Mental health conditions account for 18% of years lived with disability worldwide. 1-in-6 adults are affected in England, with most mental health conditions beginning in childhood and adolescence. Mental distress and ill health are unequally distributed in the UK, with strong associations with wider determinants of health, and higher prevalence among systemically disadvantaged groups. Currently, there is a lack of evidence to inform effective and timely policymaking for primary prevention in the UK. In recognition of these challenges, a national Population Mental Health (PMH) Consortium was established, as part of Population Health Improvement UK (PHIUK). PHIUK is a national research network which works to transform health and reduce inequalities through change at the population level. Our aim is to establish an interdisciplinary PMH Consortium, focussing on upstream determinants and the prevention of risks and onset of mental health conditions through interdisciplinary stakeholder engagement, to create new opportunities for population-based improvement of mental health in the UK.The PMH Consortium brings together leading interdisciplinary representation in population mental health, spanning from sciences to the arts, across the UK. Membership includes six academic institutions, third sector organisations, lived experience expertise, and strong links with national bodies to ensure integrated cross-national and regional policy impact. The PMH Consortium comprises four cross-cutting platforms (Partners in policy, implementation, and lived experience; Data, linkages, and causal inference; Narrowing inequalities; Training and capacity building) and three challenge areas (Children and young people's mental health; Prevention of suicide and self-harm; Multiple long-term conditions) which are highly integrated and interdependent. The work will be underpinned by a Theory of Change across an initial four-year life cycle. This paper describes the aim, objectives, and approach of the PMH Consortium, as well as anticipated challenges and strengths. The goal of the PMH Consortium is to develop a model for population mental health research and policy translation that is both scalable and sustainable. It is critical to ensure continued impact and viability beyond the initial four years, contributing to the prevention of mental health conditions in the UK, with personal, economic, social, and health benefits.
Empathy is central to humanised nursing but vulnerable to erosion in demanding academic and clinical settings. Positive mental health (PMH) encompassing emotional, psychological, and social well-being, may regulate how self-compassion is statistically linked to empathic engagement. However, evidence in nursing students remains limited. To examine the statistical association of positive mental health in the relationship between self-compassion and empathy among undergraduate nursing students within a structural equation modelling (SEM) framework. Observational, analytical, cross-sectional study. A total of 402 nursing students from a public university completed validated measures of self-compassion, empathy, and PMH. SEM with latent variables was conducted using diagonally weighted least squares (DWLS) to account for ordinal and non-normal data. Model fit was assessed using multiple indices, acknowledging the complexity of the latent structure. Self-compassion was positively associated with PMH (β = 0.772, p < 0.001), which related positively to empathy (β = 0.689, p < 0.001). The indirect effect via PMH was positive (β = 0.532, p < 0.001), while the direct effect of self-compassion on empathy was negative when controlling for PMH (β = -0.553, p < 0.001), indicating an inconsistent mediation pattern. The model explained 59.6% of the variance in positive mental health and 19.3% in empathy. PMH appears to be a key correlate in the association between self-compassion and empathy. Findings suggest that emotional well-being may be an important foundation for relational competence, although the study's cross-sectional nature precludes causal inferences and the marginal model fit warrants a cautious interpretation. Fostering empathy may require more than interpersonal skills training. Nursing curricula could benefit from integrating positive mental health promotion, including training in self-compassion and emotional regulation, to support empathic and humanised nursing practice across educational and clinical contexts.
Wearable technology holds promise for improving mental health care by enabling continuous, objective monitoring of physiologic parameters. Building on decades of psychophysiology research, wearables can provide an additional source of measurement for implementing measurement-based care in learning mental health systems. This review describes wearable use across inpatient and outpatient settings, identifying gaps and opportunities in clinical care and research. While widely studied in outpatient, wearables hold immense potential for in inpatient settings. Advancements needed include user-centered design, better understanding of complex populations and settings, and use of modern analytical methods to generate clinically actionable mental health insights for all.
Digital mental health tools-including telehealth, mobile applications, wearable devices, machine learning, and artificial intelligence-are changing the way patients and providers manage mental health care. This review summarizes the current research findings of digital interventions on patient access to care, the factors impacting personalized care, and overall patient engagement. Gaps of knowledge and future considerations are discussed, including careful observation of existing barriers to care. Clinical recommendations are discussed for clinicians who are considering implementing digital mental health tools into practice.
A number of articles have heralded the use of artificial intelligence (AI) agents to serve as a replacement for human psychotherapists. Despite the rapid advancements in the use of both rule-based and generative AI programs in the recent past, an overall review shows only small impacts on certain mental health symptoms, particularly depression, and then only in the short-term. Significant strides forward, both in terms of technology and the development of answers to ethical questions regarding AI's use in psychotherapy, must be seen before the use of such systems becomes widespread or regularly recommended to replace human mental health clinicians.
This article presents a review of examples of digital mental health technology (DMHT) for assessing and treating posttraumatic stress disorder (PTSD), including research supporting these innovative solutions. Tools for assessing PTSD are reviewed, including digital administration of self-report measures, ecological momentary assessment methods, personal sensing, electronic medical record and other naturalistic data sources, and emerging digital assessment tools. Next, DMHTs for PTSD treatment are reviewed, including Internet-based interventions, mobile mental health apps, virtual reality therapy, and several emerging digital interventions. DMHT applications for PTSD have demonstrated promise in research and are beginning to be used in clinical practice.
Obsessive-compulsive disorder (OCD) is a debilitating neuropsychiatric disorder that remains chronic unless intervened with evidence-based intervention. Cognitive behavioral therapy (CBT) with exposure and response prevention is the gold-standard psychological intervention for OCD, but many individuals do not receive this intervention due to barriers to accessing treatment. Mental health technology tools such as telehealth, computerized programs, internet-delivered CBT, and mobile applications have been adopted to expand the accessibility of CBT. An up-to-date summary of the evidence base of technology-mediated formats of CBT for OCD treatment is provided. Clinical benefits offered by such approaches, current limitations, and future research directions are discussed.
Virtual reality (VR) has generated growing enthusiasm as a tool to deliver evidence-based mental health treatments. By immersing individuals in interactive, simulated environments, VR allows for repeated exposure to feared stimuli, real-time practice, and clinician involvement. While the strongest evidence supports using VR exposure therapy for anxiety-related disorders, promising applications are emerging across a range of conditions. Despite promising findings, most studies have small samples with limited follow-up. Broader integration in clinical practice will require continued innovation, testing, and adaptation to meet patient needs, enhance therapist training, and address health care system constraints.
Depression leads to a significant societal burden worldwide, yet most individuals affected lack adequate care. Digital mental health treatments (DMHTs) offer evidence-based, accessible interventions via websites, text messaging, virtual reality, and mobile apps, among other technologies. Studies demonstrate DMHT effectiveness, often comparable to traditional therapies, with high treatment acceptability and satisfaction. Key challenges include poor engagement, high attrition, and limited integration into routine care. Despite these barriers, innovations such as human support, improved reimbursement practices, patient-treatment matching strategies, and emerging AI-driven tools promise to broaden DMHTs' impact and position these programs as a frontline treatment option for depression globally.
Body positioning is a standard nursing practice in the intensive care unit (ICU) and it is linked with physiological variations in oxygenation and hemodynamic stability. However, there is a lack of comparative data as to why different positions relate to different parameters that are critical. The aim to assess the changes in key physiological hemodynamic parameters, across different body positions and during transitions between positions in critically ill patients admitted to the ICU. A single group before and after quasi experimental design was used. A total 100 critically ill patients randomly selected from the ICU at King Abdullah University Hospital in Jordan. Each position session lasted two hours, with a 30-minute interval between transitions. Physiological data were collected before and after each session using validated tools. Among 100 ICU patients (56% female) a significant change in hemodynamic parameters were observed across positions. Temperature significantly decreased in the supine (p < 0.001) and prone (p = 0.008) positions. Pulse rate decreased significantly in the upright position (p < 0.001). Systolic BP significantly increased in supine and decreased in upright (p < 0.001). Diastolic BP also showed significant changes (p < 0.001). SpO₂ significantly increased in the supine and prone (p < 0.001) positions, while the upright position showed a significant decrease (p < 0.001). Repeated Measures ANOVA confirmed these differences (p < 0.001). Body positioning is associated with the changes in hemodynamic and oxygenation parameters in critically ill patients. In particular, prone positioning was associated with an increase in oxygenation, and upright positioning was associated with temporary decreases.
Digital phenotyping-the moment-by-moment quantification of human behavior using data from smartphones and wearables-offers new pathways for mental health research and care. This review summarizes current trends, tools, and applications of digital phenotyping, highlighting its growing clinical relevance in early detection, symptom monitoring, and personalized interventions. Although studies increasingly demonstrate its feasibility and clinical utility across conditions such as depression, anxiety, and schizophrenia, challenges persist. These challenges include inconsistent data quality, small and nonrepresentative samples, lack of methodological standardization, and pressing ethical considerations about privacy and transparency.
Substance use disorders (SUDs) are highly associated with other mental health conditions and disparities exist across sociodemographic characteristics. We aimed to estimate the incidence of specific SUDs and comorbidities with United States electronic health record data. We harnessed data from the All of Us Research Program cohort from Jan 1, 2017 to Jun 30, 2022 (N = 266,472). We identified newly documented SUDs after a two-year washout period, along with related mental health diagnoses. Multivariate logistic regression models estimated associations between incident SUDs and comorbidities. Participants included 160,792 females (60.3%) aged 51.6 years on average [SD= 16.7]. The incidence of any SUD was 4.8%; among these, 74.4% had at least one mental health comorbidity. Alcohol (1.6%) and cannabis use (1.6%) disorders were the most common. Individuals with newly documented SUDs (vs. non-SUD) were more often male, Black, socioeconomically disadvantaged, and unmarried (all p < 0.001). Overall, anxiety (25.3%) and depression (23.1%) were the most frequent comorbidities, though the prevalence of comorbid mental health conditions ranged from 48% to 77% across SUD subtypes. Most other mental health conditions were associated with elevated odds of newly documented SUDs (AOR range=4.6-9.7, p < 0.001), particularly for stimulant, cocaine, and opioid use disorders. Newly documented SUDs in this diverse cohort frequently co-occurred with other mental health conditions, with diagnostic patterns varying across sociodemographic groups. Findings underscore the importance of integrated behavioral health screening and interventions that account for comorbidity. These patterns further highlight the need for strategies that enhance equitable access to prevention and treatment for individuals with SUDs.
Pain is highly prevalent among older adults and represents a significant risk factor for cognitive frailty, but the underlying mechanism remains insufficiently understood. To examine the mediating effects of basic, instrumental, and advanced activities of daily living (ADL) in the relationship between pain and cognitive frailty in community-dwelling older adults. A cross-sectional survey was conducted among 710 Chinese adults aged 60 and above. Pain was measured using the Revised Faces Pain Scale. Cognitive frailty, defined by the FRAIL scale, subjective cognitive decline, and Short Portable Mental Status Questionnaire, was subclassified into reversible cognitive frailty (RCF) and potentially RCF (PRCF) subtypes. Katz Index for basic ADL, the Lawton Scale for instrumental ADL, and the advanced ADL scale (AADL). Parallel mediation models were tested using logistic regression, Z-test, and bootstrapping, while adjusting for covariates. Pain was significantly associated with both subtypes of cognitive frailty (RCF: OR = 3.75, 95% CI [2.36, 5.97]; PRCF: OR = 7.60, 95% CI [4.10, 14.06]). AADL, rather than basic ADL and instrumental ADL, partially mediated this association for both RCF and PRCF. Both the Z-test and bootstrapping method confirmed the indirect effect (estimate = 0.316, SE = 0.095, 95% CI [0.157, 0.533]). AADL played a key mediating role in the link between pain and cognitive frailty. Interventions targeting AADL may be crucial for preventing cognitive frailty in older adults experiencing pain.
Men show a higher mortality than women, especially at a young age (between 15 and 39 years). They are more likely to engage in unhealthy behaviours and tend not to implement preventative efforts or to seek help. While (mental) health promotion programmes aim to foster healthy behaviours, men often do not feel addressed by them and are therefore reluctant to participate. This synthesis aims at drawing together barriers to and facilitators of male participation in (mental) health promotion programmes and identifying how to best address men in health communication and programme promotion. This rapid qualitative evidence synthesis includes a sample of 21 studies. 18 are qualitative studies and 3 are mixed-methods studies with separately reported qualitative findings that captured the perspectives of males aged 12 to 79 years and of professionals working in men's health on the barriers to and facilitators of participation in (mental) health promotion programmes and on preferred health communication. Studies were purposefully selected to maximise variation across interview content, context, and participant characteristics (e.g., age, occupation). The selection was restricted to studies published between 2015 and 2025. Gender norms were one of the main barriers to participation in men's (mental) health promotion programmes. Preferably such programmes should be integrated into settings attractive or familiar to men, such as sport clubs or handicraft workshops, or the workplace. Peers and peer support played a crucial role within men's health promotion and were found to facilitate positive behavioural changes. When reaching out to men, clinical and stigmatising terminology should be avoided in favour of action-oriented language that emphasises control and practical solutions while keeping the messaging simple and focused on tangible benefits. Health promotion programmes for men require embedding interventions within male-relevant contexts, such as sports, workplaces, and peer networks, that ease participation and reduce stigma. To reach and benefit men, communication strategies should use relatable, non-stigmatising language from credible messengers and should frame self-care as compatible with masculine identities.
The development of Social and Emotional Learning (SEL) is of increasing interest in schools worldwide. Consequently, the evaluation of SEL programs such as emotional intelligence (EI) interventions, to ensure the establishment of evidence-based emotional education is relevant. The main purpose of this study was to evaluate the effects of an ultra-short (6 h) EI intervention on child psychosocial adjustment. The sample consisted of 268 children (8-12 years old). Results from ANCOVA analyses showed no statistically significant differences between the intervention and control groups at post-test across the main outcome variables (e.g., F values <2.00, p > .12), though within-group Student's t-tests revealed small but significant improvements in quality of life at post-test (d = 0.18) and in quality of life, mental health (d = 0.48), and anxiety/depression symptoms (d = 0.11) at one-year follow-up in the intervention group. These findings revealed the absence of differences between experimental and control groups at post-test, although the intervention group significantly increased health-related quality of life at post-test and follow-up, as well as mental health at follow-up assessment. This paper highlights the great importance of the way elected to evaluate the overall effectiveness of an EI intervention based on using hard techniques (i.e., ANCOVA), showing actual effectiveness, versus soft contrast techniques (e.g., Student's t-test, Repeated-Measures ANOVA), which simply show gains (fake effects). The importance of ensuring hard evidence-based emotional education is highlighted. These findings underscore the relevance of using rigorous statistical techniques-so-called hard methods like ANCOVA-to avoid inflated or false-positive interpretations often derived from gain-score analyses (fake effects) in the evaluation of overall effectiveness of an EI intervention. Overall, the intervention showed limited but promising effects, particularly in long-term mental health, suggesting potential for brief SEL programs if implemented and assessed with methodological rigor.
Health-related quality of life (HRQoL) is a vital indicator of evaluating care outcomes and prognosis, yet little is understood about its developmental trajectories in older patients with chronic pain. This study aimed to identify latent HRQoL trajectories and their predictors, and to develop explainable machine learning models for predicting HRQoL deterioration. This prospective cohort study assessed 608 older patients with chronic pain at admission and at 1, 3, and 6 months post-admission, collecting data on HRQoL, general characteristics, pain level, activities of daily living (ADL), depression, and perceived social support. Growth mixture modeling was applied to identify trajectories of physical and mental HRQoL. Predictors were selected using LASSO regression and SVM-RFE. Nine explainable machine learning models were developed for both components, and SHAP interpreted the outputs. An HRQoL decision-support dashboard was developed to facilitate potential clinical application. Three physical HRQoL trajectories were identified: Stable High, Decline and Low Stability, alongside two mental HRQoL trajectories: Improvement and Decline. Key predictors included education level, pain duration, pain level, ADL, depression, and perceived social support, with ADL and pain level being the most influential for physical and mental HRQoL, respectively. This dual-trajectory study identified five distinct HRQoL patterns in older patients with chronic pain, elucidating key predictors via explainable machine learning. The proposed HRQoL decision-support dashboard may provide an interpretable tool to support understanding of predictive relationships and assist healthcare professionals in HRQoL assessment. Not applicable.