The rising prevalence of mental health disorders such as depression and anxiety challenges traditional treatments limited by resource shortages, poor accessibility, and low adherence. Digital health technologies-particularly digital mental health interventions-offer innovative, scalable, and personalized solutions. Mobile health applications, online cognitive behavioral therapy (CBT), and AI-driven tools are becoming essential in mental health care.

This study employs bibliometric methods to examine research trends and thematic evolution in digital mental health self-management interventions, using data retrieved from the Web of Science Core Collection (WoSCC). CiteSpace, VOSviewer, and Bibliometrix were applied to quantify research output, collaboration networks, and influential topics. The analysis covered English-language publications from 2006 to 2025, with internal consistency checks and sensitivity analyses within the WoSCC dataset ensuring robustness.

A total of 2262 eligible publications were retrieved, showing a clear growth trajectory. The United States led with 932 publications, followed by the United Kingdom and Australia. Citations surged after 2016, peaking in 2023, reflecting increasing academic and clinical relevance. Research has shifted from feasibility studies to AI-enhanced and personalized interventions. Keywords such as "artificial intelligence," "digital CBT," and "personalized care" showed notable growth. International and interdisciplinary collaborations also expanded, underscoring the field's global integration.

Digital mental health interventions are evolving from traditional models to intelligent, personalized solutions, providing scalable solutions to global mental health challenges. This study offers insights into future research directions, focusing on technology integration, ethical issues, and clinical validation to drive global application.

Novice meditators often struggle to recognise and intentionally disengage from self-referential thought during meditation. We investigated whether personalised high-precision neurofeedback (NF) training improves volitional disengagement from self-referential thought during meditation and enhances meditation's outcomes.

In a single-blind, controlled study, novices received 2 days of veridical (n = 20) or sham (n = 20) 7-T fMRI NF targeting posterior cingulate cortex (PCC) deactivation during meditation. After NF training, at-home meditation practice was monitored for 1 week, followed by an in-lab behavioural assessment.

Both groups reported similar perceptions of NF contingency, performance, and expectancy (p > 0.05), suggesting effective participant blinding. PCC deactivation during NF-guided meditation was comparable across groups (p > 0.05). Veridical NF group showed significantly stronger negative functional coupling (d = 0.59) between PCC and dorsolateral prefrontal cortex (DLPFC), significantly greater mindful awareness (d = 0.41) and emotional well-being (d = 0.40) associated with 1-week practice, and significant correlation (r = 0.71, p < 0.01) between emotional well-being and PCC-DLPFC negative coupling.

These findings suggest that high-precision NF can improve novices' ability to volitionally disengage from self-referential thought during meditation, thereby fostering greater mindful awareness in real-world practice and promoting emotional well-being.

This exploratory study was not preregistered.

The online version contains supplementary material available at 10.1007/s12671-025-02671-z.

Death anxiety as a common problem in hemodialysis patients affects their quality of life and mortality. The effect of self-care training on death anxiety in hemodialysis patients is not well-documented.

This study aims to determine the effect of self-care training on death anxiety in hemodialysis patients.

Fifty hemodialysis patients were purposefully selected and allocated to two study groups equally using the random minimization method. For the intervention group and one of their family members, who had the most role in patient care, the self-care training was implemented in three at least one-hour sessions during two weeks. The data were collected using the Templer Death Anxiety Scale (DAS). Analysis was done by SPSS version 22, using Shapiro-Wilk, Chi-square, Fisher Exact, independent-t test, and paired-t tests, at a 95% confidence level.

The DAS score of the study groups were the same at baseline. In intervention group the mean ± SD of the DAS score decrease from 8.04 ± 2.35 at baseline to 5.28 ± 1.65 at after the intervention (P = 0.001). In control group the mean ± SD of the DAS score was 8.04 ± 2.42 at the baseline and reached to 8.28 2.42 in after intervention phase (P = 0.228). In intergroup comparison, the mean ± SD of DAS score in the intervention group decreased significantly compared to the control group at the after intervention (P = 0.001).

The self-care training may have a positive effect in management of the death anxiety in hemodialysis patients. The importance of self-care in managing the death anxiety of hemodialysis patients and preventing its negative consequences seems to be attributed to the health care team's emphasis on it.

Chronic kidney disease (CKD) is a global health threat to patients' physical and mental health. Effective self-management can slow disease progression in early stages. However, prolonged treatment often leads to ego depletion and subsequently impacts self-management. Interventions to address this issue remain underdeveloped.

To evaluate the effects of psychological empowerment-based motivational interviewing program on early-stage CKD patients' self-management, perceived empowerment, and ego depletion and to explore their engagement experiences and the underlying reasons for the intervention's effectiveness.

The study employed the explanatory sequential mixed methods design comprised of a randomized controlled trial and a qualitative study, which were conducted in a tertiary hospital from July 2022 to November 2023. About 70 patients with early CKD were randomly assigned to a control group (n = 35) receiving standard clinical nursing, or an intervention group receiving a 12-week nurse-led psychological empowerment-based motivational interviewing program consisting of four interview sessions and four consolidation sessions. CKD Self-Management Behavior Scale, Patient Perception of Empowerment Scale, Self-Regulation Fatigue Scale, and biochemical indicators were collected at baseline (T1), after 4 weeks of intervention (T2), immediately postintervention completion (T3), and 4 weeks after intervention completion (T4). Data were analyzed by generalized estimating equation model. Semistructured interviews were conducted with the participants in the intervention group.

The participants' mean age was 42.76 years (SD = 10.59). Compared with the control group, the intervention group had a statistically significant improvement in self-management behavior (T2: β = 18.26, T3: β = 23.73, T4: β = 23.78; p < 0.001), ego depletion (T2: β = -8.46, T3: β = -11.35, T4: β = -13.35; p < 0.001), and perceived empowerment (T2: β = 5.77, p=0.002; T3: β = 9.41, T4: β = 8.99; p < 0.001). Qualitative interviews of 14 participants indicated that the intervention could affect self-perception, improve emotion, and establish healthy behaviors, which may explain such encouraging effects.

The psychological empowerment-based motivational interviewing program produced immediate and delayed benefits on self-management, perceived empowerment, and ego depletion in patients with early CKD. These findings provide new strategies for chronic disease management and psychological nursing. Trial Registration: Chinese Registry of Clinical Trials: ChiCTR2200064257.

Identifying social determinants of mental health (SDOMH) in patients with opioid use disorder (OUD) is crucial for estimating risk and enabling early intervention. Extracting such data from unstructured clinical notes is challenging due to annotation complexity and requires advanced natural language processing (NLP) techniques. We propose the Human-in-the-Loop Large Language Model Interaction for Annotation (HLLIA) framework, combined with a Multilevel Hierarchical Clinical-Longformer Embedding (MHCLE) algorithm, to annotate and predict SDOMH variables.

We utilized 2636 annotated discharge summaries from the Medical Information Mart for Intensive Care (MIMIC-IV) dataset. High-quality annotations were ensured via a human-in-the-loop approach, refined using large language models (LLMs). The MHCLE algorithm performed multi-label classification of 13 SDOMH variables and was evaluated against baseline models, including RoBERTa, Bio_ClinicalBERT, ClinicalBERT, and ClinicalBigBird.

The MHCLE model achieved superior performance with 96.29% accuracy and a 95.41% F1score, surpassing baseline models. Training-testing policies P1, P2, and P3 yielded accuracies of 98.49%, 90.10%, and 89.04%, respectively, highlighting the importance of human intervention in refining LLM annotations.

Integrating the MHCLE model with the HLLIA framework offers an effective approach for predicting SDOMH factors from clinical notes, advancing NLP in OUD care. It highlights the importance of human oversight and sets a benchmark for future research.

Anxiety's prevalence is increasing, making it a widespread mental health concern. However, scale-based diagnostic methods have limitations. Music therapy helps regulate emotions and alleviate anxiety symptoms. Functional near-infrared spectroscopy (fNIRS) offers a novel approach to diagnosing mental disorders by measuring changes in the concentrations of oxygenated hemoglobin (HbO) and deoxygenated hemoglobin (HbR) in the superficial layers of the brain, thereby reflecting brain activation. This is the first study to use fNIRS to examine the impact of music therapy on anxiety. fNIRS was used to measure changes in HbO and HbR in the superficial brain regions of individuals with anxiety symptoms to evaluate music therapy effectiveness and identify brain regions associated with anxiety. This study recruited 83 participants: 17 comprised the healthy control group, and 66 comprised the anxiety group. The anxiety group was divided into an intervention group (34 participants) and a waiting-list group (32 participants). The intervention group underwent 12 music therapy sessions and exhibited significant changes compared with the waiting group. These changes included connectivity between Wernicke's area and the dorsolateral prefrontal cortex (DLPFC) as well as the visual association cortex and Broca's triangular area. These results suggested that the connectivity characteristics of these brain regions were associated with anxiety. Music therapy significantly improved brain network connectivity characteristics in individuals with anxiety symptoms. Furthermore, fNIRS indicators could serve as biomarkers for the auxiliary identification of anxiety symptoms, aiding early identification and intervention. Trial Registration: ClinicalTrials.gov identifier: NCT05648539.

Current assessment of depression primarily relies on psychological scales. Although the use of machine learning in depression has grown, limited reports are available on multiple neurophysiological measurements. We employed machine learning algorithms incorporating eye tracking, visual evoked potentials (VEPs), and auditory P300 to classify depression among Chinese medical students.

A total of 66 students with depression and 72 matched controls were recruited; eye tracking, VEPs, and auditory P300 data were collected. Descriptive analyses and group comparisons were performed between the depression and control groups. Then, multivariate logistic regression (LR) analysis was conducted to evaluate the relationship between eye tracking, VEPs, and auditory P300 features and Patient Health Questionnaire-9 (PHQ-9) scores. Furthermore, the study employed six classifiers to differentiate between depression and nondepression. Five-fold cross-validation was employed. Model performance was assessed using receiver operating characteristic (ROC) curves, area under the curve (AUC), precision, accuracy, recall, and F1 score. We applied SHapley Additive exPlanations (SHAP) values to explain the model.

Depression group was characterized by lower response search scores, higher D values, and prolonged P100 latencies in both eyes. No significant differences were observed in auditory P300 features. Random forest (RF) classifier demonstrated superior classification performance relative to the other five machine learning algorithms. Models utilizing combined features showed enhanced performance compared with those based solely on eye tracking or VEP features. Utilizing the SHAP method, we identified that P100 latency in the right eye was the most significant feature across all machine learning models.

Chinese medical students with depression exhibited reduced responsive search scores and extended P100 latencies, suggesting impairments in attention and visual information processing associated with depression. The combined eye tracking and VEPs proved to be more effective than single features for distinguishing depression and nondepression. P100 latency in the right eye may be the most significant predictor of depression.

Phenotypic links between psychiatric disorders and adverse perinatal events are increasingly being reported, but the mechanisms remain unclear. In this study, we aimed to assess how polygenic scores (PGSs) for 8 psychiatric conditions influence perinatal risk.

The main analysis included 13,085 mothers and their corresponding birth information. PGSs for psychiatric conditions were estimated using genome-wide association study data (excluding the iPSYCH cohort) via LDpred2 and used as exposures. Ten adverse perinatal events from Danish national registers served as outcomes. Associations were analyzed using logistic or multinomial regression, with false discovery rate correction applied.

We found that PGSs for psychiatric conditions were associated with heavy smoking (attention-deficit/hyperactivity disorder [ADHD], anxiety, and depression), lower likelihood of being overweight/obese (schizophrenia, anorexia nervosa, and obsessive-compulsive disorder [OCD]), very young maternal age (<20 years) at childbirth (ADHD, depression, and anxiety), and non-cohabitation (ADHD, schizophrenia, anxiety, and depression). Little evidence of an association between maternal PGSs for psychiatric conditions and birth weight, gestational age, and labor presentation was identified. We identified a novel dose-response relationship in which higher PGSs for ADHD, anxiety, and depression were associated with a greater cumulative burden of adverse perinatal events, whereas higher PGSs for anorexia nervosa and OCD were linked to a lower burden.

High genetic liability for psychiatric conditions may partially explain the observed phenotypic associations between maternal mental illness and adverse perinatal events, with higher genetic liability generally associated with either an increase or decrease in the cumulative burden of adverse perinatal events in a dose-response-like manner.

This study aimed to explore the challenges that patients faced when severely ill with COVID-19 and during their rehabilitation journeys following the first wave in Sweden. Eight patients that were treated in an intensive care unit were interviewed using semi-structured interviews. Three themes were generated through thematic analysis: "transition into illness" (with subthemes: underestimated severity, uncertainty and worry); "to be cared for in a hospital setting" (with subthemes: loss of responsibility, loss of memory and time, contradictory feelings of being hospitalized, physical impact as frustrating); and "after care: managing on your own" (with subthemes: appreciation for care, care gaps and insufficient care, compromised ability, mental health, and self-efficacy for self-managed rehabilitation and post-traumatic growth). The findings indicated that the Swedish open strategy may be beneficial in other countries as it facilitated post-traumatic growth and that there should be a structured rehabilitation strategy in place in case of future pandemics.

The Scholarship of Teaching and Learning (SoTL) is emphasised more than ever in today's higher education institutions. SoTL is crucial in enhancing nursing education and improving student learning outcomes, thereby preparing competent nurses to deliver high-quality healthcare. Despite its importance, the dimensions and defining characteristics of SoTL have not been adequately explored. This scoping review aims to clarify the concept of SoTL in nursing education, identify its key features and address existing knowledge gaps in this field.

The study adopts Arksey and O'Malley's established scoping review methodology, as defined by the Joanna Briggs Institute (JBI). The research process is guided by five distinct phases: the initial formulation of research questions, identification of relevant studies, selection of studies, data extraction and compilation and, finally, synthesis and reporting of results.The inclusion criteria will be based on the Population, Concept and Context framework. A comprehensive search will be conducted across PubMed, Embase, Scopus, ProQuest, Web of Science, ERIC and grey literature sources, with no time limitations and ongoing updates until the study concludes.To ensure a comprehensive review, reference lists and citations of selected studies will also be examined for additional relevant sources. Duplicate studies and studies published in languages other than Persian or English will be excluded. Two independent reviewers will select studies based on eligibility criteria and screen the title, abstract and full text. Data extraction will be conducted using the standard JBI form. A directed content analysis will be undertaken to identify and clarify key concepts in the texts.

The Research Ethics Committee of Iran University of Medical Sciences has approved this study. The results will be published in scientific publications and presented at relevant conferences.

The protocol was registered on the Open Science Framework (OSF; https://doi.org/10.17605/OSF.IO/VN5GD).