Depressive symptoms are common among older adults and can significantly impact their quality of life. However, many older adults face barriers to accessing psychological treatment. Internet-based cognitive behavioral therapy (iCBT) is a promising alternative to face-to-face treatments, but its feasibility among older adults has been less extensively studied than in adult populations.

This study evaluated the feasibility of guided iCBT for adults aged 55 years and older with mild to moderate depressive symptoms recruited from the general population.

This study is a feasibility study with a single-group, pretest-posttest design (n=21), in which all participants received guided iCBT for 8 weeks. Assessments were conducted at baseline (T0) and after the intervention (T1). The primary outcome was feasibility, conceptualized as satisfaction, usability, engagement, and uptake of iCBT. Secondary outcome measures included depression severity, working alliance, and technical alliance.

Participants were mostly highly educated (13/21, 61.9%), female (18/21, 85.7%), had an average age of 59.85 (SD 4.19; range 55-68) years, and reported moderate digital literacy. Feasibility outcomes indicated high satisfaction and engagement and moderate usability. Working alliance was rated as good by both participants and coaches, and technical alliance was rated as moderate by the participants. There was a nonsignificant modest decrease in depressive symptoms (Cohen d=0.47). Of the 20 participants who started the intervention, all completed the first 2 modules, but completion declined across the remaining 6 modules, with only 1 (5%) participant completing all modules.

This study found that guided iCBT has the potential to be a feasible option for older adults experiencing depressive symptoms, with participants reporting generally positive satisfaction, moderate engagement, and a moderate therapeutic bond with their coaches. However, below-average usability ratings and a moderate technical alliance suggest that some aspects of the platform require improvement. Future research should focus on improving usability and adherence, as well as testing the intervention in a larger and more diverse population.

Virtual reality (VR) is increasingly used for adjunctive relaxation training in psychiatric care. However, evidence remains limited among hospitalized patients with depressive disorders, particularly in routine inpatient settings in China, and little is known about whether improvement varies by session frequency.

This retrospective study examined whether adjunctive VR-based relaxation training was associated with changes in depressive and anxiety symptoms among inpatients with depressive disorders and whether improvement differed by session frequency.

We conducted a retrospective, nonrandomized natural-group comparison using complete anonymized medical records from patients hospitalized in Lishui Second People's Hospital between January 1 and December 31, 2022. Patients met International Classification of Diseases, Tenth Revision (ICD-10) diagnostic criteria for depressive episodes or recurrent depressive disorders and were screened using predefined criteria. The analytic sample included 133 inpatients: 63 (47.4%) received adjunctive VR-based relaxation training plus usual care and 70 (52.6%) received usual care only. Usual care included pharmacotherapy and physiotherapy. The VR intervention consisted of 25-minute immersive relaxation sessions delivered approximately 3 times per week. Symptoms were assessed at admission and discharge using the 17-item Hamilton Depression Scale and Hamilton Anxiety Rating Scale. Response was defined as a reduction of 50% or more from baseline, and remission was defined as a total score of 7 or less. Baseline characteristics, outcome scores, response and remission rates, and exploratory session-frequency subgroups were compared. All analyzed variables were checked against complete medical records; no missing values were identified, and no imputation was performed.

The VR and control groups did not differ significantly in baseline depressive or anxiety scores. At discharge, adjunctive VR-based relaxation training was associated with lower depressive and anxiety symptom scores than usual care alone. The VR group also showed higher response rates for both depressive and anxiety symptoms and a higher anxiety remission rate, whereas depression remission was similar. Exploratory session-frequency analyses suggested that anxiety improvement may be more consistently associated with VR exposure than depression remission; however, the pattern was not strictly linear and should be interpreted cautiously because treatment frequency was linked to hospitalization duration and routine care factors.

This study is innovative in evaluating structured VR-based relaxation training as an adjunct to routine inpatient depression care and in providing preliminary observations on session-frequency patterns in a real-world Chinese psychiatric setting. Unlike many previous VR studies conducted in noninpatient, nonclinical, or short-term experimental contexts, this study reflects everyday clinical practice among hospitalized patients with depressive disorders. The findings contribute practical evidence for integrating immersive relaxation into comprehensive inpatient care, particularly when additional anxiety relief is desired. Because the study was retrospective and nonrandomized, the findings indicate associations rather than causal effects and should be confirmed in prospective randomized controlled trials.

Depressive disorders represent the second leading cause of disability globally, and Portugal reports the second highest prevalence in Europe. Nevertheless, the role of socioeconomic factors, behavioural determinants, and potential differences in treatment remain underexplored. This study assessed socioeconomic inequalities in depression and inequity in mental healthcare utilization among Portuguese adults aged 25-65 years, and evaluated whether health behaviours mediate the socioeconomic status (SES) and depression association. We used microdata from the 2019 Portuguese National Health Interview Survey. Depression was measured through self-report and PHQ-8 (≥10, moderate and moderately-severe; ≥20, severe). Concentration curves and indices, standardized by sex and age, assessed SES-related inequality in depression; horizontal inequity in mental healthcare utilization was estimated by adjusting for morbidity. Logistic regression models estimated the SES-depression association, and mediation by health behaviours (smoking, alcohol, sedentary lifestyle, diet, BMI) was evaluated using attenuation analysis. Overall, 13.0% reported depression in the previous year and 6.8% met PHQ-8 criteria. Both were disproportionately concentrated among lower-income groups, with the strongest inequality observed for severe depression. Horizontal inequity was also observed: specialist consultations were disproportionately used by higher-income groups when adjusting for self-reported depression, whereas medication was more concentrated among lower-income individuals meeting PHQ-8 criteria. Sedentarism, BMI, and alcohol drinking partially mediated SES-depression association, reducing effect estimates by up to 23.9%. Marked socioeconomic inequalities exist in depression and mental healthcare utilization in Portugal. Strengthening equitable access to evidence-based mental healthcare and addressing upstream behavioural and socioeconomic determinants are critical to reducing the national mental health burden.

Despite the high prevalence of depressive disorders, access to effective treatment remains limited due to financial, geographic, and social barriers. Online self-help groups offer a promising and scalable form of peer-based support beyond traditional clinical settings. Integrating cognitive behavioral therapy (CBT) techniques such as cognitive restructuring and behavioral activation into self-help groups may enhance their effectiveness.

This study evaluated the efficacy of a cognitive behavioral therapy-based online self-help group (COS) that integrates structured CBT techniques with peer-led group support as a low-intensity intervention for individuals with depressive symptoms. A randomized controlled trial (RCT) comparing COS with a CBT-based mobile application was conducted. Additionally, a separately recruited waitlist control group was included as a supplementary comparison condition.

Participants were recruited online. After eligibility screening via a structured clinical interview, participants were randomly assigned to a COS group (n=79) or a CBT-based mobile application group (n=39). An additional waitlist control group (n=48) was recruited separately during the second phase of the study. The COS intervention involved 7 videoconferencing sessions that incorporated peer-led group discussions, sharing lived experiences, and core CBT techniques such as cognitive restructuring. The primary outcome measure was depressive symptoms, assessed using the Beck Depression Inventory-II, and the secondary outcome was suicidal ideation, estimated using the Beck Scale for Suicide Ideation, measured at baseline, postintervention, and 3-month follow-up. Linear mixed models were used to evaluate group × time interaction effects. Reliable change indices were also calculated to assess clinical significance. All statistical tests were 2-tailed.

Among participants assigned to the COS group, 61% (48/79) completed all 7 sessions, and 84% (66/79) attended 5 or more sessions. A significant time × group interaction was observed for depressive symptoms (F4,288.47=7.23, P<.001). The COS group exhibited a substantial reduction in depressive symptoms from baseline to postintervention (t285.76=10.77, two-tailed; P<.001), with a large within-group effect size (d=1.38); this improvement was maintained at the 3-month follow-up. Suicidal ideation also significantly decreased in the COS group (t277.11=4.55, two-tailed; P<.001), with sustained effects at follow-up. Clinically meaningful improvement in depressive symptoms, as defined by the reliable change index, was observed in 75% (56/75) of COS participants. While both the COS and app-based CBT groups achieved comparable reductions in depressive symptoms, only the COS group demonstrated a significant reduction in suicidal ideation.

This RCT provides evidence that a structured, CBT-informed online self-help group can reduce depressive symptoms and suicidal ideation. The COS program offers a scalable, accessible alternative to traditional therapy, particularly in settings with limited access to mental health professionals.

Despite the significant benefits of artificial intelligence (AI) in mental health, real-world implementation remains limited, making it essential to understand the factors that influence adoption.

This study examined the acceptability and intention to use artificial intelligence-based precision mental health technologies (AI-PMHTs) and proposed an empirical, theory-guided model that integrates traditional technology acceptance predictors (eg, perceived usefulness, risk, and ease of use) with emerging psychological factors (eg, AI anxiety, personality, and conspiratorial thinking) that may inform future implementation research, strategic planning, and training program design.

An online survey was distributed to a sample of 357 psychologists in training, including both undergraduate and master's students. A mixed methods approach was used, combining quantitative measures (via psychometrically validated questionnaires) and qualitative data (through open-ended questions). Descriptive statistics and t tests were conducted to characterize the sample, and responses to the open-ended questions on facilitators and barriers were thematically analyzed. Partial least squares structural equation modeling was used to build the empirical model.

Participants showed moderate-to-high acceptance and intention to use AI-PMHTs, yet anxiety and perceived risk varied (with higher levels among women), and more frequent use was linked to more favorable acceptance profiles without reducing fear. Thematic analysis revealed that participants viewed AI tools as efficiency-enhancing but raised concerns about reliability, usability, overdependence, and access constraints. Partial least squares structural equation modeling supported a hierarchical adoption pathway in which dispositional and demographic factors shape AI-related fear and perceived risk, which then influence cognitive evaluations and attitudes, ultimately being associated with acceptance and intention to use AI-PMHTs. Predisposing variables (particularly resistance to change and conspiratorial thinking) were the strongest predictors of AI-related anxiety, with gender and extraversion showing smaller but meaningful effects. Fear acted as a key affective mediator, increasing perceived risk and indirectly weakening positive attitudes and perceived usefulness. Acceptance was the most influential downstream construct, directly predicting satisfaction, perceived usefulness, prior experience, and future intention to use, consistent with a reinforcing feedback loop in which early acceptance supports sustained engagement.

Findings suggest a layered framework that may inform future implementation research and training program design, addressing (1) predisposing dispositional and emotional profiles; (2) precipitating fear and perceived risk via transparent regulation, explainable design, and policies that strengthen professional agency; and (3) maintenance through high-quality early experiences, usability, and sustained institutional support. This theory-guided model clarifies how psychological, contextual, and experiential factors jointly shape adoption and sustained use of AI-PMHTs among psychologists in training, informing targeted educational and implementation strategies for this population.

Background and objectives The population of older persons is increasing in India, with a projection of 230 million by 2036. With the increasing population of older persons, the prevalence of social isolation and loneliness is also increasing. The objective of the review was to assess the prevalence of social isolation and loneliness among older persons in India and to identify their risk factors. Methods The protocol for this systematic review was registered with PROSPERO (CRD420251172242). We included studies that reported the prevalence of social isolation and loneliness among older persons above 60 years of age in India. Data sources included PubMed, SCOPUS, CINAHL, and EBSCO databases. Studies were screened and selected using Rayyan software, and data were extracted and assessed for risk of bias using the Newcastle Ottawa scale. A meta-analysis was attempted, but the I2 was 99%, indicating high heterogeneity, and so a narrative synthesis was performed. The heterogeneity was due to different scales and different cut-off points. Results Out of 894 identified papers, 20 studies were included in the final review. The prevalence of loneliness obtained from 11 studies ranged from 3.8 to 66.4%, while social isolation obtained from 4 studies ranged from 3.6 to 86.4%. Key risk factors identified from 12 studies included increasing age, poor socio-economic status, poor physical health, mental health, and various social factors such as marital status, living conditions, strained relationships, elder abuse, and disengagement from social activities. Interpretations and conclusions The review highlights a significant burden of social isolation and loneliness among the elderly in India, influenced by demographic, health, and social factors. A pooled estimate of prevalence was not feasible due to the heterogeneity of studies. Future research should focus on population-based assessment using standard, validated scales to better understand the magnitude of the problem.

Tuberculosis multimorbidity is an emerging burden to the healthcare system in India, but the national programmes like the National TB Elimination Programme (NTEP), the National Programme for Prevention and Control of NCDs (NP-NCD), and the National Mental Health Programme (NMHP) are still operating in silos, leading to fragmented and inefficient care. This paper employs a SWOT (strengths, weaknesses, opportunities, threats) analysis to evaluate the integration potential of these programmes within the platform of Ayushman Arogya Mandirs (AAMs). The analysis identifies key strengths, such as NTEP's robust surveillance and NP-NCD's wide screening network, and critical weaknesses, including isolated digital platforms and a lack of cross-programme training for health workers. Significant opportunities exist through linking digital systems like Ni-kshay and the NP-NCD application, and training frontline workers in composite care. Major threats include persistent policy fragmentation and patient stigma. We conclude that a strategic shift from vertical, disease-specific programmes to a person-centred, integrated model is essential. This requires collaboration at the policy level, the integration of comprehensive digital health records, and the delegation of responsibilities to primary care teams, including AAMs, to effectively manage multimorbidity, improve patient outcomes, and advance India's goals of attaining Universal Health Coverage of tuberculosis.

Considering the surge of urban gig drivers, 111 two-wheeler delivery riders (ages 18-40) in Panvel, MMR were assessed using NMQ and GHQ-12. High (87.3%) prevalence of musculoskeletal disorders primarily in the lower back (68%), significantly correlating with age (P<0.0086) and weight carried (P<0.0001) were observed. Additionally, 42.3% reported psychological distress (GHQ-12≥3), significantly associated with weight carried (P=0.0329), distance travelled (P=0.041), work hours (P=0.006) and the number of body parts affected by musculoskeletal disorders (P=0.0007). In conclusion, this occupational group faces a high risk of early-onset musculoskeletal disorders and long-term disability. Consequently, stakeholders must establish regulatory guidelines for this emerging sector.

Given the high co-occurrence of tobacco, marijuana use, and mental health disorders, we explored behavioral health providers' perceptions of patient mental health and substance use during the COVID-19 pandemic. We conducted semi-structured interviews with 21 Ohio providers randomly sampled from state licensure records, analyzing data using constructivist grounded theory. Providers perceived broad increases in substance use and mental health symptoms, attributing these to isolation, reduced support access, and pandemic-related stress. Providers consciously de-prioritized tobacco and marijuana to focus on "more acute" concerns. Substance use providers emphasized "riskier" substances while mental health providers prioritized severe psychiatric symptoms. Tobacco was often excluded from abstinence frameworks due to its normalization in recovery settings. These findings highlight the need for targeted provider training, organizational policy reform, and educational interventions to address the systematic deprioritization of tobacco and marijuana in behavioral health settings, with implications extending beyond the COVID-19 pandemic to future public health crises.

Nursing students face considerable psychological demands in both academic and clinical training settings, which may constitute a significant source of psychological distress and give rise to distinct adaptation profiles. However, few studies have addressed these issues. This study employs an integrated analytical approach to uncover heterogeneous adaptation patterns and their network characteristics, and further simulates node-specific interventions across different subgroups.

This cross-sectional study included 1,126 Chinese nursing students. Measures included mental well-being, emotional intelligence (EI), and high education stress. Latent profile analysis (LPA) identified psychological adaptation subgroups. Gaussian graphical models estimated network structures for each subgroup, followed by centrality and bridge analyses. Ising-based simulations evaluated intervention effects of specific nodes, ranking targets by therapeutic or preventive potential.

Among these students, LPA identified three distinct subgroups: Highly Adaptive (23.4%), Moderately Adaptive (28.7%), and High-Risk (48.0%). The profiles differed significantly in mental well-being, education stress, and EI (all P < 0.001). Network analysis showed that the High-Risk subgroup had the highest connectivity, while the Highly Adaptive subgroup was more sparse and stable. EI-related nodes displayed the greatest centrality across all profiles, and the education stress node consistently functioned as a key bridge. Intervention simulations revealed subgroup-specific sensitivity patterns. For High-Risk students, EI nodes served as the most influential therapeutic and preventive targets, with a marked asymmetry ratio. The Moderately and Highly Adaptive profiles demonstrated distinct patterns of intervention efficiency and resilience.

This study identifies distinct psychological adaptation profiles among nursing students and highlights EI as a critical intervention target, especially for High-Risk subgroups. The integrated analytical framework provides a replicable approach for personalized mental health research.

Not applicable.