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 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-BasedMental 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
Research Fellow Cohort: Jorge Cuartas, Isaac Olufadewa, Richard Appiah
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:
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.
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.
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.
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.
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.
<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.
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.
<img src="https://jmir-production.s3.us-east-2.amazonaws.com/thumbs/0d8bf7410a4078dfb7635dde6d87a715" />
TORONTO, ON — October 5, 2026 – Canadian Hearing Services, in partnership with the Mental Health Commission of Canada (MHCC), through Opening Minds, an initiative of MHCC, has launched Mental Health First Aid for the Deaf Community. Through this collaboration, Canadian Hearing Services has become the first organization in Canada to offer Mental Health First Aid training specifically designed for Deaf communities and the first to deliver the program in American Sign Language (ASL), with training available nationwide.
Built through a collaborative partnership, Mental Health First Aid for the Deaf Community combines Opening Minds’ evidence-based Mental Health First Aid program with Deaf culture, Deaf lived experience, accessibility considerations, and Deaf-specific mental health knowledge. This groundbreaking initiative helps address critical gaps in accessible mental health support by equipping individuals, organizations, and communities with the knowledge, skills, and confidence to support Deaf individuals experiencing mental health or substance use challenges and crises. The program further strengthens Canadian Hearing Services’ position as a national leader in accessibility, mental health, and community support, while advancing its purpose to provide expert services that empower Deaf and hard of hearing Canadians. “Mental health support must be accessible to everyone. Through Mental Health First Aid for the Deaf Community, we are helping address a critical gap by providing training that is rooted in Deaf culture, lived experience, and accessible communication. This program will empower individuals across Canada with the skills and confidence to support mental wellness within their communities, while advancing our purpose to provide expert services that empower Deaf and hard of hearing Canadians,” said Julia N. Dumanian, President & Chief Executive Officer of Canadian Hearing Services.
“Meaningful mental health support begins with removing barriers and ensuring people can access help in ways that reflect their realities, experiences, and communities. This partnership with Canadian Hearing Services represents an important step toward making mental health education more accessible and inclusive for Deaf communities across Canada. By combining evidence-based Mental Health First Aid with lived expertise, accessible approaches, and a deep understanding of the unique experiences of Deaf communities, we are helping build stronger, more connected communities where people have the knowledge and confidence to support one another when it matters most,” Lili-Anna Pereša, President and Chief Executive Officer, Mental Health Commission of Canada.
Mental Health First Aid for the Deaf Community is designed for Deaf community members, family members, caregivers, educators, service providers, health professionals, community leaders, and anyone interested in building the skills to support mental wellness in Deaf communities. Participants will complete self-directed online learning followed by instructor-led virtual training sessions in an accessible learning environment. All instructors have completed intensive instructor certification and facilitation training through Opening Minds and bring extensive expertise in mental health and Deaf culture.
Canadian Hearing Services is a proud Canadian not-for-profit and the country’s leading provider of programs, services, education, and products that enable the Deaf and hard of hearing community to overcome barriers to participation. Building on 86 years of experience, Canadian Hearing Services has earned Accreditation Canada’s Exemplary Standing, the organization’s highest level of accreditation.
Canadian Hearing Services is home to the National Scholarship Program, the largest scholarship program in Canada exclusively supporting Deaf and hard of hearing post-secondary students, and to Hearing Research International, an international research foundation dedicated to accelerating innovative research in Hearing Health and Deaf Studies.
ABOUT OPENING MINDS
Opening Minds, an initiative of the Mental Health Commission of Canada, is a national leader in mental health training and mental health-related stigma reduction. Through evidence-based programs such as Mental Health First Aid, Opening Minds equips individuals, workplaces, and communities with the knowledge, skills, and confidence to support mental health and respond effectively to mental health and substance use challenges. Working with partners across Canada, Opening Minds develops and delivers accessible, practical training that helps create healthier, more supportive communities for everyone.
openingminds.org | mentalhealthcommission.ca
CANADIAN HEARING SERVICES MEDIA CONTACT: Rachel Winer, Manager, Communications, Canadian Hearing Services
OPENING MINDS MEDIA CONTACT: Michelle Bell, Manager, Marketing and Communications, Opening Minds
PurposeThis study aims to examine the association between elderly abuse and anxiety symptoms among older adults and explore the mechanisms of physical frailty and social frailty between them.MethodsA total of 1361 older adults were included in this study. Multiple linear regression and propensity score matching was conducted to confirm the association between elderly abuse and anxiety symptoms. Mediation and moderation analyses were performed to explore the mechanisms of physical frailty and social frailty between elderly abuse and anxiety symptoms.ResultsElderly abuse was significantly associated with higher levels of anxiety symptoms. Psychological abuse and neglect showed the strongest associations with anxiety symptoms. Social frailty played moderating roles between both psychological abuse, neglect and anxiety symptoms, while physical frailty played moderating roles between only psychological abuse and anxiety symptoms. Physical frailty and social frailty both partly mediated the relationship between psychological abuse, neglect and anxiety symptoms.ConclusionsThis study provides insight into the systematic relationship between elderly abuse and anxiety symptoms among older adults in Sichuan province. Preventive strategies should address abuse, physical health, and social support simultaneously of older adults.
ObjectiveTo assess the usability and performance of digital medication management systems (DMS) for patients with stable ambulatory schizophrenia and schizoaffective disorder.MethodsThis prospective, interventional study comprised a screening phase (up to 4 weeks), training phase (3 weeks: system use under the support), and independent phase (5 weeks under the minimized support) with a follow-up period of 2 weeks. Based on the DSM-5 criteria, patients with a diagnosis of schizophrenia or schizoaffective disorder, taking an atypical antipsychotic as their primary medication, and having access to a smartphone were included in this study.ResultsA total of 30 patients participated in the study. The main characteristics of the patients were as follows: age, 43.5 ± 11.4 years; male percentage, 40% (n = 12); duration of antipsychotic drug use, 14.2 ± 11.7 years; educational period, 13.3 ± 2.1 years; chlorpromazine (CP) equivalent dose, 369.6 ± 253.6 mg/day; and Brief Psychiatric Rating Scale (BPRS), 23.6 ± 4.8. The patients applied patches with the sensor of the DMS embedded in (hereinafter, a wearable sensors) for 93.2% of the study period. The patients could apply the patch independently and successfully and synchronize it with a smartphone application that allowed them to monitor their medication intake in 15.2 ± 8.1 days (median, 9.0; interquartile range (IQR), 1.0–15.0 days after initiation). Thirty-eight adverse events that were reported in 17 patients were classified as mild or moderate, and were related to wearable sensor use.ConclusionIt was concluded that most patients completed the task for the DMS with minimal assistance early in the study and continued to apply wearable sensors throughout the study period. Moreover, the results confirmed the feasibility of DMS in clinical practical use in stable schizophrenia.
