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Adults with intellectual disability face multiple barriers to digital participation, yet limited research has examined how motivational and demographic factors interact to influence internet self-efficacy. Structured interviews were conducted with 96 adults with mild or moderate intellectual disability and 49 adults without intellectual disability across urban and regional locations in Australia. Internet self-efficacy, attitudes and behavioural beliefs were assessed using adapted measures informed by the Theory of Planned Behaviour. Participants with intellectual disability had significantly lower internet self-efficacy than their peers, particularly those who were older, lived in regional areas or lacked professional support. Motivational variables predicted a small proportion of self-efficacy variance for participants with intellectual disability and a larger proportion for the comparison group. Findings suggest digital inequality among people with intellectual disability is shaped by contextual and motivational factors. Support programmes informed by behavioural theory may help increase digital confidence and online engagement. People with intellectual disability who are older or who live in regional areas report lower internet confidence and more limited internet use. Having help from a professional IT support person is linked to feeling more confident about using the internet. Positive beliefs about the internet are related to greater confidence and may help to guide support programmes. People with intellectual disability face digital inequality due to life circumstances and low confidence. Support programmes that build motivation and skills could help increase safe and inclusive internet use.

Depression is common in healthcare students, severely affecting their academic performance and daily life. Problematic internet use and insomnia are related to depression, yet underlying mechanisms remain unclear in Macau, China. This study aims to explore associations among problematic internet use, insomnia, and depression, and to examine the mediating role of insomnia. This cross-sectional study was implemented during January and February 2025. Two hundred and sixty-four healthcare students in Macau completed self-report questionnaires on sociodemographic variables, problematic internet use, insomnia, and depression using an online platform. Model 4 of the PROCESS macro was applied to explore the mediating effect. In this study, 40.2%, 18.6%, 6.4% and 3.4% of Macau healthcare students sustained mild, moderate, moderately severe, and severe depression, respectively. Problematic internet use was positively correlated with insomnia (r&#x2009;=&#x2009;0.363, p&#x2009;<&#x2009;0.001) and depression (r&#x2009;=&#x2009;0.484, p&#x2009;<&#x2009;0.001). Insomnia and depression were positively correlated (r&#x2009;=&#x2009;0.666, p&#x2009;<&#x2009;0.001). Insomnia served as a partial mediator between problematic internet use and depression, accounting for 35.6% of the total effect (95% Boot CI: 0.027-0.082). Depression is prevalent among Macau healthcare students. Improving sleep quality may reduce depression in students with problematic internet use. Clinical focus should shift toward assessing and treating insomnia as a core component of care for students with problematic internet use.

Health care workers face numerous occupational stressors that place them at heightened risk for burnout and poor mental health. Internet-delivered interventions have shown promise in reducing stress and related symptoms, yet adherence is often low, and users do not complete programs. Abbreviated interventions may help address engagement barriers such as high workload, limited time, and varying user preferences. There is a need to evaluate brief, accessible formats of internet-delivered programs for this population. This study aimed to examine the initial outcomes, usability, and acceptability of a 4-week abbreviated internet-delivered stress recovery intervention for health care workers. Specifically, it evaluated changes in stress recovery, perceived stress, depression and anxiety symptoms, and psychological well-being. The study also sought to understand participants' experiences with the brief format to determine whether it meets their needs. This single-arm pre-post study examined a 4-week abbreviated version of the online guided cognitive behavioral therapy-based stress recovery program FOREST among self-enrolled health care workers recruited through professional networks (N=52; mean age 39.31, SD 11.31 years; 49/52, 94.2% women). Outcomes included stress recovery (the Recovery Experience Questionnaire), perceived stress (the Perceived Stress Scale-4), depression and anxiety symptoms (the Patient Health Questionnaire-4), psychological well-being (the World Health Organization Well-being Index), and usability and acceptability ratings. We found that after the abbreviated version of the FOREST intervention participants showed moderate improvements in stress recovery (d=0.54, 95% CI 0.25-0.83); reductions in stress (d=-0.43, 95% CI -0.72 to -0.14), anxiety and depression symptoms (d=-0.51, 95% CI -0.80 to -0.22); and increase in psychological well-being (d=0.39, 95% CI 0.08-0.70). The majority (37/52, 71.2%) accessed all 6 modules. Users reported high satisfaction with the abbreviated program. While preliminary and limited by the pre-post design, these findings indicate that abbreviated internet-based stress recovery programs are a promising and practical tool for supporting the mental health of health care workers. Future research should examine the long-term effects, compare the abbreviated and standard versions, and explore implementation in routine practice.

Internet-based cognitive behaviour therapy (ICBT) is now a well-established treatment modality supported by numerous controlled trials. Platforms for delivering these interventions have become critical infrastructure for both research and clinical practice, yet platform descriptions remain rare in the literature. In this article, we provide an updated description of the Iterapi platform, originally reported by Vlaescu et al., 2016. Over the past decade, Iterapi has been used in more than 100 published research studies, spanning over 35 clinical conditions (including depression, anxiety disorders, insomnia, tinnitus, loneliness, and climate anxiety) across at least 20 countries on five continents. We describe the platform's core architecture and functionality, including treatment modules, questionnaire systems, communication tools, and automation features. We also report on significant developments since 2016, including enhanced security and regulatory compliance (GDPR, data protection impact assessments), new features for ecological momentary assessment, electronic identification and incorporation of artificial intelligence. Finally, we outline future directions including mobile application development, digital phenotyping, just-in-time adaptive interventions, and the role of emerging regulatory frameworks. This update is intended as a practical resource for researchers and clinicians considering platform-based delivery of internet interventions.

Generalized anxiety disorder (GAD) is characterized by stress-anxiety reinforcement cycles. Evidence for brief, self-guided, internet-based stress management as an adjunct to pharmacotherapy remains limited. This study aimed to evaluate the efficacy of an 8-week self-guided, internet-based stress management program (iSM) incorporating mindfulness techniques as an adjunct to treatment as usual (TAU) in adults with GAD and to examine individual predictors of response and symptom dynamics. A single-blind, parallel-group, superiority randomized controlled trial was conducted at Tongji Hospital, Wuhan, China. A total of 140 adults with GAD were randomly assigned to iSM+TAU (n=73) or TAU (n=67). Outcome assessors were blinded to group allocation. The iSM intervention consisted of 8 weekly self-guided online modules integrating mindfulness-based training and Baduanjin-based stretching exercises. TAU comprised routine pharmacotherapy. Primary outcomes were posttreatment changes in anxiety and depressive symptoms. Secondary outcomes included sleep quality, somatic symptoms, social functioning, mindfulness, rumination, and perceived stress. Exploratory cross-lagged panel network (CLPN) and random-intercept cross-lagged panel modeling (RI-CLPM) analyses were used to examine temporal symptom dynamics. All 140 randomized participants were included in the intention-to-treat analysis. Compared with TAU, iSM+TAU showed greater reductions at posttreatment in anxiety (Cohen d=-0.277, 95% CI -0.521 to -0.033) and depressive symptoms (Cohen d=-0.309, 95% CI -0.592 to -0.026). Significant between-group differences were also observed in somatic symptoms (Cohen d=-0.340, 95% CI -0.604 to -0.077), state anxiety (Cohen d=-0.537, 95% CI -0.849 to -0.224), mindfulness (Cohen d=0.666, 95% CI 0.327-1.006), rumination (Cohen d=-0.344, 95% CI -0.626 to -0.062), and perceived stress (Cohen d=-0.429, 95% CI -0.725 to -0.133), but not in sleep quality or social functioning. No serious adverse events were reported. Median session completion was 7 of 8. In exploratory analyses, higher baseline acting with awareness predicted greater treatment response (standardized &#x3b2;=0.167; 95% CI 0.031-0.335), whereas higher trait anxiety predicted poorer outcomes (standardized &#x3b2;=-0.150; 95% CI -0.234 to -0.002). CLPN and RI-CLPM identified 2 key within-person pathways, a bidirectional association between perceived stress (mean &#x3b2;=0.219; P<.001) and state anxiety (mean &#x3b2;=0.165; P=.02), and a unidirectional effect of mindfulness on subsequent anxiety reduction (mean &#x3b2;=-0.285; P<.001). To our knowledge, this is the first trial to evaluate a brief, self-guided, culturally adapted digital mindfulness intervention as an adjunct to pharmacotherapy in Chinese adults with GAD. The intervention showed clinically meaningful benefits and practical potential as a scalable, resource-efficient approach. Unlike prior studies focused mainly on symptom outcomes alone, this trial combined a randomized design with CLPN and RI-CLPM analyses to provide preliminary insight into symptom change processes over time, adding a more process-oriented analytic perspective that may inform future intervention refinement.

Insomnia and poor sleep quality are common sleep-related problems in adults and are associated with impaired quality of life, psychological distress, and increased physical health burden. Although cognitive behavioral therapy for insomnia (CBT-I) is recommended as the first-line non-pharmacological treatment for chronic insomnia, internet-based Acceptance and Commitment Therapy (iACT) has increasingly been explored as an alternative or complementary digital psychological intervention for improving sleep-related outcomes. To systematically evaluate the effects of iACT on sleep quality and insomnia-related outcomes in adults. PubMed, Web of Science, Cochrane Library, CINAHL, and Embase were systematically searched from inception to December 6, 2025. We included randomized controlled trials evaluating internet-based Acceptance and Commitment Therapy in adults that reported validated outcomes for sleep quality or insomnia. Standardized mean differences were pooled using a random-effects model, and risk of bias was assessed using the Cochrane Risk of Bias 2 tool. Eight randomized controlled trials met the inclusion criteria. Five studies were included in the meta-analysis of sleep quality outcomes and three in the meta-analysis of insomnia outcomes. Compared with passive control conditions, iACT was associated with better sleep quality outcomes (SMD&#x2009;=&#x2009;-&#x2009;0.92, 95% CI&#x2009;-&#x2009;1.45 to -&#x2009;0.38; p&#x2009;<&#x2009;0.001) and reduced insomnia symptoms (SMD&#x2009;=&#x2009;-&#x2009;0.40, 95% CI&#x2009;-&#x2009;0.64 to -&#x2009;0.16; p&#x2009;<&#x2009;0.001). Exploratory subgroup analyses suggested possible variation in sleep quality outcomes according to intervention duration, participant characteristics, and delivery format, although these findings should be interpreted cautiously. iACT may have potential to improve sleep quality and insomnia-related outcomes in adults. However, the available evidence remains limited by heterogeneous populations, intervention characteristics, delivery formats, and overall low certainty of evidence. Therefore, the findings should be considered preliminary rather than definitive.

A growing body of research links problematic internet use (PIU) in youth to anxiety, depression, and stress, yet the dynamic interactions between these negative emotions, heterogeneous PIU trajectories, and their dimensional drivers remain unclear. To address this gap, a two-wave longitudinal study of 5108 Chinese students (Mage&#xa0;=&#xa0;18.28, SD&#xa0;=&#xa0;0.84; 67.6% female) identified distinct PIU trajectories based on empirical longitudinal data and used cross-sectional/cross-lagged network analyses to explore these dynamics. Cross-sectional findings showed that the Remitted group had lower negative emotion levels and inter-network connection strength over time, whereas the Incident group exhibited the reverse; specific anxiety symptoms (e.g.,&#xa0;"restlessness" in Incident PIU) also had the strongest links to overall emotional network cohesion. Cross-lagged analyses further indicated that in the Persistent/Incident groups, stress symptoms acted as the primary trigger for subsequent negative emotions. Moreover, their cross-lagged emotional networks had significantly higher global strength than those of the Remitted/Persistent Non-PIU groups. These findings suggest that emotional network features vary by PIU trajectory group and that stress symptoms may be the core driver of emotional deterioration in the Persistent/Incident groups. The study emphasises intervention priorities: prioritise stress management for Persistent/Incident groups to prevent emotional deterioration, and monitor residual emotional connections in Remitted groups to avoid recurrence.

Healthcare Internet of Medical Things (IoMT) environments require intrusion detection systems that are accurate, safe, proportionate, and auditable. Conventional intrusion detection metrics such as accuracy and F1-score quantify classification performance but do not indicate whether automated response actions are clinically safe or operationally acceptable. This study proposes a governance-aware intrusion response framework for healthcare IoMT systems. The framework integrates a Random Forest flow-based detector, a Deep Q-Network triage agent, and an ethical rule engine informed by the NIST AI Risk Management Framework. The detector provides probabilistic and uncertainty-aware evidence, the triage agent selects one of four response actions, and the rule engine constrains unsafe actions through explicit governance rules, fallback decisions, and audit logs. The framework was evaluated using CIC-IoMT 2024 for in-domain assessment, a stratified CSE-CIC-IDS2018 sample for domain-shift stress testing, simulated Clinical Load Index sensitivity analysis, independent decision-quality metrics, and temporal streaming replay. In-domain DQN triage achieved a weighted F1-score of 0.978. Governance reduced benign blocking from 25.3% to 17.3%, showing that rule oversight reduced one form of potentially disruptive automated response. Corrected cross-domain evaluation showed substantial performance degradation, with CAS retention scores of 0.282 for the Random Forest baseline and 0.341 for the DQN triage model. In temporal streaming replay, performance declined after the transition to shifted traffic; however, the governed DQN produced lower mean decision harm cost than the unconstrained DQN under shifted replay windows, reducing harm cost from 0.502 to 0.434. These findings suggest that ethical rule oversight can improve selected decision-safety outcomes under controlled, uncertain, and shifted conditions. However, the results represent offline simulation evidence and should not be interpreted as validation for live hospital deployment.

In the modern era, Software-Defined Internet of Things (SD-IoT) networks struggle with various challenges that include heterogeneous traffic, high-throughput limitations and non-recurrent or non-stationary attacks. Therefore, to address these gaps, a unified and deployable IDS framework, namely Robust Adaptive Intrusion Detection Using Invariant State (RADIUS) is designed. In particular, the proposed RADIUS framework utilizes a Temporal Convolution Encoder (TCE) that includes dilated and causal convolutions, which assist in constructing efficient traffic embeddings. Subsequently, an Adaptive State Transition Module (ASTM) is incorporated, which identifies latent state transitions that demonstrate interpretable and adaptive detections. Further, training the RADIUS framework uses dual-objective optimization, which minimizes both prediction accuracy and requires environment-invariance and sensitivity to rare events. Thus, both anomaly scores and classification calibration are attained through Extreme Value Theory (EVT) using conformal bounds. Hence, the proposed RADIUS framework achieves 99.8% accuracy, 99.5% precision, 99.8% recall, and 99.7% F1-score consistently, which outperforms the existing Extended Wrapper Approach (EWA) model. The EWA, a hybrid of deep learning and feature optimization, offers a favorable balance between detection accuracy and deployment efficiency in resource-constrained SD-IoT environments and is therefore used as the primary quantitative baseline for ablation comparisons.

This study investigates the relationships between cybervictimization, cyberbullying, Internet Gaming Disorder (IGD), depression, and Attention-Deficit/Hyperactivity Disorder (ADHD) symptoms in adolescents with a history of cybervictimization. A clinical sample of 89 adolescents aged 12-17 years was assessed using standardized diagnostic interviews and self-report scales. Correlation and regression analyses revealed that depression severity was independently associated with cybervictimization, age, and IGD severity. Oppositional defiant disorder (ODD) symptoms were significantly associated with cyberbullying perpetration, whereas cybervictimization was only related to depression severity. Additionally, moderation analyses showed that IGD did not significantly moderate the relationship between cybervictimization and depression or between ODD symptoms and cyberbullying. These findings highlight the complex interplay between psychiatric symptoms and problematic online behaviors. Our results emphasize the importance of routine screening for cyberbullying experiences in adolescents with depression or ADHD. Further longitudinal studies are needed to clarify causal pathways and inform targeted interventions aimed at mitigating the negative mental health outcomes associated with cyberbullying and IGD.

Associations between problematic internet use (PIU) and borderline personality disorder (BPD) have been reported, but the literature is highly heterogeneous, with some studies assessing overall BPD severity and others examining individual traits (e.g., emotional lability, impulsivity, dissociation). This review synthesizes these approaches using the Alternative Model of Personality&#xa0;Disorders (AMPD), examining how BPD-relevant pathological personality traits (Criterion&#xa0;B) and impairments in self and interpersonal functioning (Criterion A) relate to PIU. Following PRISMA 2020 guidelines, we searched PsycINFO, Web of Science, PubMed, Embase, and Scopus for studies examining associations between PIU (e.g., general use, social media, gaming, smartphone use) and personality traits or functioning in adults. Study quality was assessed using the NIH Quality Assessment Tool for Observational Cohort and Cross-Sectional Studies. Thirty studies (N&#xa0;=&#xa0;21,358) met inclusion criteria. Emotional lability and impulsivity showed the most consistent associations with PIU across outcome types. Few studies examined impairments in personality functioning, but available evidence suggests that identity disturbance may predict PIU over time and that interpersonal dysfunction is associated with greater use of online environments for reassurance and emotion regulation. Associations involving dissociation and detachment were relatively weak and less consistent. Studies varied widely in design, measures, and theoretical approach, limiting direct comparison across studies. Despite this heterogeneity, findings indicate that emotional lability, impulsivity, and identity disturbance appear to represent transdiagnostic vulnerabilities that increase risk for PIU and shape patterns of online engagement.

Previous research has shown that perceived social support may play a protective role in internet gaming disorder (IGD) symptoms. However, the potential psychological mechanism underlying this linkage is still unclear. This study aimed to examine a serial mediation model involving perceived social support, psychological distress, intolerance of uncertainty, and IGD symptoms. A two-wave follow-up design was employed in a sample of 478 Chinese adolescents. Results indicated that perceived social support was significantly related to IGD symptoms, and that psychological distress and intolerance of uncertainty serially mediated the association between perceived social support and IGD symptoms. This two-wave longitudinal study highlights the possible key role of alleviating psychological distress and reducing intolerance of uncertainty in reducing the severity of IGD symptoms.

The insular cortex is a pivotal hub for interoception and salience processing, yet subregion-specific circuit abnormalities in Internet Gaming Disorder (IGD) and their molecular correlates remain unclear. Using resting-state fMRI data from 71 IGD patients and 80 healthy controls, we conducted seed-based functional connectivity (FC) analyses across six bilateral insular subregions and applied Allen Human Brain Atlas (AHBA)-based imaging transcriptomics to characterize associated gene-expression patterns. IGD patients showed increased FC between the bilateral dorsal anterior insula (dAI) and paracingulate gyrus, reduced FC between the left dAI and frontal pole, and decreased FC between the bilateral posterior insula and postcentral gyrus. These findings suggest altered salience-network coordination with default-mode and executive-control systems, together with disrupted somatosensory-interoceptive integration. Right dAI-paracingulate FC was positively associated with symptom severity, suggesting clinical relevance of this circuit. Transcriptomic decoding revealed non-random spatial correspondence between right dAI FC abnormalities and AHBA gene-expression profiles. Associated genes were enriched in two molecular contexts: neuronal signal transmission and metabolic homeostasis (Corr+), and neurodevelopment and structural plasticity (Corr-). They further showed enrichment in neuronal and glial cell-type signatures, with the highest overlap ratios during three key developmental windows: early infancy, adolescence, and young adulthood. These findings reveal dissociable, subregion-specific insular circuit abnormalities in IGD, provide a multi-scale mechanistic account linking macroscale dysconnectivity to molecular and cellular substrates, consistent with and extending the triple network model in the context of behavioral addiction, and provide circuit-to-cellular candidate targets for intervention.

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Multi-institutional healthcare Internet of Things (IoT) networks face a core challenge between combined intrusion detection and patient data privacy. Raw traffic records cannot be shared across institutional boundaries, yet local models trained on institution-specific data alone generalize poorly to attack distributions that differ from those practical in real healthcare IoT deployments. Federated Learning (FL) addresses privacy constraints by storing data locally at each institution, but it introduces statistical heterogeneity across institutions. Local data at each institution are non-independent and non-identically distributed due to clinical specialization, protocol diversity, and deployment-scale asymmetry. Standard federated averaging cannot handle this non-IID condition, and detection performance lowers significantly as a result. Existing Federated Intrusion Detection Systems (FIDS) implied that all participating institutions were honest. But this hypothesis cannot hold in real multi-institutional consortia, because Byzantine participants can corrupt the global model by submitting manipulated gradient updates. In this work, a Non-IID-Aware Federated Intrusion Detection System (N-IID-AFIDS) is proposed for multi-institutional healthcare IoT networks and is designed to address both challenges simultaneously. A cluster-weighted aggregation mechanism is used in this N-IID-AFIDS, grouping institutions by distributional similarity through spectral clustering of a Wasserstein-based affinity matrix and applying gradient divergence correction to submitted updates before aggregation. Protocol-aware Deep Sparse Autoencoder (DSAE) adaptation is also part of this model, and it uses local feature normalization based on an institutional protocol mixture and a distribution alignment regularizer. This regularizer operates without raw data exchange across institutions. This work extends practical Byzantine Fault Tolerance (PBFT) consensus from event logging for detection to model update validation. Geometric median-based Byzantine filtering, together with reputation-based participation control, is also added to this model. The proposed model is evaluated on the IoT-Flock and CICIoT2023 datasets across three non-IID severity levels, based on combined skew in quantity, labels, and features. It achieved non-IID detection accuracies of 93.17% on IoT-Flock and 89.84% on CICIoT2023. And this model is the only federated method in the comparison that reports non-IID performance on both datasets simultaneously. It also retains 83.61% accuracy with four Byzantine participants and meets a 16 ms clinical real-time detection constraint, converging in 29-67 rounds, faster than other federated baselines.