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PubMed2026

Whole cell inactivated poly-bacterial preparation MV130 effect on nasal mucosal immunity and experimental human pneumococcal carriage: double-blind randomised controlled trial with controlled human infection model.

BACKGROUND: Bacterial mucosal immunotherapy has shown protection of children and adults from both viral and bacterial respiratory infections, offering the potential to reduce antimicrobial use, and hence also control antimicrobial resistance (AMR). Pneumococcal carriage of vaccine type Streptococcus pneumoniae remains high in Malawi despite infant conjugate vaccination and AMR is increasing. We compared nasal inflammation following sublingual bacterial immunotherapy including S. pneumoniae (MV130, Inmunotek, Spain) or placebo and determined the effect in an experimental human pneumococcal carriage model. METHODS: A double-blind, randomised, placebo-controlled trial in healthy adult volunteers was conducted at Queen Elizabeth Central Hospital in Blantyre, Malawi. Participants were randomly allocated to receive MV130 or placebo sublingually once daily for 42 days. Mucosal inflammation (neutrophil to T cell ratio, NTR) was measured in nasal micro-biopsies. Post-treatment, participants were challenged with 160,000 CFU/naris S. pneumoniae 6B (Spn6b). Experimental pneumococcal carriage rates post inoculation were compared between the two arms. All participants completing the study were included in the analysis. Prospective trial registration: PACTR202403820001276. FINDINGS: 107 participants were enrolled and randomised to MV130/placebo between May and December 2024. There were no serious adverse events, complete compliance was good (72%) and all adverse events were mild. 96 participants (53 male, 43 female) completed the study with 52 participants randomised to MV130 and 44 to placebo. There was no difference in mucosal inflammation (neutrophil to T cell ratio) at day 14 of the intervention MV130 NTR median = 0.737 (IQR 0.294, 2.059) and placebo NTR = 0.831 (IQR 0.450, 2.073), p = 0.64. Secondary analyses showed a rise in mucosal neutrophils after MV130 treatment and after experimental pneumococcal inoculation. There was no difference in nasal or serum anti-pneumococcal immunoglobulin or in experimental pneumococcal carriage proportion between MV130 (12/52, 23%) and placebo (10/44, 23%) groups (unadjusted risk ratio 1.02 (CI 0.49-2.12) p = 1.0). INTERPRETATION: MV130 induced non-specific mild neutrophil inflammation of the nasal mucosa but had no protective effect against experimental human pneumococcal carriage. FUNDING: Wellcome Trust.

Humans↗

PubMed2026

Ten years on, still out of reach: barriers to PrEP access and retention in France according to frontline actors (QualiPrEP Study).

Pre-exposure prophylaxis (PrEP) for HIV has been available in France since 2014, and reimbursed since 2016, with general practitioners allowed to prescribe it since 2021. Despite these policy advances, uptake remains low among some of the most affected populations. This community-based qualitative study explored barriers to PrEP access and retention ten years into its implementation.Interviews were conducted with 28 PrEP frontline actors (healthcare professionals and community-based workers involved in promoting, prescribing, or supporting PrEP). The sample included one group discussion (n = 5), two triads (n = 6), two dyads (n = 4), and nine individual interviews (n = 13). Thematic analysis was inductive, with barriers classified across four main domains.Participants were mostly cisgender men, median age 48, born in France and abroad, and employed by NGOs in Paris. Thirteen barriers and four major themes emerged: (1) Internal psychosocial barriers: lack of knowledge, negative health-related reactions; HIV stigma; STI risk perception, taboos; (2) Internal pragmatic barriers: perceived limits of protection, usage and follow-up constraints; (3) External psychosocial barriers: limited physician knowledge and reluctance; (4) External pragmatic barriers: communication failures; structural constraints, lack of human and financial resources.Findings call for more targeted messaging, simplified care models and provider training. They highlight the need to address social and symbolic dimensions of PrEP, with insights from those supporting users to ensure more equitable implementation.

Humans↗

PubMed2026

Construction of precision clinical-proteomics risk model based on machine learning for predicting heart failure in type II diabetes mellitus.

BACKGROUND AND AIMS: Heart failure (HF) is a severe complication in type 2 diabetes mellitus (T2DM), but current risk stratification scores have limited predictive accuracy. We aimed to develop novel prediction tools integrating clinical variables with proteomics to improve risk stratification of hospitalization for HF in T2DM. METHODS AND RESULTS: In this study, we included 2111 UK Biobank participants with T2DM but no prior HF, and profiled 2920 proteins to predict 10-year incident HF hospitalization. Participants were randomly divided into training (70%), tuning (10%), and validation (20%) sets.Three prediction models were developed: a Clinical model based on demographic characteristics, comorbidities, medication use, and laboratory indices; a Protein model based on 40 proteins selected by the Light Gradient Boosting Machine (LGBM); and the Clinical OMics and Protein ASSessment for Heart Failure (COMPASS-HF) model, which integrated both clinical variables and the LGBM-selected proteins. Models were evaluated for area under the curve (AUC), sensitivity, and specificity. During follow-up, 168 participants (7.96%) developed incident HF. The COMPASS-HF model showed better discrimination than the Clinical model, with an AUC of 0.897 (95% CI: 0.850-0.945) versus 0.790 (95% CI: 0.723-0.856). It also demonstrated higher sensitivity (0.882; 95% CI: 0.725-0.967) and consistent performance in subgroups. COMPASS-HF effectively stratified risk of hospitalization for HF, with cumulative incidence rates of 31.9% in the high-risk group and 1.2% in the low-risk group. CONCLUSIONS: By combining clinical and proteomic variables, we developed a high-performance HF prediction model for T2DM, enabling precise risk stratification and informing early intervention strategies.

Humans↗

PubMed2026

Implementing Prolonged Exposure Therapy in a Community Substance Use Treatment Program: A Qualitative Study.

INTRODUCTION: Post-traumatic stress disorder (PTSD) commonly co-occurs with substance use disorders (SUD), yet few community-based SUD programs incorporate evidence-based trauma-focused. Prolonged exposure (PE), including its massed format (M-PE) with session frequency of 3-4 times per week, is a gold standard intervention for PTSD; however, concerns about client readiness, logistical demands and relapse risk have limited its adoption within SUD settings. This study examined staff perspectives on the feasibility and acceptability of integrating M-PE into a community-based SUD program. METHODS: Prior to launching a Hybrid Type 1 effectiveness-implementation trial (Project COMET), we conducted semi-structured virtual interviews with 15 community clinic staff: providers (n = 8), administrators (n = 2) and peer specialists (n = 5). Interviews were recorded, transcribed and analysed using a rapid qualitative analysis framework with matrix techniques to compare themes across roles. RESULTS: Four overarching themes captured staff perspectives on integrating M-PE: (Theme 1) Prior Knowledge and Experiences: Most staff were familiar with EMDR, while direct knowledge of PE/M-PE was limited. (Theme 2) Perceptions of M-PE: M-PE was widely viewed as a promising, structured intervention that fits the pacing and duration of SUD care. (Theme 3) Symptom Reduction and Client Impact: Staff anticipated improvements in PTSD and SUD symptoms through trauma-focused treatment. (Theme 4) Barriers and Constraints: Participants identified several potential implementation challenges, including logistical barriers and client readiness. DISCUSSION AND CONCLUSIONS: Findings suggest that staff generally viewed M-PE favourably but emphasised the importance of ensuring client readiness and organisational support. Enhancing feasibility and long-term sustainability may require expanded psychoeducation, targeted provider training and flexible delivery models. TRIAL REGISTRATION: ClinicalTrials.gov identifier: NCT06968832.

Adult↗

PubMed2026

Reliability-aware hierarchical learning for Chagas disease screening from 12-lead ECGs: tackling label uncertainty and class imbalance.

Objective.Chagas disease, a neglected tropical disease (NTD) with significant cardiovascular impact, remains underdiagnosed in resource-limited regions. Electrocardiogram (ECG) screening offers a low-cost tool for detecting cardiac involvement, yet algorithm development is challenged by label noise, data scarcity, and the latent nature of infection. This study proposes a robust ECG-based screening framework that explicitly addresses these constraints.Approach.We introduce aReliability-Aware Hierarchical Learningstrategy that calibrates supervision according to data provenance, prioritizing serology-confirmed labels over noisy self-reports. To mitigate data scarcity, we compare a specialized convolutional neural network (CNN) trained from scratch with a transfer learning approach based on a Spatio-Temporal ECG foundation Model (FM). Performance is evaluated across varying data scales, and the representation structure is analyzed to interpret model behavior.Main results.On the official hidden test set of the George B. Moody PhysioNet/Computing in Cardiology Challenge 2025, our approach achieved a Challenge Score of 0.163. We observe that while the specialized CNN performs competitively in data-rich regimes, the FM exhibits superior robustness in extreme low-resource settings. Furthermore, performance reaches a plateau imposed by underlying disease physiology. Bimodal score distributions suggest that models distinguish established cardiomyopathy from indeterminate infection, which remains electrophysiologically indistinguishable from healthy controls.Significance.These findings clarify both the potential and intrinsic limits of ECG-based AI screening for NTD-associated cardiac involvement. Reliability-aware supervision and data-efficient transfer learning provide a practical framework toward scalable and clinically meaningful ECG screening systems in resource-constrained environments.

Humans↗

PubMed2026

The value of international collaborations for supporting neuroanesthesia practice, education, and research in resource-constrained settings.

PURPOSE OF REVIEW: Neuroanesthesia practice in low- and middle-income countries is constrained by workforce shortages, limited infrastructure, and variability in clinical practice. Growing global interest in collaboration makes it timely to evaluate how international partnerships can address these gaps and improve equity in care, education, and research. RECENT FINDINGS: Recent literature highlights substantial variability in neuroanesthesia practice and limited access to context-appropriate guidelines and advanced technologies. International collaborations, including training partnerships, scholarship programs, and research networks, have improved knowledge exchange, workforce development, and the adoption of standardized practices. Evidence suggests that specialized training is associated with improved clinical outcomes. However, persistent inequities in research participation, authorship, and leadership, as well as concerns regarding sustainability and 'parachute research', remain. SUMMARY: International collaboration is a key strategy for advancing neuroanesthesia in resource-constrained settings. Sustainable, equitable partnerships that prioritize local ownership, capacity building, and contextual adaptation are essential to improving clinical practice, strengthening education, and enhancing global research representation.

Humans↗

PubMed2026

Predicting ACL injury risk in athletes: A systematic review of machine learning-based models.

BACKGROUND: Early ACL injury risk identification in athletes is essential. This systematic review examines machine learning (ML) models for predicting ACL injuries, evaluating their methodological quality, performance, and reliability. METHOD: A comprehensive electronic search was conducted across PubMed, Scopus, Web of Science, and IEEE Xplore databases, supplemented by Google Scholar for grey literature, covering articles published between January 1, 2015, and August 30, 2025. Eligible studies were appraised using the Prediction Model Study Risk of Bias Assessment Tool (PROBAST) for methodological quality and risk of bias, and the Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD) guidelines for quality of evidence. RESULTS: Ten studies were included. PROBAST showed eight studies had moderate risk of bias and two low risk. TRIPOD found only two studies met quality criteria. ML models included logistic regression (n = 5), support vector machines (n = 4), k-nearest neighbor (n = 3), decision trees (n = 3), random forests (n = 5), neural networks (n = 2), linear discriminant analysis (n = 1), and pre-trained CNNs (n = 1). AUC ranged from 0.63 to 0.98. Accuracy (reported in six studies) ranged from 26% to 95%; however, these values should be interpreted with caution due to the absence of confidence intervals, lack of class imbalance handling, and limited external validation across studies. Tree-based ensemble methods such as random forest achieved competitive accuracy (74-86%), while SVM, a non-ensemble classifier, reported accuracy ranging from 71% to 95%; however, the highest values were obtained in studies with notably small sample sizes (n = 12 to n = 39), raising concerns about overfitting and generalizability. CONCLUSION: Current ML algorithms show promise for identifying athletes at high ACL injury risk and detecting relevant risk factors. Although study quality was generally satisfactory, future research should prioritize external validation and model interpretability to support clinical translation.

Humans↗

PubMed2026

Weight-bearing locomotion mitigates the progression of post-traumatic knee osteoarthritis in male rodents: A systematic review and meta-analysis.

OBJECTIVE: Although knee post-traumatic osteoarthritis (PTOA) has been associated with altered loading, how weight-bearing (WB) activities should be modulated post-injury to preserve knee health remains unknown. This systematic review and meta-analysis examined the effects of WB locomotion on knee PTOA in rodents to provide insight into potential clinical implications. DESIGN: Studies published in PubMed, Cochrane Library, Embase, and CINAHL through 02/2025 and meeting the following criteria were included: 1) rodents with knee PTOA, 2) compared locomotor exercises (treadmill/wheel) to no exercise, 3) outcome measures of PTOA (OARSI/Mankin OA scores, bone quality, osteophyte condition, cartilage quality/morphology). If significant heterogeneity across studies was observed, locomotion speed, time/number of intervention sessions, frequency and duration of intervention, exercise initiation, follow-up time, animal species, PTOA model were analyzed as potential moderator variables that may influence findings. RESULTS: Twenty-two studies met the criteria, resulting in a total of 156 effect sizes (ESs) for meta-analysis (all males). A positive ES denotes less PTOA. Based on the CAMARADES checklist, 5 studies were ranked low risk of bias and 14 studies were ranked moderate risk of bias. Locomotion significantly reduced PTOA severity evaluated using OARSI/Mankin scores (ES=0.927, 95% CI: [0.590, 1.264]) and improved bone quality (ES=0.379, 95% CI: [-0.023, 0.780]) with substantial heterogeneity across studies. Follow-up analyses indicated that greater ESs for reducing OA scores were associated with a shorter duration of each training session, more training sessions, longer intervention duration, and longer follow-up time (all P≤0.004). Greater ESs for improving bone quality were associated with a longer follow-up time and later exercise initiation post-injury (both P≤0.014). Locomotion resulted in small to moderate but non-significant positive effects for isolated measures assessing osteophyte condition (ES=0.379, 95% CI: [-0.023, 0.780]), cartilage quality (ES=0.430, 95% CI: [-0.034, 0.893]), and cartilage morphology (ES=0.464, 95% CI: [-0.006, 0.934]). CONCLUSION: WB locomotion reduced the overall degree of knee PTOA in male rodents. Composite knee OA scores may be more responsive to exercise interventions than isolated cartilage/osteophyte measures. The benefits of locomotion may be more prominent with a longer follow-up time or with exercise protocols consisting of shorter but more individual sessions and longer overall intervention durations.

Animals↗

PubMed2026

Protocol for project FIERCE: A randomized controlled trial to evaluate a positive emotion-focused meditation intervention for physicians with elevated stress.

INTRODUCTION: Nearly 80% of healthcare providers experience adverse psychological symptoms (e.g., depression, burnout, sleep disturbance) stemming from workplace stressors. Elevated levels of stress have been associated with unfavorable occupational, patient, and provider-related outcomes, imposing a heavy burden on a strained system. Given the impact of stress on both employee and patient health, effective interventions are urgently needed to reduce distress and promote well-being among healthcare professionals. Mindfulness-based interventions show promise for addressing these challenges. We developed a six-week, remotely delivered mindfulness intervention, the Building Emotional Strength Training (BEST) program, based on the Buddhist Four Immeasurables practice to cultivate the distinct emotional qualities of loving-kindness, compassion, joy, and equanimity. The present study aims to evaluate the feasibility and efficacy of a Four Immeasurables-based mindfulness intervention on perceived stress (primary outcome), burnout, depressive symptoms, and inflammatory biomarkers, while enhancing psychological well-being and sleep quality (secondary outcomes) in physicians. We will also investigate potential mediators of intervention effects, including compassion, positive affect, equanimity, and mindfulness. METHOD: We will enroll 90 full-time physicians in a remote, two-arm randomized controlled trial with 1:1 allocation to either the meditation intervention or waitlist control. Participants will complete self-report questionnaires and provide blood samples at baseline, mid-course, and post-intervention to assess outcomes and mediators. DISCUSSION: The project aims to advance the study of mindfulness-based interventions that reduce distress and promote well-being through practices that cultivate prosocial and altruistic feelings toward oneself and others. While mindfulness interventions have gained considerable interest, none have specifically drawn from the Four Immeasurables practice to target loving-kindness, compassion, joy, and equanimity. This novel investigation could expand our understanding of practices that foster kindness and compassion to reduce distress in an at-risk population. TRIAL REGISTRATION: ClinicalTrials.gov NCT07283744, registered on 2025/10/14. The Open Science Framework, registered on 2026/06/26.

Humans↗

PubMed2026

The Role of Artificial Intelligence for Intimate Partner Violence Prevention: A Systematic Review.

INTRODUCTION: Intimate partner violence (IPV), encompassing physical, sexual, emotional and economic abuse, remains a pervasive global health concern. Traditional prevention efforts face obstacles such as underreporting, delayed detection and limited personalised support. Emerging artificial intelligence (AI) approaches offer new opportunities to enhance IPV prevention. AIM: This systematic review maps and synthesises evidence on AI-driven tools in IPV prevention based on studies published between 2004 and 2024. METHODS: Following PRISMA 2020 guidelines and PROSPERO registration, we searched PubMed, Embase, CINAHL, PsycINFO, IEEE Xplore and Web of Science. Eligible studies explicitly evaluated AI technologies targeting IPV prediction, screening, intervention or support delivery. Study quality was appraised using the Mixed Methods Appraisal Tool (MMAT). RESULTS: Of 1304 records initially identified, 41 studies met eligibility criteria. AI applications ranged from machine learning (ML) for risk prediction and natural language processing (NLP) for IPV detection in clinical and social media data, to image analysis for forensic evaluation and chatbot-based support. Predictive modelling demonstrated strong discriminative performance, while NLP-based screening detected IPV with notable sensitivity. Chatbots showed feasibility and user acceptability, but evidence of their direct impact on reducing IPV incidence was limited, with one randomised controlled trial showing a modest reduction. Key challenges identified included algorithmic bias, data privacy risks and barriers to integration across health and social care systems. DISCUSSION: AI-informed interventions show promise for improving IPV detection, risk assessment, and scalable support, but questions remain about long-term effectiveness, ethical fairness, transparency and equitable implementation. Future interdisciplinary research should address these concerns to responsibly deploy AI in IPV prevention. RELEVANCE TO CLINICAL PRACTICE: The findings highlight the importance of trauma-informed, culturally responsive care and provider training in AI applications. Nurse-led innovation and policy advocacy will be crucial for safe, equitable integration of AI in IPV prevention.

Artificial Intelligence↗

PubMed2026

Machine learning-based prediction of unplanned readmission and construction of an online calculator for elderly patients with mild ischemic stroke.

OBJECTIVE: To screen for independent risk factors for unplanned readmission in elderly patients with mild ischemic stroke, and to construct and validate an online risk prediction calculator based on an interpretable machine learning model, thereby providing a promising practical tool for accurate clinical assessment of 30‑day all‑cause unplanned readmission risk in this population. METHODS: A prospective cohort study was conducted, including 1050 patients aged ≥ 60 years with mild ischemic stroke admitted between August 2023 and September 2024. Participants were randomly divided into a training set (840 cases) and a test set (210 cases) at a ratio of 8:2. Risk factors were screened by univariate analysis and multivariable Logistic regression. Four machine learning models, namely LightGBM, XGBoost, Random Forest, and K‑Nearest Neighbors (KNN), were developed and their performance was evaluated using AUC, accuracy, sensitivity, and specificity as metrics. The SHAP framework was used for interpretability analysis, and an online calculator was subsequently developed based on the optimal model. RESULTS: Univariate analysis showed significant differences (P < 0.05) in 13 factors including age, smoking, AIP, TyG index, HALP score, etc. Multivariable Logistic regression identified age (OR = 9.752), smoking (OR = 5.171), AIP (OR = 6.691), TyG index (OR = 4.393), HALP score (OR = 2.831), and ≥ 2 comorbidities (OR = 3.664) as independent risk factors. All four machine learning models demonstrated good predictive performance. Based on a comprehensive evaluation of multiple metrics and computational efficiency, the LightGBM model exhibited the best predictive performance (AUC = 0.884, accuracy = 0.829, sensitivity = 0.812, specificity = 0.875). SHAP analysis showed that age, AIP, TyG index, smoking, and HALP score were key predictors. An online calculator developed based on this model enables individualized risk predictions. CONCLUSION: Key risk factors associated with 30‑day unplanned readmission in elderly patients with mild ischemic stroke were identified. The LightGBM model demonstrated high predictive accuracy, and together with the interpretability analysis and online calculator, offers a practical tool to support clinical risk assessment. However, this tool requires future external validation.

Humans↗

PubMed2026

Exploring Primary Care Clinicians' Sexual and Reproductive Health Care Delivery to Male Adolescents and Young Adults.

INTRODUCTION: Despite existing guidance for adolescent sexual and reproductive health (SRH) care, male adolescent SRH care receipt is inadequate. Limited research has explored factors affecting clinicians' provision of SRH care to male adolescents, specifically. METHODS: This mixed-method study with 12 primary care clinicians included a brief survey assessing care delivery practices and confidence, followed by an in-depth interview to explore factors affecting SRH care delivery. RESULTS: Clinicians reported high confidence delivering male adolescent SRH care (mean ± SD: 8.31 ± 1.71 out of 10), but delivered only about half the recommended services (20 items out of 38). Factors influencing care delivery included gaps in education/training, assumptions about male SRH care, and behavioral constraints at various levels. DISCUSSION: Findings underscore the need to strengthen male adolescent SRH care by enhancing provider training, increasing clinic-level supports, and addressing structural barriers. CONCLUSIONS: Findings can inform strategies to improve the quality and comprehensiveness of SRH services for male adolescents in primary care settings.

Humans↗

PubMed2026

Workplace Safety Champions: Strengthening safety culture through nurse engagement.

Workplace violence is a growing concern in health care, disproportionately affecting frontline nurses and nursing assistants. Despite high prevalence, underreporting remains a barrier to effective prevention and response. This article describes the development, implementation, and outcomes of a Workplace Safety Champion program designed to increase reporting of violent incidents and strengthen a culture of safety. A multidisciplinary task force developed an evidence-based Workplace Safety Champion course that emphasizes de-escalation strategies, reporting processes, and staff support. Champions were appointed across inpatient and emergency units and integrated into a hospital-wide Workplace Safety Champion Council. Program evaluation used course completion data and posttraining surveys. The organizational goal of having at least one trained champion in 90% of inpatient and emergency units was exceeded, with 98% of units represented (N = 93 champions). More than 85% of learners reported intent to change their response to workplace violence, and 90% endorsed improved knowledge of resources and de-escalation strategies. The Workplace Safety Champion program successfully improved staff awareness, reporting, and engagement in workplace violence prevention. Embedding champions across units can serve as a sustainable strategy to strengthen safety culture and support frontline health care workers.

Humans↗

PubMed2026

An exploratory analysis of decision-making in population affinity estimation among forensic anthropology practitioners in the United States.

Population affinity estimation in forensic anthropology often involves the integration of multiple pieces of information, including visual (nonmetric) and metric data. This study examines how practitioners interpret and synthesize visual and metric information and their decision-making processes. A Qualtrics survey was developed using two cases: Case 1 presented clear nonmetric signal but ambiguous metric signal, while Case 2 showed more ambiguous nonmetric signal but clear metric signal. Practitioners were asked to estimate population affinity based on visual assessment, Fordisc data, and provide a final, integrated assessment. A total of 22 valid survey responses were received, with the majority of survey respondents reporting more than 10 years of forensic anthropology experience and holding a PhD degree. Results showed that there is substantial variability in Fordisc use and interpretation. Across both cases, participants synthesized conflicting visual and metric information, converged toward the stronger signal, and came to more consistent final estimates relative to the more ambiguous input. These findings highlight variability in practitioner decision-making but suggest that integration of nonmetric and metric information in population affinity estimation can moderate decision-making uncertainty. The results have implications for forensic anthropology education, training, and proficiency testing.

Humans↗

PubMed2026

Improving Sleep and PTSD Outcomes in Service Members: A Randomized Controlled Trial Examining the Long-Term Effects of an Integrated Treatment.

Trauma-induced sleep disturbances often persist after successful posttraumatic stress disorder (PTSD) treatment. While integrated protocols combining sleep and exposure-based treatments may maximize outcomes, prior studies are limited and have largely relied on subjective sleep measures or failed to include long-term follow up assessments. Active-duty service members with PTSD (n = 82) were randomly assigned to Compressed Prolonged Exposure (CPE) treatment or Trauma Management Therapy (TMT), which integrates exposure therapy with sleep hygiene training and other skills-based interventions. PTSD symptoms and actigraphy-based sleep were measured at baseline, posttreatment, 3- and 6-month follow-up and data were compared between groups and across time. Posttreatment, both groups showed negligible to small changes in sleep compared to baseline. However, the TMT group evidenced improvements in most sleep parameters by the 3- and 6-month follow-ups, while sleep health generally worsened in the CPE group over time. Between groups, those randomized to TMT exhibited better sleep efficiency (g = 0.24) and onset latency (g = -0.34) at 3-month follow-up, and better sleep quality (g = 0.70), efficiency (g = 0.51), and wake after sleep onset (g = -0.52) at 6-month follow-up. Within both treatment groups, poorer sleep at the 6-month follow-up was correlated with greater PTSD symptom severity measured at the same time point. Integrated treatment for sleep and PTSD produced superior objective sleep outcomes compared to exposure alone, with the most meaningful improvements in sleep observed 6 months after treatment completion. Several critical directions for future studies are discussed.

Humans↗

PubMed2025

Psychological interventions for children with chronic physical conditions: a systematic review assessing the role of coping, emotional and cognitive processes.

OBJECTIVES: Coping, emotional and cognitive processes are crucial in child development, particularly in children with pediatric chronic physical conditions (CPC). No systematic review in pediatric psychology has investigated the effectiveness of interventions on these processes concurrently. This review addresses this gap by focusing on the effectiveness of psychological interventions on coping, emotional and cognitive processes in children with CPCs. METHODS: Five electronic databases were searched for studies assessing at least one of these processes. Only randomized-controlled trials with children (8-12 years) with a CPC (e.g. diabetes, asthma), which implemented a psychological intervention were included. This study is registered in (CRD42021233505). RESULTS: Ten intervention studies were identified. While cognitive interventions (Cogmed) showed some improvements in working memory, the effects varied across studies despite similar methodologies. Coping interventions (e.g. Coping Skills Training) showed little effect on coping strategies or psychological health variables and were no more beneficial than control groups. No study trained coping, emotional processes and cognitive processes together. CONCLUSION: This review shows current limitations in evaluating psychological interventions targeting coping, cognitive or emotional processes in children with CPCs, limiting a comprehensive understanding of the interventions' action mechanisms. Systematically including underlying processes in intervention studies could help to better adjust those interventions.

Humans↗

PubMed2026

A systematic review of sleep hygiene interventions in physically and operationally demanding occupations.

Workers in physically and operationally demanding (POD) occupations (e.g. military personnel, emergency responders, law enforcement) experience disrupted sleep due to irregular schedules and high-stress work environments. Given the importance of sleep for physical and cognitive performance, interventions to improve sleep in POD occupations are crucial yet remain under-researched. To investigate the effectiveness of sleep hygiene interventions in improving sleep quality and quantity outcomes for POD occupations, a systematic review was conducted across five databases (PubMed, CINAHL, SPORTDiscus, PsycINFO, Web of Science) in September 2024. Eligible studies involved workers in POD occupations where a sleep hygiene intervention was compared against a waitlist or passive control. Ten studies were included, eight randomised controlled trials (RCTs) and two non-randomised controlled trials (NRCTs). Narrative synthesis identified Structured Training Programs (n = 7), Phone Assistance Programs (n = 2), and Single Lecture Interventions (n = 1). Evidence certainty ranged from Very Low to Moderate, converging towards Low quality due to imprecision, risk of bias, and indirectness. Structured Training Programs showed moderate improvements in sleep quality and small-to-moderate effects on sleep duration. Phone Assistance Programs had minimal effects, and Single Lecture Interventions had negligible impact. Higher-quality research is urgently needed to assess sleep hygiene interventions in POD occupations.

Humans↗

PubMed2026

Assessing AI literacy and attitudes among medical students: implications for integration into healthcare practice.

PURPOSE: This study aims to assess AI literacy and attitudes among medical students and explore their implications for integrating AI into healthcare practice. DESIGN/METHODOLOGY/APPROACH: A quantitative research design was employed to comprehensively evaluate AI literacy and attitudes among 374 Lusaka Apex Medical University medical students. Data were collected from April 3, 2024, to April 30, 2024, using a closed-ended questionnaire. The questionnaire covered various aspects of AI literacy, perceived benefits of AI in healthcare, strategies for staying informed about AI, relevant AI applications for future practice, concerns related to AI algorithm training and AI-based chatbots in healthcare. FINDINGS: The study revealed varying levels of AI literacy among medical students with a basic understanding of AI principles. Perceptions regarding AI's role in healthcare varied, with recognition of key benefits such as improved diagnosis accuracy and enhanced treatment planning. Students relied predominantly on online resources to stay informed about AI. Concerns included bias reinforcement, data privacy and over-reliance on technology. ORIGINALITY/VALUE: This study contributes original insights into medical students' AI literacy and attitudes, highlighting the need for targeted educational interventions and ethical considerations in AI integration within medical education and practice.

Students, Medical↗