Search Results (380)

Background/Objectives: In the age of digitalization, nomophobia has emerged as a relevant issue, especially among university students who utilize smartphones heavily for academic and social purposes. The Stressor–Strain–Outcome (SSO) framework explains the relationship between stressors, strain, and outcomes. Stressors such as nomophobia induce psychological strain. This strain subsequently influences outcomes like mindfulness. Nomophobia has been linked to higher distress, including depression, anxiety, and stress, that can negatively impact students’ focus. However, the mechanisms by which nomophobia impacts mindfulness remain less explored. Hence, this study aims to analyze the mediating effect of distress on the relation between student’s nomophobia and mindfulness. Methods: In this quantitative study, the researcher employed a structured close-ended survey to collect data from 723 students at the University of Ha’il in Saudi Arabia. Nomophobia was measured using the Nomophobia Questionnaire (NMP-Q). The level of distress was measured using the Depression, Anxiety, and Stress scale (DASS-21) Furthermore, the assessment of mindfulness was conducted using the Mindful Attention Awareness Scale (MAAS). Structural equation modeling was utilized to test the hypotheses of this study. Results: The results from PLS-SEM indicate that nomophobia did not directly reduce mindfulness, as its effect was statistically non-significant (β_1 = −0.052, p-value = 0.168). This suggests that nomophobia alone may not weaken focus. However, it significantly increased distress, particularly depression (β_2a = 0.327, p-value < 0.001), anxiety (β_2b = 0.294, p-value < 0.001) and stress (β_2c = 0.259, p-value < 0.001). In plain terms, students with higher nomophobia reported more depression and stress, which in turn reduced mindfulness. Anxiety, however, did not significantly affect mindfulness (β_3b = 0.006, p-value < 0.933), indicating its influence may be negligible or context-specific. Mediation analysis confirmed indirect effects of nomophobia on mindfulness through depression (β_4a = −0.096, p-value < 0.001) and stress (β_4c = −0.045, p-value < 0.020). Together, these mediators explained a substantial portion of the variance in mindfulness. Conclusions: The findings align with the SSO model, indicating that nomophobia acts as a stressor, exacerbating distress, which in turn reduces mindfulness. From a practical perspective, the results highlight the need for comprehensive student support. Universities should integrate digital wellness programs, stress-management resources, and mindfulness training into their services. Limitations and Future Research: The cross-sectional design and convenience sampling restrict causal inference and generalizability. Future studies should employ longitudinal research designs. They should also examine diverse cultural contexts. In addition, researchers should investigate potential mediators such as social support and sleep quality. Full article

Background: The widespread use of social media among children and adolescents has raised increasing concern about its potential impact on mental health. Given the unique neurodevelopmental vulnerabilities during adolescence, understanding how digital platforms influence psychiatric outcomes is critical. Objectives: This narrative review aims to synthesize current evidence on the relationship between social media exposure and key psychiatric symptoms in youth, including depression, anxiety, body image disturbances, suicidality, and emotional dysregulation. Methods: We conducted a comprehensive narrative review of the literature, drawing from longitudinal, cross-sectional, and neuroimaging studies published in peer-reviewed journals. Specific attention was given to moderators (e.g., age, gender, and personality traits) and mediators (e.g., sleep, emotion regulation, and family context) influencing the relationship between social media use and mental health outcomes. Results: Evidence indicates that certain patterns of social media use, especially passive or compulsive engagement, are associated with increased risk of depression, anxiety, body dissatisfaction, and suicidal ideation. Adolescent girls, younger users, and those with low self-esteem or poor emotional regulation are particularly vulnerable. Neuroimaging studies show that social media activates reward-related brain regions, which may reinforce problematic use. Family support and digital literacy appear to mitigate negative effects. Conclusions: Social media use is not uniformly harmful; its psychological impact depends on how, why, and by whom it is used. Multilevel prevention strategies, including media education, parental involvement, and responsible platform design, are essential to support healthy adolescent development in the digital age. Full article

Background: Rapid-eye-movement (REM) sleep disinhibition—shorter REM latency and a larger nightly REM fraction—is a well-described laboratory correlate of major depression. Whether the same pattern can be captured efficiently with consumer wearables in everyday settings remains unclear. We therefore quantified REM latency and proportion of REM sleep out of total sleep duration (labeled “REM sleep coefficient”) from Apple Watch recordings and examined their association with depressive symptoms. Methods: 191 adults wore an Apple Watch for 15 consecutive nights while a custom iOS app streamed raw accelerometry and heart-rate data. Sleep stages were scored with a neural-network model previously validated against polysomnography. REM latency and REM sleep coefficient were averaged per participant. Depressive severity was assessed twice with the Beck Depression Inventory and averaged. Descriptive statistics, normality tests, Spearman correlations, and ordinary-least-squares regressions were performed. Results: Mean ± SD values were BDI 13.52 ± 6.79, REM sleep coefficient 24.05 ± 6.52, and REM latency 103.63 ± 15.44 min. REM latency correlated negatively with BDI (Spearman ρ = −0.673, p < 0.001), whereas REM sleep coefficient correlated positively (ρ = 0.678, p < 0.001). Combined in a bivariate model, the two REM metrics explained 62% of variance in depressive severity. Conclusions: Wearable-derived REM latency and REM proportion jointly capture a large share of depressive-symptom variability, indicating their potential utility as accessible digital biomarkers. Larger longitudinal and interventional studies are needed to determine whether modifying REM architecture can alter the course of depression. Full article

Psychedelic-assisted therapy offers rapid and profound benefits for treatment-resistant psychiatric conditions but remains constrained by the need for intensive, clinic-based administration. Concurrently, advances in digital health technologies have introduced scalable tools. This systematic review evaluates the safety, efficacy, and implementation of digitally enabled psychedelic-assisted therapy delivered in non-clinical settings. A comprehensive search of five databases, registered in PROSPERO (CRD420251020968) and conducted in accordance with PRISMA guidelines, identified six eligible studies including real-world analyses, clinical trials, qualitative research, and case reports, representing a total of 12,731 participants. Most studies examined at-home ketamine or esketamine therapy supported by telehealth platforms or mobile applications. Data were synthesized narratively given the heterogeneity of designs and outcomes. Digital enablement was associated with high response rates (ranging from 56.4% to 62.8% for depression) and rapid symptom improvement, particularly in depression and anxiety. Remote monitoring and digital tools demonstrated feasibility and acceptability, but serious safety concerns—including psychiatric adverse events and one unintentional overdose—underscore the need for strict oversight. Risk of bias was moderate to serious across non-randomized studies, limiting confidence in the findings. One study on virtual ayahuasca rituals highlighted the sociocultural potential and limitations of online practices. Despite promising preliminary findings, the field is marked by low methodological rigor and absence of controlled trials. Digitally supported at-home psychedelic therapy represents a transformative but high-stakes frontier, requiring robust research and safeguards to ensure safe, equitable, and effective implementation. No funding was received for this review, and the authors declare no conflicts of interest. Full article

Cold-air pooling (CAP) and frost risk represent significant climate-related hazards in karstic and agricultural environments, where local topography and surface cover strongly modulate microclimatic conditions. This study focuses on the Mohos sinkhole, Hungary’s cold pole, situated on the Bükk Plateau, to investigate the formation, structure, and persistence of CAPs in a Central European karst depression. High-resolution terrain-based modeling was conducted using UAV-derived digital surface models combined with multiple GIS tools (Sky-View Factor, Wind Exposition Index, Cold Air Flow, and Diurnal Anisotropic Heat). These models were validated and enriched by multi-level temperature measurements and thermal imaging under various synoptic conditions. Results reveal that temperature inversions frequently form during clear, calm nights, leading to extreme near-surface cold accumulation within the sinkhole. Inversions may persist into the day due to topographic shading and density stratification. Vegetation and basin geometry influence radiative and turbulent fluxes, shaping the spatial extent and intensity of cold-air layers. The CAP is interpreted as part of a broader interconnected multi-sinkhole system. This integrated approach offers a transferable, cost-effective framework for terrain-driven frost hazard assessment, with direct relevance to precision agriculture, mesoscale model refinement, and site-specific climate adaptation in mountainous or frost-sensitive regions. Full article

Background: During COVID-19 Arab American and Arab/Middle Eastern (AME) international students faced disproportionately higher psychosocial stressors like other racial populations that may influence their mental health and help-seeking behaviors. Objective: This study examined and compared the prevalence of depression, anxiety, and loneliness among Arab American and AME international college students in addition to how loneliness is associated with perceived mental health need and the use of digital mental health (DMH) services. Methods: Data from the 2022–2024 Healthy Minds Study (HMS) comprised a final analytic sample of 3249 AME students (2662 Arab American; 587 AME international). Logistic regression and generalized structural equation modeling (GSEM) were used to examine associations and mediating pathways. Results: Depression and anxiety were prevalent among Arab American students (44.3% and 40.0%) compared to AME international students (40.9% and 37.0%). Rates of loneliness were similar (56.2% vs. 56.9%). Loneliness was bidirectionally associated with both depression and anxiety (p < 0.001). Perceived need for care emerged as a significant mediator linking psychological symptoms to DMH service use (OR = 2.56, p < 0.001), while loneliness did not directly predict DMH utilization. Only 10% of students reported using DMH services. Conclusions: Mental health disparities among AME students exist. Institutions should prioritize culturally responsive approaches to support the mental health needs of diverse student populations. Full article

Background: Postpartum depression is a common mental health condition that can occur up to one year after childbirth. Recent studies have increasingly used machine learning techniques to predict its occurrence; however, few have comprehensively explored the use of electronic health record data, particularly in tertiary care settings where such data can be fragmented. Methods: We analyzed electronic health record data from 12,284 women who delivered at The Birth Center at Atrium Health Wake Forest Baptist Medical Center, excluding those with missing data or no prenatal or postpartum visits. To define the target variable, we examined different combinations of depression screening tools (Edinburgh Postnatal Depression Scale and Patient Health Questionnaire-9), along with diagnosis codes specific to postpartum depression. We then trained a random forest classification model to predict postpartum depression. Results: The model achieved an area under the receiver operating characteristic curve of 0.733 ± 0.008, which is comparable to previous studies. Adding socioeconomic features from census tract data did not improve predictive performance, underscoring the importance of individual-level data. Incorporating national survey data, such as the Pregnancy Risk Assessment Monitoring System, also did not improve performance due to limited overlap in data features. Interestingly, model performance was slightly lower among Hispanic patients (area under the curve = 0.713 ± 0.040), although this difference was not statistically significant (p = 0.17), likely due to the small sample size. A similar, but statistically significant trend was observed in the larger national survey dataset (area under the curve = 0.699 ± 0.019 for Hispanic patients versus 0.735 ± 0.010 for White patients, p < 0.01). Conclusions: While our model demonstrates moderate predictive capability, further validation and prospective testing are needed before clinical implementation. This work also identified an optimal approach for digital phenotyping postpartum depression in electronic health record data and highlighted key gaps in data quality and completeness. These findings emphasize the importance of robust data when developing predictive models for real-world clinical use. Full article

The COVID-19 pandemic increased mental health issues and heavily affected the academic performance of college students. The study aimed to assess the association of psychological health and behavioral factors with academic performance among undergraduate students during the third year of the COVID-19 pandemic. The study was conducted at a small liberal arts undergraduate college in rural Appalachia. A cross-sectional research design was followed. Data was collected online using Qualtrics in person in July 2021. Participants were selected randomly. World Health Organization-5, Perceived Stress Scale 4, The Pittsburgh Sleep Quality Index, Generalized Anxiety Disorder-7, and Patient Health Questionnaire scales for well-being, stress, sleep quality, anxiety, and depression were used, respectively. Spearman’s correlation, t-test, analysis of variance, and multiple regression were conducted. Overall well-being, perceived stress, generalized anxiety, depression, and sleep quality were found to be significantly different by gender and exercise. The Grade Point Average (GPA) was found to be negatively associated with depression and positively associated with Sleep quality. Findings suggest that students who have scored higher on the depression and sleep quality scale may be affected more during this COVID-19 pandemic in maintaining a good GPA. The findings of this study can help generate hypotheses for further research and guide interventions to address poor academic performance. Full article

Metal oxides are the most widely used material for the resistive switching layer of memristors. Nevertheless, the majority of oxide-based memristors exhibit binary switching, restricting the emulation of neuronal synaptic behaviors. In this paper, the shift from digital-to-analog switching behavior is achieved by inserting an Al₂O₃ layer atop Zr-doped HfO₂. The TiN/Al₂O₃/HZO/W/Si device exhibits long resistance state retention time and consistency. In addition, by applying a varying voltage, the device exhibits up to 20 continuous resistance states, which is highly significant for high-density storage. Upon the application of a programmable pulse signal, the device’s conductance undergoes continual alteration, reflecting long-term potentiation (LTP) and long-term depression (LTD) synaptic characteristics. The conduction mechanism of the device is studied through physical model fitting and schematic diagrams. Full article

Two-dimensional (2D) materials are promising candidates for neuromorphic computing owing to their atomically thin structure and tunable optoelectronic properties. However, achieving controllable synaptic behavior via defect engineering remains challenging. In this work, we introduce X-ray irradiation as a facile strategy to modulate defect states and enhance synaptic plasticity in WSe₂-based optoelectronic synapses. The introduction of selenium vacancies via irradiation significantly improved both electrical and optical responses. Under electrical stimulation, short-term potentiation (STP) exhibited enhanced excitatory postsynaptic current (EPSC) retention exceeding 10%, measured 20 s after the stimulation peak. In addition, the nonlinearity of long-term potentiation (LTP) and long-term depression (LTD) was reduced, and the signal decay time was extended. Under optical stimulation, STP showed more than 4% improvement in EPSC retention at 16 s with similar relaxation enhancement. These effects are attributed to irradiation-induced defect states that facilitate charge carrier trapping and extend signal persistence. Moreover, the reduced nonlinearity in synaptic weight modulation improved the recognition accuracy of handwritten digits in a CrossSim-simulated MNIST task, increasing from 88.5% to 93.75%. This study demonstrates that X-ray irradiation is an effective method for modulating synaptic weights in 2D materials, offering a universal strategy for defect engineering in neuromorphic device applications. Full article

Depression, stress, and anxiety are mental health disorders that are increasingly becoming a huge challenge in the digital age; at the same time, it is critical that they are detected early. Social media is a rich and complex source of emotional expressions that requires intelligent systems that can decode subtle psychological states from natural language. This paper presents RAMHA (RoBERTa with Adapter-based Mental Health Analyzer), a hybrid deep learning model that combines RoBERTa, parameter-efficient adapter layers, BiLSTM, and attention mechanisms and is further optimized with focal loss to address the class imbalance problem. When tested on three filtered versions of the GoEmotions dataset, RAMHA shows outstanding results, with a maximum accuracy of 92% in binary classification and 88% in multiclass tasks. A large number of experiments are performed to compare RAMHA with eight standard baseline models, including SVM, LSTM, and BERT. In these experiments, RAMHA is able to consistently outperform the other models in terms of accuracy, precision, recall, and F1-score. Ablation studies further confirm the contributions of the individual components of the architecture, and comparative analysis demonstrates that RAMHA outperforms the best previously reported F1-scores by a substantial margin. The results of our study not only indicate the potential of the adapter-enhanced transformer in emotion-aware mental health screening but also establish a solid basis for its use in clinical and social settings. Full article

Objective: We aimed to explore associations between plasma neurofilament light chain (pNfL) and cognition through patient-reported outcomes (PROs) in multiple sclerosis (MS). Methods: In this cross-sectional study, we included 211 people with MS (PwMS) and collected data from pNfL (fully automated chemiluminescent enzyme immunoassay), EDSS, education, cognition (the Symbol Digit Modalities Test (SDMT), California Verbal Learning Test-II (CVLT II), and Brief Visuospatial Memory Test–Revised (BVMT-R)), the Modified Fatigue Impact Scale (MFIS), Beck Depression Inventory (BDI-II), Beck Anxiety Inventory (BAI), and Pittsburgh Sleep Quality Index (PSQI). Results: On multivariate linear regression models, higher educational attainment was significantly associated with lower pNfL (high school: Coeff = −0.22, 95% CI = −0.41 to −0.04, p = 0.019; university: Coeff = −0.22, 95% CI = −0.42 to −0.02, p = 0.030). In logistic regression models, the likelihood of having pNfL levels above normal thresholds increased by 56% for each one-point increment in the EDSS score (OR = 1.56, 95% CI = 1.23 to 1.98, p < 0.001) and was 2.5 times greater in individuals with impaired SDMT (OR = 2.50, 95% CI = 2.20 to 5.21, p = 0.014). No statistically significant associations were observed between pNfL and CVLT-II, BVMT-R, BDI-II, MFIS, BAI, or PSQI. Conclusions: Neuro-axonal damage in people with MS manifests clinically as increased disability and reduced attention and processing speed. However, these effects may be mitigated by greater brain resilience, as suggested by the protective role of higher educational attainment. The PROs assessed in this study showed no significant associations with pNfL levels, possibly due to measurement errors and heterogeneity, with limited sensitivity to neuro-axonal damage. Full article

Background/Objectives: Use of social media among college students is ubiquitous. Excessive use of social media has been linked to distractions, reduced academic focus, and poor mental health outcomes such as anxiety and depression. The determinants of social media use among college students are not well understood. Hence, the purpose of this study was to conduct a scoping review on the behavioral, demographic, and psychosocial determinants, explore theoretical frameworks, and suggest evidence-based recommendations. Methods: This scoping review was conducted between January 2024 and May 2025 following PRISMA-ScR guidelines, using MEDLINE (PubMed), CINAHL, and ERIC databases. Peer-reviewed studies were included if they focused on college students (ages 18–30), investigated determinants of social media use, and met predefined inclusion criteria. Results: A total of 22 studies met the inclusion criteria. Studies were conducted in Bangladesh, Canada, China, Egypt, India, Nigeria, Pakistan, Saudi Arabia, Turkey, and the United States, and the majority used cross-sectional designs (n = 20). A consistent finding across the reviewed studies was the strong association between social media overuse and symptoms of depression, anxiety, stress, and emotional dysregulation. Very few theoretical frameworks for understanding the determinants of social media were used. According to the reviewed studies, factors such as fear of missing out, sleep quality, and prolonged social media use consistently emerged as significant predictors of adverse mental health outcomes (p < 0.05). Conclusions: In this study, problematic social media use (PSMU) was linked to increased mental health issues, suggesting that students frequently engage in social comparison and experience feelings of missing out (FoMO), which exacerbate emotional distress. There is a need for integrated approaches in addressing PSMU within educational environments, particularly in fostering healthier digital habits among students. There is a need to conduct more concerted research using longitudinal designs and contemporary theoretical frameworks in this area. Full article

Background: COVID-19 has limited pregnant and postpartum women’s access to mental health services, leading to the introduction of online interventions. Objectives: This study aims to compare the effectiveness of telepsychotherapy (i.e., psychotherapy provided through digital technology supporting real-time interactivity in the audio or audiovisual modality) with the one yielded by face-to-face interventions in treating perinatal depression and anxiety and to assess the therapist’s perceived alliance in both interventions. Methods: We collected anamnestic information and obstetrical risk factors for 61 women. We evaluated the effectiveness of face-to-face (N = 31) vs. telepsychotherapy (N = 30) interventions on depressive and anxiety symptoms at baseline (T0) and the end of treatment (T1) using the Edinburgh Postnatal Depression Scale (EPDS) and the State-Trait Anxiety Inventory (STAI-Y 1 and 2). We assessed the degree of alliance perceived by therapists with the Working Alliance Inventory (WAI-T). Results: Both groups showed significant decreases in depressive (EPDS face-to-face: T0 12.65 ± 5.81, T1 5.77 ± 4.63, p < 0.001; EPDS remote: T0 11.93 ± 5.24, T1 5.70 ± 4.46, p < 0.001; effect size: 0.002) and state anxiety (STAI-Y 1 face-to-face: T0 51.19 ± 13.73, T1 40.23 ± 12.86, p < 0.001; STAI-Y 1 remote: T0 51.10 ± 11.29, T1 38.00 ± 10.90, p < 0.001; effect size: 0.007//STAI-Y 2 face-to-face: T0 43.13 ± 12.11, T1 41.03 ± 13.06, p = 0.302; STAI-Y 2 remote: T0 44.20 ± 8.70, T1 39.30 ± 9.58, p = 0.003; effect size: <0.001) symptoms by the end of treatment. Women treated remotely also experienced a significant reduction in trait anxiety at T1 (p = 0.003). We found no significant differences in either symptomatology (EPDS; STAI-Y) between the two interventions at baseline or in the therapist-perceived alliance. Conclusions: Synchronous telepsychotherapy for perinatal depression and anxiety showed comparable treatment response to face-to-face interventions, with both modalities associated with significant symptom reduction and the establishment of a working alliance. These findings support the potential of telepsychotherapy as a valuable alternative when in-person services are not accessible, especially during emergency contexts. Full article

Background: In the social context characterized by digitization and population aging, empty-nest older individuals have emerged as a particularly vulnerable group. This study examines the impact of digital finance on the mental health of empty-nest older individuals using data from the China Health and Retirement Longitudinal Study (CHARLS) spanning 2011 to 2018 and investigates its underlying mechanisms through the three dimensions of active aging. Results: The findings suggest that digital finance is associated with reduced likelihood and degree of depression among empty-nest older individuals. The beneficial effect of digital finance on depression is more pronounced among empty-nest older individuals with lower educational attainment, without supplementary pensions, and residing in rural areas. Furthermore, mechanism analysis shows that digital finance relates to better health outcomes, greater security, and increased social participation among empty-nest older individuals. Conclusions: These findings underscore the potential of digital finance to facilitate active aging among empty-nest older individuals. Full article