Background: Suicide helplines increasingly employ chat services to aid those in urgent need, but the wording and structure of text-driven exchanges may affect their effectiveness. Objective: Given the association of cognitive distortions with depression and anxiety, this study investigated their prevalence in the language of individuals seeking help from the Dutch 113 suicide helpline. Methods: We observed the prevalence of cognitive distortions for both help seekers and counselors in a large volume of chat sessions (N=71,148) of the Dutch 113 suicide chat helpline using natural language processing. The results were compared to 2 large collections of online text data from Dutch social media and web content. Results: We found that nearly all types of cognitive distortions are more prevalent in the language of help seekers compared to the control group of helpline counselors. Distortions of the personalizing, emotional reasoning, and mental filtering types were, respectively, 20.22, 7.87, and 4.53 times more prevalent among help seekers, revealing a distinct pattern of thought and language among individuals affected by suicidality. Conclusions: Our results raise the prospect of improving the effectiveness of online therapeutic interventions that target cognitive distortions through lexical analysis that detects the cognitive and lexical markers of suicidality.
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Oxygen extraction fraction is differentially associated with pathological biomarkers in Alzheimer’s disease and non-Alzheimer’s dementias
IntroductionWe aimed to understand the pathophysiological differences between 16 Alzheimer’s disease (AD) and 15 non-AD dementia patients by quantifying oxygen extraction fraction (OEF) in cortical (CGM) and deep gray matter (DGM) regions.MethodsTo achieve this, we used a novel MRI-based OEF mapping technique, QQ, which estimates OEF from routine multi-echo gradient echo data. Multiple linear regression analyses were performed to compare the associations between OEF and white matter hyperintensities (WMH) or cognitive impairment (measured by Montreal Cognitive Assessment (MoCA) between the two groups.ResultsIn the AD and non-AD group, OEF showed negative associations with WMH in DGM and positive associations with MoCA in DGM and CGM.DiscussionOur study suggests that QQ is a promising tool for differentiating between AD and non-AD dementias, by revealing abnormalities in tissue oxygen usage and their relationships to microvascular changes and cognitive impairment.
Quality, reliability, and transparency of late-life depression videos on Chinese social media: a cross-sectional study of Douyin, Rednotes, and BiliBili
BackgroundLate-life depression is common in older adults and is often under-recognized. Short-video platforms have become a major source of mental health information. However, content quality and transparency remain uncertain.MethodsWe conducted a cross-sectional assessment of highly viewed videos on late-life depression on three Chinese platforms. We searched each platform using the keyword in Chinese “Late-life depression”. We selected the top 200 videos by view count on Douyin, Rednotes (Xiaohongshu), and BiliBili. After exclusions, 562 videos were included (Douyin, n=188; Rednotes, n=188; BiliBili, n=186). Two medically trained raters scored videos using the Global Quality Score (GQS), modified DISCERN (mDISCERN), and JAMA benchmark criteria. We also coded content categories and creator types. We assessed platform differences using non-parametric tests. We examined associations between a limited engagement proxy, defined as the comment-to-view ratio, and quality scores using Spearman correlation.ResultsVideo duration differed across platforms (p<0.001). Engagement indicators were higher on Douyin and Rednotes than on BiliBili. Symptoms were the most common topic on all platforms. Prevention and intervention ranked second on Douyin and Rednotes. On BiliBili, causes and case-based analysis were also common. Overall quality was moderate. Mean GQS ranged from 2.96 to 3.05. Transparency was limited. Mean JAMA ranged from 1.91 to 2.04. Reliability was slightly higher on BiliBili based on mDISCERN. Creator type was strongly associated with scores. Expert and institutional videos scored higher than general and marketing-oriented accounts. Correlations between visible audience interaction and quality were weak.ConclusionHighly viewed late-life depression videos on major Chinese platforms show moderate quality and limited transparency. Exposure does not reliably signal higher-quality information. Platforms and health authorities should strengthen source disclosure and promote evidence-based content from qualified creators.
Real-world effectiveness of medication-assisted treatment and psychotherapy for opioid use disorder: a national multi–health care organization analysis
BackgroundHarm reduction strategies for opioid use disorder (OUD) emphasize pragmatic, evidence-based approaches that reduce overdose risk, relapse, and other adverse outcomes without requiring abstinence. Medication for opioid use disorder (MOUD) and structured psychotherapy represent core harm-reduction modalities, yet their real-world comparative effectiveness, alone and in combination, remains underexplored at scale.MethodsA retrospective cohort study was conducted using the TriNetX Research Network, comprising de-identified electronic health records from 112 U.S. health systems. 18,047 adults aged 18–45 were identified with a diagnosis of opioid dependence (ICD-10 F11.20) between 2016 and 2025. Subjects were assigned to eight mutually exclusive treatment cohorts: no treatment (Cohort 1); buprenorphine alone (Cohort 2); methadone alone (Cohort 3); psychotherapy alone (30 minutes (Cohort 4), 45 minutes (Cohort 5), or 60 minutes (Cohort 6)); buprenorphine + psychotherapy (Cohort 7); and methadone + psychotherapy (Cohort 8), with combination treatments defined within a ±30-day window. Cox proportional hazards models estimated adjusted hazard ratios (aHRs) for remission (F11.21, F11.11) within 12 months.ResultsBuprenorphine (aHR = 2.33; 95% CI: 1.85–2.94), methadone (aHR = 2.50; 95% CI: 2.05–3.04), and psychotherapy (30 min: aHR = 2.18; 45 min: aHR = 2.38) were each independently associated with significantly higher remission compared to no treatment. The combination of buprenorphine + psychotherapy yielded the strongest effect (aHR = 5.26; 95% CI: 2.68–10.32). Anxiety diagnoses and gabapentinoid prescriptions were positively associated with remission; benzodiazepine co-prescription was negatively associated.ConclusionsIn this first national-scale, multi–health-care-organization analysis, both pharmacologic and psychosocial harm-reduction interventions were independently associated with improved OUD remission, with additive benefit when integrated. These findings underscore the value of embedding comprehensive, multimodal harm-reduction services within routine care and support policies promoting equitable access to both MOUD and behavioral health supports across diverse health systems.
Scoping review of therapeutic approaches among individuals with secondary exercise addiction
Secondary exercise addiction shows high comorbidity with eating and body image disorders. Despite its substantial impact on physical and mental health and daily functioning, evidence on effective therapeutic interventions remains limited. The aim of this scoping review was to identify and describe therapeutic interventions applied to adult individuals with secondary exercise addiction. This review followed the PRISMA Sc-R guidelines and covered the years 2002–2024. Ultimately, five studies were included (four randomized controlled trials and one quasi-experimental study). Three studies applied psychotherapeutic interventions based on cognitive-behavioral models (Cognitive Behavioral Therapy, Lifestyle, Exercise, Attitudes, and Relationships Program, Physical Exercise and Dietary Therapy), while two integrated physical or nutritional components. A secondary analysis published in 2024 based on the LEAP trial dataset was identified but not treated as an independent study to avoid duplication. EBSCOhost, Web of Science, PubMed, and Google Scholar were searched from January to May 2025 using terms related to exercise addiction, exercise abuse, psychotherapy, intervention, and treatment. English-language studies were eligible if they described an intervention with at least one treated group with pre- and post-test measures; the participants of the study were adult patients suffering from eating disorders and exercise addiction (the therapy programs involved one inpatient and four outpatient treatments) and therapeutic intervention was carried out with outcomes based on exercise addiction level data. Four out of five included studies reported improvements in variables related to compulsivity, although these did not always imply a reduction in the amount of exercise, indicating that qualitative changes may be more relevant. Longer interventions showed more consistent effects, but even brief treatments generated positive changes in non-clinical populations. The examination of the research revealed a gap in studies addressing interventions for those with secondary exercise addiction, especially highlighting the need for randomized controlled trials (RCTs) with proper randomization methods.
Human Ventral Tegmental Local Field Potentials in Treatment-Resistant Depression and Obsessive-Compulsive Disorder
The ventral tegmental area (VTA) is a key node within the limbic circuitry. Through dense dopaminergic, glutamatergic, and GABAergic projections, the VTA forms reciprocal loops with prefrontal and limbic cortices that are consistently implicated in major depressive disorder (MDD) and obsessive–compulsive disorder (OCD) (1,2). Decades of animal research have established the VTA as a central hub for motivational drive and reward prediction error signaling (3,4). Despite its presumed critical role in mental disorders, direct electrophysiological recordings from the human VTA have so far remained absent.
Sociodemographic factors, anxiety and attitudes toward generative artificial intelligence among nurses
BackgroundAlthough generative artificial intelligence offers substantial potential benefits in healthcare, negative attitudes and elevated anxiety among nurses may hinder its effective integration into clinical practice. Evidence regarding the psychological impact of generative artificial intelligence on nurses remains limited.ObjectiveThis study examined the relationships among sociodemographic characteristics, anxiety, and attitudes toward generative artificial intelligence among nurses.MethodsA cross-sectional correlational design was employed. Data were collected from 312 hospital nurses using online questionnaires assessing sociodemographic characteristics, attitudes toward artificial intelligence, and artificial intelligence-related anxiety. Data were analyzed using IBM Statistical Package for the Social Sciences (SPSS) Statistics software version 28.ResultsHigher levels of artificial intelligence-related anxiety were associated with less favorable attitudes toward artificial intelligence. Sociodemographic characteristics and anxiety scores collectively explained 49.4% of the total variance in attitudes toward artificial intelligence. Gender, experience with artificial intelligence, use of artificial intelligence in nursing care, awareness of artificial intelligence applications in healthcare, hours spent on the internet, age, and professional experience accounted for 24.7% of the variance in negative attitudes toward generative artificial intelligence.ConclusionAnxiety and experiential factors play a central role in shaping nurses’ attitudes toward generative artificial intelligence. Increasing nurses’ exposure to and awareness of artificial intelligence in nursing practice may reduce anxiety and support its acceptance and appropriate use.
Multimodal Depression Detection Through Conversational Interactions with an Emotion-Aware Social Robot: Pilot Study
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.
Comparing Perceptions of ChatGPT Use in Health Attitude Contexts Among Users and Nonusers: Cross-Sectional Study
Background: In light of the growing use of artificial intelligence (AI) in health care, individuals’ access to and use of health information are transforming. ChatGPT, an AI chatbot, provides immediate responses to health queries, with the potential to influence health-related attitudes, thereby raising concerns related to privacy, reliability, and security. Objective: This study aimed to investigate the perceived usefulness, risks, anxiety, and social influence of ChatGPT on health attitudes among users and nonusers in Saudi Arabia. Methods: A cross-sectional study was conducted using an online survey based on a validated tool. In total, 337 participants aged 18 years and older responded to questions assessing their perceptions of ChatGPT on health-related attitudes. Results: Data showed that 76.1% (194/255) of the respondents used ChatGPT, with the majority being younger and more highly educated. The main uses for health-related purposes were health education (43/194, 22.2%) and physical activity guidance (31/194, 16%). The analysis showed that users considered ChatGPT useful for health-related decisions, with 45.9% (89/194) finding it easy to learn and use, but concerns about privacy (106/194, 54.7%) and reliability (87/194, 44.9%) remained. Among nonusers, security risks (39/61, 63.9%) were the major barrier to using AI-based tools for health purposes, and 68.9% (42/61) found such tools attractive and engaging. There were no statistically significant differences between users and nonusers across all examined sociodemographic characteristics (>.05). Conclusions: The study established the potential of ChatGPT in improving health decision-making and revealed cultural, privacy, and trust issues that may affect its implementation. These findings underscore the importance of strengthening the security of AI-based applications to enhance public acceptability of related health policies and to support the safe integration of tools such as ChatGPT into the health care system.
