Background: Children of parents with mental health illness (COPMI) have the right to receive preventive interventions to avoid developing their own mental health and socioeconomic problems. However, access to interventions varies widely within health care and social services depending on their geographical location in Sweden. In this regard, mobile health (mHealth) interventions for COPMI provide a pathway for more equitable and sustainable preventive support. Objective: This project aims to (1) map and assess the quality of available mHealth apps relevant to COPMI; (2) investigate experts’ perceptions of COPMI needs for digital health solutions (particularly mHealth) by consulting and collaborating with an interdisciplinary reference group (scholars, child rights organizations, and IT professionals); and (3) explore the prerequisites for development, implementation, and sustainable access to future digital solutions for COPMI. Methods: We collaborated with a reference group through a series of meetings and workshops, identifying and assessing the quality of 9 free, highly ranked apps (ie, using the well-known Mobile App Rating Scale [MARS]). All the workshop sessions were recorded and transcribed for further qualitative analysis, allowing us to document and derive our main findings. Results: Three out of 9 apps received high scores across the MARS dimensions of aesthetics, perceived functionality, information, and engagement in the expert heuristic evaluation using recorded demonstrations of the apps, indicating a significant lack of high-quality apps relevant to COPMI. Further findings were derived, such as the experts’ preference for more general apps that are not specifically targeted to COPMI, as these could promote self-identification and reduce stigmatization. Regarding the third aim, the results showed that it was important to find an app that protected users’ privacy by allowing anonymous access to digital support and that mobile apps should be complemented (or replaced) by web-based applications to improve accessibility for children who may not be allowed to download apps without parental permission. Conclusions: The sustainability of digital solutions (web applications or mobile apps) for COPMI is the biggest challenge for future developments. Long-term impact requires stable funding, continuous maintenance, and partnering with established organizations that already possess the infrastructure, expertise, and reach necessary to provide ongoing support. To address the limitations in the study design and deployment issues, it is important to actively involve children through participatory and co-creational approaches when designing and developing mHealth solutions. Also, future evaluations should include privacy and data security assessments such as app permissions, privacy-policy presence, and third-party sharing.
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Incorporating Objective Behavioral and Biological Measures in a Large-Scale Decentralized Mobile Health Trial for Depression: Randomized Controlled Trial
Background: Decentralized (ie, fully remote) clinical trials (DCTs) offer a scalable and ecologically valid approach to evaluating digital mental health interventions (DMHIs). Yet, incorporating objective measures of well-being within DCTs poses novel methodological and feasibility challenges that can limit the rigor and precision of data collection in the field. Objective: We recently conducted the Behavior, Biology, and Well-being (BeWell) Study, a large-scale, 3-arm randomized DCT evaluating the Healthy Minds Program app (Humin)—a self-guided DMHI that includes didactic well-being information and meditation components. The current paper’s objective is to describe the methodological approach to collecting and processing a multimodal battery of psychological (questionnaires and structured clinical interview), biological (blood and stool assays), and behavioral (stimulus-elicited affect [SEA] task, video log, and Mnemonic Similarity Task [MST]) measures, longitudinally across baseline, the 4-week intervention period, and 17-week follow-up. Secondarily, we evaluate its feasibility. Methods: The BeWell Study methodology is described. Descriptive statistics characterized recruitment and retention, as well as data completion and data quality. Binary logistic regressions evaluated whether recruitment sources, demographics, and depression severity predicted measure completion and data quality. Results: From June 1, 2022, through July 15, 2024, 52,052 individuals started the screening questionnaire. Of these, 2.22% (n=1156 participants; mean age 38.69, SD 12.14 years; women=75.50%; non-Hispanic White=68.88%) completed the screener, met eligibility criteria (including elevated depressive symptoms [Patient Health Questionnaire-9 [PHQ-9] score≥5]), completed baseline measures, were enrolled, and were randomized. Participants were recruited from Craigslist (694/1154, 60.14%), a US-based advertisement website, and university email messages (107/1154, 9.27%); participants were enrolled from every US state. Across the study, 84% (971/1156) of participants were retained (completed ≥1 measure at 17-week follow-up). Across time points, ≥81.40% (941/1156) of participants completed questionnaires and ≥75.17% (869/1156) completed biological samples. Of those completing questionnaires, ≥84.14% (796/946) completed SEA and video log tasks and ≥91.75% (868/946) completed the MST. Of completed assessments, high-quality or analyzable data rates across all time points were ≥97.89% (926/946) for questionnaires, ≥90.20% (773/857) for SEA, ≥84.60% (725/857) for video logs, ≥94.20% (1056/1121) for the MST, ≥98.84% (849/859) for dried blood spots, and ≥99.43% (865/870) for microbiome samples. Greater depressive symptoms at screening, but not age, predicted completion of baseline measures (odds ratios 0.96‐0.99; <.001). For enrolled participants, recruitment sources and baseline depressive symptoms were not predictive of data completion or quality. Older age and having a college degree predicted significantly higher rates of data completion across all data types (questionnaire, behavioral, and biological) at week 4 or week 17 follow-ups. Conclusions: This methodological approach offers one model of incorporating objective measures of well-being into DCTs testing DMHIs. Implications and limitations of our approach are discussed. Trial Registration: ClinicalTrials.gov NCT05183867; https://clinicaltrials.gov/study/NCT05183867
The SNF Global Center Names New Research and Communicator Fellows Ahead of World Mental Health Day 2026
The latest cohort of Fellows will join an international network of experts leading scalable, community-driven innovations in low- and middle-income countries (LMICs).
New York, NY—The Stavros Niarchos Foundation (SNF) Global Center for Child and Adolescent Mental Health at the Child Mind Institute has announced its most recent 2026 cohort of Research and Communicator Fellows ahead of World Mental Health Day. The fellowships are part of the Child Mind Institute’s broader global effort to strengthen local leadership, advance evidence-based solutions, and expand access to culturally responsive mental health care for children and adolescents worldwide. Through its Global Fellowships Program, the SNF Global Center is able to invest in the people and institutions best positioned to develop and scale solutions within their own communities.
The SNF Global Center Communicator Fellowship targets professionals shaping important conversations through evidence-based approaches, while the Research Fellowship invites creative, early-career scientists with records of conducting innovative projects — with both programs addressing child and adolescent mental health gaps and needs across low- and middle-income countries.
The Research Fellowship provides up to $550,000 over a period of 4–5 years and offers structured mentorship opportunities, strategic career development, training in implementation science, and access to a global network of experts. Each year, two fellows and one honorable mention are named. For the Communicator Fellowship, financial support of up to $100,000 is provided; and professionals working in public communications — including journalists, broadcasters, and podcasters — also receive training, mentorship, and networking opportunities.
The Global Fellowships Program’s growing relevance is evident through the sharp rise in applications for both rounds of the Communicator (64 percent increase) and Research Fellowship (113 percent increase) from last year.
“We are honored to expand our international footprint by introducing new countries and impactful communications and research projects to the program, including Colombia, Ghana, and Peru,” says Peter Raucci, director of Global Fellowship Strategy at the SNF Global Center. “This strategic investment in exceptional youth mental health experts allows us to continue developing a network of innovators leading evidence-based transformation across continents, alongside valued partners. As of September 2026, across all three of our fellowship programs, we have selected 35 fellows in 14 countries.”
The new cohort of Research Fellows includes:
- Jorge Cuartas, Universidad de los Andes, Colombia
Project: Apapacho: Transforming Child and Adolescent Mental Health through Scalable Parenting Support Embedded in Government Systems - Isaac Olufadewa, Slum and Rural Health Initiative (SRHIN), Nigeria
Project: Brave Heart AI Buddies: Effectiveness of a Culturally Adapted, Human-Centered and Offline-Capable Generative AI-Enhanced Non-Specialist Model for Community-Based Mental Health Care Among Adolescent Migrants — A Cluster Randomized Controlled Trial - Richard Appiah (Honorable Mention), Mental Health Authority, Ghana
Project: THRIVE-GH: Co-Designing and Feasibility-Testing a Whole-School Framework for Early Identification and Psychosocial Support in Ghanaian Basic Schools
In a first for the SNF Global Center Fellowships, two applicants were awarded fellowships from the same institution — the Slum and Rural Health Initiative (SRHIN) in Ibadan, Nigeria.
Isaac Olufadewa, a 2026 SNF Global Center Research Fellow from Nigeria, believes the fellowship will help turn local ingenuity into global learning, while helping build a future where no young person’s access to mental health care is determined by where they live.
“My fellowship will develop the Brave Heart AI Buddies program, a culturally responsive, human-centered, and digital model for bringing mental health care to adolescents and young people living in displacement in Nigeria,” Olufadewa explains. “The SNF Global Center Fellowship is the doorway to new knowledge, new partnerships, and a new generation of culturally grounded mental health solutions.”
While only one Communicator Fellow was named in 2025, this year three Communicator Fellows were named:
- Lucero Andaluz Llerena, De-Mentes, Peru
Project: The Youth Lived Experience Leadership Lab - Peace Oregbesan, Slum and Rural Health Initiative (SRHIN), Nigeria
Project: #UnspokenStories - Stacey-Ann Pillay, The Healthy Brains Global Initiative, South Africa
Project: Voices to Action: Adolescent Maternal Mental Health
For Peace Oregbesan, a 2026 SNF Global Center Communicator Fellow from Nigeria, the heart of her project #UnspokenStories is bringing the experiences of young people in Nigeria into the wider, global conversation on child and adolescent mental health.
“This project will create spaces for out-of-school youth in Nigeria to share, shape, and safely tell stories about their mental health in ways that feel true to their lives,” says Oregbesan. “I’m drawn to the SNF Global Center community because I want to learn from people doing thoughtful work in mental health, communication, and storytelling. And I want to be challenged by perspectives beyond my own.”
At a time when one in five young people are experiencing mental health or learning challenges, evidence-based and culturally grounded communication is urgently needed. Adolescents are also more exposed to a high volume of mental health content. Dispelling misinformation, reducing stigma, and increasing youth engagement will be components of the priorities embedded into each Communicator project.
With the theme for this year’s World Mental Health Day being, “Lived experiences heard: Real voices, Real change,” the SNF Global Center remains committed to collaboratively improving systems of care for children and adolescents in often underserved communities. This is also made possible through our partnerships around the world, where efforts are designed to transform the future of youth mental health for all young people, everywhere. everywhere.
Learn more about the Global Fellowships Program
About the SNF Global Center at the Child Mind Institute
The Stavros Niarchos Foundation (SNF) Global Center for Child and Adolescent Mental Health at the Child Mind Institute brings together the Child Mind Institute’s expertise as a leading independent nonprofit in children’s mental health and the Stavros Niarchos Foundation’s deep commitment to supporting collaborative projects to improve access to quality health care worldwide. The center is building partnerships to drive advances in under-researched areas of children and adolescents’ mental health, and expand access to culturally appropriate training, resources, and treatment in low- and middle-income countries. This work is conducted by the Child Mind Institute with support from SNF through its Global Health Initiative (GHI).
About Child Mind Institute
The Child Mind Institute is the leading nonprofit dedicated to transforming the lives of children and families experiencing mental health and learning disorders. Since its founding in 2009, the Child Mind Institute has become a global institution reaching millions of families each year through excellence in evidence-based clinical care, renowned educational resources and training, and science to advance the next generation of treatment.
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For press questions, contact us at childmindinstitute@ssmandl.com or mediaoffice@childmind.org.
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Efficacy of artificial intelligence-driven conversational agents (chatbots) for mental health promotion among university students: a systematic review and meta-analysis
BackgroundUniversity students experience substantial mental-health burdens, while access to timely support remains uneven. AI conversational agents may provide low-threshold support, but the evidence base has expanded rapidly and prior reviews have often combined different age groups, intervention types, outcome domains, and comparator conditions.ObjectiveTo evaluate the effects of AI-mediated conversational interventions on depressive and anxiety symptoms in university students, while distinguishing outcome domains, comparator intensity, intervention architecture, and the evidential requirements for standardized post-intervention effect estimation.MethodsWe updated the search in PubMed, Embase, PsycINFO, CENTRAL, and Web of Science to the revised search date and rebuilt an auditable cross-database record ledger. The updated workflow included 3,786 exported records, 451 DOI/PMID duplicates, 41 confirmed title/author duplicates, and 3,294 unique records screened. All title/abstract records completed a two-reviewer screening and consensus chain. Forty-six update-search records entered research-level/full-text assessment. Randomized controlled trials were eligible for causal quantitative synthesis; relevant feasibility, acceptability, usability, and non-comparable controlled studies were retained narratively. Standard post-intervention analyses used Hedges g, REML random-effects models, and Hartung-Knapp adjustment, and were restricted to attributable two-group post-intervention data.ResultsIn the final locked non-active/low-intensity comparator analysis, three depression comparisons favored conversational interventions (k=3; Hedges g=-0.413, 95% CI -0.557 to -0.268; I2 = 0.0%) and four anxiety comparisons also favored them (k=4; Hedges g=-0.435, 95% CI -0.603 to -0.268; I2 = 2.1%). In the separate active conversational-control analysis, two MYLO comparisons did not show a reliable incremental effect for depression (k=2; g=0.165, 95% CI -0.580 to 0.911) or anxiety (k=2; g=0.190, 95% CI -0.556 to 0.937). The evidence base remained small, clinically heterogeneous, and limited by attrition, self-reported outcomes, and uncertainty regarding generalizability.ConclusionsAI conversational interventions may improve depressive and anxiety symptoms relative to non-active or low-intensity comparators in university students, but the certainty of evidence is low and estimates are based on few comparisons. Evidence against active conversational controls is very uncertain. Outcome-domain differences and architecture-related claims should be treated as hypothesis-generating. Secure, consent-based, human-supervised implementation and objective outcome assessment are priorities for future research.Systematic review registrationhttps://www.crd.york.ac.uk/PROSPERO/view/CRD420261323382, identifier CRD420261323382.
Exercise for the negative symptoms of schizophrenia: a review
Negative symptoms of schizophrenia, comprising avolition, asociality, anhedonia, blunted affect, and alogia, are a primary driver of functional impairment, tend to persist, and have no treatment approved specifically for them. Exercise has drawn interest as a low-cost, widely available, adjunctive intervention. This review synthesizes three recent evidence syntheses: an umbrella review of previously published meta-analyses and two network meta-analyses of randomized controlled trials. All three report significant reductions in negative symptoms, with yoga consistently the most effective modality; aerobic and mind-body exercise also show benefit, while the evidence for resistance exercise is inconsistent across analyses. Reported effect sizes range from small to large, and this divergence appears to reflect differences in comparator group and analytic method rather than differences in study quality. A dose-response analysis estimated an optimal total dose of 1200 metabolic equivalent minutes per week but proved unstable under sensitivity analysis. Certainty of evidence was rated low to very low throughout. Two constraints recur across the literature: the rating scales used cannot distinguish primary from secondary negative symptoms, and avolition itself likely limits sustained participation in exercise. Exercise is a reasonable adjunctive intervention, but its specific benefit for primary negative symptoms remains poorly understood.
Cognitive remediation in schizophrenia: efficacy and role of neuroplasticity in “top-down” and “bottom-up” mechanisms—a randomized, double-blind, treatment-as-usual-controlled study protocol
ObjectivesSchizophrenia is characterized by positive, negative and cognitive symptoms, including different degrees of impairment in several neurocognitive domains, such as executive function, processing speed, learning and memory, which impact a patient´s functioning and quality of life. Cognitive remediation can help restore cognitive functioning through repeated exercises, following at least two possible strategies: a “top-down” approach, focused on training executive functions, or perceptual training, a “bottom-up” approach, aimed at first addressing impairments in perceptual processing. This study aims to clarify whether both approaches have comparable efficacy in improving neurocognitive performance, and to explore the timing of their cognitive and molecular effects. It will also examine the relationship between these approaches and changes in BDNF levels, considered a biomarker of neuroplasticity.Materials and methodsIn order to have a control group, patients will undergo an initial 10-week observation period receiving treatment as usual, followed by randomization into either executive training or perceptual training during the active phase of the trial. Clinical and neurocognitive assessments, as well as blood sampling, will be conducted at the beginning and end of each stage. Finally, assessments will be repeated after a 12-week follow-up period.DiscussionThe study aims to answer these research questions concerning clinical efficacy and molecular mechanisms and expects to identify possible clinical or molecular biomarkers (such as BDNF levels or the Val66Met polymorphism) that could be helpful as predictors of neurocognitive treatment response for each remediation strategy, thereby contributing to a personalized treatment approach. Study registered on clinicaltrials.gov number NCT06482918.
Feasibility Trial of a Parent Peer-Co-Led Caregiver Group for Pediatric Early Onset Schizophrenia Spectrum Disorders
Conditions: Schizophrenia; Psychosis
Interventions: Behavioral: Group Therapy; Other: Treatment as Usual (TAU)
Sponsors: Boston Children’s Hospital
Not yet recruiting
Interventions: Behavioral: Group Therapy; Other: Treatment as Usual (TAU)
Sponsors: Boston Children’s Hospital
Not yet recruiting
Novel and Conventional Interventions for Cognitive and Emotional Flexibility in Patient Trauma
Conditions: Depression; Anxiety; Trauma Patients
Interventions: Drug: D-Cycloserine (DCS); Device: Transcranial Magnetic Stimulation; Behavioral: Unified Protocol; Behavioral: Psychoeducation
Sponsors: Mclean Hospital
Active, not recruiting
Interventions: Drug: D-Cycloserine (DCS); Device: Transcranial Magnetic Stimulation; Behavioral: Unified Protocol; Behavioral: Psychoeducation
Sponsors: Mclean Hospital
Active, not recruiting
Ensemble of Domain-Specific Natural Language Processing and Large Language Models for Detecting Suicidal Ideation and Mental Health Conditions in Social Media Text: Development and Evaluation Study
<strong>Background:</strong> Mental illness contributes substantially to global disability, and public adoption of AI for mental health support is accelerating without commensurate safety evaluation. General-purpose large language models can fail to recognize naturalistic text expressing suicidal ideation, with immediate clinical consequences. Domain-specific natural language processing methods offer a contrasting approach, but ensembles integrating the two have not been formally characterized. <strong>Objective:</strong> We aimed to (1) benchmark 5 modeling approaches for classifying mental health–related social media text, (2) develop and optimize ensembles integrating a domain-specific natural language processing classifier with a fine-tuned large language model, and (3) derive a closed-form framework constraining ensemble weighting in safety-critical classification. <strong>Methods:</strong> We analyzed 60,889 publicly available Reddit and Twitter texts spanning 9 categories (anxiety, bipolar disorder, depression, a normal baseline, personality disorder, stress, suicidal ideation, attention-deficit/hyperactivity disorder, and autism spectrum disorder). Labels were derived from the originating subreddit or a self-stated condition and are not clinical diagnoses. Five architectures were compared: prompt-engineered base GPT-4o-mini; fine-tuned GPT-4o-mini; a domain-specific classifier (support vector machine over symptom-informed lexical features); and 2 ensembles of these models, hybrid probability–indicator fusion and soft probability fusion. Data were split into 70%, 10%, and 20% at the post level (n=42,622, n=6089, and n=12,178); weights were selected on the validation split and all reported performance comes from the held-out test split. Closed-form bounds on permissible language model weights were derived for both strategies. CIs are paired bootstrap intervals (n=10,000 replicates); accuracy differences were tested with exact McNemar tests. <strong>Results:</strong> The domain-specific classifier achieved 91.9% accuracy (95% CI 91.4%-92.4%), exceeding the base (58.7%) and fine-tuned (88.6%) large language models. Both ensembles outperformed either constituent model, reaching 93.9% (soft fusion; 95% CI 93.5%-94.3%) and 93.8% (hybrid fusion; 95% CI 93.4%-94.2%) at a weighting of 60% classifier to 40% fine-tuned model, selected on validation (<i>P</i><.001 for both against the classifier alone; the 2 ensembles did not differ from each other [<i>P</i>=.16]). The hybrid ensemble reduced the miss rate for the suicidal ideation category from 32.5% (650/2000) to 1.9% (37/2000), a 17.6-fold reduction relative to the base model, although its advantage over the classifier alone within that category was not significant (<i>P</i>=.68). Accuracy fell discontinuously above a 50% language model weight, where the hybrid ensemble reduced exactly to the fine-tuned model; the selected weight satisfies the derived instance-level bound of 44.0%. <strong>Conclusions:</strong> Domain-specific clinical grounding remained necessary for stable ensemble performance in this corpus, and probabilistic fusion outperformed both constituent models when language model weight was bounded by a derivable, model-agnostic constraint. Because labels were community-inferred and only one corpus was analyzed, these results are hypotheses requiring external validation on clinically characterized data before use in decision support.
Using App Store Reviews to Explore User Experiences and Perceptions of Mental Health Mobile Apps: Qualitative Study
Background: With the rise of digital health, mental health apps (MHapps) have become an increasingly popular tool in the self-management of depression, anxiety, and stress. Star ratings are often used as indicators of app quality to guide selection. However, ratings do not fully capture if the MHapp’s features, usability, or design elements exhibit the heterogeneous real-life user experiences. Instead, examining the patterns of engagement and app usage can highlight whether high ratings reflect diverse user needs. Objective: This study aimed to examine user experience of top-rated depression and anxiety apps to assess if user perceptions, as reported in app reviews, align with star ratings. Methods: To identify relevant mobile apps, we used a systematic search strategy that mimicked a typical user’s journey and applied search terms related to depression, anxiety, and stress management. Apps were screened using a predefined inclusion criteria that guided the identification of the 5 most highly rated MHapps, yielding approximately 750 user reviews across all apps. Reviews were analyzed using hybrid inductive and deductive coding techniques, with codes generated iteratively to capture the spectrum of user perceptions and experiences. Codes were then clustered, leading to theme generation that provided a deeper understanding of user experiences. Results: User experiences demonstrated a complex interplay between perceived benefits and frustration. Users valued ease of navigation, convenience, accessibility, and low-effort features that allowed easier entry to self-management of mental health. Users expressed their affinity toward specific app features and desire for the addition of new features that could further support their mental wellness. However, limited or restrictive features due to paywall, in-app advertisement placement, and functionality issues resulting from technical issues or bugs garnered substantial criticism. Conclusions: This study highlights the complexity of user perceptions influencing experience with self-management MHapps. Despite high star ratings, app usability elicited paradoxical responses and showed multiple overlapping themes, showcasing features that may support some users but hinder others. The results emphasize that star ratings alone are insufficient to showcase if a MHapp meets user needs. Continued improvement in app design such as interface usability, feature development, and pricing transparency is needed to meet the diverse needs of users for better engagement and satisfaction.
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