure (U=45.5; P=.37) as compared to the chatbots. The chatbots used affirming (U=28; P=.045) and reassuring (U=23; P=.02) language more often than the therapists. The chatbots also used psychoeducation (U=22.5; P=.02) and suggestions (U=12.5; P=.003) more often than the therapists.</p><p><strong>Conclusions: </strong>Our study demonstrates the unsuitability of general-purpose chatbots to safely engage in mental health conversations, particularly in crisis situations. While chatbots display elements of good therapy, such as validation and reassurance, overuse of directive advice without sufficient inquiry and use of generic interventions make them unsuitable as therapeutic agents. Careful research and evaluation will be necessary to de","PeriodicalId":48616,"journal":{"name":"Jmir Mental Health","volume":"12 ","pages":"e69709"},"PeriodicalIF":4.8,"publicationDate":"2025-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144121072","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":"Evaluating Generative AI in Mental Health: Systematic Review of Capabilities and Limitations.","authors":"Liying Wang, Tanmay Bhanushali, Zhuoran Huang, Jingyi Yang, Sukriti Badami, Lisa Hightow-Weidman","doi":"10.2196/70014","DOIUrl":"10.2196/70014","url":null,"abstract":"<p><strong>Background: </strong>The global shortage of mental health professionals, exacerbated by increasing mental health needs post COVID-19, has stimulated growing interest in leveraging large language models to address these challenges.</p><p><strong>Objectives: </strong>This systematic review aims to evaluate the current capabilities of generative artificial intelligence (GenAI) models in the context of mental health applications.</p><p><strong>Methods: </strong>A comprehensive search across 5 databases yielded 1046 references, of which 8 studies met the inclusion criteria. The included studies were original research with experimental designs (eg, Turing tests, sociocognitive tasks, trials, or qualitative methods); a focus on GenAI models; and explicit measurement of sociocognitive abilities (eg, empathy and emotional awareness), mental health outcomes, and user experience (eg, perceived trust and empathy).</p><p><strong>Results: </strong>The studies, published between 2023 and 2024, primarily evaluated models such as ChatGPT-3.5 and 4.0, Bard, and Claude in tasks such as psychoeducation, diagnosis, emotional awareness, and clinical interventions. Most studies used zero-shot prompting and human evaluators to assess the AI responses, using standardized rating scales or qualitative analysis. However, these methods were often insufficient to fully capture the complexity of GenAI capabilities. The reliance on single-shot prompting techniques, limited comparisons, and task-based assessments isolated from a context may oversimplify GenAI's abilities and overlook the nuances of human-artificial intelligence interaction, especially in clinical applications that require contextual reasoning and cultural sensitivity. The findings suggest that while GenAI models demonstrate strengths in psychoeducation and emotional awareness, their diagnostic accuracy, cultural competence, and ability to engage users emotionally remain limited. Users frequently reported concerns about trustworthiness, accuracy, and the lack of emotional engagement.</p><p><strong>Conclusions: </strong>Future research could use more sophisticated evaluation methods, such as few-shot and chain-of-thought prompting to fully uncover GenAI's potential. Longitudinal studies and broader comparisons with human benchmarks are needed to explore the effects of GenAI-integrated mental health care.</p>","PeriodicalId":48616,"journal":{"name":"Jmir Mental Health","volume":"12 ","pages":"e70014"},"PeriodicalIF":4.8,"publicationDate":"2025-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12097452/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144081328","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":"Prescriptive Predictors of Mindfulness Ecological Momentary Intervention for Social Anxiety Disorder: Machine Learning Analysis of Randomized Controlled Trial Data.","authors":"Nur Hani Zainal, Hui Han Tan, Ryan Yee Shiun Hong, Michelle Gayle Newman","doi":"10.2196/67210","DOIUrl":"10.2196/67210","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Shame and stigma often prevent individuals with social anxiety disorder (SAD) from seeking and attending costly and time-intensive psychotherapies, highlighting the importance of brief, low-cost, and scalable treatments. Creating prescriptive outcome prediction models is thus crucial for identifying which clients with SAD might gain the most from a unique scalable treatment option. Nevertheless, widely used classical regression methods might not optimally capture complex nonlinear associations and interactions.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;Precision medicine approaches were thus harnessed to examine prescriptive predictors of optimization to a 14-day fully self-guided mindfulness ecological momentary intervention (MEMI) over a self-monitoring app (SM).&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;This study involved 191 participants who had probable SAD. Participants were randomly assigned to MEMI (n=96) or SM (n=95). They completed self-reports of symptoms, risk factors, treatment, and sociodemographics at baseline, posttreatment, and 1-month follow-up (1MFU). Machine learning (ML) models with 17 predictors of optimization to MEMI over SM, defined as a higher probability of SAD remission from MEMI at posttreatment and 1MFU, were evaluated. The Social Phobia Diagnostic Questionnaire, structurally equivalent to the Diagnostic and Statistical Manual SAD criteria, was used to define remission. These ML models included random forest and support vector machines (radial basis function kernel) and 10-fold nested cross-validation that separated model training, minimal tuning in inner folds, and model testing in outer folds.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;ML models outperformed logistic regression. The multivariable ML models using the 10 most important predictors achieved good performance, with the area under the receiver operating characteristic curve (AU-ROC) values ranging from .71 to .72 at posttreatment and 1MFU. These prerandomization and early-stage prescriptive predictors consistently identified which participants had the highest probability of optimization of MEMI over SM after 14 days and 6 weeks from baseline. Significant predictors included 4 strengths (higher trait mindfulness, lower SAD severity, presence of university education, no current psychotropic medication use), 2 weaknesses (higher generalized anxiety severity and clinician-diagnosed depression or anxiety disorder), and 1 sociodemographic variable (Chinese ethnicity). Emotion dysregulation and current psychotherapy predicted remission with inconsistent signs across time points.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;The AU-ROC values indicated moderately meaningful effect sizes in identifying prescriptive predictors within multivariable models for clients with SAD. Focusing on the identified notable client strengths, weaknesses, and Chinese ethnicity may enhance our ability to predict future responses to scalable treatments. Estimating the likelihood of SAD re","PeriodicalId":48616,"journal":{"name":"Jmir Mental Health","volume":"12 ","pages":"e67210"},"PeriodicalIF":4.8,"publicationDate":"2025-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144054863","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 Integrated Interventions for Comorbid Depression and Substance Use Disorder: Narrative Review and Content Analysis.","authors":"Geneva K Jonathan, Qiuzuo Guo, Heyli Arcese, A Eden Evins, Sabine Wilhelm","doi":"10.2196/67670","DOIUrl":"10.2196/67670","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Integrated digital interventions for the treatment of comorbid depression and substance use disorder have been developed, and evidence of their effectiveness is mixed.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;This study aimed to explore potential reasons for mixed findings in the literature on integrated digital treatments. We described the methodologies and core characteristics of these interventions, identified the presence of evidence-based treatment strategies, examined patterns across digital modalities, and highlighted areas of overlap as well as critical gaps in the existing evidence base.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;In June 2024, a literature search was conducted in Google Scholar to identify digital integrated interventions for comorbid major depressive disorder and substance use disorder. Articles were included if they described interventions targeting both conditions simultaneously; were grounded in cognitive behavioral therapy, motivational interviewing, or motivational enhancement therapy; and were delivered at least in part via digital modalities. In total, 14 studies meeting these criteria were coded using an open-coding approach to identify intervention characteristics and treatment strategies (n=25). Statistical analyses summarized descriptive statistics to capture the frequency and overlap of these strategies.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;Studies included a range of digital modalities: internet (n=6, 43%), computer (n=3, 21%), smartphone (n=2, 14%), and supportive text messaging interventions (n=3, 21%). Half (n=7, 50%) of the studies included participants with mild to moderate depression symptom severity and hazardous substance use. Only 36% (n=5) of the studies required participants to meet full diagnostic criteria for major depressive disorder for inclusion and 21% (n=3) required a substance use disorder diagnosis. Most interventions targeted adults (n=11, 79%), with few targeting young or emerging adults (n=4, 29%), and only 36% (n=5) reported detailed demographic data. Treatment duration averaged 10.3 (SD 6.8) weeks. Internet-based interventions offered the widest range of treatment strategies (mean 11.7), while supportive text messaging used the fewest (mean 4.6). Common treatment strategies included self-monitoring (n=11, 79%), psychoeducation (n=10, 71%), and coping skills (n=9, 64%). Interventions often combined therapeutic strategies, with psychoeducation frequently paired with self-monitoring (n=9, 64%), assessment (n=7, 50%), coping skills (n=7, 50%), decisional balance (n=7, 50%), feedback (n=7, 50%), and goal setting (n=7, 50%).&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;Among integrated digital interventions for comorbid depression and substance use, there was noteworthy variability in methodology, inclusion criteria, digital modalities, and embedded treatment strategies. Without standardized methods, comparison of the clinical outcomes across studies is challenging. These results emphasiz","PeriodicalId":48616,"journal":{"name":"Jmir Mental Health","volume":" ","pages":"e67670"},"PeriodicalIF":4.8,"publicationDate":"2025-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12102630/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143651471","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 Prevalence and Incidence of Suicidal Thoughts and Behavior in a Smartphone-Delivered Treatment Trial for Body Dysmorphic Disorder: Cohort Study.","authors":"Adam C Jaroszewski, Natasha Bailen, Simay I Ipek, Jennifer L Greenberg, Susanne S Hoeppner, Hilary Weingarden, Ivar Snorrason, Sabine Wilhelm","doi":"10.2196/63605","DOIUrl":"10.2196/63605","url":null,"abstract":"<p><strong>Background: </strong>People with past suicidal thoughts and behavior (STB) are often excluded from digital mental health intervention (DMHI) treatment trials. This may perpetuate barriers to care and reduce treatment generalizability, especially in populations with elevated rates of STB, such as body dysmorphic disorder (BDD). We conducted a cohort study of randomized controlled trial (RCT) participants (N=80) who received a smartphone-based cognitive behavioral therapy (CBT) treatment for BDD that allowed for most forms of past STB, except for past-month active suicidal ideation.</p><p><strong>Objective: </strong>This study had two objectives: (1) to characterize the sample's lifetime prevalence of STB and (2) to estimate and predict STB incidence during the trial.</p><p><strong>Methods: </strong>We completed secondary analyses on data from an RCT of smartphone-delivered CBT for BDD. The primary outcomes consisted of STB severity and suicide attempt assessed at baseline with the Columbia-Suicide Severity Rating Scale (C-SSRS) and weekly during the trial via one item from the Quick Inventory of Depressive Symptomatology-Self Report (QIDS-SR item #12; 1043 observations). We computed descriptive statistics (n, %) and ran a series of bi- and multivariate linear regressions predicting STB incidence during the 3-month trial.</p><p><strong>Results: </strong>At baseline, 40% of participants reported a lifetime history of active suicidal thoughts and 10% reported lifetime suicide attempts. During the 3-month trial, 42.5% reporting thinking about death or suicide via weekly assessment. No participants reported frequent or acute suicidal thoughts, plans, or attempts. Lifetime suicide attempt (odds ratio 11, 95% CI 2.14-59.14; P<.01) and lifetime severity of suicidal thoughts (odds ratio 1.76, 95% CI 1.21-2.77; P<.01) were significant bivariate predictors of death- or suicide-related thought incidence reported during the trial. Multivariate models including STB risk factor covariates (eg, age, and sexual orientation) modestly improved prediction of death- or suicide-related thoughts (eg, positive predictive value=0.91, negative predictive value=0.75, and area under the receiver operating characteristic curve=0.83).</p><p><strong>Conclusions: </strong>Although some participants may think about death and suicide during a DMHI trial, it may be safe and feasible to include participants with most forms of past STB. Among other procedures, researchers should carefully select eligibility criteria, use frequent, ongoing, low-burden, and valid monitoring procedures, and implement risk mitigation protocols tailored to the presenting problem.</p>","PeriodicalId":48616,"journal":{"name":"Jmir Mental Health","volume":"12 ","pages":"e63605"},"PeriodicalIF":4.8,"publicationDate":"2025-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12074616/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144046465","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":"Game Design, Effectiveness, and Implementation of Serious Games Promoting Aspects of Mental Health Literacy Among Children and Adolescents: Systematic Review.","authors":"Michael Zeiler, Sandra Vögl, Ursula Prinz, Nino Werner, Gudrun Wagner, Andreas Karwautz, Natalie Zeller, Lorenz Ackermann, Karin Waldherr","doi":"10.2196/67418","DOIUrl":"10.2196/67418","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;The effects of traditional health-promoting and preventive interventions in mental health and mental health literacy are often attenuated by low adherence and user engagement. Gamified approaches such as serious games (SGs) may be useful to reach and engage youth for mental health prevention and promotion.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;This study aims to systematically review the literature on SGs designed to promote aspects of mental health literacy among adolescents aged 10 to 14 years, focusing on game design characteristics and the evaluation of user engagement, as well as efficacy, effectiveness, and implementation-related factors.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;We searched PubMed, Scopus, and PsycINFO for original studies, intervention development studies, and study protocols that described the development, characteristics, and evaluation of SG interventions promoting aspects of mental health literacy among adolescents aged 10 to 14 years. We included SGs developed for both universal and selected prevention. Using the co.LAB framework, which considers aspects of learning design, game mechanics, and game design, we coded the design elements of the SGs described in the studies. We coded the characteristics of the evaluation studies; indicators of efficacy, effectiveness, and user engagement; and factors potentially fostering or hindering the reach, efficacy and effectiveness, organizational adoption, implementation, and maintenance of the SGs.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;We retrieved 1454 records through database searches and other sources. Of these, 36 (2.48%) studies describing 17 distinct SGs were included in the review. Most of the SGs (14/17, 82%) were targeted to a universal population of youth, with learning objectives mainly focusing on how to obtain and maintain good mental health and on enhancing help-seeking efficacy. All SGs were single-player games, and many (7/17, 41%) were embedded within a wider pedagogical scenario. Diverse game mechanics and game elements (eg, minigames and quizzes) were used to foster user engagement. Most of the SGs (12/17, 71%) featured an overarching storyline resembling real-world scenarios, fictional scenarios, or a combination of both. The evaluation studies provided evidence for the short-term efficacy and effectiveness of SGs in improving aspects of mental health literacy as well as their feasibility. However, the evidence was mostly based on small samples, and user adherence was sometimes low.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;The results of this review may inform the future development and implementation of SGs for adolescents. Intervention co-design, the involvement of facilitators (eg, teachers), and the use of diverse game mechanics and customization to meet the needs of diverse users are examples of elements that may promote intervention success. Although there is promising evidence for the efficacy and effectiveness of SGs for promoting mental health","PeriodicalId":48616,"journal":{"name":"Jmir Mental Health","volume":"12 ","pages":"e67418"},"PeriodicalIF":4.8,"publicationDate":"2025-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12089885/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143992726","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":"Impact of Conversational and Animation Features of a Mental Health App Virtual Agent on Depressive Symptoms and User Experience Among College Students: Randomized Controlled Trial.","authors":"Stephanie Six, Elizabeth Schlesener, Victoria Hill, Sabarish V Babu, Kaileigh Byrne","doi":"10.2196/67381","DOIUrl":"https://doi.org/10.2196/67381","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Numerous mental health apps purport to alleviate depressive symptoms. Strong evidence suggests that brief cognitive behavioral therapy (bCBT)-based mental health apps can decrease depressive symptoms, yet there is limited research elucidating the specific features that may augment its therapeutic benefits. One potential design feature that may influence effectiveness and user experience is the inclusion of virtual agents that can mimic realistic, human face-to-face interactions.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;The goal of the current experiment was to determine the effect of conversational and animation features of a virtual agent within a bCBT-based mental health app on depressive symptoms and user experience in college students with and without depressive symptoms.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;College students (N=209) completed a 2-week intervention in which they engaged with a bCBT-based mental health app with a customizable therapeutic virtual agent that varied in conversational and animation features. A 2 (time: baseline vs 2-week follow-up) × 2 (conversational vs non-conversational agent) × 2 (animated vs non-animated agent) randomized controlled trial was used to assess mental health symptoms (Patient Health Questionnaire-8, Perceived Stress Scale-10, and Response Rumination Scale questionnaires) and user experience (mHealth App Usability Questionnaire, MAUQ) in college students with and without current depressive symptoms. The mental health app usability and qualitative questions regarding users' perceptions of their therapeutic virtual agent interactions and customization process were assessed at follow-up.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;Mixed ANOVA (analysis of variance) results demonstrated a significant decrease in symptoms of depression (P=.002; mean [SD]=5.5 [4.86] at follow-up vs mean [SD]=6.35 [4.71] at baseline), stress (P=.005; mean [SD]=15.91 [7.67] at follow-up vs mean [SD]=17.02 [6.81] at baseline), and rumination (P=.03; mean [SD]=40.42 [12.96] at follow-up vs mean [SD]=41.92 [13.61] at baseline); however, no significant effect of conversation or animation was observed. Findings also indicate a significant increase in user experience in animated conditions. This significant increase in animated conditions is also reflected in the user's ease of use and satisfaction (F(1, 201)=102.60, P&lt;.001), system information arrangement (F(1, 201)=123.12, P&lt;.001), and usefulness of the application (F(1, 201)=3667.62, P&lt;.001).&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;The current experiment provides support for bCBT-based mental health apps featuring customizable, humanlike therapeutic virtual agents and their ability to significantly reduce negative symptomology over a brief timeframe. The app intervention reduced mental health symptoms, regardless of whether the agent included conversational or animation features, but animation features enhanced the user experience. These effects were observed in both user","PeriodicalId":48616,"journal":{"name":"Jmir Mental Health","volume":"12 ","pages":"e67381"},"PeriodicalIF":4.8,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12007843/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144054870","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":"Health Care Professionals' Engagement With Digital Mental Health Interventions in the United Kingdom and China: Mixed Methods Study on Engagement Factors and Design Implications.","authors":"Zheyuan Zhang, Sijin Sun, Laura Moradbakhti, Andrew Hall, Celine Mougenot, Juan Chen, Rafael A Calvo","doi":"10.2196/67190","DOIUrl":"10.2196/67190","url":null,"abstract":"<p><strong>Background: </strong>Mental health issues like occupational stress and burnout, compounded with the after-effects of COVID-19, have affected health care professionals (HCPs) around the world. Digital mental health interventions (DMHIs) can be accessible and effective in supporting well-being among HCPs. However, low engagement rates of DMHIs are frequently reported, limiting the potential effectiveness. More evidence is needed to reveal the factors that impact HCPs' decision to adopt and engage with DMHIs.</p><p><strong>Objective: </strong>This study aims to explore HCPs' motivation to engage with DMHIs and identify key factors affecting their engagement. Amongst these, we include cultural factors impacting DMHI perception and engagement among HCPs.</p><p><strong>Methods: </strong>We used a mixed method approach, with a cross-sectional survey (n=438) and semistructured interviews (n=25) with HCPs from the United Kingdom and China. Participants were recruited from one major public hospital in each country.</p><p><strong>Results: </strong>Our results demonstrated a generally low engagement rate with DMHIs among HCPs from the 2 countries. Several key factors that affect DMHI engagement were identified, including belonging to underrepresented cultural and ethnic groups, limited mental health knowledge, low perceived need, lack of time, needs for relevance and personal-based support, and cultural elements like self-stigma. The results support recommendations for DMHIs for HCPs.</p><p><strong>Conclusions: </strong>Although DMHIs can be an ideal alternative mental health support for HCPs, engagement rates among HCPs in China and the United Kingdom are still low due to multiple factors and barriers. More research is needed to develop and evaluate tailored DMHIs with unique designs and content that HCPs can engage from various cultural backgrounds.</p>","PeriodicalId":48616,"journal":{"name":"Jmir Mental Health","volume":"12 ","pages":"e67190"},"PeriodicalIF":4.8,"publicationDate":"2025-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11990651/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143784595","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}