Background: Depression affects more than 300 million people worldwide and is a leading contributor to the global disease burden. Traditional diagnostic methods, such as structured clinical interviews, are reliable but impractical for frequent or large-scale screening. Self-report tools like the Patient Health Questionnaire-8 (PHQ-8) require disclosure and clinician oversight, limiting accessibility. Recent artificial intelligence–based approaches leverage multimodal behavioral cues (linguistic, acoustic, and visual) for automated depression detection but remain constrained by limited adaptability, scarce annotated data, weak emotional expression in real-world settings, and the high computational cost of deployment of socially assistive robots (SARs). Objective: This study introduces Depression Social Assistant Robot (DEPRESAR)-Fusion, a lightweight multimodal depression detection framework designed for natural interactions with emotion-aware SARs. The objective of this study was to enhance detection accuracy in everyday conversations while addressing the challenges of data scarcity, weak emotional cues, and computational efficiency. Methods: DEPRESAR-Fusion integrates acoustic, linguistic, and visual features with an emotion-aware response module powered by large language models to adapt conversational strategies dynamically. To stimulate richer emotional expression, participants were exposed to emotionally evocative videos before SAR interactions. To overcome data scarcity, we augmented training with (1) public depression-related social media corpora and (2) synthetic samples generated via large language models. The proposed multimodal fusion architecture was evaluated on benchmark clinical datasets for both binary depression classification and PHQ-8 regression tasks. Performance was compared against prior multimodal baselines using root mean square error, mean absolute error, and standard classification metrics. Results: Participants who viewed emotional stimuli before interacting with SARs exhibited significantly higher emotional expressiveness, leading to improved model performance. Regression tasks showed lower root mean square error and mean absolute error, while classification tasks achieved significantly higher accuracy than the nonstimulus condition. DEPRESAR-Fusion outperformed prior multimodal baselines across multiple benchmark datasets, achieving state-of-the-art performance in both binary classification and PHQ-8 regression. The system maintained a lightweight architecture suitable for real-time deployment on SARs. Conclusions: DEPRESAR-Fusion demonstrates that integrating emotion induction, data augmentation, and lightweight multimodal fusion can enable accurate and scalable depression detection in naturalistic SAR interactions. By bridging the gap between structured clinical assessments and everyday conversations, this approach highlights the potential of SAR-based systems as nonintrusive, artificial intelligence–driven tools for proactive mental health support.
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Enhancing Sleep and Mental Health: Longitudinal, Observational, Real-World Study From a Digital Mental Health Platform
Background: Poor sleep is closely linked to mental health challenges and workplace burnout. Mental health and workplace stressors can impair sleep, while good sleep quality supports cognitive and emotional resources to cope with daily challenges. Despite positive outcomes of maintaining good sleep, many people struggle to get enough restorative sleep at night. Given the bidirectional relationship between sleep and mental health, evidence-based digital mental health solutions may offer an accessible and scalable approach to improving sleep quality. Objective: This study examines whether engagement with an employer-sponsored, multimodal digital mental health platform is associated with improvements in sleep quality over time, and whether changes in sleep quality are associated with concurrent changes in mental health and burnout outcomes. Methods: This 12-month prospective, observational study followed working adults who were newly registered to an employer-sponsored digital mental health platform (Modern Health). The platform leveraged technology (mobile and web) to connect employees with comprehensive provider-led and self-guided care through therapy, coaching, on-demand digital resources, and group psychoeducational sessions. Participants [N=578; 61.1% (n=353) women; mean age 33.88, SD 8.73 years; 40.3% (n=233) people of color] completed measures of self-rated sleep quality, depression, anxiety, and burnout (exhaustion, cynicism, and professional efficacy) at baseline and after 3 and 12 months of accessing the platform. Upon registering for the platform, participants were given an initial care recommendation, but could flexibly engage in any combination of services. Participants in this study engaged with at least one care modality, including therapy, coaching, psychoeducation sessions, and self-guided mental health resources. We examined perceived sleep quality and associations with other study variables at baseline, changes in perceived sleep quality over time, and whether changes in sleep quality correlated with concurrent changes in mental health and burnout. Results: At baseline, 42% (243/578) reported poor sleep quality and were more likely to have higher levels of depression, anxiety, and burnout. A generalized linear mixed-effects model showed that each additional month of platform access was related to an increased odds of having good sleep quality by 3.7% (=.02). Linear mixed-effects models found that higher sleep quality over time was associated with lower depression, anxiety, exhaustion, cynicism, and efficacy (all <.001). Among participants reporting poor sleep quality at baseline, 44% (62/141) reported good sleep quality at 12 months. Within this subgroup, paired sample tests showed significant reductions in depression (−48.3%) and anxiety (−38.3%), and increased cynicism, burnout, though cynicism levels remained below the cutoff for high burnout (23.9%; all <.01). Conclusions: Use of an employer-sponsored digital mental health platform was associated with meaningful improvements in self-reported sleep quality over 12 months. These gains were associated with significant reductions in depression, anxiety, and burnout symptoms, highlighting broader well-being benefits of comprehensive mental health care.
The Effectiveness and Mechanisms of Action of App-Based Interventions for Improving Mental Health and Workplace Well-Being: Randomized Controlled Trial
Background: Depression is the most common mental health disorder worldwide and frequently leads to workplace absence. As face-to-face treatment can be difficult to access, app-based interventions are a popular solution, although their effectiveness in working populations and their mechanisms of action are unclear. Deficits in executive function may contribute to the onset and maintenance of depression, and executive function training is proposed to improve symptoms by enhancing executive function. Responders to cognitive behavioral therapy (CBT) show improvements in executive function, suggesting that this may be one mechanism of action. Objective: This study investigated the effectiveness of app-based interventions (executive function or CBT-based) for reducing depressive and anxiety symptoms and improving workplace well-being, and assessed whether changes in executive function mediated improvements. Methods: A total of 228 participants (147 female participants) with mild-to-moderate symptoms of depression and anxiety were recruited online and randomly assigned to a waitlist control group, an executive function training group (NeuroNation app, Synaptikon GmbH), or a self-guided CBT group (Moodfit app, Roble Ridge LLC) for a 4-week intervention period. Participants assigned to the active intervention groups were asked to use their apps a minimum of 21 times during the intervention. Participants completed measures of depressive symptoms, anxiety symptoms, and workplace well-being, and a working memory task at baseline, postintervention, and follow-up (12 weeks). Results: Executive function training reduced anxiety (β=−2.79; =.004) and depressive (β=−2.77; =.02) symptoms at follow-up but not at postintervention, and it did not affect workplace well-being. There were no reductions in depressive or anxiety symptoms in the self-guided CBT group, though workplace well-being was improved at postintervention (β=3.72; =.02) and follow-up (β=4.46; =.02). Improvements in executive function did not mediate intervention-related changes in symptoms or workplace well-being. Self-reported adherence rates were high (executive function training: 48/54, 89%; self-guided CBT: 52/54, 96%), although attrition was high at follow-up (58% missing). Conclusions: These results suggest that app-based executive function training may be effective at managing symptoms of anxiety and depression in a working population, while self-guided CBT apps may improve workplace well-being. However, improving executive function did not appear to be a mechanism of action of either intervention. Trial Registration: ISRCTN 12730006; https://www.isrctn.com/ISRCTN12730006
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Trump administration warns against using federal dollars on fentanyl test strips
The Trump administration is doubling down on its opposition to harm reduction services for people who use illicit drugs.
In an open letter on April 24, the federal agency overseeing addiction and mental health policy warned its grantees against using federal funds to buy harm reduction supplies including sterile syringes and pipes, or to distribute test strips for common drug supply adulterants like fentanyl, xylazine, and medetomidine.
How adolescent cannabis use reshapes the developing brain — a systematic review
Background and hypothesisCannabis use initiation during adolescence has increased globally, raising concerns about neurodevelopmental consequences during this critical period when the brain undergoes extensive remodeling in cannabinoid receptor-rich regions.Study designThis systematic review examines neurodevelopmental consequences of adolescent cannabis use, focusing on structural brain changes, cognitive impacts, addiction vulnerability, and long-term outcomes. We searched PubMed, EMBASE, PsycINFO, and Web of Science (2000-2025) for studies examining cannabis effects in adolescent populations. Following PRISMA guidelines, two reviewers screened 3,421 records and assessed 156 full-text articles, including studies with neuroimaging, cognitive assessments, or longitudinal follow-up.Study resultsThirty-six studies involving 8,432 participants met criteria: 23 longitudinal cohorts (62.2%), 8 cross-sectional (22.2%), 4 RCTs (11.1%), and 1 case-control study (2.8%). Neuroimaging revealed dose-dependent alterations including reduced prefrontal cortical and hippocampal/amygdala volumes, accelerated cortical thinning in longitudinal studies, and impaired white matter connectivity correlating with initiation age. Cognitive findings were mixed — some showed persistent deficits after prolonged abstinence in adolescent-onset users, others found no effects after controlling for confounders. Epidemiological studies consistently showed elevated addiction risk (ORs 3.9–7.2) in adolescents versus adults. Long-term associations included educational difficulties, mental health problems, and functional impairment, though causal relationships remained unclear.ConclusionsAdolescent cannabis use associates with structural brain changes, elevated addiction risk, and variable cognitive effects, suggesting greater vulnerability versus adult-onset use. However, methodological limitations including confounders, heterogeneous definitions, and observational designs limit causal inference. Findings support age-specific prevention and specialized interventions while highlighting needs for rigorous longitudinal research establishing causality.Systematic review registrationhttps://www.crd.york.ac.uk/prospero/, identifierCRD420251165329.
Electrophysiological and morphological alteration in the visual pathway of children with attention-deficit/hyperactivity disorder
IntroductionAttention-deficit/hyperactivity disorder (ADHD) is one of the most common neurodevelopmental disorders in children. Optical Coherence Tomography (OCT) and Visual evoked potentials (VEP) are common non-invasive diagnostic techniques. Researchers can use these techniques to identify possible biomarkers and explore the neurodevelopmental mechanisms underlying ADHD.MethodsThe ADHD group (37 cases, average age 8.81 ± 1.44 years) and the healthy controls (38 cases, average age 8.97 ± 1.43 years), had the OCT and VEP. The retinal nerve fibre layer (RNFL), optic disc parameters, and macular parameters were measured through OCT. The latencies of P100 and the amplitudes of N75-P100 and P100-N135 waves at three different spatial frequencies (visual angles of 15’, 30’, and 60’) were tested through VEP.ResultsThe average RNFL and RNFL in each quadrant between the two groups were no statistically significant (all p > 0.05). The optic disc area, average cup-to-disc ratio, and cup volume in the ADHD group were all significantly larger than those in the control group (all p < 0.05). At three visual angles (15’, 30’, 60’), P100-latency in the ADHD group were all more significant than those in the control group (all p < 0.05). The amplitudes of N75-P100 and P100-N135 in the ADHD group were all statistically significantly lower than those in the control group (all p ≤ 0.001).DiscussionFrom the perspective of electroencephalophysiology, children with ADHD may have early visual information processing disorders. This provides a theoretical and practical basis for further early intervention in children with ADHD from the field of visual perception. The study protocol followed the tenets of the Declaration of Helsinki, was approved by the local ethics committee (No 2023-2240), and was registered on ClinicalTrials.gov (ChiCTR2400086223).
From collective restriction to critical action: the indirect effects of critical motivation and radical hope
IntroductionHistorically, Women of Color (WOC) in the United States have experienced systemic restrictions to their freedom and autonomy, which can have a lasting impact on their mental health and wellbeing. Conceptually, this type of collective autonomy restriction (CAR) experience may be associated with increased critical consciousness (CC), reflected in greater awareness of social and systemic oppression, commitment to and belief in one’s capacity to address social issues, and engagement in action; however, there is a dearth of research examining this association. Building on critical consciousness and hope literatures, we hypothesized that the association between CAR and critical action would be explained through serial pathways of increased critical motivation and greater radical or collective hope.Materials and MethodsA sample of 408 WOC completed an online survey administered through Prolific and hosted on Qualtrics. The survey included indicators of CAR, critical consciousness (critical motivation and critical actions), psychological hope, and radical hope. ResultsWe conducted structural equation modeling to test a serial mediation model exploring the associations among CAR, critical motivation, hope, and critical action. Findings indicated the association between CAR and critical action was fully mediated by the proposed serial mediation pathways (CAR → Critical Motivation → Radical Hope → Critical Action). The pathway through radical hope was stronger than through psychological hope. The direct effect of CAR on critical action was non-significant, indicating full mediation.DiscussionThese results highlight the role of radical hope as a potential pathway connecting critical awareness of collective autonomy restriction and critical motivation to engage in critical action aimed at social change. We extend the existing literature by demonstrating that awareness of oppression and motivation alone may be insufficient to explain the link between the first two dimensions of critical consciousness (critical reflection and critical motivation) and critical action. Limitations and implications for research and practice are discussed.
Ngā māuiui kai: a cross-sectional study of elevated eating disorder risk and related experiences among trans people in Aotearoa
PurposeLittle is known about disordered eating and eating disorders (ngā māuiui kai) among transgender and non-binary (trans) communities in Aotearoa New Zealand. This cross-sectional study sought to provide evidence of the prevalence and experiences of ngā māuiui kai among these communities.MethodsWe analyzed data from a national trans health survey of people using chi-square tests of independence to examine associations between sociodemographic characteristics and elevated eating disorder risk measured by the SCOFF screening tool. A content analysis of open-text survey comments identified themes across participants’ self-reported experiences of ngā māuiui kai.ResultsOverall, 34.3% of participants met criteria for increased risk for an eating disorder. Age, neurodivergence, material hardship, functional impairment, and Māori ethnicity were associated with elevated risk among this sample. No associations were found for gender, self-identified disability, or other ethnicities. The content analysis found that several participants reported connections between their māuiui kai and gender incongruence, broader mental health issues, or structural barriers. Some reported challenges seeking related healthcare, and a lack of providers’ awareness of the relationship between gender-affirming healthcare needs and ngā māuiui kai.ConclusionsA high proportion of trans participants met the criteria for elevated risk of eating disorders, with higher risk among those belonging to other marginalized groups. These findings highlight the unique risk factors among trans people who belong to multiple marginalized groups. They signal need for appropriate prevention and provision of responsive care for trans people at the intersections of ngā māuiui kai and gender-affirming healthcare.
3 things to know before Tardive Dyskinesia Awareness Week
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3 common myths about workplace mental health and wellness
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