Background and objectiveThe rapid popularization of the Internet among Chinese adolescents has resulted in the emergence of a public major concern known as Internet Gaming Disorder (IGD). As demonstrated by previous studies, an association has been demonstrated among emotional distress, gaming motives and IGD. Nevertheless, the specific pathways connecting these constructs remain to be elucidated. The present study aims to explore the network structure characterizing the interactions among these three constructs and to identify potential targets for psychological interventions.MethodsThis was a cross-sectional survey conducted in city of Hangzhou. A total of 3,795 middle school students were included in the analysis. The 21-item Depression Anxiety Stress Scale (DASS-21), the Motives for Online Gaming Questionnaire (MOGQ), and the Chinese version of the Ten-Item Internet Gaming Disorder Test (IGDT-10) were used to assess emotional distress, gaming motives and IGD symptoms, respectively. Network analyses were performed using R4.5.1 software to explore the interrelationships among emotional distress, gaming motives and IGD symptoms, and identify the core symptoms and bridge symptoms.ResultsIn the depression combined network model, the presence of bridge symptoms was indicated by no initiative (D2), gaming for escape or mood relief (IGD8) and fantasy motive (fan). In anxiety combined network model, the bridge symptoms included coping motive(cop), gaming for escape or mood relief (IGD8), withdrawal (IGD2), mouth dryness (A1), and fear of embarrassment (A4). The bridge symptoms in the stress combined network model were gaming for escape or mood relief (IGD8), difficulty winding down (S1), withdrawal (IGD2), nervous energy expenditure (S3), and coping motive (cop).ConclusionThe present study explored complex network structure among psychological distress, gaming motivation, and IGD. and suggested fantasy and coping motive as bridges connecting psychological distress and IGD. Besides, our research identified no initiative, mouth dryness, difficulty winding down, fear of embarrassment, and nervous energy expenditure as the best targets for intervention to reduce IGD.
STAT+: FDA to speed up review of three psychedelics as mental health treatments
The Food and Drug Administration will accelerate its review of psychedelic drugs developed by Compass Pathways, the Usona Institute, and Transcend Therapeutics for mental health disorders, as part of the Trump administration’s plan to boost access to the controversial yet promising medications.
The agency will grant priority review vouchers specifically to Compass’ psilocybin product for treatment-resistant depression, Usona’s similar medicine for major depressive disorder, and an MDMA-like treatment for post-traumatic stress disorder from Transcend.
The FDA identified the medications receiving the vouchers, but not the companies developing them. Compass, Usona, and Transcend confirmed they received vouchers.
<![CDATA[Definium’s CMO says FDA talks stay aligned with their plans for advancing DT120 ODT for the treatment of depression, anxiety, and now PTSD.]]>
<![CDATA[In this CME article, learn how depression hides in organ failure, transplants, neurologic disease, and hormonal shifts—plus screening tips and safer treatment choices.]]>
Impact of extremely low frequency electromagnetic fields exposure on sleep quality and mental health in a Tunisian power plant: a cross-sectional study
IntroductionExtremely low-frequency electromagnetic fields (ELF-EMFs) are ubiquitous in our daily life. They may have an impact not only on physical health but also on mental health.ObjectivesTo assess the impact of occupational exposure to the ELF-EMFs on sleep quality, depression, anxiety and stress among workers at the Tunisian Electricity and Gas Company (TEGC).MethodsThis was a cross-sectional study. The study population included two groups: an exposed group (EG), consisting of power plant employees, and a non-exposed group (NEG), consisting of administrative workers. Exposure to ELF-EMFs was assessed via spot measurements using a magnetometer. Sleep quality, depression, anxiety and stress were assessed by the French versions of the Pittsburgh Sleep Quality Index (PSQI) and the Depression, Anxiety and Stress Scale (DASS-21).ResultsSeventy-seven participants in the EG and 88 participants in the NEG were included in the study. The median value of the ELF-EMFs was 5.86 μT at the power plant [min 0.1, max 40.34 μT]. According to the PSQI global score, 64.9% of the EG had poor sleep quality versus 29.5% of the NEG. Depression was registered in 24.7% of EG and in 3.4% of NEG. Anxiety was noted in 23.4% of the EG and in none of the NEG. Stress was found in 46.8% of the EG and none of the NEG. After multivariate analysis, ELF-EMF exposure was significantly associated with poor sleep quality and depression.ConclusionThe present study revealed that ELF-EMFs can affect sleep and mental health. Further studies are needed to explain the mechanism involved.
Psychedelics get a boost from the White House
President Trump recently signed an executive order which aims to increase access to psychedelic drug treatments. He was joined at the signing by podcaster Joe Rogan, who said he’ ha’d messaged the president about research on the psychedelic ibogaine.
In this week’s STATus Report, host Alex Hogan chats with STAT Washington correspondent Daniel Payne about what the executive order does and doesn’t do. Hogan also looks at why ibogaine, and psychedelic drugs more broadly, are increasingly being taken seriously for stubbornly hard-to-treat conditions like addiction, depression, and PTSD.
Cyclothymic and anxious affective temperament in perinatal depression: findings from an exploratory cross-sectional study
IntroductionThe perinatal period represents a vulnerable period in which women may experience high psychic distress due to psychological, biological and social changes. The prevalence of perinatal depression (PND) is estimated around 15%-20% during pregnancy and 16%-18% after childbirth. Although several risk factors have been investigated in the PND development, few studies explored the role of affective temperaments, well known to exert a role in any mood disorders. The aim of our study was to explore which is the most represented affective temperamental profile in PND as well as which is its role in the development and severity of depressive symptoms during perinatal period.MethodsAll pregnant women admitted at the Perinatal Mental Health Outpatient Service, Unit of Clinical Psychiatry, University Hospital of Marche, Polytechnic University of Marche, Ancona, Italy, between April 2021 and July 2025, were screened for PND through Edinburgh Postnatal Depression Scale (EPDS) and a semi-structured clinical interview (SCID-5-CV). Temperament Evaluation of Memphis, Pisa, Paris and San Diego (TEMPS-M) was administered to all pregnant women. ResultsThe PND prevalence was 33.1%. PND was significantly associated with higher cyclothymic (B = 0.356, p = 0.001) and anxious TEMPS-M scores (B = 0.247, p = 0.026) and a positive psychiatric history (B = 5.245, p < 0.001) (R = 0.6, R2 = 0.36, F(3,129) = 24.189, p < 0.001). Logistic regression indicated that cyclothymic (Exp(B)=1.118, p=0.008), hyperthymic (Exp(B)=0.911, p=0.049), anxious temperaments (Exp(B)=1.109, p=0.029), presence of medical comorbidities (Exp(B)=0.224, p=0.003) and psychiatric history (Exp(B)=5.144, p=0.001) were independent predictors of PND.DiscussionAffective temperaments, particularly cyclothymic and anxious profiles, and prior psychiatric history are predictors of perinatal depression. Incorporating temperament assessment alongside standard screening tools such as the EPDS may improve early identification of women at risk, supporting tailored preventive and therapeutic strategies.
Growth, Empowerment, and Mindfulness (GEM): A Mindfulness-based Intervention to Address Mental Health in Young Adults With Early Life Adversity
Conditions: Anxiety; Depression
Interventions: Behavioral: Growth, Empowerment, and Mindfulness (GEM)
Sponsors: Brown University
Recruiting
Interventions: Behavioral: Growth, Empowerment, and Mindfulness (GEM)
Sponsors: Brown University
Recruiting
Effectiveness of a Fully Automated Mobile Therapeutic Versus a General Chatbot in Reducing Depression and Anxiety and Improving Well-Being: Feasibility Randomized Controlled Trial
Background: Given the increasing prevalence of depression and anxiety disorders and enduring barriers to care, there is a critical need for alternative treatment options. Generative artificial intelligence (AI) chatbots show promise for increasing access to mental health care, though more direct research is needed to establish their efficacy. Objective: This pilot study aimed to test the efficacy of a generative mental health chatbot rooted in solution-focused therapy compared to the general-purpose ChatGPT and an assessment-only control (AOC) group on depression, anxiety, and well-being. Methods: A total of 185 English-speaking adults were recruited online and randomly assigned to one of three groups: AI therapy, ChatGPT, or AOC. Of these, 147 eligible participants filled out a pretreatment assessment. Over a 3-week period, the AI therapy group (n=44) was instructed to complete 3 structured, fully automated app-based sessions per week (9 total), while the ChatGPT group (n=60) was instructed to engage in 9 unstructured conversations with ChatGPT (GPT-4o–based models). The control group (n=43) received no intervention. In the AI therapy group, 39% (n=17) completed all sessions, as did 62% (n=38) of those in the ChatGPT group. Primary outcome measures, self-assessed online at baseline and postintervention, included the Patient Health Questionnaire-9 (PHQ-9), Overall Depression Severity and Impairment Scale (ODSIS) (depression), 7-item Generalized Anxiety Disorder Scale (anxiety), and World Health Organization Well-Being Index (5-item version) (well-being). Linear mixed effects models were used for data analysis. Results: Compared to AOC, both the AI therapy group (=−0.47; =.01) and the ChatGPT group (=−0.44; =.02) demonstrated significant reductions in depression scores measured by PHQ-9. The AI therapy group showed nonsignificant reductions in anxiety (=−0.37; =.11) and ODSIS depression scores (=−0.25; =.22) and an increase in well-being (=0.12; =.53) compared to AOC. Similarly, a nonsignificant reduction in anxiety (=−0.27; =.22) and ODSIS depression scores (=−0.12; =.53) and an increase in well-being (=0.20; =.29) were observed in the ChatGPT group compared to AOC. The AI therapy group did not significantly outperform the ChatGPT group on any outcomes (PHQ-9: =−0.19; =0.03; =.87; 7-item Generalized Anxiety Disorder Scale: =−0.57; =−0.11; =.62; ODSIS: =−0.59; =−0.13; =.50; and WHO: =−0.38; =−0.07; =.69). Conclusions: Both the structured generative AI chatbot and ChatGPT showed a significant reduction in depression scores compared to the control group. No significant effects were observed across other outcomes, although descriptive trends indicated improvements in anxiety. While the AI therapy group showed descriptively better outcomes for depression and anxiety, differences between groups were not significant. A larger sample and longer intervention may be needed for the emerging trends to yield clinically meaningful effect sizes. Trial Registration: OSF Registries osf.io/r76ef;
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<![CDATA[Depression care speeds up when clinicians measure symptoms, adjust doses fast, and tackle side effects—helping patients reach remission sooner.]]>