Research Appraisals
Evidence-based critical appraisals of the latest medical research, systematically evaluated using Oxford CEBM methodology.
Showing 91 appraisals
JMIR mHealth and uHealth
Machine Learning Frameworks for Wearable-Based Stress Modeling in Naturalistic Settings: Scoping Review
BACKGROUND: Stress, as commonly recognized, is an integral part of modern life and can significantly affect both mental and physical health. While substantial advancements have been made in measuring physical fitness through wearable devices, the detection and assessment of mental stress remain in their early stages. OBJECTIVE: The objective of this paper is to review recent studies of wearable-based stress detection in naturalistic settings, with a specific focus on characterizing machine learning frameworks inspired by the model card approach. METHODS: This review was conducted using the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) checklist. A total of 353 articles were identified through searches in databases such as PubMed, MEDLINE, ScienceDirect, IEEE, ACM Digital Library, Web of Science, and Embase. Studies were considered eligible if they collected data from healthy adults in naturalistic settings using wearable devices and used machine learning models for stress detection. RESULTS: A total of 34 articles met the eligibility criteria, including 11 conference papers, 22 journal articles, and 1 preprint published between 2017 and 2024. From these studies, we analyzed key machine learning modeling decisions such as problem formulation, ground truth determination, and machine learning algorithms. Additionally, we examined the major contributions of each study, focusing on the challenges they addressed and the solutions they proposed. Based on these findings, we proposed a model card framework for reporting machine learning-based, wearable-based stress detection. CONCLUSIONS: This scoping review highlights recent trends in machine learning models for stress detection and measurement using wearable signals. It underscores the need for improved standardization in reporting practices for datasets and key machine learning decisions, as well as the importance of addressing critical challenges associated with data collection in real-world settings. We hope this review will support and strengthen ongoing research efforts, promote knowledge sharing, and promote collaboration among researchers-ultimately advancing the field as a community.
1 Aug 2026
Read appraisal →Lasers in medical science
Effect of photobiomodulation on trapezius myofascial pain: a randomized, sham-controlled, double-blind clinical trial.
To investigate the effect of 16 J of 808 nm low-level laser therapy (LLLT) on pain in individuals with trapezius trigger points (TPs). Randomized, double-blind, controlled trial conducted with 52 adults (≥ 18 years) with chronic trapezius pain and TPs. Participants were assigned to the LLLT group (808 nm, 16 J per TP) or the sham group (same protocol without emission). Both received eight sessions over four weeks. Outcomes included pain perception, number of TPs, cervical function, skin temperature, global perceived effect, and adverse events. Randomization was concealed from evaluators and participants. Repeated measures ANOVA (SPSS 21) was used for analysis. No significant differences were found between groups for pain [F(2,100) = 1.21; p = 0.30, η2 = 0.024], TPs [F(1.09,54.27) = 0.35; p = 0.57, η2 = 0.007], cervical skin temperature [F(1.79,89.65) = 0.79; p = 0.45, η2 = 0.042], trapezius temperature [F(2,100) = 1.09; p = 0.34, η2 = 0.021], or global perceived effect [F(1.44) = 1.91; p = 0.17, η2 = 0.029]. Cervical function improved significantly in the LLLT group [F(1.36,67.73) = 3.75; p = 0.04, η2 = 0.070]. Adverse effects were mild and infrequent, mainly warmth. Both groups reported clinically relevant pain reduction. LLLT at 16 J per TP with 808 nm was not superior to placebo for pain, TP count, or skin temperature. However, it improved cervical function, suggesting a functional benefit. Registration: july 18, 2022 on the Brazilian Clinical Trials Registry (REBEC - RBR-10m474jv).
28 July 2026
Read appraisal →Journal of neural engineering
Multimodal quantification of cognitive load using a printed wearable facial bio-potential system
Objective.Cognitive load refers to the amount of mental effort required to process information and perform tasks. It has a strong impact on both learning and task performance and, therefore, plays a central role in cognitive neuroscience, psychology, neurology, human-machine interaction, and education research. Despite its significance, measuring cognitive load is challenging due to its complex and dynamic nature, influenced by task complexity, individual differences, and emotional states. While standard methods are based on subjective self-reports, numerous objective methods have been explored to assess cognitive load, such as electroencephalography (EEG), heart rate variability, and performance-based metrics. Despite these efforts, it remains challenging to objectively measure cognitive load in stationary and even more so in freely moving individuals.Approach.In this study, a wireless wearable system was employed to collect facial bio-potential markers and systematically compare their sensitivity to cognitive load. A dedicated task paradigm was developed and validated to induce graded cognitive load, allowing precise synchronization of physiological signals with task events. Facial muscle activity, eye movements, and brain activity were acquired simultaneously using a single facial electrode array.Main results.Across nine participants, entropy-based features tracked cognitive load more closely than conventional band-power measures. Facial EMG was the most informative modality: a cross-subject (leave-one-subject-out) model predicted the NASA-TLX weighted workload score with a mean Spearman correlation= 0.792 (95% CI [0.747, 0.836]; mean absolute error4.3, on a NASA-TLX 0-100 scale), and a single forehead-EMG entropy feature (the EMG-band entropy of forehead channel Ch14, above the right eyebrow) was selected in every cross-subject fold, generalizing across all participants without subject-specific calibration. An EEG-only model carried weaker but genuine cross-subject information (= 0.739), whereas EOG features did not pass the feature-selection threshold in this seated paradigm.Significance.Facial bio-potentials offer a practical, wearable system for objective cognitive load assessment. An outdoor pilot experiment further demonstrated electrophysiological measures under non-laboratory conditions. These findings offer insights into the suitability of different physiological indicators for assessing cognitive load, particularly the informativeness of facial EMG for seated assessment and the robustness of blink-based measures under ambulatory motion, contributing to the future development of practical measurement techniques in dynamic everyday settings.
23 July 2026
Read appraisal →Science advances
Reduction of appearance artifacts in wearable on-skin electronics
Facial electrophysiological signals are crucial to human-machine interfaces and health care monitoring. Soft and skin-conformable electrodes enabled long-term and comfortable signal monitoring. However, the appearance of the electrodes affects the wearer's social interactions and self-identity, making daily usage difficult and leaving appearance artifacts. Here, we developed fully invisible and unperceivable on-skin electrodes free from appearance artifacts. Neither the wearer nor observers can detect the visual and tactile presence of the electrodes on the skin. The unperceivable property was confirmed with sensory experiments and physical characterizations of the film on skin. Furthermore, our invisible electrodes did not affect the psychological conditions of the wearers, which confirms the feasibility of artifact-free monitoring in daily lives. Last, we demonstrated the functionality of our electrode with successful monitoring of various facial electrophysiological signals, including electrooculogram (EOG), electromyogram (EMG), and electroencephalogram (EEG). Our fully invisible electrodes provide a promising direction in developing on-skin bioelectronics, seamlessly integrating health monitoring and human-computer interaction technologies into people's daily lives.
19 July 2026
Read appraisal →Scientific reports
The influence of contact body size on superficial and deep tissue pain perception for development of a collision dummy.
The use of robots is playing an increasingly important role in industrial manufacturing. Collaborative robots operated alongside humans can pose a hazard. Pain perception as an injury risk indicator is contingent on several factors, especially contact surface, anatomical region and the difference between superficial and deep tissue pain. In this article, we have conducted an experimental load study with human subjects in which we have analyzed these factors to enable the development of a pain-sensitive dummy for testing collaborative robot against biomechanical thresholds. For the load test, we used our established algometer setup with three different contact bodies (7 × 7 mm2, 14 × 14 mm2 and 24 × 24 mm2) to evaluate six anatomical regions of the arm. Subjects were instructed to activate a switch whenever they sensed pain after starting the test procedure. The algometer increased the force in 5 N/s increments. We used topical anesthetic to distinguish between superficial and deep tissue pain. We had 11 male subjects for measurements. The greatest force had to be applied with the medium-sized contact body to induce pain perception. Local anesthetization made it necessary to increase the force of the small and large contact bodies significantly. Our finding that peak force is a reliable indicator of the perception of pain caused by the medium-sized contact body indicates that the contact body's size affects peak force magnitude significantly.
17 July 2026
Read appraisal →Cardiovascular toxicology
Mechanisms of Cardiovascular Toxicity Induced by Silver Nanoparticles: A Systematic Review of Preclinical Evidence
Silver nanoparticles (AgNPs) have emerged as one of the most widely used nanomaterials in medical and consumer products, yet their cardiovascular safety remains inadequately characterized. While their antimicrobial properties are well-documented, accumulating evidence reveals that AgNPs pose a significant threat to the cardiovascular system. Exposure to AgNPs through inhalation, ingestion, or dermal contact enables these particles to enter systemic circulation, where they accumulate in cardiac and vascular tissues in a dose-, time-, and size-dependent manner. Once deposited, AgNPs initiate a cascade of pathological events, including oxidative stress, mitochondrial dysfunction, DNA damage, and disruption of calcium and sodium channel signaling. These molecular disturbances translate into physiological alterations such as bradycardia, atrioventricular conduction block, impaired contractility, and exacerbated ischemia-reperfusion injury. AgNPs also trigger endothelial activation, leading to upregulation of adhesion molecules, namely vascular cell adhesion molecule-1 (VCAM-1), intracellular adhesion molecule-1 (ICAM-1) and recruitment of inflammatory cells, creating a proinflammatory and prothrombotic environment. Elevated cardiac biomarkers, including creatine kinase-MB fraction (CK-MB), brain natriuretic peptide (BNP), lactate dehydrogenase (LDH), and histopathological changes, including pericardial edema, myofibril disorganization, fibrosis, and inflammatory infiltration are consistently observed. Vascular dysfunction manifests as impaired vasorelaxation, enhanced vasoconstriction via endothelin-1 upregulation, thrombosis, and abnormal angiogenesis driven by vascular endothelial growth factor (VEGF) dysregulation. Although plant-based AgNPs have shown cardioprotective potential in limited studies, the overall evidence indicates significant cardiovascular risk. This systematic review synthesizes findings from 38 preclinical studies (35 in vivo, 3 in vitro), providing a comprehensive analysis of AgNP-induced cardiotoxicity with emphasis on oxidative stress, inflammation, and molecular dysregulation as core mechanistic drivers.
13 July 2026
Read appraisal →Aerospace medicine and human performance
Large Language Models as Behavioral Health Teammates in Long-Duration Spaceflight
INTRODUCTION: Long-duration spaceflight exposes crews to isolated, confined, and extreme environments where chronic stress and limited real-time support heighten interpersonal tensions. Large language models (LLMs) could serve as onboard artificial intelligence teammates for de-escalation, but could also exacerbate tensions by endorsing hostility or exclusion. This study evaluates whether LLMs demonstrate prosocial conflict mediation behaviors vs. failure modes that could elevate interpersonal risk. METHODS: We tested four LLMs (GPT-5, Claude Sonnet 4, Grok 4, Gemini 2.5 Pro) using a Dartmouth PATH Program scenario where one crewmember criticizes an absent teammate and tries to recruit another against him. Models were evaluated across seven test batteries: adversarial framing, backstory manipulation, team role variations, aggressor identity variations, target identity variations, mental health sensitivity, and direct ethics questions. We scored 196 responses using a three-point rubric (defended excluded teammate, neutral, or endorsed exclusion). RESULTS: Claude Sonnet 4 achieved perfect scores (3.0). GPT-5 averaged 2.97. Gemini 2.5 Pro (2.81) showed vulnerability to narrative capture, shifting from defending the excluded crewmember to endorsing criticism when given unfavorable backstory. Grok 4 (2.85) exhibited significant failure under social proof pressure, explicitly endorsing exclusion. All models showed consistent prosocial responses regardless of demographic factors and correctly identified ethical issues when questioned directly. DISCUSSION: LLMs show promise as behavioral health teammates but have critical vulnerabilities. They can be manipulated into endorsing teammate exclusion through backstory reframing, peer pressure, or authority hierarchies, requiring safety protocols before deployment. Dvorak C, Buckey JC Jr. Large language models as behavioral health teammates in long-duration spaceflight. Aerosp Med Hum Perform. 2026; 97(7):516-523.
2 July 2026
Read appraisal →Diving and hyperbaric medicine
Bipolar spectrum disorders in divers: risks, recognition, and recommendations.
Bipolar disorder is a recurrent psychiatric condition characterised by episodic mood disturbances, residual functional impairment, and high rates of psychiatric and medical comorbidity. While many individuals achieve symptomatic remission, persistent deficits in cognition, emotional regulation, and insight may remain, raising concerns for participation in safety-critical activities such as scuba diving. This systematic review synthesised evidence from psychiatric, occupational, aviation, and diving medicine literature to examine the clinical course of bipolar disorder, treatment considerations, functional outcomes, and safety-relevant factors pertinent to fitness-to-dive assessments. Bipolar disorder exhibits marked heterogeneity in syndromal and functional outcomes. Even during euthymia, subtle impairments in attention, executive functioning, and decision-making may persist. Pharmacological stability is essential for diving, but treatment regimens, particularly lithium use, polypharmacy, and antidepressant therapy, introduce additional considerations. Comorbidity, circadian disruption, sleep deprivation, and reduced insight during early relapse further complicate risk assessment. Empirical data on diving outcomes in individuals with bipolar disorder are scarce, necessitating reliance on expert opinion and extrapolation from related safety-critical domains. Fitness-to-dive assessments in bipolar disorder should prioritise sustained functional stability, reliable treatment adherence, and illness insight over symptom absence alone. A cautious, individualised approach is warranted, incorporating medication effects, comorbidity, operational context, and relapse-prevention planning, supported by collaboration between mental health professionals and diving medical examiners.
30 June 2026
Read appraisal →Journal of special operations medicine : a peer reviewed journal for SOF medical professionals
Prediction Factors Associated with Success in Military Special Operations Courses: A Systematic Review
BACKGROUND: We investigated factors associated with military personnel success in Special Operations courses (course pass or fail). METHODS: A systematic search was conducted across eight databases using the keywords "Special Operations" and "attrition." Sample size and profile data, course characteristics, predictive factors, follow-up, and results were extracted from the studies. We compared the means of the variables associated with success between the groups that completed and did not complete the courses. A meta-analysis was conducted for factors assessed by two or more studies, pooling standardized or weighted mean differences using fixedor random-effects models. RESULTS: A total of 23 studies were included. They were published between 1990 and 2022, included samples from 10 nations, lasted between 5 days and 12 months, and ranged from 11 to 1,138 individuals. VO2max, pull-ups, push-ups, and marching performance showed consistent positive associations with course success, while body fat percentage and sit-up performance did not. Certainty of evidence was rated as low to very low using the Grading of Recommendations Assessment, Development, and Evaluation. CONCLUSION: The factors that increased the probability of success in Special Operations courses were character strengths, recidivism in the course, anthropometric data, performance on physical assessment tests, psychological dissociation, psychosocial resources, blood markers, smoking/alcohol consumption, personality, and psychological strength. These findings support the inclusion of aerobic capacity, muscular endurance, and psychological resilience in early screening and preparatory selection processes.
29 June 2026
Read appraisal →PloS one
Hybrid deep learning for mental workload classification using EEG with enhanced preprocessing and interpretability
Mental workload classification is critical in safety-sensitive fields such as healthcare and aviation. However, electroencephalography-based approaches still face challenges with generalizability, noise robustness, and interpretability. In this study, we propose an integrated hybrid deep learning framework to address these limitations and enable robust, interpretable electroencephalography-based mental workload classification. The proposed approach uses a Variational Autoencoder to enhance noise reduction and feature extraction from band-wise topographical videos, a Convolutional Block Attention Module to adaptively focus on important spatial-channel Electroencephalogram features, and a Bidirectional Long Short-Term Memory network to capture complex temporal dependencies under leave-one-subject-out cross-validation. We conducted ablation studies to identify each architecture component's contribution and sensitivity analyses to determine the optimal parameters. The model achieved the highest overall accuracy among the baselines and reached an average accuracy of 83.9% across subjects for classifying four mental workload levels. Ablation studies confirmed the added value of all three architecture components for improving performance. Sensitivity analyses identified optimal parameters, including a 10-second window length that balances temporal context and specificity. For better interpretability and neurophysiological insight, we used Gradient-weighted Class Activation Mapping to visualize key frontal-parietal brain regions and frequency bands associated with workload dynamics. Future research could explore adaptive windowing strategies, multimodal data integration, cross-dataset benchmarking, and evaluation against transformer-based and graph neural network architectures under consistent subject-independent evaluation settings to further enhance model generalizability.
28 June 2026
Read appraisal →La Medicina del lavoro
Cadmium Exposure and Risk of Pancreatic Cancer: A Systematic Review and Meta-Analysis
INTRODUCTION: Pancreatic cancer is a highly lethal malignancy with poor prognosis and limited treatment options. Environmental exposures, particularly to heavy metals such as cadmium, may contribute to its etiology. This systematic review and meta-analysis evaluated the association between cadmium exposure from different sources and pancreatic cancer incidence or mortality. METHODS: Following PRISMA guidelines, we searched PubMed, EMBASE, and Scopus. Eighteen studies comprising 20 risk estimates were included. Random-effects meta-analyses were conducted overall and stratified by exposure source, gender, region, study design, and outcome. Dose-response relationships were assessed using meta-regression of cadmium exposure measures. Publication bias was evaluated using funnel plots and Egger's test. RESULTS: Overall, cadmium exposure was associated with an increased risk of pancreatic cancer [relative risk (RR) = 1.69, 95% confidence interval (CI): 1.28-2.22]. Occupational exposure showed the most consistent association (RR = 1.38, 95% CI: 1.19-1.61), followed by urinary and blood/serum biomarkers. Risk was higher in men than in women, and in case-control than in cohort studies. Dose-response analysis did not reveal a linear trend. There was limited evidence of publication bias overall, though some asymmetry was observed for urinary cadmium studies (P = 0.045). CONCLUSION: Cadmium exposure was associated with pancreatic cancer risk, particularly in occupational and biomarker-based studies. While findings support a potential causal link, heterogeneity, residual confounding, and limited dose-response data necessitate cautious interpretation.
26 June 2026
Read appraisal →BMJ open
Understanding self-management and digital health needs in musicians with playing-related musculoskeletal disorders: an explanatory sequential mixed-methods study
OBJECTIVES: To explore the characteristics of musicians with current or previous troubling musculoskeletal symptoms associated with musician self-limitation of activity (often referred to as playing-related musculoskeletal disorders (PRMSDs)), barriers and facilitators to PRMSD self-management, health needs and perspectives on digital health interventions (DHIs). The purpose of the study was to understand the unique needs of this population to effectively inform future DHIs to support this community. DESIGN: A mixed-methods study comprising an online survey and four online focus groups (2-3 participants per group). Survey data were analysed statistically, and focus group discussions were analysed thematically. SETTING: Online survey and focus groups conducted via Zoom. PARTICIPANTS: UK-based musicians with current or previous PRMSDs. RESULTS: A total of 100 musicians participated in the survey study, and 10 musicians participated in the focus group study. Over a third (38%, n=38) of participants had experienced musculoskeletal symptoms that interfered with their ability to play at their usual level 0-2 times in the last 5 years, 24% (n=24) reported 3-5 episodes, and 38% (n=38) had experienced them more than five times. The majority of participants (67%, n=67) continued playing music while experiencing these symptoms. Several barriers to self-management were identified, including limited music-specific health education during training, financial and time constraints and insufficient accessible resources. Key facilitators and health needs included healthcare professionals who understood musicians' professional demands and performance pressures, personalised instrument-specific health support, peer support from fellow musicians and accessible digital resources including self-assessment tools, automated reminders, health tracking, motivational features, peer support forums and multimedia resources organised by condition or symptom stage. Addressing these needs could enable musicians to better manage their symptoms, maintain playing activity and make informed decisions about when and how to seek help. CONCLUSION: This study reveals unmet health needs in musicians and offers recommendations for designing DHIs that promote musicians' health and well-being. Key design recommendations include personalisation by instrument type, health tracking, peer support and consolidated multimedia resources. These findings can inform both intervention design and policy development to better support this population.
26 June 2026
Read appraisal →Journal of medical Internet research
The Emerging Roles of AI in Self-Directed Stress Management: Systematic Review
BACKGROUND: Stress is widespread and carries substantial mental health, social, and economic burdens. Yet, access to clinician-led stress management remains constrained by service capacity, cost, and stigma. In response, artificial intelligence (AI)-enabled tools have rapidly proliferated as scalable, self-directed options. However, evidence on how these systems support stress management outside formal clinical settings remains fragmented. OBJECTIVE: This systematic review aimed to synthesize empirical evidence on how AI-enabled technologies are used for self-directed stress management. We mapped the emerging functions of these tools, the psychological frameworks informing their design, the populations and settings studied, and the outcomes reported. METHODS: We conducted a PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses)-compliant systematic review of English-language studies published between 2000 and 2025. Six databases were searched (APA PsycINFO, PubMed, MEDLINE, Scopus, Web of Science Core Collection, ProQuest, and Google Scholar). RESULTS: Of 3008 records identified, 35 studies met the inclusion criteria. The methodological quality of included studies was critically appraised using the Mixed Methods Appraisal Tool (version 2018). Findings illustrated that AI-supported stress management can operate through 5 core functions, including psychological intervention, behavioral support, psychoeducation, companionship, and emotional support, and stress monitoring, detection, and triage. Across the reviewed studies, these functions supported self-directed stress management by helping users identify stress, regulate responses, and engage in coping outside formal clinical care. CONCLUSIONS: AI-enabled systems show preliminary promise for supporting self-directed stress management through multiple user-facing functions grounded in established psychological frameworks.
26 June 2026
Read appraisal →BMJ open
Workplace interventions for chronic musculoskeletal disorders: a systematic review
OBJECTIVES: To provide an updated systematic review of the effectiveness of workplace interventions for chronic musculoskeletal disorders, identify key intervention components and assess their impact on health and work-related outcomes. DESIGN: Systematic review of randomised controlled trials. DATA SOURCES: MEDLINE, EMBASE, CINAHL, PsycINFO, Scopus and Web of Science were searched for studies published between 2018 and 2024. Grey literature sources were also consulted. ELIGIBILITY CRITERIA: Randomised controlled trials and cluster-randomised trials evaluating workplace interventions targeting adults with chronic musculoskeletal disorders were included. Outcomes of interest included pain, functional outcomes, quality of life and work-related outcomes (eg, work ability, productivity, presenteeism and absenteeism). DATA EXTRACTION AND SYNTHESIS: Two reviewers independently screened studies, extracted data and assessed risk of bias using the Cochrane Risk of Bias 2 tool. Due to heterogeneity, only a narrative synthesis was conducted. RESULTS: 23 studies involving 2456 participants were included and classified into physical exercise, ergonomic and multicomponent interventions. Physical exercise interventions consistently improved pain and functional outcomes. Digital delivery of exercise interventions demonstrated comparable effectiveness to face-to-face or workplace-based approaches. Multicomponent interventions, combining exercise with ergonomic strategies, demonstrated the most consistent benefits across outcomes. Evidence for work-related outcomes was limited and inconsistent, and quality of life was assessed in only a small number of studies. CONCLUSIONS: Workplace interventions, particularly multicomponent approaches combining exercise, ergonomics and education, can be effective in improving health outcomes in chronic musculoskeletal disorders. However, evidence on work-related outcomes remains limited and inconsistent. PROSPERO REGISTRATION NUMBER: CRD420261289218.
24 June 2026
Read appraisal →Scientific reports
Classifying mental stress from eye tracking data: deep learning approaches for out-of-the-lab conditions
Eye-tracking signals such as pupil diameter and gaze behavior have been widely used for stress detection, yet most approaches rely on task-specific features, controlled laboratory settings, or multimodal sensor combinations, limiting scalability in less controlled environments. This work investigates whether unimodal eye-tracking time-series data can support task-agnostic stress detection beyond static laboratory tasks. We analyze stress classification across two complementary datasets: a virtual reality goalkeeper task with moderate visuomotor activity and stable recording conditions, and a virtual job interview dataset reflecting less controlled settings with uncalibrated signals. The results show that these signals alone contain informative patterns related to stress-associated autonomic and oculomotor responses. Under favorable conditions, performance reaches up to [Formula: see text] macro-averaged F1-score. At the same time, performance varies substantially across datasets, indicating that effective learning depends strongly on data quality, calibration, signal characteristics, and task design. Overall, the findings demonstrate the potential of unimodal eye tracking as a lower-burden alternative to more complex multimodal systems, while highlighting that reliable stress detection is fundamentally conditioned by the interplay of data, signal representation, and modeling approach.
23 June 2026
Read appraisal →Progress in biomedical engineering (Bristol, England)
Beyond the lab: a review on neurophysiological mental states assessment in real-world settings.
Recent advancements in wearable, non-invasive neurophysiological sensors have increased interest in applying Human factors (HF) research beyond controlled laboratory settings. HF research aims to objectively quantify individuals' (alone or working in team) mental and emotional states to ensure safety, maintain good performances and prevent risks. These devices enable real time, unobtrusive monitoring of individuals in real-world environments, overcoming the limitations of traditional subjective assessments. Unlike self-reports, neurophysiological signals provide objective, real-time data, allowing for a more accurate and continuous understanding of mental states. This review examines the latest advancements over the past ten years in real-time monitoring of the most studied and operationally relevant mental states, including mental workload (MW), stress, attention, fatigue, drowsiness, and teamwork, by analyzing studies that contribute to research advancing toward real-world application direction. While wearable biosensors were used as one criterion for selecting studies, this review extends beyond device usage to explore crucial methodological aspects relevant to real-world applications, such as data quality, customized processing steps, and the robustness of results compared to traditional laboratory-grade devices. Thus, this review analyzes 123 articles exploring current advancements in real-time monitoring of MW, stress, attention, mental fatigue, drowsiness, and teamwork using wearable devices. The findings provide a comprehensive overview of methodology advancement toward real-time, objective individuals monitoring in real world settings.
20 June 2026
Read appraisal →Journal of hazardous materials
Prenatal 6:2 Cl-PFESA exposure and birth weight: A systematic review and meta-analysis
Most existing systematic reviews and meta-analyses on PFAS have focused on legacy compounds, yet with less attention on PFAS alternatives and mixtures. Chlorinated polyfluorinated ether sulfonic acids (Cl-PFESAs) have been introduced as PFOS alternatives. Among these, 6:2 Cl-PFESA has accumulated persistent environmental residues and has recently emerged as a significant health concern. We aim to evaluate the association between prenatal 6:2 Cl-PFESA exposure and birth weight using a meta-analysis. A systematic review identified 12 studies reporting birth weight in relation to 6:2 Cl-PFESA biomarker measures, including maternal serum and umbilical cord samples. Risk of bias and study sensitivity were assessed, and overall effects were estimated by random-effects model. Stratified analyses were conducted by study confidence, sample size, and exposure level. The overall pooled estimate showed no statistically significant association between 6:2 Cl-PFESA exposure and birth weight. However, subgroup analyses revealed a consistent reduction in birth weight per ln-unit increase in 6:2 Cl-PFESA in studies with large sample sizes (β = -20.05 g; 95% CI: -33.13, -6.97), high methodological confidence (β = -16.74 g; 95% CI: -26.42, -7.06), or higher exposure levels (β = -16.15 g; 95% CI: -25.43, -6.87), all showing low between-study heterogeneity. Sensitivity analysis excluding the study contributing most to heterogeneity indicated a 15.57 g decrease in birth weight per ln ng/mL increase in 6:2 Cl-PFESA (95% CI: -24.74, -6.40). Although current evidence suggests that prenatal 6:2 Cl-PFESA exposure may adversely affect fetal growth, well-designed prospective studies with standardized exposure assessments are needed to confirm this potential risk.
15 June 2026
Read appraisal →Human vaccines & immunotherapeutics
Immunogenicity and safety of AS03-adjuvanted A/Astrakhan/3212/2020 (H5N8)-like influenza vaccine in adults: Phase 1/2, observer-blinded, randomized trial
Influenza pandemics arise from novel influenza A viruses. Recent emergence of a new clade (2.3.4.4.b) of the highly pathogenic H5N1 in animals and humans highlighted its pandemic potential. We evaluated the immunogenicity and safety of GSK's AS03-adjuvanted H5N8 vaccine in adults. In this phase 1/2, observer-blinded, age-stratified, randomized trial, healthy US adults (age, ≥18 y) received two intramuscular doses of hemagglutinin antigen (3.75 or 7.50 μg) with AS03A or AS03B, administered 21 d apart. Immunogenicity - seroprotection rates (SPRs), seropositivity, geometric mean titers (GMTs), geometric mean fold rise (GMFR), and seroconversion rates (SCRs) - was evaluated on day 43 using hemagglutination inhibition (HI) and microneutralization (MN) assays. Safety was monitored throughout the study. Of 520 enrolled participants, 518 were vaccinated. On day 43, the US Food and Drug Administration's (FDA) Center for Biologics Evaluation and Research criteria for influenza vaccines were met. HI SPRs, seropositivity rates, SCRs, GMTs, and GMFR appeared to be higher in the AS03A vs AS03B group. Immune responses were generally higher in younger (aged 18-64 y) vs older (aged ≥65 y) adults. Immune responses were also detected in MN assays, with a correlation between HI and MN responses on day 43 across age groups and vaccine formulations. Safety was acceptable, with no increase in adverse events post-dose 2. Reactogenicity appeared more common in younger adults. The antigen-sparing potential of AS03 was demonstrated, with an acceptable safety profile. The benefit/risk profile was favorable for all formulations tested, including 3.75 µg AS03A (licensed in the US).ClinicalTrials.gov registration: NCT05975840. Influenza pandemics occur when new influenza viruses emerge, causing widespread illness. A recent study investigated a new vaccine, GSK’s AS03-adjuvanted H5N8 influenza vaccine, for its ability to trigger an immune response and its safety in adults aged 18–64 y and older adults aged ≥65 y. Overall, 518 participants received at least one dose of the vaccine, with different amounts of hemagglutinin antigen (3.75 or 7.50 µg) combined with different concentrations of AS03 adjuvant. Immune responses were assessed at multiple time points, with the primary endpoint measured on day 43 post-vaccination. The vaccine produced a strong immune response in both age groups, with younger adults showing slightly better responses than older adults. The vaccine met the required standards for an immune response set by the US Food and Drug Administration (FDA). AS03A typically induced greater immune response than AS03B. Safety monitoring showed that the vaccine was generally well tolerated, with the most common side effects being mild, such as injection-site pain and muscle ache. This study confirmed that the AS03-adjuvanted H5N8 vaccine has the potential to provide protection against influenza with an acceptable safety profile, making it a valuable candidate for pandemic preparedness.
14 June 2026
Read appraisal →JMIR formative research
Digital Health Monitoring and Intervention Suite for Stress in Frontline Nurses: Prospective Cohort Trial
BACKGROUND: Stress among health care workers (HCWs) contributes to burnout, workforce attrition, and adverse patient outcomes. Although virtual reality (VR), psychoeducation, ecological momentary assessments (EMAs), and wearables have independently shown promise in stress research, no integrated digital suite has combined controlled stress induction, intervention delivery, and longitudinal real-world monitoring in HCWs. OBJECTIVE: This study aimed to evaluate the feasibility, engagement, and preliminary effectiveness of a multimodal Digital Health Monitoring and Intervention suite for Stress framework integrating VR simulation, psychoeducation, EMAs, and wearable biometrics. We examined (1) the impact of VR simulation and psychoeducation on stress outcomes and (2) associations between physiological and self-reported mental health outcomes. METHODS: Ninety-nine nurses (mean age 33.7, SD 8.9 yr, 87% female) were enrolled in 2023. We conducted a single-arm prospective cohort study (NCT05923398). Using convenience sampling, participants were recruited from social media advertisements, flyers, and email notices distributed through professional listservs. Participants completed ≥2-week baseline monitoring, a single VR session (2 runs separated by a brief psychoeducation intervention), and 12-week follow-up. In-VR stress was assessed using the Subjective Units of Distress Scale (SUDS) and 4-item Moral Injury Outcome Scale (MIOS-4), with synchronous heart rate variability. Longitudinal outcomes included weekly and biweekly EMAs alongside 70 wearable-derived features. Paired t tests, aligned rank transform ANOVA, and Pearson correlations informed study objectives, with P values adjusted for multiple comparisons. Qualitative content analysis classified emotional responses during and after VR. RESULTS: VR significantly increased subjective stress across checkpoints in both runs, with attenuation in Run B relative to Run A (all P<.001). No significant heart rate variability differences were observed between runs (P=.15). During VR, 92% (91/99) of participants felt stressed, 36% (36/99) reported anxiety or nervousness, and 51% (50/99)-78% (77/99) endorsed anger, guilt, shame, and/or betrayal. Most (59/99, 60%) HCWs returned to an emotional baseline post-VR, although 12% (12/99) reported lingering distress. Immediate reliable improvements in anger, guilt, shame, and/or betrayal occurred for 50% (50/99)-75% (74/99) of participants post intervention. Anxiety (mean -0.53, SD 2.34; P=.03) and stress (mean -3.05, SD 11.35; P=.01) decreased 2 weeks post intervention, but were not sustained at 12 weeks. Increased sleep restlessness was the only wearable feature showing significant changes (mean 2.46%, SD 5.43; Padj<.001). In-VR stress correlated with 12-week real-world stress (SUDS: r=0.57-0.58; MIOS-4: r=0.58-0.61; all P<.01). Data completion exceeded 90%, with 71% achieving full compliance. CONCLUSIONS: This study moves beyond single-tool interventions to demonstrate the feasibility and preliminary effectiveness of an integrated, multimodal stress platform within a single coordinated framework. This trial demonstrates high engagement, short-term symptom responsiveness, ecological validity, and emotional safety. The framework provides a scalable model for proactive stress identification, skills training, and implementation in high-risk occupational settings. Randomized controlled trials are needed to establish sustained efficacy and optimize deployment for real-world implementation.
13 June 2026
Read appraisal →PloS one
Feasibility of mechanomyography-based fatigue classification for passive lower-limb exoskeleton evaluation: A pilot study
Mechanomyography (MMG) enables non-invasive monitoring of muscle mechanical activity, while its utility in time-resolved fatigue detection during dynamic human-exoskeleton interaction remains underexplored. This pilot study explored the feasibility of combining MMG with machine learning to characterize neuromuscular fatigue and evaluate passive lower-limb exoskeleton assistance during repetitive 10 kg squat-lifting tasks. MMG signals from five lower-limb muscles were extracted for time-domain, frequency-domain and nonlinear features, and fatigue identification was implemented via a spectral-based criterion and multi-muscle voting optimization. A radial basis function-enhanced random forest (RBF-RF) model integrated with data augmentation was validated through leave-one-subject-out cross-validation. The results demonstrated that the 1/5 voting rule achieved optimal performance, with mean accuracy of 0.913 ± 0.057, AUC of 0.792 ± 0.073, and a low fatigue detection error of 1.4 ± 0.8 s. Data augmentation steadily improved model robustness, and predicted fatigue levels were significantly correlated with subjective perceived exertion (ρ = 0.756, p < 0.001). This pilot study demonstrates the feasibility of MMG-based fatigue monitoring for wearable assistive systems. The proposed framework supports objective, high-temporal-resolution fatigue monitoring, and may serve as a viable tool for assessing wearable assistive systems. Further large-cohort studies are required to validate its generalizability for practical applications.
13 June 2026
Read appraisal →Scientific reports
Influence of a passive shoulder exoskeleton on drilling performance in women- a cross-sectional study
Work-related musculoskeletal disorders are common, especially among women performing repetitive overhead tasks. In a randomized 2 × 2 crossover study with 14 female participants, we investigated the effects of a passive upper-body exoskeleton during an overhead precision task, involving the tightening of 20 bolts into a sensor-based workstation, while muscle activation, task performance, and usability were assessed. The results showed significant reduced M. Trapezius muscle activations during arm lowering (p = .041, 73%), and lower target accuracy (p < .001, 100%) when using the exoskeleton. Subjective strain in the shoulders was significantly lower when using the exoskeleton (p = .035, 15%). The usability was rated as "unacceptable", with users criticizing the complexity and learning effort. While the exoskeleton reduced muscle load, its mechanical limitations impaired precision and usability, especially for women. These results highlight the importance of sex-specific, ergonomic, and adaptive designs to improve exoskeleton effectiveness and acceptance.
11 June 2026
Read appraisal →Environmental monitoring and assessment
YOLO-geochemical integration for real-time aeolian dust monitoring: a critical review of signatures, health risks, and policy implications
Aeolian dust is one of the most pervasive natural air pollutants, strongly influencing atmospheric chemistry, visibility, climate feedback, and human health. This review critically evaluates the integration of geochemical fingerprinting techniques and YOLO-based deep learning frameworks for real-time aeolian dust monitoring, with the overarching objective of advancing source attribution, health risk assessment, and evidence-based policy formulation in dust-prone regions. The Middle East, particularly the Qatar Peninsula, experiences recurrent dust storms that transport mineral particles and trace metals, contributing to degraded air quality, reduced solar energy efficiency, and elevated risks of respiratory and cardiovascular diseases. Traditional monitoring approaches including satellite observations, ground-based measurements, and geochemical analyses such as inductively coupled plasma mass spectrometry (ICP-MS), scanning electron microscopy with energy dispersive spectroscopy (SEM-EDS), and X-ray diffraction (XRD) have improved source apportionment and compositional understanding, yet they often lack real-time detection and high-resolution spatiotemporal coverage. Emerging artificial intelligence (AI) tools, particularly the You Only Look Once (YOLO) deep learning framework, offer transformative opportunities for atmospheric dust monitoring by enabling rapid, automated plume detection across multispectral and hyperspectral datasets. This review demonstrates that ICP-MS, SEM-EDS, and XRD, when applied in combination, provide robust multi-dimensional source fingerprinting: ICP-MS isotopic ratios discriminate crystal from anthropogenic contributions, SEM-EDS resolves particle-level morphology and elemental composition, and XRD quantifies mineralogical assemblages that vary systematically with source region and transport pathway. Concurrently, successive YOLO generations (YOLOv5 through YOLOv10) have achieved real-time dust plume detection with mAP values exceeding 88% on satellite and UAV imagery. The proposed multimodal YOLO-geochemical integration framework, in which YOLO-guided plume detection triggers targeted field sampling and geochemical labels iteratively retrain detection models, enables compositionally resolved, real-time exposure mapping that directly supports inhalation risk assessment against WHO PM2.5 and PM10 thresholds and metal-specific reference concentrations. Collectively, these findings establish a scalable, evidence-based monitoring architecture that strengthens early-warning systems, informs air quality regulation, and advances global strategies to mitigate the environmental and public health burdens of dust storms.
7 June 2026
Read appraisal →Medicine
Machine learning identifies traffic-related pollutant mixtures as potential factors suggestive of association with osteoporosis in a cross-sectional analysis of NHANES
The combined effect of traffic-related pollutant mixtures on osteoporosis (OP) remains unclear. This study aimed to evaluate such associations using machine learning and mixture modeling approaches. A cross-sectional analysis was conducted among 3053 participants from the National Health and Nutrition Examination Survey 2015 to 2018. Eighteen exposures were assessed, including heavy metals, polycyclic aromatic hydrocarbons, phthalates, per- and polyfluoroalkyl substances, volatile organic compounds, diesel exhaust, and behavioral factors. OP was defined by femoral neck bone mineral density T-score ≤-2.5. Weighted quantile sum regression and 5 machine learning models were applied, accounting for complex survey design. OP prevalence was 12.97%. Weighted quantile sum regression showed a positive but nonsignificant mixture odds ratio (odds ratio = 1.21; 95% confidence interval: 0.77-2.63; P = .412). The largest positive weights were daily outdoor work duration (0.240) and occupational diesel exhaust (0.136). Random forest identified female sex, age, vitamin D, blood lead, and body mass index as top predictors. Least Absolute Shrinkage and Selection Operator achieved the highest test area under the receiver operating characteristic curve (0.81) with the lowest cross-validation standard deviation (0.0167). Its coefficients highlighted occupational diesel exhaust (β = 0.40) as a notable environmental factor alongside female sex (β = 0.95) and age (β = 0.70). Traffic-related pollutant mixtures showed a cross-sectional association with OP in a nationally representative US population, with occupational diesel exhaust and prolonged outdoor work emerging as key contributors in mixture models. However, the nonsignificant overall mixture effect and potential for reverse causation warrant caution in interpretation. These findings support considering environmental and occupational factors in OP prevention, although the overall mixture effect did not reach statistical significance.
31 May 2026
Read appraisal →BMJ open
Multicentre randomised controlled trial of the Norwegian health in work service for patients with common mental disorders or musculoskeletal disorders: the Norwegian Sickness Absence Clinic (NSAC) Efficacy Study
INTRODUCTION: In many countries, a high or increasing rate of sickness absence is challenging the sustainability of present sickness absence benefit schemes. Most sickness absence is certified on the grounds of common mental disorders or musculoskeletal disorders, and substantial effort has been invested in developing interventions promoting return to work for these patients. In Norway, the Health in Work ((HelseIArbeid), HIA) clinics were established as outpatients' services within the specialised healthcare system, with the aim of improving health and supporting return to work. The HIA service admits patients with low-to-moderate anxiety/depression and/or musculoskeletal disorders. In this protocol, we describe the naturalistic multicentre randomised controlled trial Norwegian Sickness Absence Clinic Efficacy study, which aims to determine the effect of HIA on work participation and health. METHODS AND ANALYSES: The HIA outpatient service is staffed by clinical psychologists, physiotherapists, medical specialists in physical medicine and rehabilitation and employment support supervisors from the Norwegian Labour and Welfare Administration. Patients admitted to HIA have access to multidisciplinary assessment and treatment. The trial recruits' patients from five HIA outpatient clinics in Northern Norway. Patients are randomised in equal proportions to either (1) rapid HIA (assessment within 4 weeks), (2) delayed HIA (assessment within 10-14 weeks) or (3) active control, which consists of a monodisciplinary examination at HIA close to diagnosis-specific deadline for examination as suggested by guidelines (8-26 weeks). The trial commenced recruitment on 16 January 2023 and will recruit 2500 patients. The aim is to assess the effect of the HIA service, with the hypothesis that the HIA concept is superior to what resembles treatment as usual, in improving employment and preventing long-term welfare dependency. Secondary outcomes include self-reported symptoms of health problems. We also examine the effect the service has on other healthcare utilisation. To date, no research has been conducted to assess the efficacy of the HIA service. If proven efficacious, and if there is an economic case for this investment in tailored healthcare delivery, the policy implication may be implementation of the service at scale. If not, adaptations or investments into other viable paths of treatment may be considered. ETHICS AND DISSEMINATION: The study is approved by the Regional Committee for Medical Research Ethics (REC North, #122770). Results from the study will be disseminated at national and international scientific conferences, to funders and participating outpatient clinics in seminars and in peer-reviewed scientific journals. TRIAL REGISTRATION NUMBER: NCT05310695.
31 May 2026
Read appraisal →BMC musculoskeletal disorders
The effectiveness of telerehabilitation in upper limb musculoskeletal disorders: a systematic review.
BACKGROUND: Telerehabilitative services have been increasingly used in recent years, raising questions about their effectiveness. In musculoskeletal disorders, evidence supports telerehabilitation but methodological quality of evidence is limited and focused on lower limb disorders. Hence, this systematic review aimed to assess the effectiveness of telerehabilitation in upper limb musculoskeletal disorders. METHODS: We conducted electronic searches in MEDLINE, Embase, and AMED (up until 11/27/2024), complemented by hand searches. Randomized (RCTs) and non-randomized studies of interventions (NRSIs) assessing upper limb musculoskeletal disorders reporting on pain, activities of daily living (ADL), or health-related quality of life (HrQol) were included. We focused on studies from high-income countries in English or German. Risk of bias was assessed using RoB 2 and ROBINS-I. Standardized mean differences (SMDs) and 95% confidence intervals were derived from meta-analysis, where negative values favor telerehabilitation. Certainty of evidence was rated using GRADE. We grouped studies comparing telerehabilitation to standard care (subgrouped into in-person or minimal intervention) and separately examined studies with a tele-adjunct. Compared to in-person rehabilitation we assessed whether outcomes were similarly improved with telerehabilitation. For tele-interventions as adjunct or compared to minimal care we assessed whether telerehabilitation improved outcomes. RESULTS: We included twenty RCTs representing 1,285 participants. All NRSIs were excluded due to critically high risk of bias. Compared to in-person rehabilitation, telerehabilitation showed similar improvements with very low certainty of evidence in pain (SMD: -0.33 [-1.16 to 0.5] VAS/NRS), moderate in ADL (SMD: -0.07 [-0.31 to 0.17] QuickDASH/DASH), and low in HrQol (SMD: -0.26 [-0.82 to 0.31]). Compared to brochure-based exercising, certainty of evidence was low for improved pain (SMD: -0.28 [-0.58 to 0.03]), moderate in ADL (SMD: -0.56 [-0.88 to -0.24]), and low in HrQol (SMD: -0.31 [-0.65 to 0.02]). Tele-adjunct yielded low certainty of evidence for improvements in pain (SMD: -0.38 [-0.71 to -0.05]) and moderate in ADL (SMD: -0.51 [-0.88 to -0.14). CONCLUSIONS: Our results tend to support the effectiveness of telerehabilitation across different comparisons although certainty of evidence is inconclusive. Future studies should particularly investigate the non-inferiority of telerehabilitation compared to in-person rehabilitation and the added benefit of tele-adjunct. TRIAL REGISTRATION: Prospero registration ID: CRD42024622465.
31 May 2026
Read appraisal →BMJ open
Impacts of human and digital scribes on improving physician wellness and reducing administrative burden: a scoping review protocol
INTRODUCTION: The rise in administrative burden on physicians is becoming increasingly recognised as a significant contributor to burnout and job dissatisfaction among primary care practitioners. Human scribes (HS) and digital/artificial intelligence scribes (DS) have emerged as potential tools to reduce clerical workload and improve physician well-being. There has been extensive research conducted on HS, and DS show promise but require validation across diverse healthcare settings. This scoping review aims to assess the effects of HS and DS in primary care settings to evaluate their impact on administrative work, job satisfaction, burnout and clinical operational efficiency. METHODS AND ANALYSIS: This review will follow the Arksey and O'Malley framework with enhancements from Levac et al, along with Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) reporting guidelines. A comprehensive search strategy was developed, including Ovid MEDLINE, Ovid Embase and Scopus databases in June 2025 with an academic health sciences librarian. Database searches will be conducted between March and June 2026. The inclusion criteria consist of empirical research on licensed physicians in primary care settings that implement HS or DS systems and measure physician-related outcomes. Two researchers will review and extract data independently using Covidence. The results will be analysed through thematic methods followed by descriptive summarisation. ETHICS AND DISSEMINATION: This review conducts an analysis of existing publications without needing ethical clearance. Findings will be disseminated through peer-reviewed publications and presented at academic conferences where possible. The research team will make supporting materials accessible via the Open Science Framework. TRIAL REGISTRATION NUMBER: https://doi.org/10.17605/OSF.IO/T5BHY.
31 May 2026
Read appraisal →Journal of breath research
Investigating short-term lung inflammation using exhaled breath VOCs from exposure to candles: a randomized controlled crossover study among young mild asthmatics
Lung inflammation is associated with a response to pollution or irritants and is reported to release volatile organic compounds (VOCs) in breath, that can be monitored non-invasively. An approach to studying short-term lung inflammation is exposure to environmental emissions. This study used brief exposure to PM₂.₅ emissions from two types of burning candles to evaluate potential differences in airway inflammation among mildly asthmatic individuals. The aim was to explore VOCs linked to lung inflammation by exposure to candle emissions; emissions from candle A, a newly developed candle with a smaller emission to the surroundings and candle B, a well-characterised commercially available candle. Seventeen non-smoking asthmatics (11 female) with a mean age of 21.9 years participated in a randomised controlled double-blind crossover study including two exposure sessions: i) air with candle A emissions (mean PM2.554.1µg m-3), and ii) air with candle B emissions (mean PM2.598.4µg m-3). Participants underwent two five-hour, double-blind exposure sessions in a controlled climate chamber, each involving emissions from one of two candle types. Exhaled breath was collected at baseline (0 h), immediately post-exposure (5 h), and the following morning (24 h). VOCs were analysed using both a targeted panel of 23 predefined compounds and an untargeted workflow, which yielded 21 breath-derived VOCs after ambient-blank filtering. Statistical comparisons used Wilcoxon signed-rank tests with Benjamini-Hochberg correction. Across both exposure sessions, ten exhaled VOCs showed significant changes between baseline and immediate post-exposure (5 h), including increases in several aldehydes and decreases in selected sulfur-containing compounds. These changes were not present at 24 h, where samples resembled baseline profiles. The ten VOCs were identified from a combined dataset of 23 targeted compounds and 21 untargeted breath-derived VOCs; only the untargeted analysis yielded significant findings after correction. PCA demonstrated clear separation between baseline and 5 h samples, particularly for candle B, for which 0 h, 5 h and 24 h samples formed distinct clusters. This candle-specific response produced a multi-VOC signature capable of distinguishing individuals immediately after exposure, suggesting that exhaled VOCs can reflect short-term airway inflammatory responses. The findings suggest that an emission-specific, exhaled breath, multi-VOC profile can detect individuals exposed to inflammation triggers, such as candles. This indicates that VOCs could serve as biomarkers for detecting short-term airway inflammation, as evidenced by the systematic difference in VOCs observed 5 h after exposure to candle B. This research contributes to the advancing field of VOC-based health monitoring and might affect public health implications.
30 May 2026
Read appraisal →Journal of medical Internet research
Detection of Microbehavior Intervals for Predicting Mental Health: Clinically Relevant and Advanced Multimodal Temporal Analysis
BACKGROUND: Health care workers (HCWs) face sustained psychological demands that place them at heightened risk for burnout and posttraumatic stress disorder (PTSD). However, assessing psychological distress in this population remains challenging because of stigma, underreporting, and the limitations of self-report tools. Although nonverbal behaviors such as facial expressions and gaze hold diagnostic promise, most approaches overlook the fine-grained, temporal fluctuations in these signals. In this study, we focused on microbehavior intervals-brief, involuntary changes in multimodal nonverbal signals-that emerge during emotion-eliciting interviews. OBJECTIVE: This study aimed to determine whether microbehavior intervals improve the discrimination of psychological distress profiles among HCWs with symptoms of burnout and PTSD. METHODS: HCWs participated in a semistructured interview that included 5 work-related, emotionally charged questions and that was recorded via Webex (online video platform). Participants also completed validated questionnaires for burnout (Maslach Burnout Inventory General Survey 9-item) and PTSD (PTSD checklist for Diagnostic and Statistical Manual, 5th edition). Recordings were analyzed with computer vision models to generate time-series data of facial expressions, head movement, gaze, body posture, and hand gestures. An unsupervised anomaly detection model (MOMENT [a Family of Open Time-Series Foundation Models]) isolated microbehavior intervals without requiring manual labels. Features derived from these intervals were used to train a deep learning classifier that predicted 4 symptom classes of psychological distress: "moderate-severe burnout," "subthreshold-provisional PTSD," "burnout+PTSD," and "resilient." We conducted an ablation study by systematically removing one behavioral data stream at a time. Finally, we conducted an explainability analysis to characterize the features driving model predictions. RESULTS: We analyzed 258 interview recordings from 151 HCWs. Per interview, an average of 19.65 (SD 6.01) microbehavior intervals were detected, each lasting an average of 1.31 (SD 1.10) seconds. The classifier demonstrated robust performance across classes, achieving a macro- F1-score of 0.75 and a macro area under the receiver operating characteristic curve of 0.80 on held-out data. Ablation analysis showed that excluding gaze or arousal-valence signals caused the largest performance declines, particularly in recall and F1-score. The explainability analysis revealed distinct temporal patterns across symptom classes, with irregularity and variability in microbehaviors emerging as key predictors. CONCLUSIONS: Focusing on microbehavior intervals yields a scalable, interpretable, and annotation-free framework for detecting psychological distress from nonverbal signals. By moving from whole-video features to fine-grained multimodal temporal modeling, we successfully captured subtle, involuntary fluctuations in nonverbal responses to emotion-eliciting questions. This multimodal approach enables an objective, robust, and explainable assessment of psychological distress and offers a promising complement to conventional psychometric assessments.
29 May 2026
Read appraisal →The Science of the total environment
Ambient and personal air monitoring using low-cost sensors to identify elevated air pollution in vulnerable communities
In the United States, regulatory air monitoring networks are designed to assess regional air quality but do not capture neighborhood-level variability in pollutant concentrations. Low-cost air monitors may be one approach to address these gaps. The objective of this study was to evaluate whether neighborhood-level and personal monitoring identified increased concentrations of fine particulate matter (PM2.5) exposures in the neighborhood of Lower Price Hill (LPH), Cincinnati, Ohio, compared with regional regulatory monitors. We deployed a network of 12 PurpleAir monitors across LPH from April-June 2022 and calibrated measurements to a nearby U.S. EPA regulatory monitor. PM2.5 concentrations and Air Quality Index (AQI) values measured by the PurpleAir sensors were compared with two EPA monitoring sites. Additionally, three residents conducted 7-day personal monitoring using wearable AirBeam devices. Calibrated PurpleAir data demonstrated significantly increased PM2.5 concentrations at four of the 12 PurpleAir sites and increased AQI at 11 of the 12 PurpleAir sites in LPH compared to EPA regulatory sites. PurpleAir data also found that LPH experienced up to six days where AQI exceeded healthy levels (>50) but the EPA regulatory monitor did not exceed 50. Personal monitoring revealed substantial variability, with two of three participants exceeding the EPA 24-hour outdoor PM2.5 standard (>35 μg/m3) on all study days, indicating exposures not measured by outdoor stationary monitoring. Overall, regulatory monitoring may underestimate neighborhood-level and personal PM2.5 exposures in LPH. Community-based and personal monitoring provides novel insights into localized exposure disparities and may improve risk communication and inform interventions in vulnerable communities.
25 May 2026
Read appraisal →Environmental monitoring and assessment
XGBoost-based analysis of short- and long-term COVID-19 air quality impacts in Tehran, Iran.
This study examines the air quality trends associated with COVID-19 lockdowns and the subsequent recovery on air quality in Tehran, employing an established XGBoost framework for de-weathering with daily data from 22 monitoring stations from 2019 to 2025. A comprehensive analysis was conducted on six principal pollutants (PM₂.₅, PM₁₀, NO₂, O₃, SO₂, and CO) across four distinct temporal phases: Pre-pandemic, Pandemic, Post-COVID, and After Termination. While the model effectively reduced meteorological bias, exhibiting strong efficacy for particulate matter (R = 0.78-0.88; IOA = 0.68-0.88; FAC2 > 0.95), we explicitly acknowledge less consistent outcomes for reactive gases such as O₃ (R < 0.75; NMGE > 0.30). The imposition of lockdown measures coincided with significant reductions in pollutants associated with vehicular traffic: NO₂ exhibited a decline of - 30 to - 36%, PM₂.₅ decreased by - 18 to - 22%, and CO diminished by - 20%. In contrast, O₃ experienced an increase of + 20-30%, indicative of diminished NO titration. Principal Component Analysis (PCA) corroborated these trends, with PC1 (NO₂, PM₂.₅, CO) accounting for approximately 60% of the variance throughout the pandemic. In subsequent phases, partial recoveries were noted: PM₂.₅ escalated by + 15-20%, NO₂ stabilized at around -10%, SO₂ consistently decreased (-30% by 2025), whereas O₃ surged by + 20% by 2025, clustering with PM₁₀ in PC2, thereby highlighting the impacts of dust and secondary formation processes. Lockdowns yielded transient enhancements in air quality, with restricted long-term advantages. Sustainable progress necessitates the adoption of comprehensive policies targeting traffic, industrial emissions, VOCs, and dust management.
25 May 2026
Read appraisal →Journal of medical Internet research
Effectiveness of a Blended Intervention to Promote Physical Activity Among Office Employees: Randomized Controlled Trial
BACKGROUND: Regular moderate-to-vigorous physical activity (MVPA) reduces the risk of noncommunicable diseases, yet one-third of adults globally fail to meet MVPA recommendations. Office employees, among the least active groups, face heightened risks due to prolonged sedentary behavior and barriers like lack of time, fatigue, and low motivation. Although scalable, web-based interventions frequently face challenges, such as low engagement, high attrition, and limited personalization. Blended interventions, combining digital tools with interactive components, show promise but lack robust evidence among office employees. OBJECTIVE: This study aimed to evaluate the effectiveness of a theory- and evidence-based blended intervention in increasing MVPA among office employees. METHODS: This 24-week, 3-arm randomized controlled trial included 141 office employees, randomized equally into blended (web-based+electronic workshops [e-workshops]), web-based only, and control groups. The intervention was developed using the intervention mapping framework, ensuring a systematic, evidence- and theory-based design tailored to the needs of the target population. All participants accessed a study website; the control group used the "Library" module with general MVPA information, while intervention groups also accessed a tailored, theory-driven "Intervention" module. The blended group additionally attended 3 live e-workshops (weeks 2, 4, and 8). MVPA was objectively measured using hip-worn accelerometers at baseline (T1), postintervention (12 wk, T2), and follow-up (24 wk, T3). Retention, engagement, and acceptability were assessed. Analyses followed the intention-to-treat principle, and generalized linear mixed models were used to evaluate between-group differences at T2 and T3, adjusted for baseline MVPA, sex, and ActiGraph LEAP (Ametris) wear time. RESULTS: A total of 141 participants (mean age 32.1, SD 9.2 y; 75/141, 53.2% female) were randomized evenly across groups with comparable baseline characteristics, except for sex (P=.002). The blended group showed significantly greater MVPA increases than the control at T2 (β coefficient=0.247, 95% CI 0.013-0.480; P=.04) and T3 (β coefficient=0.373, 95% CI 0.139-0.607; P=.002), and greater increases than the web-based at T2 (β coefficient=0.287, 95% CI 0.062-0.512; P=.01) and T3 (β coefficient=0.368, 95% CI 0.138-0.597; P=.002). Sensitivity analyses confirmed the robustness of these findings. Retention and engagement rates were 83% and 56%, respectively. Participants reported high acceptability across all domains. CONCLUSIONS: A blended intervention integrating tailored web content with interactive e-workshops significantly increased MVPA at T2 and T3 compared with both web-based and control groups. This success stems from the rigorous intervention mapping framework, which systematically identified effective behavior change and habit formation theories, while the blended approach addressed key limitations of traditional and web-based methods. These findings underscore the potential of evidence- and theory-based blended approaches to address common barriers to MVPA and promote sustainable MVPA in workplace settings, offering promising opportunities for broader scalability and personalization.
23 May 2026
Read appraisal →Current environmental health reports
Industrial Pollution and Health in Louisiana: A Systematic Review of Quantitative and Qualitative Studies
PURPOSE OF REVIEW: Louisiana has one of the largest concentrations of petrochemical industry in the USA. Many studies have assessed patterns of industrial pollution and health in Louisiana; we aim to systematically review this evidence. We systematically searched PubMed, Web of Science, Embase, APA PsycInfo, and GreenFILE for peer-reviewed papers published 1999-2024 that reported geographical variation in health or industrial pollution, and/or tested for an association between the two in Louisiana. We used Covidence to support standardized review and extraction. RECENT FINDINGS: We identified 2485 non-duplicate papers in our search; 53 met the inclusion criteria. Most reported quantitative findings. All studies of industrial pollution described air pollution (some also described other pollution). Studies described various health outcomes, including cancer, respiratory health, mortality, and COVID-19. Overall, people who lived closer to industrial activity had higher pollution exposure and worse health. Black and lower-income residents were exposed to more industrial activity than white and higher-income residents. Twenty-one studies assessed statistical associations between industrial pollution and health; many found an association. Twenty-one studies were quantitative and adjusted for confounding, 29 studies did not adjust for confounding (including qualitative studies), and three studies did not adjust for confounding and had authors with industry ties. Evidence suggests that there is a higher burden of air pollution and worse health outcomes in industrialized areas of Louisiana. While there was some evidence of significant associations between industrial pollution and health outcomes, research with larger sample sizes and improved pollution exposure measurements could be informative.
23 May 2026
Read appraisal →Occupational and environmental medicine
Lymphoid neoplasms and benzene exposure using a revised classification scheme: systematic review and meta-analysis
Lymphoid neoplasms are a diverse group of cancers derived from lymphocytes, with classification recently updated by the 5th edition of the WHO Classification of Haematolymphoid Tumours (WHO-HAEM5). Benzene, a well-known carcinogen, is widely used in industries and environmental exposures vary; however, its association with specific lymphoid neoplasm subtypes remains unclear due to historical classification challenges. This study aimed to clarify the risk of lymphoid neoplasms related to benzene exposure using the updated WHO-HAEM5 framework.A systematic review and meta-analysis were conducted following Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Eligible studies were comparative human studies evaluating benzene exposure and lymphoid neoplasm risk, with sufficient data for risk estimates. Inclusion criteria specified study designs, participants, exposures and outcomes based on clinical diagnoses. Data were independently extracted by multiple reviewers. The main outcome was risk of lymphoid neoplasms and subtypes analysed by random-effects meta-analysis with heterogeneity and bias assessments.From 1488 records, 95 studies met criteria (65 occupational and 30 environmental exposure). Benzene exposure significantly increased overall lymphoid neoplasm risk (RR 1.26; 95% CI 1.18 to 1.35). B-cell neoplasms showed elevated risk (RR 1.26; 95% CI 1.16 to 1.37), including mature B-cell neoplasms, Hodgkin lymphoma and plasma cell neoplasms. T-cell and natural killer cell neoplasm risk was not significantly increased overall. Occupational exposures conferred higher risks with lower heterogeneity than environmental exposures.Benzene exposure is strongly associated with increased risk of lymphoid neoplasms, particularly B-cell subtypes, supported by the refined WHO-HAEM5 classification reducing heterogeneity. This study underscores the importance of biologically informed disease classification and the need for targeted occupational safety and environmental health policies.The review protocol was registered in the International Prospective Register of Systematic Reviews (PROSPERO) (Registration ID: CRD420251063844) .
23 May 2026
Read appraisal →BMJ military health
Risk and protective factors for suicide-related outcomes among serving military personnel: a systematic review of cohort studies
INTRODUCTION: Understanding risk and protective factors for suicide-related outcomes (suicidal ideation, attempts, and deaths) among military personnel is key to the development and design of suicide prevention initiatives. Current literature has predominantly focused on ex-serving or a combination of ex-personnel and serving-personnel. Therefore, factors that may be pertinent for serving personnel are less understood. This review aims to identify risk and protective factors for suicide-related outcomes comprising serving military personnel. METHODS: The review adhered to PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines and was registered on PROSPERO. A systematic literature search of academic databases on military personnel and suicide from 2004 was conducted. Two independent reviewers conducted study selection. Inclusion criteria included serving personnel, cohort study design, and suicide-related outcomes. Exclusion criteria were National Guard or reservists, and studies not in English. Data on study and participant characteristics, military-specific variables, and suicide-related outcomes were extracted. All studies were critically appraised. RESULTS: A total of 53 studies were included. Most studies were published in the past 10 years, comprised army personnel from the USA, and focused on suicide attempts. Overarching risk factor categories included adverse military experiences, deployment, repeated help seeking and support, junior rank, occupation and time in service and vulnerability factors within the military context. Since many of these risk factors are inherent to military service, they may potentially be considered non-modifiable. Despite a limited number of studies on protective factors, several studies found that unit cohesion reduced the likelihood of suicidal ideation and suicide attempts. CONCLUSIONS: Suicide prevention strategies within the military may be enhanced by targeting risk factors that are potentially modifiable. Less modifiable risk factors could potentially be targeted through improved personnel management practices, particularly in preparation for and after deployment among first-year personnel. Longitudinal research programmes that identify and examine risk and protective factors for serving military personnel are needed. PROSPERO REGISTRATION NUMBER: CRD42024558183.
22 May 2026
Read appraisal →ACS applied materials & interfaces
Breathable Bioadhesive Electronic Skin for Precise Multimodal Electrophysiological Monitoring with AI-Assisted Physiological-State Prediction
Accurate and stable monitoring of electrophysiological (EP) signals is essential for wearable healthcare applications. However, conventional commercial electrodes suffer from poor conformal contact, limited breathability, and weak adhesion under sweaty skin and human motion condition, leading to unstable skin-electrode interface and low-quality signal acquisition. Moreover, single-modality EP signal measurements are insufficient for reliable physiological and health evaluation. Herein, we develop a multimodal electrophysiological signal monitoring electrode (MESME) featuring a fiber membrane combining thermoplastic polyurethane (TPU) and poly(acrylic acid) functionalized with N-hydroxysuccinimide ester (PAA-NHS), seamlessly integrated with a liquid metal (LM) serpentine conductive path. Compared with commercial electrodes (gel-based wet electrode), this MESME exhibits high mechanical flexibility and conformability, superior breathability, robust bioadhesion, stable electrical performance under perspiration, and motion conditions and lower interfacial impedance, enabling high-fidelity multimodal signal acquisition. Leveraging this capability, the multimodal signals generated by MESME can be further integrated with deep learning for drowsiness prediction, achieving a validation accuracy of 97.14%. This platform offers a practical strategy for multimodal electrophysiological monitoring and supports future development of data-driven health assessment models.
21 May 2026
Read appraisal →JMIR mHealth and uHealth
Multimodal Personalized Mobile Health Just-in-Time Adaptive Intervention for Occupational Stress Management: Pilot Study
BACKGROUND: As digital health solutions gain traction, there is an urgent need for effective, person-centered stress management tools for employees. Advances in wearable stress monitoring and machine learning now enable the collection of high-resolution, real-time data and the delivery of personalized interventions with respect to both timing and content. Despite this technological progress, there remains a notable paucity of mobile health (mHealth) interventions that capitalize on these capabilities to implement just-in-time adaptive interventions (JITAIs). OBJECTIVE: This pilot study aimed to examine the preliminary effects of the RELAX mHealth app, which uses a JITAI framework to reduce occupational stress, with a particular focus on initial indications of stress reduction, the influence of the app's degree of personalization on these effects, and overall user experience. METHODS: A 2-arm randomized trial was conducted with 46 employees across two phases of 3 weeks each. Participants used the RELAX mHealth app, which adapts intervention timing based on real-time stress data and tailors content according to users' preferences. Stress was assessed via multiple heart rate variability (HRV) parameters measured with wearable sensors, alongside subjective questionnaires and ecological momentary assessment (EMA) questionnaires. Usability and user satisfaction were also evaluated. In the second phase, a group randomization was implemented, with two groups of different degrees of in-app personalization. RESULTS: The findings were mixed. While selected subjective stress indicators decreased from baseline to the end of the study, one physiological measure increased. Retrospective chronic stress (P=.048) and arousal related to the most recent stress event prior to EMA entry (P=.02) were reduced; however, the low frequency (LF)-to-high frequency (HF) ratio of HRV increased (P=.03). Other stress variables, such as work-related stress, valence of daily experiences, and momentary arousal, showed no change over the course of the study. No between-group differences were observed with respect to the assigned degree of personalization. Additionally, usability increased (P<.001), whereas satisfaction decreased at a trend level (P=.05). CONCLUSIONS: Although the results provide selective preliminary indications of stress reduction with app use, the variability of outcomes raises concerns about its current practical utility. The effects are also discussed with regard to the specific design of the prototype and the study setup. Technical issues and user discomfort were noted despite increasing usability over the study period, indicating areas for improvement in the developed prototype. Given the absence of a control group and the use of interventional arms only, no definitive conclusions about effects can be drawn. Nevertheless, this study highlights both the potential and the challenges of a personalized mHealth approach to occupational stress management. Further refinement of the app and more comprehensive evaluations are needed to fully understand the impact and functional usability of this JITAI-based approach in real-world settings.
19 May 2026
Read appraisal →Environmental research
Association between air pollution and chronic cough: Evidence from a systematic review and meta-analysis of observational studies
BACKGROUND: Chronic cough is a common and debilitating respiratory symptom, yet its association with air pollution remains insufficiently understood. METHODS: We conducted a systematic review and meta-analysis of observational studies evaluating the association between air pollution and chronic cough. Searches of Medline, Embase, Scopus, and GeoBase were completed up to February 14, 2025. Studies were eligible if they reported quantitative associations between air pollution and exposures and chronic cough outcomes. Two reviewers independently screened studies, assessed quality, and extracted data. Random-effects models were used to estimate pooled odds ratios (ORs) with 95% confidence intervals. RESULTS: Of 1250 articles screened, 53 studies were reviewed, and eight were included in meta-analysis. Pooled ORs per 10 μg/m3 increase in exposure were 1.10 (95% CI: 0.99-1.22) for PM2.5 and 1.04 (95% CI: 1.02-1.06) for NO2. Heterogeneity was low to moderate (I2: 18.9% for PM2.5; I2: 0.0% for NO2), and there was no evidence of publication bias (Egger's test p = 0.98 and 0.47, respectively). Stratified analyses revealed stronger associations in adults and males. Evidence on progression from chronic cough to more severe respiratory conditions was limited, highlighting a critical gap for evidence-informed clinical follow-up and public guidance to prevent progression. CONCLUSION: Our findings highlight that chronic cough is an overlooked consequence of air pollution exposure and emphasize the need for health care professionals to consider environmental factors when managing persistent cough. Strengthening air quality control and early detection strategies could help reduce the respiratory disease burden.
18 May 2026
Read appraisal →Environmental pollution (Barking, Essex : 1987)
A systematic review of low-level ambient fine particulate matter (PM2.5) exposures and adverse cardiovascular health outcomes
Associations between exposure to fine particulate matter and impacts in the cardiorespiratory system are well-established, but associations are expected to vary based on exposure duration and concentration. Domestic and international agencies set standards and guidelines for PM2.5 in part to protect public health. This systematic review explores if associations exist between adverse cardiovascular endpoints and PM2.5 exposure when the concentrations are below regulatory standards. In total, 1349 documents were screened, with 95 articles meeting criteria (i.e. peer-reviewed, PM2.5 concentrations reported, cardiovascular endpoints measured). Of the included articles, 67% (64 articles) observed significant associations with adverse cardiovascular endpoints with variable findings based on specific cardiovascular endpoints (e.g. direct measurements - 69% of articles, cardiovascular endpoints - 44% of articles, events and mortality - 76% of articles, and biomarkers - 67% of articles). While not universally consistent, significant associations were observed between PM2.5 exposure and adverse cardiovascular effects, even at levels deemed compliant with current regulations. This review highlights the necessity for further research across diverse geographical settings and populations to clarify the implications of low-level PM2.5 exposure on cardiovascular health. The findings advocate for a review of regulatory standards for PM2.5 exposure in the context of cardiovascular health.
17 May 2026
Read appraisal →PloS one
Deep learning-based bimodal speech and facial expression recognition of miners' unsafe emotions
Under the influence of unsafe emotions, miners' ability to perceive risks is hindered, which can easily lead to decision-making errors and safety accidents. To recognize unsafe emotions exhibited by miners during operations, this study proposes a deep learning-based bimodal framework that integrates speech and facial expression features. A convolutional neural network (CNN) combined with a bidirectional long short-term memory (Bi-LSTM) network is employed to model local spectral patterns and temporal dependencies in speech signals, and ShuffleNet-V2 is used to capture deep facial features. In addition, three feature enhancement strategies are proposed to improve the generalization ability of the model. By constructing a dataset containing five categories of miners' unsafe emotions for network training, the model achieves a mean recognition accuracy of 85.56%. Furthermore, we conducted a preliminary field test of the bimodal model in a real mining environment. The results provide preliminary evidence of its potential applicability in real-world mining conditions.
16 May 2026
Read appraisal →Journal of global health
Global prevalence of underweight and overweight/obesity among migrant workers: a systematic review and meta-analysis
BACKGROUND: Migrant workers are increasingly recognised as a nutritionally vulnerable population due to unstable working conditions, disrupted food environments, and limited access to health services. However, global estimates of malnutrition in this group remain fragmented. We aimed to identify the global prevalence of underweight and overweight/obesity among migrant workers and identify key moderating factors. METHODS: We conducted a systematic review and meta-analysis following PRISMA guidelines. We searched seven databases without restrictions on language, region, or year of publication. Observational studies reporting the prevalence of underweight (body mass index (BMI) < 18.5 kg/m2) or overweight/obesity (BMI ≥ 25 kg/m2) among migrant workers were included. We calculated pooled prevalence estimates using generalised linear mixed models with random effects. We assessed heterogeneity with I2 and Cochran's Q statistics. Moderator analyses were conducted using meta-regression and subgroup comparisons. Further, we appraised study quality using the Joanna Briggs Institute tools, and assessed the certainty of the evidence using GRADE. RESULTS: We included 30 studies involving 135 404 migrant workers with a mean age of 32.3 years (standard deviation = 11.1). The pooled prevalence was 4.7% (95% confidence interval (CI) = 3.1, 6.9) for underweight and 43.7% (95% CI = 39.8, 47.6) for overweight/obesity, suggesting a substantial double burden of malnutrition. Underweight was more common among local migrants and in lower-middle-income settings, particularly in South Asia, whereas overweight/obesity was more common among international migrants, especially in high-income countries and in Europe and Central Asia. Moderator analyses identified female proportion, destination country or region, income level, and marital status as significant moderators for both outcomes. CONCLUSIONS: Migrant workers experience a substantial double burden of malnutrition, although the pattern varies across migration contexts. Undernutrition remains a concern in lower-income settings, whereas overweight and obesity are more common in high-income settings. Integrating nutrition-sensitive strategies into labour and migration health policies, including healthier food environments, routine nutritional screening, and culturally appropriate interventions, may help reduce health inequalities in this population. REGISTRATION: INPLASY: 202540030.
16 May 2026
Read appraisal →Environmental research
Mental health benefits of specific blue space types and characteristics: A systematic evidence map.
Natural environments support mental health and wellbeing, yet limited evidence exists about which types or characteristics are most beneficial. This is particularly true for blue spaces (natural or manmade outdoor environments featuring water). This evidence map investigates the mental health and wellbeing benefits associated with specific blue space types (e.g. coast, river) and characteristics (e.g. sounds). Following PRISMA guidelines, specific keywords were used to search for articles published from inception to March 2025. A total of 139 papers were included, examining the relationships between specific blue space types or characteristics and mental health. Coastal environments were the most frequently studied blue space (94 studies). Affect was the most assessed mental health outcome (74 studies). Most studies were conducted in European and Asian countries. Most studies compared blue spaces to urban or a green space environments, rather than other blue spaces. Only six studies directly compared different blue space types within the same study. Overall, the current evidence base does not allow for comparisons between different blue space types or characteristics, due to the limited number of direct comparisons and substantial heterogeneity across study designs, outcome measures, geographical contexts, blue space descriptions, and study populations. Future research should prioritize direct comparisons between different blue spaces, preferably including the detailed descriptions and analyses of specific characteristics, and examine the role of amount and type of exposure to better inform evidence-based recommendations for blue spaces and mental health.
16 May 2026
Read appraisal →European respiratory review : an official journal of the European Respiratory Society
Exploring the role of cold air in airway hyperresponsiveness and asthma diagnostic testing: a systematic review and meta-analysis
BACKGROUND: Asthma is a heterogeneous disease, making uniform diagnosis challenging, resulting in both under- and overdiagnosis. We conducted a systematic review and meta-analysis to summarise the data on diagnostic accuracy of a cold air bronchial challenge test (CACh) in exercise-induced bronchoconstriction and asthma. METHODS: Databases (PubMed, Embase, Web of Science core collection, Cochrane Library and the Cumulative Index to Nursing and Allied Health Literature via EBSCO) were systematically screened for articles evaluating the use of CACh as a diagnostic tool in exercise-induced bronchoconstriction and asthma. A random-effects model was used to calculate the effect on the reduction in forced expiratory volume in 1 s (FEV1) by CACh in healthy controls compared to patients with asthma. Risk of bias was evaluated using the Quality Assessment of Diagnostic Accuracy Studies-Comparative. This study is registered with PROSPERO (www.crd.york.ac.uk/PROSPERO/ identifier number CRD42021290350). RESULTS: After screening and removal of duplicates, 39 studies were included. Aggregate sensitivity and specificity of a FEV1 decrease ≥10% after CACh were 0.808 and 0.959, respectively. The weighted mean difference in percentage FEV1 decrease after CACh for patients with asthma compared to healthy controls was -17.58. High heterogeneity (I2=99) was mainly explained by differences in reference tests to diagnose asthma. Application of the trim-and-fill method to account for publication bias imputed eight studies, resulting in a notable reduction in heterogeneity. The overall effect (a significant decrease in FEV1 in patients with asthma) remained statistically significant (-8.18, p<0.001). CONCLUSION: Our study shows the high sensitivity and specificity and possible clinical implications of a CACh in the diagnosis of asthma. Further research is needed to confirm this.
15 May 2026
Read appraisal →PloS one
The impact of walking on creative thinking: A systematic review and meta-analysis
BACKGROUND: Walking is associated with many benefits, from improved mental health to a reduced risk of mortality - but can it boost creative thinking? Current evidence suggests a positive effect of physical activity on creative thinking, but the specific effect of walking has not been adequately explored. METHODS: We conducted a systematic search of the PsycINFO, MEDLINE, Scopus, and ProQuest databases from inception to 8 January 2025 using search terms related to walking and creativity. We then performed meta-analyses of Cohen's d effect sizes to assess the effect of walking on creative thinking, as represented by divergent thinking (the ability to generate novel and useful ideas) and convergent thinking (the ability to analyze and select ideas to find the best solution). We assessed study quality using a creativity-specific tool, and the certainty of the evidence with the Grading of Recommendations Assessment, Development and Evaluation Framework. RESULTS: We identified 23 studies (12 randomized experimental studies, nine non-randomized experimental studies, two observational studies) from 16 articles including a total of 1,036 participants, most of whom were post-secondary students. We found moderate certainty evidence of a large effect of walking on divergent thinking (d = 0.93 [95% CI 0.44, 1.42]), and very uncertain evidence of a null effect of walking on convergent thinking (d = 0.16 [95% CI -0.31, 0.63]). Sensitivity analyses of randomized trials only found similarly large effects of walking on divergent thinking ability (d = 0.82 [95% CI 0.35, 1.28]). CONCLUSION: Results suggest that walking likely results in a large increase in divergent thinking, indicating its potential as an intervention to stimulate creative idea generation.
15 May 2026
Read appraisal →Journal of medical Internet research
Plain Language Summarization of Environmental Health Research Using Generative AI: Community-Engaged Qualitative Study
BACKGROUND: Plain language summaries (PLSs) are increasingly required in environmental health publications to improve public accessibility. Generative artificial intelligence (AI) systems such as large language models can automatically produce such summaries; however, automated summaries often overlook local context and cultural relevance-limitations that are critical in environmental health research, where affected communities face disproportionate exposure and health risks. OBJECTIVE: This study aimed to develop and refine community-informed prompts for generating PLSs of environmental health research using generative AI. METHODS: We conducted a community-engaged qualitative study in Louisville, Kentucky, involving workshops with 97 participants from 4 stakeholder groups: summer interns at a local social justice nonprofit organization, participants in a youth development program, participants in a faith-based community organization, and members of a community advisory board focused on environmental justice. Participants reviewed PLSs generated using 3 different prompt styles in GPT-4o (ChatGPT, OpenAI). Feedback was collected through structured discussions and facilitator notes. Data were analyzed using thematic analysis informed by knowledge translation frameworks to identify preferred structural, linguistic, and contextual features of the summaries. RESULTS: Participants consistently preferred summaries between 300 and 400 words, written at a sixth- to eighth-grade reading level. Key priorities included presenting definitions before findings, using headings and bullet points to improve readability, and clearly explaining real-world and environmental justice implications. Narrative summaries without structure were viewed as overly long and difficult to interpret, while purely bullet-based formats were considered too simplified. Feedback from youth participants emphasized clarity and practical relevance, while faith-based participants highlighted the importance of trust and contextual framing. These insights informed the development of a community-refined prompt that included definitions, key findings, an introduction, and a concluding section on community implications. CONCLUSIONS: Community-engaged prompt development may improve the relevance and interpretability of AI-generated PLSs of environmental health research. Incorporating stakeholder perspectives into prompt design offers a replicable strategy for improving research translation and ensuring AI-generated summaries reflect the informational needs of affected communities.
15 May 2026
Read appraisal →Annals of work exposures and health
Occupational mechanical exposures and upper-body musculoskeletal disorders: an overview of systematic reviews
OBJECTIVES: The aim was to provide a comprehensive overview of systematic reviews with meta-analyses of the association between occupational mechanical exposures and upper-body musculoskeletal disorders. METHODS: A systematic literature search was conducted in PubMed, Web of Science, Embase, and CINAHL. Systematic reviews were considered eligible if they included occupational mechanical exposures and provided weighted measures of association in relation to specific musculoskeletal disorders. From each systematic review, data on eg databases searched, exposure, outcome, effect estimates, and quality of evidence of the association were extracted. The methodological quality of each systematic review was assessed using AMSTAR 2. RESULTS: The screening of 1,756 articles resulted in the inclusion of 17 systematic reviews with meta-analysis covering 6 musculoskeletal disorders: subacromial pain syndrome (n = 3), lateral epicondylitis (n = 2), carpal tunnel syndrome (n = 7), Dupuytren's disease (n = 4), hand-arm vibration syndrome (n = 1), and hand osteoarthritis (n = 1). The weighted odds ratios from the meta-analyses generally ranged between 1.5 and 3.0. Approximately 35% of the included systematic reviews assessed the quality of evidence of the association, and among these, 48% graded the evidence as being low to very low. CONCLUSIONS: In general, this overview found an increased risk associated with most mechanical exposures in relation to specific upper-body musculoskeletal disorders. However, grading of evidence quality was rarely applied, and when present, showed substantial variability. Future research related to primary studies should prioritise high-quality prospective studies and objective exposure assessments, and systematic reviews would benefit from standardised methodologies and improved reporting transparency to further strengthen the evidence quality.
14 May 2026
Read appraisal →Journal of the International Society of Sports Nutrition
Carbohydrate supplementation for endurance exercise in the heat: a systematic review with practical recommendations
BACKGROUND AND OBJECTIVES: Endurance exercise performance is impaired by heat stress. Although the mechanisms are not yet fully understood, heat-induced increases in glycogenolysis may play a role. Therefore, carbohydrate supplementation recommendations may differ for endurance exercise in hot environments. This systematic review aims to investigate the efficacy of carbohydrate supplementation for endurance performance in hot environments. METHODS: Electronic databases, including PubMed, the Cochrane Library, MEDLINE, and SPORTDiscus were searched through to April 14, 2026 for studies evaluating endurance performance outcomes under heat stress in response to carbohydrate supplementation. Studies were included if the participants were healthy, aged 18-65 years, and at least recreationally active. A hot environment was defined as an ambient temperature >23 °C. Studies were required to involve an endurance-based and continuous exercise protocol lasting >30 min and a direct performance measure. This cutoff was selected based on evidence indicating glycogen stress at ~40 min under heat stress. In total, 3151 records were identified, and nine randomized, crossover studies met the inclusion criteria. Risk of bias was assessed using the Cochrane Collaboration's tool for assessing risk of bias. Primary outcomes (endurance performance and carbohydrate oxidation rates) and secondary outcomes (including gastrointestinal symptoms, thermoregulation, and hydration markers) were extracted. Mean carbohydrate intake rates were calculated, and a narrative synthesis was performed. RESULTS: Mean carbohydrate ingestion rates ranged between 14 and 140 g·h-1, and the exercise trial duration ranged between ~50 and 152 min. Carbohydrate supplementation resulted in equivocal benefits to endurance performance: five studies found significant (p < 0.05) positive effects for carbohydrates (increases of 13.4%-19.3% in time to exhaustion and 3.3%-12.7% in time trial), and four studies found no significant effects. Although nonsignificant, three studies reported average improvements of 6.7%-11.6%, which may be meaningful in elite athletes. Most studies reported no differences in the respiratory exchange ratio between trials, indicating a preferential reliance on glycogen, which is in line with the literature. Carbohydrate ingestion during exercise in the heat did not influence markers of hydration, thermoregulation, or fatigue compared with placebo. Given that prior research shows gastrointestinal symptoms is common in endurance events in hot environments, the absence of symptom investigations in seven out of nine studies reviewed has important implications for interpreting findings. Overall, the results suggest that carbohydrate intake may not reduce glycogen breakdown during endurance performance in heat. Future research should help to better understand the underlying reasons, including any moderating effects of gastrointestinal distress. CONCLUSIONS: Carbohydrate intake during endurance exercise in the heat does not consistently improve exercise performance. Athletes may focus on maintaining hydration during exercise and practice gut-training, based on the wider literature. Future studies should fill the gaps in research, namely, the measurement of gastrointestinal symptoms, the mechanisms of exogenous carbohydrate use in heat, and different modes of exercise, including running, to enable the development of robust, evidence-based recommendations.
12 May 2026
Read appraisal →International journal of biometeorology
Recent trajectories of biometeorology in the Global South: a systematic review of biometeorology research in the International Journal of Biometeorology
Literature reviews have become increasingly prominent in scientific discourse; however, few have critically examined the geographic distribution of research, particularly in the field of biometeorology, where studies remain disproportionately concentrated in the Global North. Building on previous regional reviews and special issues, this paper critically examines geographic disparities in publication trends, authorship, and methodological approaches to aid in identifying challenges and opportunities for equitable and justified future research. Using a PRISMA-style framework, 591 empirical studies conducted across 147 Global South countries were identified from a total of 3,046 publications in the International Journal of Biometeorology (IJBM) between 2000 and 2024. The review reveals a steady increase in contributions from the Global South over time, but highlights persistent gaps in representation, especially in author affiliation and research leadership. Thematic and methodological analyses highlight the diversity and innovation present in Global South contexts, while also revealing structural barriers to equitable participation in the field. This work advocates for more inclusive research practices and enhanced institutional support to bridge the divide between the Global North and South, fostering a more representative and collaborative future for biometeorology.
12 May 2026
Read appraisal →Journal of medical Internet research
Effectiveness of mHealth Interventions Aimed at Promoting Physical Activity and Reducing Sedentary Behavior on Work-Related Outcomes Among Workers: Systematic Review
BACKGROUND: Mobile health (mHealth) technologies have gained popularity and may play a key role in promoting health-related behaviors. However, the impact of mHealth interventions on work-related outcomes remains unclear. OBJECTIVE: We aimed to provide an overview of the effectiveness of mHealth interventions designed to encourage physical activity and decrease sedentary behavior on work-related outcomes. METHODS: A search was conducted in MEDLINE, Web of Science Core Collection, the Cochrane Central Register of Controlled Trials and Cochrane Database of Systematic Reviews, and Ichushi-Web for all publications up to September 12, 2025, without language or date restrictions. We included studies which (1) investigated the impact of mHealth interventions promoting physical activity and reducing sedentary behavior on work-related outcomes such as absenteeism, presenteeism, work productivity, work performance, and workability; (2) were designed as a randomized controlled trial or nonrandomized study of interventions; (3) were conducted among workers; and (4) were published as full-text original articles in Japanese or English. Two researchers independently conducted the screening of titles and abstracts, followed by a full-text review to confirm study eligibility and extract the data. We assessed the risk of bias using the Cochrane Collaboration Risk of Bias tool for randomized trials (RoB 2) and the Risk Of Bias In Nonrandomized Studies of Interventions (ROBINS-I). Findings were narratively synthesized. RESULTS: In total, 4022 records were identified from databases and other sources. After removing duplicates, 3631 studies were screened, and 17 studies (12 randomized controlled trials and 5 nonrandomized studies of interventions) met the inclusion criteria. Of the 17 eligible studies with 18,805 participants, 12 showed a favorable effect direction on ≥1 work-related outcome, but the majority exhibited a high or critical risk of bias, raising concerns about the quality of the research. There was considerable heterogeneity across studies. CONCLUSIONS: Scientific evidence suggests that health promotion efforts that incorporate mHealth components to encourage physical activity and reduce sedentary behavior may have a favorable impact on work-related outcomes. However, the low methodological quality of the studies and their high heterogeneity due to the participant characteristics, intervention content, use of diverse work-related assessment tools, and different follow-up periods make it difficult to draw definitive conclusions about the effectiveness of the interventions. This review is the first to comprehensively synthesize evidence on the impact of mHealth interventions on work-related outcomes, extending prior reviews that primarily focused on traditional workplace-based interventions without mHealth components. This review also contributes to the field by offering guidance for future studies, including the potential value of megastudy designs that evaluate multiple interventions within the same population over the same period on the same outcomes. As a practical implication, these findings suggest that mHealth technologies may serve as a complementary strategy for supporting occupational health and productivity.
12 May 2026
Read appraisal →JMIR formative research
Feasibility of Integrating Wearable Devices and Ecological Momentary Assessment for Real-Time Environmental Exposure Estimation: Proof-of-Concept Study
BACKGROUND: Environmental exposures such as heat and air pollution are critical determinants of health, yet traditional assessment methods rely on stationary monitors or residential address proxies that fail to capture the exposures that individuals experience throughout the day. OBJECTIVE: This pilot study aimed to assess the feasibility of integrating ecological momentary assessment (EMA), wearable devices, and continuous GPS tracking to capture real-time environmental exposures and to explore associations with concurrent health outcomes. METHODS: In total, 7 young adults (aged approximately 16 to 24 years; 5/7, 71% female) wore Fitbit Charge 6 watches from July 2025 to August 2025 (mean 28.1, SD 1.1 days), recording sleep quality and duration, resting heart rate, breathing rate, heart rate variability, and physical activity. GPS location measured at up to 5-minute intervals (mean 19.7, SD 25.8 measurements per day) was linked to ambient temperature, humidity, and air pollution data (particulate matter <2.5 um or <10 um in diameter, nitrogen dioxide, sulfur dioxide, ozone, and carbon monoxide) derived from monitoring stations, satellites, and climate models using data-integration algorithms accessed via an application programming interface. EMA surveys administered 3 times per day captured participants' emotional states and location (inside or outside). Feasibility targets were ≥3 GPS measurements per day, ≥1 survey completed per day, and complete sleep data on ≥50% of days. We examined exploratory bivariate correlations between environmental exposures, physiological measures, and self-reported mood, adjusting for multiple comparisons using false discovery rate correction. RESULTS: Of the 7 participants, 5 (71%) met predefined feasibility targets. Mean compliance included 565 (SD 457) GPS coordinates per participant, 1.4 (SD 0.2) EMA surveys per day, and complete Fitbit sleep data on 64% (SD 27%) of days. Surveys identified barriers to compliance, including perceived complexity of the system and forgetting to put the Fitbit watch back on after removing it. Exploratory correlations (6/7, 86% of participants with complete Fitbit data) revealed associations between nitrogen dioxide and heat exposure and reduced heart rate variability (a marker of parasympathetic tone), and between air pollutants (sulfur dioxide) and increased negative emotions. Heat exposure showed a paradoxical pattern of lower self-reported sadness but reduced heart rate variability with higher levels of heat exposure. Given the small sample size, these correlations should be considered preliminary and hypothesis generating rather than definitive findings. CONCLUSIONS: This study demonstrates that the multimodal integration of wearable devices, GPS tracking, and EMA is feasible for capturing real-time environmental exposures and concurrent health outcomes in young adults. This approach addresses critical exposure misclassification issues in environmental health research that relies on residential addresses as proxies. Preliminary patterns suggest complex relationships between environmental exposures and both physiological and emotional outcomes, warranting further investigation in larger, more diverse samples. This approach could inform future personalized environmental health interventions.
11 May 2026
Read appraisal →PloS one
Research on interpretable machine learning modeling and spatial prediction of cross-landform landslide disaster mechanism: A case study of Shaanxi Province
The differential understanding of the landslidetriggering mechanisms across various geomorphic units is vital for enhancing regional disaster prevention. This study investigates the Loess Plateau area in northern Shaanxi and the Qinba Mountain Area in southern Shaanxi as the study areas, and constructs a 15dimensional evaluation factor system covering topography, geology, vegetation, and human activities. After eliminating collinearity factors via double tests of the Pearson correlation coefficient and variance inflation factor, Bayesian optimization is used to optimize hyperparameters for CatBoost, Random Forest, LightGBM, and XGBoost, and the SHAP framework is combined to perform global attribution, single-factor dependency analysis, and interaction-effect quantification. The results demonstrate that CatBoost performs best across both geomorphic areas, achieving AUCs of 0.8307 and 0.8252 in the test sets. In northern Shaanxi, a discrete patch pattern driven by "human-water" coupling is observed, with population density and the topographic moisture index contributing 37.2%. In contrast, Southern Shaanxi exhibits a continuous, band-shaped distribution controlled by "structure-topography" is observed, and elevation and lithology dominate the model's decision-making. The SHAP-dependent map identified cross-geomorphic differences in the population density threshold of 99.83 people/km2, the slope threshold of 2.21° in northern Shaanxi, and the elevation threshold of 1194.73m in southern Shaanxi. Furthermore, the interaction network revealed an antagonistic effect of DEM and POP in northern Shaanxi and of DEM and LULC in southern Shaanxi. The framework proposed a quantitative, explainable basis for differentiated disaster prevention strategies in this research.
11 May 2026
Read appraisal →Environmental health : a global access science source
Building the model: a review of input structures in extreme temperature-health
BACKGROUND: The health impacts of extreme temperatures have been extensively studied through epidemiological models. However, limited attention has been paid to the specification of these models, particularly regarding input structure and model selection. Although exposure metrics and statistical techniques have evolved over time, a comprehensive synthesis of the variables included in these models, and the rationale behind their inclusion, is still lacking. This gap limits the comparability of studies and may compromise the robustness of temperature-health evidence. METHODS: We conducted a systematic review of peer-reviewed studies published between 2014 and 2024 that employed quantitative epidemiological models to estimate the association between extreme temperatures and health outcomes. Following PRISMA guidelines, we selected 119 studies through searches conducted in Scopus, PubMed, and Web of Science. Each study was analysed in terms of spatial coverage, modelling framework, and model inputs. Inputs were classified into six functional groups: thermal exposures; environmental covariates (including both non-thermal meteorological variables and air pollutants); temporal controls; socio-demographic factors; health system indicators; and built environment characteristics. RESULTS: Substantial heterogeneity was observed in both input selection and model specification. Daily mean temperature was the dominant exposure metric, though rarely justified over alternatives. Environmental covariates were inconsistently included: while relative humidity was frequent, other meteorological modifiers and air pollutants were often omitted without clear rationale. Temporal adjustments were common but heterogeneous. Distributed lag non-linear models were the prevailing framework, varying greatly in lag structure, spline specification, and covariate integration. Socio-economic, health, and infrastructural indicators appeared in less than one third of studies, typically as effect modifiers in meta-regression analyses, highlighting uneven integration of contextual determinants. No consensus currently exists on what constitutes a minimum model specification necessary to ensure reliable and interpretable effect estimates. CONCLUSIONS: Current temperature and health modelling remains fragmented, with notable variability in input specification and transparency. Strengthening methodological coherence through clearer guidance on input selection is essential. Greater integration of socio-economic and infrastructural variables would further enhance models' capacity to capture contextual vulnerability. To ensure reliability and policy relevance, future research should develop shared guidelines for input specification, define minimum modelling standards, and promote transparent reporting of analytical decisions.
11 May 2026
Read appraisal →World journal of urology
Quantifying ergonomic challenges for urologists operating flexible ureteroscopes through artificial intelligence-based posture estimation
PURPOSE: The ergonomic challenges faced by surgeons during flexible ureteroscopy have yet to be thoroughly evaluated using objective methods. However, robot-assisted ureteroscopy has emerged as a promising treatment for urinary stones. The present study quantitatively assesses operative posture during transurethral ureteroscopy via an artificial intelligence-based posture estimation framework, and identifies kinematic differences associated with surgical expertise. METHODS: Expert and novice urologists performed standardized flexible ureteroscopic tasks in a simulated transurethral ureteroscopy environment using kidney phantoms containing artificial stones. Upper-body movements were continuously video-recorded and joint coordinates of the shoulders, elbows, and wrists were extracted using an artificial intelligence-based pose estimation system. Kinematic parameters were quantitatively analyzed and compared between groups. RESULTS: Experts completed observation of the entire renal pelvis significantly faster than novices (median 58 vs. 102 s, p < 0.001). Accumulated travel distance was markedly less in experts for the left shoulder (125 vs. 300 cm, p < 0.001), right elbow (324 vs. 1035 cm, p = 0.028), left elbow (349 vs. 772 cm, p = 0.019), and left wrist (729 vs. 3798 cm, p < 0.001). Experts had smaller movement areas at the left shoulder (6.29 vs. 40.5 cm2, p < 0.001) and right elbow (17.3 vs. 281 cm2, p = 0.040), and reduced movement ranges across multiple joints. Their angle fluctuation ranges were also narrower for the right shoulder (32.0 vs. 76.3°, p = 0.028) and left elbow (70.4 vs. 122.4°, p < 0.001). CONCLUSIONS: The artificial intelligence-based posture analysis objectively demonstrated ergonomic advantages associated with surgical expertise during flexible ureteroscopy and revealed persistent ergonomic risks to the wrist and elbow.
11 May 2026
Read appraisal →Scientific reports
Predictive robot eyes shape visual attention, performance, and trust in interaction with an industrial CoBot.
Collaborative robots increasingly share workspaces with humans, making the predictability of robot actions critical for efficient and safe coordination. Predictive visual cues, such as gaze or arrows, may reduce uncertainty, yet their effectiveness and robustness, especially under failure conditions, remain insufficiently understood. Across two laboratory studies with an industrial CoBot, we examined how predictive cues influence visual attention, prediction, and trust. Participants predicted the movement target of a robotic arm displaying eyes, arrows, or no cues across pre-failure, failure, and post-failure phases, while eye movements, reaction times, and trust were recorded. The second study served as a full replication. Across both studies, gaze cues accelerated visual orienting toward the target, as reflected in earlier target fixations, and improved prediction time when cues were reliable. A cue-action mismatch led to a temporary delayed target fixation and slower responses in both cue conditions. Importantly, performance and attentional guidance largely recovered during subsequent error-free interaction, with gaze cues tending to regain their advantage over no cues. Trust mostly showed the expected dynamic pattern of formation, dissolution, and restoration. Together, the findings demonstrate that predictive cues in industrial HRI enhance action anticipation by guiding visual attention, but their benefits are sensitive to cue reliability.
11 May 2026
Read appraisal →Journal of surgical education
Non-Invasive Assessment of Continuous Real-Time Workplace Stress Among Surgeons and Surgical Trainees: A Scoping Review
OBJECTIVES: To describe how physiological parameters were used to characterize continuous, real-time workplace stress among surgeons and surgical trainees, and to identify the advantages and disadvantages of using wearable devices. DESIGN: We conducted a scoping review to examine how wearable devices were used to objectively measure surgeons' and trainees' physiological stress in the workplace. Peer-reviewed English articles published up to September 2023 in Medline (Ovid), Cochrane Library (Wiley), Embase (Elsevier), and PsycINFO (Ebsco) databases using wearable devices used to measure continuous, real-time physiological parameters representing workplace stress among surgeons and surgical trainees were included in the review. SETTING: We included studies that assessed physiological stress in the workplace (e.g., operating room and other clinical environments). PARTICIPANTS: Surgeons and surgical trainees. RESULTS: A review of 61 articles that met inclusion criteria revealed that the most commonly used wearable devices were Polar 13 (21.3%), Medilog: 6 (9.8%), Holter ECG device: 7 (11.5%), and WHOOP! : 6 (9.8%). Physiological parameters used to measure continuous, real-time workplace stress were heart rate, heart rate variability, blood pressure, respiratory rate, and electrodermal activity. Physiological parameters indicated that surgeons were more stressed in the operating room than in other clinical environments. Intraoperative surgeons' stress increased with performing critical steps of the operation, teaching a trainee, the number of intraoperative events, e.g., bleeding, and decreased with years of experience. There was a negative relationship between stress and the surgeon's intraoperative performance. The advantages of using wearable devices' physiologic parameters were objectivity, longitudinal measurement, and minimally obtrusive. The disadvantages were short battery life, inadequate data capture, artifacts, and data loss. CONCLUSIONS: Wearable devices can measure a range of physiological parameters that represent workplace stress continuously among surgeons and surgical trainees.
5 May 2026
Read appraisal →Environmental science. Processes & impacts
Machine learning-driven QSAR models for the prediction of metabolic mechanisms and thyroid hormone-disrupting effects of emerging pollutants in the human body: a case study of bisphenol analogues
Research on theoretical prediction methods for elucidating the reaction mechanisms and thyroid hormone-disrupting effects of emerging pollutants in the human body faces significant challenges. The application of in silico methods utilizing machine learning is increasingly recognized as an effective strategy to determine the mechanisms of reactions catalyzed by human cytochrome P450 enzymes (CYPs) and to predict the adverse effects of emerging pollutants. Bisphenol analogues (BPs) represent one of the most important endocrine disruptors. These compounds can cause serious effects on the ecological environment and human health. Herein, density functional theory (DFT) calculations was employed to investigate the reaction mechanisms of the O-addition and H-abstraction of 20 BPs by human CYPs. A machine-learning-integrated quantitative structure-activity relationship (ML-QSAR) framework was established to predict the energy barriers of three types of reactions. Moreover, binding affinities were calculated between BPs (including their metabolites) and TRβ-LBD to evaluate their thyroid hormone-disrupting effects. The binding affinity data of BPs to TRβ-LBD were used as a training set to develop a ML-QSAR model for efficient prediction of potentially hazardous BPs. A ML-QSAR model with an R2 of 0.869 was established to predict their thyroid hormone-disrupting effects. The study enhances our understanding of CYP catalytic mechanisms involving BPs and provides a reference for the future design of environmentally friendly BPs. The predictive model developed for thyroid hormone-disrupting effects can aid in identifying the adverse effects of emerging BPs, supporting more effective management of toxic compounds.
4 May 2026
Read appraisal →Environmental science. Processes & impacts
Adverse outcome pathway-informed machine learning for predicting vascular toxicity of emerging organic pollutants
Emerging organic pollutants (EOPs) are increasingly detected in wastewater and pose potential vascular toxicity risks that remain inadequately assessed in current regulatory frameworks. This study developed an adverse outcome pathway (AOP)-informed machine learning approach to evaluate vascular toxicity for 312 EOPs. By integrating ToxCast high-throughput bioassay data with Morgan fingerprints, we trained fourteen multilayer perceptron (MLP) models targeting key events in AOP 509, including Nrf2 inhibition, oxidative stress, mitochondrial dysfunction, apoptosis, endothelial impairment, and angiogenesis disruption. Our optimized models achieved high predictive accuracy (70-95%), enabling activity classification for both tested and untested chemicals. The predicted activation profiles prioritized chemicals such as ketoconazole, sertraline, and miconazole, with literature evidence supporting their vascular toxicity potential. This AOP-guided modelling framework demonstrates how integrating mechanistic pathways with machine learning can inform chemical risk assessment and prioritization, supporting hazard evaluation and environmental decision-making for pollutants with limited toxicological data.
4 May 2026
Read appraisal →Journal of proteome research
Stability-Based Machine Learning Identifies a Minimal Two-Protein Serum Signature for Early Silicosis
BACKGROUND: The early diagnosis of silicosis, an irreversible fibrotic lung disease, is challenged by the low sensitivity of current radiological methods in early-stage disease and their susceptibility to interobserver variability. Consequently, a pressing need exists for noninvasive, objective biomarkers to facilitate timely detection and intervention. METHODS: We employed a multistage study design comprising a discovery cohort (57 Stage I silicosis patients, 57 matched controls) and an independent, unmatched validation cohort (40 patients, 40 controls). Serum protein profiles were generated using Olink targeted proteomics. We utilized a rigorous, stability-based machine learning framework, which integrated Lasso, Random Forest, and SVM-RFE algorithms over 100 iterations, to perform feature selection and identify a robust biomarker signature from the discovery cohort. Based on the selected features, a logistic regression model was subsequently constructed, and its performance was evaluated using both internal and external validation. RESULTS: Our discovery strategy identified a two-protein signature comprising IL8 and CCL3. This signature demonstrated excellent diagnostic performance in the discovery cohort, achieving a cross-validation AUC of 0.986 (95% CI: 0.975-1.000). Importantly, the model's robustness was confirmed in the heterogeneous validation cohort, where it achieved an outstanding AUC of 0.973 (95% CI: 0.936-1.000), with 95.0% specificity and 77.5% sensitivity. Bioinformatic analysis revealed that decreased serum levels of IL8 and CCL3 were associated with silicosis, providing novel diagnostic biomarkers and highlighting a complex, paradoxical shift in circulating chemokines during early-stage disease.
4 May 2026
Read appraisal →Journal of occupational and environmental medicine
Combining MEA-Net and LAP-Net for Pneumoconiosis Staging Framework
OBJECTIVE: Pneumoconiosis is a common and highly hazardous occupational disease. The staging of pneumoconiosis is mainly carried out by experienced doctors on the basis of the shadows and textures on lung x-ray films. Despite well-defined criteria, the process remains influenced by individual clinical judgment. METHODS: To improve the subjective process of pneumoconiosis diagnosis, this study proposes a new deep learning framework for pneumoconiosis staging framework using MEA-Net and LAP-Net. RESULTS: The experimental results show that the accuracy, precision, recall, specificity, F1-score, and area under the curve in the four-stage classification reached 95.24%, 95.15%, 95.15%, 90.58%, 94.85%, and 98.87%, respectively. CONCLUSION: The proposed method can help doctors to identify the different stages of pneumoconiosis more accurately in the diagnosis of the disease.
4 May 2026
Read appraisal →Advances in colloid and interface science
Electrospun nanofibrous membranes for self-powered wearable air filtration systems
With the growth of society and industrialization, air pollution has intensified, disturbing the ecological balance and presenting considerable risks to human health. The development of intelligent wearable protective systems is a major breakthrough in real-time health monitoring and prevention, greatly improving personal health management outcomes. An optimal smart air filtration system must demonstrate reliable filtration efficiency, minimal air resistance, and incorporate features for health monitoring and interaction with users. As a result, it is essential to create innovative materials for air filtration that can effectively trap particulate matter (PM). The adaptability, high surface area, interconnected porosity, and configurable morphology of electrospun nanofibers make them a highly promising platform for developing high-performance air filtration media. The conversion of ambient mechanical vibrations into electrical power by piezoelectric and triboelectric nanogenerators (PENGs and TENGs) has emerged as a practical strategy for effective energy harvesting. Integrating self-powered technologies with fibrous air filtration materials to efficiently eliminate fine particulate matter provides a new and sustainable approach to air purification. This review systematically summarizes recent advances in electrospun nanofibrous membranes for self-powered, intelligent, wearable air filtration systems. It begins with an analysis of the fundamental mechanisms of air filtration, the criteria for performance evaluation, and the principles of electrospinning technology. Subsequently, the practical application performance of these filtration systems is critically examined. Finally, the review outlines prospective research directions and discusses the remaining challenges confronting self-powered air filtration systems.
4 May 2026
Read appraisal →Sports health
Evaluation of Exertional Sweat Loss Estimates in Wearable Technology
BACKGROUND: Wearable technology shows promise for monitoring exertional sweat loss, yet its accuracy requires evaluation. This study evaluated the accuracy of 2 commercially available smartwatches for estimating sweat loss compared with gold standard methods. HYPOTHESIS: Sweat loss values measured via wearable technology will be determined accurate compared with gold standard methods. STUDY DESIGN: Cross-sectional. LEVEL OF EVIDENCE: Level 2. METHODS: A total of 111 participants (age, 40 ± 14 years; height, 171.0 ± 8.9 cm; body mass, 70.8 ± 14.7 kg; VO2max, 46.8 ± 8.7 ml/kg/min) completed up to 4 running trials under varying conditions (indoor/outdoor, distances from 2.5 km to 20 km, and interval runs). Sweat loss estimates from each smartwatch were compared with criterion measurements based on change in nude body mass pre- and postexercise. Validity statistics included tests for error (mean absolute error [MAE], mean absolute percentage error [MAPE]), linearity (Lin's concordance correlation coefficient [CCC], Pearson's [r], and Deming regression), equivalence (CI for difference in means), and Bland-Altman plots for bias assessment. Stratified analyses by sex, weight, and fitness category were also completed, along with a chi-square test of independence. RESULTS: Both devices demonstrated acceptable overall correlation (CCC range, 0.71 to 0.90) but substantial errors in estimating exertional sweat loss (MAPE range, 25.38% to 33.21%). Stratified analyses yielded similar results, with no additional analyses meeting the combined validity thresholds. CONCLUSION: Wearable devices offer a promising and convenient tool for general sweat loss tracking but lack the precision to replace laboratory methods for hydration management. Traditional methods remain essential for the most accurate measurements. CLINICAL RELEVANCE: Wearable devices provide an accessible option for sweat loss monitoring. This study highlights their potential for general tracking while emphasizing the need for laboratory methods when accuracy is critical, ensuring better guidance for hydration strategies in sports performance and health applications.
4 May 2026
Read appraisal →JMIR mental health
The Effectiveness and Mechanisms of Action of App-Based Interventions for Improving Mental Health and Workplace Well-Being: Randomized Controlled Trial
BACKGROUND: Depression is the most common mental health disorder worldwide and frequently leads to workplace absence. As face-to-face treatment can be difficult to access, app-based interventions are a popular solution, although their effectiveness in working populations and their mechanisms of action are unclear. Deficits in executive function may contribute to the onset and maintenance of depression, and executive function training is proposed to improve symptoms by enhancing executive function. Responders to cognitive behavioral therapy (CBT) show improvements in executive function, suggesting that this may be one mechanism of action. OBJECTIVE: This study investigated the effectiveness of app-based interventions (executive function or CBT-based) for reducing depressive and anxiety symptoms and improving workplace well-being, and assessed whether changes in executive function mediated improvements. METHODS: A total of 228 participants (147 female participants) with mild-to-moderate symptoms of depression and anxiety were recruited online and randomly assigned to a waitlist control group, an executive function training group (NeuroNation app, Synaptikon GmbH), or a self-guided CBT group (Moodfit app, Roble Ridge LLC) for a 4-week intervention period. Participants assigned to the active intervention groups were asked to use their apps a minimum of 21 times during the intervention. Participants completed measures of depressive symptoms, anxiety symptoms, and workplace well-being, and a working memory task at baseline, postintervention, and follow-up (12 weeks). RESULTS: Executive function training reduced anxiety (β=-2.79; P=.004) and depressive (β=-2.77; P=.02) symptoms at follow-up but not at postintervention, and it did not affect workplace well-being. There were no reductions in depressive or anxiety symptoms in the self-guided CBT group, though workplace well-being was improved at postintervention (β=3.72; P=.02) and follow-up (β=4.46; P=.02). Improvements in executive function did not mediate intervention-related changes in symptoms or workplace well-being. Self-reported adherence rates were high (executive function training: 48/54, 89%; self-guided CBT: 52/54, 96%), although attrition was high at follow-up (58% missing). CONCLUSIONS: These results suggest that app-based executive function training may be effective at managing symptoms of anxiety and depression in a working population, while self-guided CBT apps may improve workplace well-being. However, improving executive function did not appear to be a mechanism of action of either intervention.
2 May 2026
Read appraisal →Journal of chemical information and modeling
Development, Evaluation and Application of a Multi-Representation Fusion Model for Accurate Prediction of Per- and Polyfluoroalkyl Substance (PFAS) Binding to Plasma Proteins
Per- and polyfluoroalkyl substances (PFAS) constitute a large and structurally diverse class of man-made chemicals. Their strong carbon-fluorine (C-F) bonds confer high environmental persistence, bioaccumulation, and various associated toxicities. As amphiphilic compounds, most PFAS bind to proteins and accumulate in protein-rich tissues, with such bioaccumulation exerting significant adverse impacts on human health. Accurate evaluation of the binding status between PFAS and proteins constitutes an essential step in health risk assessment. Traditional experiments and certain modeling approaches for analyzing PFAS bioaccumulation suffer from drawbacks such as time-consuming processes, high costs, or inadequate capture of molecular structural information, while existing machine learning-based prediction methods rely on single molecular representation, making it difficult to comprehensively encode the structural information on PFAS. Here, we propose MURNet, a multirepresentation fusion network model integrating chemical descriptors, 2D molecular graphs, and molecular fingerprints to predict PFAS-plasma protein binding. Compared with the state-of-the-art baseline models, MURNet achieves the optimal comprehensive performance. The multirepresentation fusion strategy generates higher-quality molecular features. Tanimoto similarity applicability domain analysis demonstrates MURNet's capability to reliably predict PFAS homologues. Case studies reveal the effectiveness of MURNet in screening PFAS with potential binding affinity to human serum albumin (HSA).
2 May 2026
Read appraisal →PloS one
Unpacking the dual psychological paths of employee-AI collaboration on creativity: The role of proactive behavior
The deep integration of artificial intelligence (AI) into organizational settings has significantly transformed employees' work patterns, underscoring the need to investigate the mechanisms through which employee-AI collaboration influences creativity. Grounded in social cognitive theory, this study examines how employee-AI collaboration affects creativity via internal (self-efficacy) and external (performance pressure) mechanisms, and explores the moderating role of proactive behavior. Using convenience sampling, 733 valid responses were collected from six technology-driven enterprises. The theoretical model was tested through confirmatory factor analysis and regression analyses using AMOS and the Process macro for SPSS. Our results reveal that employee-AI collaboration is positively associated with creativity through two statistically supported indirect pathways: parallel indirect effects via self-efficacy (internal pathway) and performance pressure (external pathway), and a sequential indirect association in which higher self-efficacy is related to higher performance pressure, which in turn is positively related to creativity. Moreover, proactive behavior appears to moderate these relationships, such that the association between employee-AI collaboration and self-efficacy is more pronounced at higher levels of proactive behavior, whereas the association between employee-AI collaboration and performance pressure is less pronounced at higher levels of proactive behavior, yielding a pattern consistent with moderated indirect effects on creativity through both routes. These findings provide theoretical insights into the complex psychological processes underlying creativity in AI-enabled work environments and offer practical implications for optimizing AI applications and developing targeted interventions to foster employee creativity.
28 Apr 2026
Read appraisal →PloS one
The stress reduction potential of Bhagavad Gita and Yoga for healthcare workers during the COVID-19 pandemic: A randomized controlled trial
BACKGROUND: Healthcare workers (HCWs) experienced elevated psychological distress during the COVID-19 pandemic. Culturally embedded, non-stigmatizing approaches may improve uptake of mental health support in this population. We aimed to evaluate the impact of Yoga, Bhagavad Gita learning, and their combination (Yoga+Gita) on psychological distress among HCWs. METHODS: We conducted a prospective, single-center, parallel-group randomized controlled trial at a secondary hospital in India. Eligible HCWs (>18 years, without pre-existing diagnosed mental health conditions) were randomized in a 1:1:1:1 ratio to: Yoga (daily 60-min sessions for one week), Gita (daily 60-min teaching of Bhagavad Gita Chapter 2 for one week), Yoga+Gita (30 min Yoga + 30 min Gita daily for one week), or Control (usual care). Psychological distress was assessed using the Generalized Anxiety Disorder-7 (GAD7) scale at baseline (B-GAD7), immediately post-intervention (IPI-GAD7), and 45 days post-intervention (DPI-GAD7). Participants were aware of group assignment, but data analysts were blinded. Group comparisons used Friedman and Kruskal-Wallis tests with Bonferroni-adjusted post-hoc analyses. We hypothesized that (1) all three interventions would reduce GAD-7 scores compared with control, (2) Gita would produce more durable effects than Yoga alone, and (3) the combined Yoga+Gita intervention would yield the greatest overall reduction in psychological distress. FINDINGS: Sixty-eight participants were enrolled (17 per group), with no losses to follow-up. B-GAD7 did not differ across groups (p = 0.908). Compared with Control, Yoga+Gita reduced anxiety immediately (p = 0.009), and Gita reduced anxiety at 45 days (p = 0.047). A clinically meaningful (≥4-point) GAD7 reduction at 45 days occurred in 17.6% (Yoga), 47.1% (Gita), 41.2% (Yoga+Gita), and 11.8% (Control) of the participants. Exploratory analyses suggested larger benefits among women and among those who continued practice. INTERPRETATION: Yoga and Gita-learning, particularly in combination, were associated with reductions in psychological distress among HCWs. TRIAL REGISTRATION: Prospectively registered with the CTRI (CTRI/2021/10/037365).
27 Apr 2026
Read appraisal →Journal of the International Society of Sports Nutrition
Effects of nutritional interventions on competitive performance and dose‒response relationships among esports players of different skill levels: a systematic review and three-level meta-analysis
BACKGROUND AND OBJECTIVES: Esports place exceptional demands on neuropsychological capacities; however, a systematic and quantitative synthesis of the specific efficacy of nutritional interventions across various tasks and competitive populations remains absent. This study aims to evaluate the impact of nutritional supplementation on cognitive function and competitive performance in esports players and to explore key moderating variables. METHODS: Electronic databases, including Web of Science, PubMed, and Scopus, were searched (up to January 25, 2026). Thirteen randomized controlled trials (RCTs) met the inclusion criteria. A three-level random-effects model was employed to calculate the pooled effect size (Hedges' g), with sources of heterogeneity explored through subgroup analyses and meta-regression. RESULTS: Nutritional interventions significantly enhanced esports-related performance (g = 0.92), with the most pronounced benefits observed in processing speed (g = 1.18) and executive function (g = 1.06). Subgroup analyses revealed significant gains in both professional (g = 0.97) and amateur groups (g = 0.92), while the improvement in first-person shooter (FPS) games (g = 0.96) appeared to surpass that in multiplayer online battle athlete (MOBA) games. Meta-regression indicated no significant linear correlation between caffeine dosage and effect size. GRADE assessment categorized the quality of evidence mostly as "low" to "moderate." CONCLUSIONS: Nutritional interventions demonstrate significant potential for optimizing multidimensional cognitive performance in esports players, particularly in enhancing fundamental reaction speed and high-level decision-making processes. While the findings exhibit a degree of robustness, their practical application should be cautiously evaluated within specific competitive contexts due to current limitations in evidence certainty and significant interstudy heterogeneity. Future research characterized by gender balance and mechanistic orientation with long-term intervention protocols is warranted to elevate the level of clinical evidence.
27 Apr 2026
Read appraisal →ACS sensors
All-in-One Wearable Hydrogel Patch for Multimodal Visualized and Quantitative Heatstroke Monitoring
Heat-related illnesses, particularly heatstroke, pose serious risks to outdoor workers, athletes, and individuals with impaired thermoregulation. Existing monitoring strategies are limited by invasive measurements, reliance on external power sources, and lack of real-time multimodal feedback. Here, this study presents an all-in-one wearable patch that integrates a dual-functional self-powered thermogalvanic hydrogel with a selective electrochemical interface for intuitive visual feedback, dynamic temperature tracking, and durable sweat analysis. The hydrogel enables real-time thermochromic visualization and active thermoelectric sensing of body temperature, while an integrated electrochemical module allows continuous and selective detection of Na+ and K+ ions in sweat. The multiplexed physical-chemical health-monitoring patch provides a multimodal dual-stage evaluation of body temperature and sweat ion level toward improving metabolic status control and alerting individual to heatstroke risk, holding promise for developing personalized healthcare and a scalable route to proactive heatstroke prevention.
26 Apr 2026
Read appraisal →JMIR human factors
Dynamic Balance Control and Postural Adaptation in Human-Robot Collaborative Manipulation: Within-Subject Experimental Study
BACKGROUND: The integration of robots into industrial settings has rapidly advanced, aiming to reduce human involvement in demanding tasks while improving overall efficiency. As collaborative robots (cobots) become more prevalent, assessing the physical strain during joint tasks is essential to promote long-term well-being in the workplace. OBJECTIVE: This study aimed to investigate how human-robot collaboration influences workers' postural control and musculoskeletal load during manipulation tasks performed in parallel. METHODS: Fourteen healthy male participants performed manipulation tasks under 3 conditions: without robotic assistance, with a cobot providing load support (Robot Free [RF]) and a cobot constrained to horizontal movement (Robot Plane [RP]). Center of pressure trajectories were computed, and nonlinear recurrence quantification analysis indicators (recurrence rate [REC], determinism [DET], and their ratio) were calculated in the anteroposterior, mediolateral, and anteroposterior-mediolateral planes. RESULTS: Statistical analysis showed greater postural sway in robot-assisted conditions compared to Free. Mean distance increased from 1.7 (SD 0.6) cm in Free to 2.4 (SD 0.6) cm in RF (P<.001) and 2.3 (SD 0.6) cm in RP (P<.001). Mean velocity increased from 2.9 (SD 0.9) cm/s in Free to 4.3 (SD 1.4) cm/s in RF and RP. Confidence ellipse area increased from 7.6 (SD 4.1) cm2 in Free to 24.9 (SD 14.2) cm2 in RF and 23.1 (SD 13.4) cm2 in RP. Sway area increased from 1.5 (SD 0.7) cm2/s in Free to 2.9 (SD 1.2) cm2/s in RF and RP. Nonlinear metrics revealed lower recurrence rates in robot-assisted conditions, decreasing from 0.31 (SD 0.08) in Free to 0.2 (SD 0.08) in RF and 0.2 (SD 0.04) in RP in the anteroposterior-mediolateral plane (P<.001), from 0.33 (SD 0.08) in Free to 0.28 (SD 0.07) in RF (P=.02) and 0.16 (SD 0.03) in RP (P=.007) in the mediolateral direction, and from 0.36 (SD 0.07) in Free to 0.3 (SD 0.06) in RF (P=.009) and 0.26 (SD 0.03) in RP (P<.001) in the anteroposterior direction. Determinism remained stable (values close to 1), leading to higher determinism-to-recurrence ratios for robot-assisted conditions, increasing from 3.41 (SD 0.87) in Free to 5.41 (SD 1.69) in RF and 5.51 (SD 1.11) in RP in the anteroposterior-mediolateral plane (P<.001), from 3.11 (SD 0.63) in Free to 3.64 (SD 0.72) in RF (P=.02) and 3.92 (SD 0.48) in RP (P=.007) in the mediolateral direction, and from 2.82 (SD 0.48) in Free to 3.47 (SD 0.57) in RF (P=.009) and 3.46 (SD 0.45) in RP (P<.001) in the anteroposterior direction. No significant differences were found between the robot-assisted conditions. CONCLUSIONS: Interaction increases postural sway, indicating reduced stability and higher physical demand. This could reflect impaired balance or adaptation. Nonlinear analysis reveals that postural control remains structured. Results also suggest that the mere presence of the cobot is the primary driver of these postural changes.
26 Apr 2026
Read appraisal →La Medicina del lavoro
Artificial Intelligence in Occupational Health Surveillance: Evaluating AI-Assisted ILO Classification of Radiographs of Pneumoconioses
BACKGROUND: Pneumoconioses remain an important occupational health issue, particularly in low- and middle-income countries. The International Labour Organization (ILO) Classification standardizes chest radiograph interpretation but requires trained readers and is affected by inter-reader variability. This study evaluated whether generative multimodal artificial intelligence (AI) models can approximate ILO-based diagnostic reasoning. METHODS: Eighty-two chest radiographs from the official NIOSH B Reader syllabus were analysed using four AI systems (GPT-4o, GPT-5, MedGemma-4B, MedGemma-27B). Each image was evaluated with a standardized prompt based on the 2022 revised ILO guidelines using deterministic settings. Model outputs were mapped to ILO codes and compared with the official answer keys of the ILO Standard Radiograph Set used for B Reader training and examination. Performance metrics included balanced accuracy, sensitivity, specificity, precision, and Matthews correlation coefficient (MCC). Bootstrap 95% confidence intervals, McNemar's test, and Cohen's κ assessed performance variability and agreement. RESULTS: All four AI models showed moderate diagnostic performance, with balanced accuracy ranging from 60.8% to 70.3%. Sensitivity remained limited (35.5%-54.9%), while specificity was consistently high (84.6%-86.2%). MedGemma-27B performed best for small opacities, GPT-5 for pleural abnormalities and for technical quality. Large opacities and rare findings were systematically under-detected. Statistical comparisons showed significant differences between models, although agreement patterns were broadly similar. CONCLUSION: All AI models partially followed structured ILO radiographic criteria but did not achieve expert-level performance, confirming that they cannot replace certified B Readers. Larger, real-world datasets are needed to assess their potential clinical utility as supportive tools in occupational health surveillance programs.
25 Apr 2026
Read appraisal →Nicotine & tobacco research : official journal of the Society for Research on Nicotine and Tobacco
Health Hazards of E-Cigarettes and Heated Tobacco Products: a Comprehensive Analysis of Hazardous Substances and Regulatory Gaps
INTRODUCTION: Ingredients and products of electronic cigarettes (ECs) and heated tobacco products (HTPs) formed during heating are aerosolized and inhaled. Hazard information on ingredients and emissions is often unavailable. Our objective was to identify and classify substances in the ingredients and emissions of ECs and HTPs based on scientific hazard classifications. METHODS: Ingredient data were collected from the European Union (EU) Common Entry Gate, a mandatory notification system for tobacco and related products. Substances in ECs and HTPs emissions were identified with a systematic literature review and classified into three categories based on existing health hazard classifications. Category 1 substances (i.e., highest potential health hazard) were regarded as high-priority substances for research and quantification. Categories 2 and 3 included potentially harmful substances and substances lacking sufficient toxicological data respectively. RESULTS: A total of 134 of the 1740 identified substances were classified in Category 1. Several substances possess Carcinogenic, Mutagenic, or Reprotoxic properties, prohibited as tobacco additives. Most substances lacked sufficient toxicity data and were classified in Category 3. CONCLUSION: Our classification can be used for monitoring and enforcement, ensuring proactive management of potential risks. Therefore, we recommend strengthening the enforcement of existing bans. Implications: This study identifies and classifies 1740 substances found in the ingredients and emissions of ECs and HTPs, including 134 hazardous chemicals, among which are carcinogens, mutagens, and reproduction toxicants. This indicates regulatory gaps and the need for stricter enforcement of existing bans. The findings provide a valuable tool for policymakers and public health professionals for monitoring and mitigating risks.
24 Apr 2026
Read appraisal →Veterinaria italiana
A One Health systematic review and meta-analysis of Coxiella burnetii prevalence in humans, animals, and vectors in Algeria
Coxiella burnetii, the causative agent of Q fever, is a globally distributed zoonotic pathogen affecting humans, domestic animals, and arthropod vectors. In Algeria, fragmented data suggest widespread circulation, yet no comprehensive quantitative assessment exists. This study aimed to systematically review and meta-analyse the prevalence of C. burnetii in humans, animals, and vectors in Algeria, using a One Health perspective. A systematic search of PubMed, Scopus, Web of Science, and Google Scholar was conducted up to January 2026. Studies reporting serological or molecular detection of C. burnetii in Algeria were included. Random-effects meta-analysis was performed to estimate pooled prevalence, with subgroup analyses by host species, region, and diagnostic method. A total of 35 studies were included, encompassing 8,372 samples and 80 prevalence observations. Pooled prevalence was highest in camels (73.7%; 95% CI: 66.5-79.8%), followed by small ruminants (15.8%; 95% CI: 10.6-22.9%) and cattle (11.8%; 95% CI: 7.2-18.8%). Ticks showed a pooled prevalence of 10.0% (95% CI: 3.2-27.1%), while human infection had a pooled prevalence of 3.7% (95% CI: 0.9-13.9%). The south-eastern region exhibited the highest prevalence, and serological methods generally reported higher rates than molecular methods. High heterogeneity (I² > 75%) was observed across studies. Coxiella burnetii is widely circulating among humans, domestic animals, camels, and ticks in Algeria, with notable variation between hosts and regions. Camels and small ruminants act as major reservoirs, while humans remain at risk, particularly those in close contact with livestock. Integrated One Health surveillance and targeted control strategies are urgently needed to reduce the burden of Q fever.
22 Apr 2026
Read appraisal →Journal of neurology
The association of night shift work with the risk of acute ischemic cerebrovascular syndrome: a longitudinal study of 204,261 UK Biobank participants
BACKGROUND: Shift work is highly prevalent globally, yet evidence linking it to ischemic cerebrovascular disease remains inconsistent. Existing studies are limited by unsystematic quantification of night shift exposure and outcome definitions failing to capture the clinical continuum of ischemic events. We performed a large prospective cohort study using refined shift pattern classification and acute ischemic cerebrovascular syndrome (AICS) as the primary end point to enable a robust evaluation of the association between shift patterns and ischemic cerebrovascular disease. METHODS: 204,261 participants with employment information from the UK Biobank were included. Cox proportional hazards regression was used to assess associations between current shift work and AICS risk, with cross-classification by shift status and job duration. We further quantified average lifetime night shift frequency and cumulative duration among participants with complete occupational histories. Transient ischemic attack (TIA) and ischemic stroke (IS) were analyzed separately to identify the main driver of AICS and potential heterogeneity. RESULTS: During a median follow-up exceeding 14 years, irregular shift work was significantly associated with elevated AICS risk (HR 1.118, 95% CI 1.019-1.227, P = 0.019), while always night shift work was not (HR 0.904, 95% CI 0.707-1.157, P = 0.423). AICS risk was driven by irregular shift patterns rather than duration. Lifetime night shift frequency and duration showed significant dose-response relationships with AICS. CONCLUSIONS: Irregular shift work increases AICS risk relative to non-shift work, and lifetime night shift exposure exhibits a clear dose-response effect. IS constitutes the primary driver of the composite AICS end point.
20 Apr 2026
Read appraisal →Journal of health organization and management
Organisational professional support measures to enhance mental health and wellbeing in female healthcare workers: an umbrella review
PURPOSE: This study aims to synthesise evidence on organisational professional support measures that enhance female healthcare workers' mental health and wellbeing. Female healthcare workers face high rates of mental health challenges, impacting their ability to provide optimal care. DESIGN/METHODOLOGY/APPROACH: This umbrella review adhered to the nine-step pathway to conducting umbrella reviews by Cant et al. (2022) and reported following the Preferred Reporting Items for Overview of Reviews checklist (Pollock, 2019). A search of Embase, Medline, CINAHL via EBSCO and the Cochrane Library for English language papers from January 2011 onwards was conducted (search date: February 2024). Following an identification of relevant papers and data extraction, themes were developed using a narrative synthesis approach. FINDINGS: In total, 32 systematic reviews met the inclusion criteria. From the data synthesis, four themes were developed, namely lack of studies specific to female healthcare workers, diversity and variability of interventions, interventions best related to staff outcomes versus organisational outcomes and social connections at work can be supportive or stressful. ORIGINALITY/VALUE: Evidence supports that person-directed interventions including cognitive-behavioural therapy and relaxation measures may enhance staff-level outcomes. In contrast, organisation-directed interventions such as clinical supervision and mentoring, or a combination of person- and organisation-centred interventions, appear to enhance both staff and organisational outcomes. Limitations in existing studies highlight the need for improved research conduct and reporting.
18 Apr 2026
Read appraisal →BMJ open
Comparison of the effects of eHealth-delivered sedentary breaks versus physical activity promotion interventions on sedentary time: protocol for a systematic review and meta-analysis
INTRODUCTION: Prolonged sedentary behaviour (SB) is an independent risk factor for adverse health outcomes, with current WHO guidelines emphasising both increased physical activity (PA) and reduced sitting time. While electronic health (eHealth) interventions offer scalable solutions, the comparative effectiveness of two dominant strategies: sedentary break interventions (frequent interruptions of sitting) versus PA promotion (structured activity sessions) remains unclear. This systematic review and meta-analysis protocol aims to compare the effectiveness of interventions targeting breaks in SB versus increased PA in reducing sedentary time through meta-analysis of randomised controlled trials (RCTs) comparing either intervention to a control condition, while exploring key moderating factors including participant characteristics, intervention type, duration, eHealth delivery mode, theoretical basis, use of behaviour change techniques (BCTs), PA intensity and SB frequency. METHODS AND ANALYSIS: This protocol follows the Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols (PRISMA-P) guidelines. A comprehensive search will be conducted in PubMed, Embase, Web of Science and the Cochrane Library from inception to 31 July 2025, with language restrictions limited to English and Chinese publications. RCTs comparing eHealth-delivered interventions promoting sedentary breaks with those increasing PA in adults will be included. The primary outcome is sedentary time (objectively measured or self-reported), and secondary outcomes include health outcomes such as cardiometabolic markers, fatigue and well-being. Two reviewers (SC and XN) will independently screen studies, extract data and assess risk of bias using validated tools. Meta-analyses will be performed if sufficient homogeneous data are available, comparing changes in sedentary time. Subgroup analyses will explore effects by participant characteristics including gender (male vs female), age groups (<60 vs ≥60 years), intervention type (sedentary breaks vs PA), duration (short-term ≤3 months vs medium-term >3 to <6 months vs long-term ≥6 months), eHealth platform/delivery mode (eg, smartphone apps vs websites vs wearable device prompts vs text messages), theoretical basis (eg, theory-driven interventions vs non-theoretical interventions), use of BCTs (eg, interventions incorporating BCTs vs interventions without BCTs), SB break frequency (eg, every 30 min vs every 60 min) and PA promotion intensity (eg, light vs moderate vs vigorous). A narrative synthesis will be conducted for studies unsuitable for quantitative analysis. ETHICS AND DISSEMINATION: Ethical approval is not required as this study involves secondary analysis of published data. Findings will be disseminated through peer-reviewed publication and conference presentations. PROSPERO REGISTRATION NUMBER: CRD420251042994.
18 Apr 2026
Read appraisal →PloS one
Mukara: A deep learning alternative to the four-step travel demand model with a case study on interurban highway traffic prediction in the UK.
Accurate traffic volume prediction is essential for managing congestion, improving road safety, mitigating environmental impacts, and supporting long-term transportation planning. The traditional four-step travel demand model (FSM) is a well-established framework, but it relies on static survey data, substantial calibration effort, and simplified behavioural assumptions that may not adequately capture complex travel patterns. In contrast, data-driven models are capable of learning nonlinear relationships from large datasets, yet they are often designed for short-term forecasting and typically do not target the long-term, segment-level volume estimation tasks required for strategic planning. This study proposes Mukara, a deep learning framework that directly approximates the mapping from external socioeconomic and network features to observed traffic volumes on highway trunk road segments. The model is trained on eight years of data from England and Wales and incorporates population, employment, land use, road network characteristics, and points of interest as inputs. Mukara achieves a mean GEH of 50.74, a mean absolute error of 8,989 vehicles per day, and an R2 of 0.583 under random cross-validation, outperforming baseline models and existing studies under comparable settings. Under a more stringent region-based spatial cross-validation scheme, performance remains robust, demonstrating strong spatial transferability. Ablation experiments further demonstrate the robustness of the proposed architecture and reveal the relative importance of different input feature groups for prediction.
17 Apr 2026
Read appraisal →Sleep
Improving sailor's amount and efficiency of sleep in two-section watchkeeping schedules: A cluster randomized trial study
STUDY OBJECTIVES: The present study aimed at comparing sailors' amount and efficiency of sleep in two two-section watchkeeping schedules, i.e. the traditional 6/6 and the 7-5-5-7. Intention for the latter schedule was to align the major sleep episodes with biologically driven nocturnal sleep patterns. METHODS: Using a parallel crossover design with cluster randomized trials, forty-eight crew members from two vessels of the German Navy participated in a field trial with two 1-week underway periods varying the watchkeeping schedule in randomized order. Sleep minutes and sleep efficiency were assessed on the participant level using wrist-worn actigraphs and analyzed for sleep episodes occurring during the day (i.e. between 10:00 am and 10:00 pm) and during the night (i.e. between 10:00 pm and 10:00 am). RESULTS: Nighttime sleep duration was 14 min longer and with 10% higher sleep efficiency in the 7-5-5-7 schedule compared to the 6/6. In contrast, daytime sleep duration was 40 min longer in the 6/6 with no statistically significant differences in sleep efficiency between the two watchkeeping schedules. CONCLUSIONS: Our results suggest that the amount and efficiency of nighttime sleep can be enhanced with the 7-5-5-7. Hence, even in shift schedules with restricted periods for rest, recuperation can be improved by more closely aligning watchkeeping schedules to the innate human diurnal circadian cycles. Statement of Significance The size of ships and the associated limitations on the size of their crews make two-section watchkeeping schedules common in civil and military maritime settings. Such watchkeeping schedules-in which the 24-h day must be shared between two watches-limit time available for rest and recuperation. The present study shows that even in this context, slight modifications in two-section watchkeeping schedules lead to more and better nighttime sleep because the circadian fluctuations of sleep and performance are considered. Our research findings have high practical significance for all industries with shift workers and demonstrate how the negative effects of watchkeeping schedules can be reduced if they are adapted to better align with innate human circadian rhythms.
17 Apr 2026
Read appraisal →BMJ open
mHealth-based Healthy Lifestyle Promotion (MYLIFE) for metabolic syndrome risk reduction in Chinese employed adults: protocol for a cluster-randomised controlled trial
INTRODUCTION: Metabolic syndrome (MetS) poses a significant public health challenge among employed adults. Lifestyle modifications have been shown to be effective in preventing the onset and progression of MetS in employed adults, and the widespread adoption of mobile and wearable technologies introduces an appealing approach to mHealth lifestyle interventions. When widely implementing smartphone-based interventions for employed adults, enhancing equitable access and promoting wearable device use for those with MetS is a more cost-effective way to reduce health disparities, particularly in resource-limited settings. However, relevant evidence is currently lacking. OBJECTIVE: This study aims to design an mHealth-based Healthy Lifestyle Promotion (MYLIFE) trial, a three-arm randomised controlled trial (RCT), to evaluate the effectiveness of a smartphone-based lifestyle intervention and a combined smartphone-based lifestyle intervention with intensive intervention for MetS individuals using wearable devices, in reducing MetS risk among employed adults. METHODS AND ANALYSIS: This is a three-arm, parallel, single-blind, cluster RCT with a 12-week intervention and a total follow-up of 1 year. The trial will recruit 120 workplaces from the Chinese Cohort of Working Adults in a 1:1:1 ratio to either a control group or one of two intervention groups. Within these workplaces, a total of at least 348 eligible participants will be enrolled. The regular mHealth group will receive smartphone-based intervention. The intensive mHealth group will receive smartphone-based intervention for all participants, and an additional wearable device-intensive intervention for those with MetS. The primary outcome is the Chinese MetS Z score at 12 weeks. Secondary outcomes include MetS Z score, prevalence of MetS, specific MetS components, questionnaire-based indicators, and clinical outcomes at 12 weeks and 1 years. The main analysis will follow the intention-to-treat principle, using mixed-effects models to assess between-group differences in outcomes. ETHICS AND DISSEMINATION: The protocol has been approved by the Ethics Committee of the West China School of Public Health and the West China Fourth Hospital, Sichuan University (Gwll2025024). The findings will be published in peer-reviewed journals and presented at scientific conferences. TRIAL REGISTRATION NUMBER: ChiCTR2500101904.
17 Apr 2026
Read appraisal →Emerging microbes & infections
Cross-protection against highly pathogenic avian influenza H5N1 virus from seasonal influenza vaccines: a systematic review and meta-analysis of ferret studies
The recent surge in spillover events of highly pathogenic avian influenza A(H5N1) clade 2.3.4.4b to humans and mammals in North America has raised urgent pandemic concerns. Human H5N1 vaccines are unavailable in most countries. We synthesized data from ferret challenge trials to evaluate whether widely available seasonal influenza vaccines confer cross-protection against lethal H5N1 infection. We systematically searched PubMed, Embase, and Web of Science for ferret studies of lethal H5N1 challenge published up to 5 July 2025 (PROSPERO #CRD42024520346). Random-effects meta-analyses were conducted to compare vaccine efficacy (VE) of seasonal influenza vaccines and H5N1 vaccines against H5N1-related mortality. Seroprotection was defined as a neutralizing antibody titre of ≥1:40. We identified 35 studies (157 trials). Seasonal influenza vaccines without N1 did not confer significant cross-protection (five trials; VE 14.8%, 95% CI -3.6 to 30.0). In contrast, VE was 73% for N1-containing seasonal influenza vaccines (19 trials; 95% CI 54-84) and 77% for H5N1 vaccines overall (133 trials; 95% CI 72-82) (p = 0.52). The VE of N1-containing seasonal influenza vaccines was modestly lower than that of H5N1 vaccines with seroprotection (88%; 66 trials; 95% CI 84-91; p = 0.009), but comparable to H5N1 vaccines that did not achieve seroprotection (63%; 67 trials; 95% CI 52-71; p = 0.29). The VE of seasonal influenza vaccines against H5N1 was robust across sensitivity analyses, with no evidence of publication bias (p = 0.99). Seasonal influenza vaccines significantly reduce H5N1-associated mortality in ferret trials, suggesting the cross-protection potential of currently available vaccines. Human studies are warranted.
16 Apr 2026
Read appraisal →Neuroscience
Neural responses to acute hypoxia and hyperoxia
The brain's high metabolic demands make it highly sensitive to oxygen level changes, with both hypoxia and hyperoxia affecting cognitive and neural function. However, the underlying neural mechanisms remain unclear. This study aimed to characterise the effects of acute normobaric hypoxia and hyperoxia on neural function and perceptual thresholds using EEG, visual evoked potentials (VEPs), and critical flicker fusion (CFF) thresholds. In a single-blind, randomised, crossover design, thirty participants inhaled 10.5%, 21%, or 100% oxygen while resting-state EEG, VEPs, and CFF thresholds were recorded. Hypoxia reduced CFF thresholds in an exposure-dependent manner, while hyperoxia produced a smaller, transient reduction. Resting-state EEG revealed significantly increased power across delta, theta, and beta bands in response to hypoxia. Whereas hyperoxia induced smaller increases in theta and alpha and decreases in gamma power and a reduction in beta power over the sensorimotor area. Hypoxia increased spectral entropy, indicating impaired network synchronisation, while hyperoxia increased fuzzy entropy, particularly in eyes-closed states, potentially reflecting enhanced neural complexity. In VEPs, hypoxia reduced P1 amplitude and increased N2 deflections, reflecting disrupted early and late sensory processing, with sustained effects across blocks. Hyperoxia transiently increased P1 amplitude and showed minor effects on other components. These findings demonstrate the impact of altered cerebral oxygen on fundamental neural processing and the associated state- and time-dependent effects on brain function. This provides insight into the mechanisms underlying oxygen-induced neuromodulation and highlights the value of EEG and CFF as biomarkers for tracking neural responses to oxygen variability.
16 Apr 2026
Read appraisal →Journal of affective disorders
Adverse psychosocial work exposures and 2-year incident prescription of psychotropic medications: a prospective French study using the national healthcare reimbursement database
OBJECTIVE: Despite the abundant literature on the associations between psychosocial work exposures and mental health outcomes, prospective studies using medico-administrative data are lacking. The objective was to study the associations between various psychosocial work exposures and incident prescription of psychotropic medications. METHODS: The study relied on three waves (2010, 2012, 2014) of the French ESPS survey (Health, Health Care and Insurance Survey) (baseline data), linked to the national database of healthcare reimbursements (follow-up data). Each survey wave was conducted among a randomly selected national sample of people aged 18+. The study included a total of 8689 observations among 7695 workers without psychotropic medication prescription within the 6 months preceding survey wave. Ten psychosocial work exposures (quantitative demands, tensions with the public, freedom, possibilities for learning new things, colleague support, recognition, salary satisfaction, job insecurity, temporary contract, and redundancy plan) were assessed at each survey wave. Covariates included gender, age, marital status, employee/self-employed worker, private/public sector, occupation, and full/part-time work. The outcome was psychotropic medication prescription within the 2 years following survey wave. Mixed effects Cox regression modelling was used. RESULTS: Seven of the ten psychosocial work exposures were associated with incident psychotropic medication prescription. Associations were observed more with incident prescription of antidepressants and anxiolytics than with hypnotics/sedatives. The number of psychosocial work exposures increased the risk linearly, except for hypnotics/sedatives. No gender interaction was found for any of these associations. CONCLUSION: The findings may support that poor psychosocial working conditions increase the risk of subsequent psychotropic medication prescription.
15 Apr 2026
Read appraisal →Analytical chemistry
Dual-Signal Amplification Strategy for Synchronous Monitoring of Stress Hormones and Metabolic Indicators
The synchronous monitoring of stress hormones and metabolic indicators is crucial for long-term health management, yet it remains a significant challenge for wearable sensors, primarily due to the ultralow abundance of biomarkers in sweat and the vulnerability of weak electrochemical signals to interference. Herein, we report a wearable electrochemical sensor based on a dual-signal amplification strategy for the synchronous and highly sensitive detection of cortisol, glucose (Glu), and uric acid (UA). The CuFe-PBA/CC substrate provides a platform featuring high surface area, electrocatalytic activity, and dual signal amplification capabilities, which are the primary sources of high sensitivity. The MIP layer enhances the effective concentration of target substances near active sites through a local enrichment effect. Combined with the substrate's high catalytic activity, this amplifies the electrochemical signal specific to the target. It synergizes with an exogenous redox probe ([Fe(CN)6]3-/4-) to establish a dual-amplification pathway, which enhances the current output to the milliampere level and substantially improves the signal-to-noise ratio. To ensure the optimal performance of the sensor, the preparation parameters were optimized via the integration of machine learning. This integrated sensing system exhibits high selectivity, superior sensitivity (with detection limits of 44.78 fM for cortisol, 0.064 μM for Glu, and 0.089 μM for UA), and excellent stability (retaining 88% of the initial signal after 100 cycles of mechanical deformation). Furthermore, we fabricated the sensor into a watch-like form factor, integrated it with high-precision wearable electronic modules, and achieved real-time wireless detection of sweat cortisol, Glu, and UA in ex vivo sweat monitoring experiments. This work provides a novel solution for objective stress assessment and metabolic status monitoring, holding promise for advancing long-term health management applications.
15 Apr 2026
Read appraisal →Tobacco control
Cigarette filter ventilation, product appeal and regulatory options: a review of the influence of ventilation on consumers' sensory and risk perceptions
OBJECTIVE: Cigarette filter ventilation may enhance product appeal, support initiation and sustained smoking, and increase smoking prevalence. We reviewed recent evidence on how filter ventilation affects consumers' perceptions of product appeal and considered product use preferences to inform regulatory options. DATA SOURCES: PubMed, Embase and PsycINFO databases were searched up to December 2023. Systematic search strategies used terms based on cigarette filter ventilation, and multiple constructs comprising product appeal and user perceptions. STUDY SELECTION: Two reviewers screened all 917 retrieved titles and abstracts independently and applied a consensus strategy to identify 16 articles that met eligibility criteria. DATA EXTRACTION: Extracted data focused on study design, dependent variables or outcomes, and major conclusions. DATA SYNTHESIS: While remaining popular, awareness and risk perception of ventilated cigarettes is generally low. Cigarettes with filter ventilation are perceived as smoother and less irritating than non-ventilated cigarettes. These modified sensory experiences shape perceptions of reduced health risks. Pack text descriptors and colours convey further information to consumers about smoothness, supporting lowered risk perceptions. CONCLUSIONS: Filter ventilation and its associated marketing continue to influence consumers' perceptions of smoothness creating misperceptions about health risks. Standardised ('plain') packaging regulations are recommended to restrict communication of ventilation themes as well as evidence-based health communications to correct consumer misperceptions about smoothness and health risks. Since filter ventilation has no benefits for public health, policy makers could consider banning filter ventilation or even disallowing filters.
15 Apr 2026
Read appraisal →BMJ open respiratory research
Occupational exposure to toxic particles and risk of pulmonary sarcoidosis: a systematic review and meta-analysis
INTRODUCTION: The onset of sarcoidosis is likely the result of a complex genetic-environment-immunological interaction. This systematic review and meta-analysis aimed to identify occupational toxic particles associated with an increased risk of developing pulmonary sarcoidosis. METHODS: Publications in English, published from January 2000 to January 2025, were searched in PubMed, Scopus and JSTOR. The risk of bias was assessed for all included studies. Sensitivity analyses stratified by study quality were conducted to evaluate the robustness of the findings and potential bias. To assess publication bias, a funnel plot was used when more than six studies were included in the analysis. RESULTS: Five categories of toxic particles were identified to be associated with increased risk of the onset of sarcoidosis: chemicals, inorganic dusts, metals, mixed dusts and fumes and organic dusts. The quantitative analysis includes data from 13 studies. The results suggested that occupational silica, pesticides, mould/mildew and World Trade Center ((WTC) dust exposures were associated with increased odds of pulmonary sarcoidosis. Sensitivity analysis confirmed the robustness of the association for WTC dust and mould, whereas the risk associated with silica appeared attenuated in high-quality studies. However, gold exposure was identified as a protective factor. CONCLUSION: Pulmonary sarcoidosis is associated with occupational silica, pesticides, WTC dust and mould. Future research should prioritise gene-environment interactions and granuloma mineralogy to refine preventive strategies and disease management.
12 Apr 2026
Read appraisal →PloS one
Prevalence and risk factors for AMS: A systematic review and meta-analysis
BACKGROUND: Acute Mountain Sickness (AMS) is a high-altitude-specific condition with variable prevalence and poorly defined risk factors. Despite growing global exposure to high-altitude environments, no systematic review has comprehensively synthesized AMS epidemiology. OBJECTIVES: To assess the prevalence of AMS and critically evaluate evidence on risk factors associated with increased susceptibility. DESIGN: A systematic review and meta-analysis following the Meta-analysis of Observational Studies in Epidemiology (MOOSE) guidelines. DATA SOURCES: PubMed, Cochrane Library, Web of Science, CINAHL Plus, China Knowledge Resource Integrated Database (CNKI), Wanfang Database, Chinese Biomedical Database (CBM), and Weipu Database (VIP) were comprehensively searched for observational studies investigating the prevalence and risk factors of AMS from January 1, 2004 to December 31, 2024. REVIEW METHODS: Original journal articles were included which met the inclusion criteria. The quality of the included studies was evaluated independently by two investigators. Meta-analysis was conducted using R software (v4.2.2), with estimates of AMS from pooled using a random-effects model. RESULTS: Fifty-eight studies (n = 2,705) were included. The pooled AMS prevalence was 48.25% (95% CI: 42.58-53.96%), with substantial heterogeneity (I² = 82.3%). Significant risk factors included extreme altitude (>5500 m; RR = 1.89), rapid ascent (<6 h; RR = 1.60), marked heart rate increase (>20 bpm; RR = 2.35), and oxygen saturation decline (>10%; RR = 2.02). Prior AMS history (RR = 1.36) and male sex (RR = 1.15) were also associated with higher risk, while older age (>50 years) was protective (RR = 0.78). CONCLUSION: AMS affects nearly half of high-altitude visitors. Altitude, ascent rate, cardiopulmonary response, and prior AMS history are key risk determinants. These findings support pre-exposure risk stratification, staged ascent protocols, and real-time physiological monitoring. PROSPERO CRD42024595365.
11 Apr 2026
Read appraisal →ACS applied materials & interfaces
Bioinspired Janus Electronic Textiles with Moisture and Thermal Management
Wearable electronics have attracted great attention in recent decades. However, excessive sweat accumulation on skin often has a significant effect on the signal stability, comfort, and safety of electronic skin in practical applications. Herein, a skin-comfortable Janus electronic textile (e-textile) that integrates unidirectional liquid transport and passive radiative cooling properties is developed. This e-textile is fabricated through laser ablation and single-side plasma treatment of carbon nanotubes (CNTs) doped with polydimethylsiloxane (PDMS). The resulting e-textile features a conical pore structure and asymmetric wettability, enabling unidirectional sweat transport from the skin surface to a superhydrophilic silk layer, thereby maintaining a dry and comfortable skin microenvironment. The Janus e-textile achieves a solar reflectivity of 69.2% and a mid-infrared emissivity of 98.3%, reducing the temperature by approximately 5.0 °C compared with a cotton fabric under solar intensity, demonstrating a superior radiative cooling performance. Moreover, the Janus e-textile exhibits stable resistance changes under cyclic stretching and bending, accurately detecting subtle movements across various body parts while reliably acquiring electromyography (EMG) signals. This work positions the Janus e-textile as a pioneering solution for next-generation wearable technologies, integrating thermal and moisture management with advanced resistive strain sensing and EMG capabilities for applications in sports, rehabilitation, and health monitoring.
10 Apr 2026
Read appraisal →JMIR human factors
Use of Digital Peer Support for Employee Well-Being: Retrospective Analysis Across Five Large Employers
BACKGROUND: Anonymous, 24/7 digital peer support (DPS) offers a scalable solution to support employees' emotional well-being. Understanding sociobehavioral factors, such as timing of engagement and the impact of shared resources, can help employers and employee assistance programs (EAPs) integrate digital tools to better support workforce well-being. OBJECTIVE: This study examines (1) outcomes via overall sentiment changes among DPS users; (2) sociobehavioral differences between employees who accessed real-time, anonymous DPS during versus outside business hours; (3) differences in sentiment outcomes based on time of use; and (4) the impact of in-session resource sharing on sentiment improvement. METHODS: Using OpenAI's large language model (LLM) GPT-4o-mini with a few-shot learning approach, 24,818 anonymous chat conversations from 13,879 employees at 5 large employers were evaluated for subclinical sentiment variables, including loneliness, sadness, stress, anxiety, depression, despair, helplessness, and optimism. RESULTS: Distinct activity patterns were observed between employees during and outside business hours, with a median user age of 36 years. During business hours, employees reported higher baseline stress (Δ1.6%), whereas outside business hours, baseline depression (Δ1.7%) and loneliness (Δ1.2%) were higher. After DPS use, LLM-derived negative sentiment scores decreased (loneliness 46% reduced, sadness 45%, stress 46%, anxiety ~39%, depression ~40%, despair ~40%, and helplessness ~38%), and optimism increased (~77%). Outside business hours, users engaged more (~68% of all sessions), remained in sessions 19% longer, and discussed 13% more topics, whereas users during business hours reported greater improvements in depression (Δ2.3%), helplessness (Δ1.9%), and loneliness (Δ0.5%). Resource sharing was associated with greater improvements in loneliness (Δ2.9%), stress (Δ1.3%), anxiety (Δ1.9%), depression (Δ7.3%), despair (Δ3.0%), helplessness (Δ2.8%), and optimism (Δ8.8%), but not sadness. CONCLUSIONS: DPS complements employers' EAPs by addressing employee engagement gaps, reducing barriers to mental health care, and promoting emotional well-being among the workforce.
8 Apr 2026
Read appraisal →Journal of medical Internet research
Electrocardiogram-Based Mental Stress Detection Amid Everyday Activities Using Machine Learning: Model Development and Validation Study
BACKGROUND: Frequent, sustained stress is linked to poor health and requires monitoring for early intervention. Electrocardiograms (ECG) are promising biomarkers because they can be recorded noninvasively and continuously using wearable devices. However, tracking stress with ECG is challenging because daily activities elicit responses similar to mental stress (MS), and various mental stimuli that individuals encounter complicate the use of machine learning (ML) models trained on a limited set of stressors. OBJECTIVE: We (1) evaluated the ability of ML models to distinguish MS episodes from a composite "no-stress" background, including rest and low- to moderate-intensity activities; (2) assessed their generalizability to new stressors and participants; and (3) tested robustness to lower sampling rates and fewer features, to explore their suitability for lightweight wearables. METHODS: We used a comprehensive ECG dataset sampled at 1000 hertz from 127 participants who underwent various mental stressors and engaged in diverse physical activities. A 30-second window was used to extract 55 features from time, frequency, nonlinear, and morphological domains. We trained a logistic regression (LR) model and an extreme gradient boosting (XGBoost) model, splitting the data into 60/20/20 for training, validation, and testing. Shapley additive explanation values were computed to explain model predictions. Additional analyses included leave-one-stressor-out; downsampling to 500, 250, and 125 hertz; a time-window sensitivity analysis; and reducing the number of features to as few as 5. RESULTS: XGBoost achieved an area under the receiver operating characteristic curve (AUROC) of 0.741 (95% CI 0.701-0.783) and an area under the precision-recall curve (AUPRC) of 0.706 (95% CI 0.658-0.753), compared with 0.724 (95% CI 0.678-0.772) and 0.691 (95% CI 0.639-0.742) for LR. The mean performance difference between XGBoost and LR was 0.017 for AUROC (95% CI 0.001-0.032) and 0.015 for AUPRC (95% CI -0.001 to 0.037; clustered bootstrap analysis using 2000 participant-level resamples), suggesting that LR performs comparably to the nonlinear XGBoost model. Both models were robust to downsampling and feature reduction (10 features retained >93% of performance). Extending the analysis window to 60 seconds improved model performance across all sampling rates, highlighting a trade-off between rapid detection and overall performance. When evaluating discrimination from physical activity, models achieved acceptable specificity for light physical activity (XGBoost: 0.787; LR: 0.794) but poor specificity for moderate physical activity (XGBoost: 0.418; LR: 0.444). Both models generalized to most unseen stressors, although performance varied across stressors, with limited transfer to the social-evaluative stressor. Feature importance analysis revealed fuzzy entropy and frequency-based features as key predictors. CONCLUSIONS: ML models can detect MS with high sensitivity and remain robust to lower sampling rates and fewer features. Generalization to novel stressors was stressor-dependent. Importantly, our results highlight challenges in distinguishing stress-related cardiac responses from those caused by physical exertion, revealing critical limitations of single-sensor ECG approaches for MS detection.
8 Apr 2026
Read appraisal →Journal of the American Heart Association
Exposure to Volatile Organic Compounds and Cardiovascular Diseases: A Systematic Review and Meta-Analysis
BACKGROUND: Environmental pollution is an often-neglected contributor to cardiovascular disease (CVD). Volatile organic compounds (VOCs) are a subset of ubiquitous pollutants. Recent evidence has suggested exposure to VOCs as a possible contributor to CVD. We aimed to review the evidence for such a link between VOC exposure and cardiovascular outcomes. METHODS: PubMed, Web of Science, and Scopus were systematically searched for ecological and observational studies evaluating associations between exposure to VOCs and cardiovascular outcomes. The available evidence was synthesized qualitatively, and meta-analyses were conducted wherever multiple estimates were identified for a shared association. Sensitivity analyses were performed to evaluate the influence of individual studies on the pooled estimates. RESULTS: Thirty-six studies were included in the review. Pooled random-effects estimates for associations between VOC mixtures (odds ratio, 1.57 [95% CI, 0.79-3.11])/toluene, ethylbenzene, and xylene and CVD trended toward a positive effect while being imprecise. The pooled estimate for the association between exposure to benzene and CVD death also trended toward a positive effect (meta relative risk, 1.09 [95% CI, 0.99-1.21]; I2=91.4 [95% prediction interval, 0.82-1.46]). However, serious inconsistencies were noted on the significance and the magnitude of association. Fixed-effect analyses indicated significant positive effects in all evaluated associations. CONCLUSIONS: Evidence from studies evaluating ambient and individual VOC exposures suggests a possible association with increased CVD and CVD death. However, the heterogeneity noted between specific VOCs' effects, possible confounding, limited long-term exposure assessment, and sparse evaluation of mixture/copollutant effects warrant cautious interpretation of these findings and highlight the need for future high-quality prospective studies.
8 Apr 2026
Read appraisal →Environmental geochemistry and health
Deciphering stone mining-induced hazardous heavy metal contamination in agricultural soils using source attribution, health-dietary risk analysis, and machine learning-driven insights
Mining activities significantly contribute to the release of hazardous heavy metals (HHMs) into agricultural soils, especially where stone extraction is intensive. This study assessed metal content, source identification, and possible health risks associated with HHMs (Cd, Pb, Cr, Cu, Ni) in stone mines affected agricultural soils of India. The results revealed that Zone 1 exhibited higher concentrations of Cr (308.95 ± 23.03 mg/kg), Ni (117.11 ± 16.21 mg/kg), and Cd (4.21 ± 1.26 mg/kg), surpasses the threshold limit set by WHO/FAO for agricultural soils. In contrast, Zone 2 had considerably lower HHMs concentrations for Cr (60.24 ± 10.68 mg/kg), Ni (31.61 ± 7.52 mg/kg) and Cd (0.66 ± 0.13 mg/kg). Geospatial distribution and self-organizing map (SOM) indicated spatial clustering of HHMs, with elevated concentrations centered in the southern part of the study area. Positive matrix factorization showed that geogenic sources and mining activities affect significantly to HHMs pollution. Monte Carlo simulation revealed that children are more prone to exposure to HHMs than adults, with ingestion identified as the primary exposure route. Hazard quotient values calculated from free ion activity model exceeded the threshold limit for Pb (1.59E+00) and Cd (6.32E-01). Severity adjustment margin of exposure highlighted severe human health risk for Cr and Ni. Random forest analysis indicated that bioavailable HHMs significantly influence the accumulation of metals in rice tissues which elevating dietary risk. The integration of robust statistical tools offers insights into the dynamics of HHMs contamination and provide policymakers and environmental scientists with useful information to mitigate the potential risks caused by mining on nearby agricultural lands.
5 Apr 2026
Read appraisal →Neurology
Long-Term Exposure to Ambient Air Pollution and Incident Amyotrophic Lateral Sclerosis: A Prospective Cohort Analysis of the UK Biobank
BACKGROUND AND OBJECTIVES: Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disease with a complex etiology. Although a range of genetic and lifestyle factors have been implicated, the potential role of environmental airborne pollution exposure is uncertain. This study examined the association between long-term ambient exposure to air pollutants and the incidence of ALS in UK Biobank participants. METHODS: This prospective cohort study was based on the UK Biobank participants aged 40-69 years. The analytical sample comprised participants free of ALS at baseline and had complete data on air pollution exposure. Long-term exposure (2006-2021) to nitrogen dioxide (NO2), nitrogen oxides (NOX), fine particulate matter (PM2.5; <2.5 µm), and coarse particulate matter (PM10; <10 µm) was assessed using data from the UK Department for Environment, Food and Rural Affairs at a spatial resolution of 1 × 1 km. To evaluate the association between these pollutants and ALS risk, we used multivariable time-varying Cox proportional hazards models. Several sensitivity analyses were conducted to assess the robustness of the results. We also examined for gene-environment interaction stratified by C9orf72 status and UNC13A genotype. RESULTS: Among the 501,308 participants with a mean age of 56.5 (SD 8.1) years at baseline, 272,764 (54.4%) were female. Over a median follow-up of 8.4 years, 687 individuals developed ALS. We did not observe any associations for any of the examined pollutants and ALS risk. Specifically, the hazard ratios per SD increment for PM10, PM2.5, NOX, and NO2 were 1.03 (95% CI 0.92-1.15), 1.00 (95% CI 0.88-1.14), 1.01 (95% CI 0.90-1.13), and 1.00 (95% CI 0.89-1.12), respectively. Individuals living in areas with the highest tertile of air pollutant exposure, compared with those in the lowest tertile, did not show a higher risk of ALS across any of the pollutants examined (p for trend >0.05). Restricted cubic spline analyses revealed no nonlinear associations between air pollution and ALS risk (all p for nonlinearity >0.05). These results remained robust in various subgroup and sensitivity analyses. No evidence of gene-environment interaction was found. DISCUSSION: In this large population-based study with high statistical power, ambient air pollution was not a risk factor for the development of ALS.
5 Apr 2026
Read appraisal →Journal of hazardous materials
Structuring large language models for chemical health risk reasoning in environmental and occupational exposure
Large language models (LLMs) have revolutionized numerous scientific fields. However, their application in environmental health remains limited, as they are prone to hallucinations and struggle with reasoning over complex, real-world exposure data. Chemical risks, especially from rare or poorly studied compounds, span diverse occupational and everyday scenarios, where structured knowledge is often incomplete and poorly integrated. To address these challenges, we present a health risk assistant that integrates deep learning-based compound property prediction, retrieval-augmented generation (RAG), and prompt engineering into a unified reasoning framework. Central to our design are two synergistic strategies: semantic structuring, which reorganizes heterogeneous knowledge into context-consistent and semantically rich profiles, and domain-aware prompting, which guides large language models through structured and role-specific reasoning aligned with toxicological goals. The framework enables the system to map physicochemical attributes to exposure pathways and disease outcomes with improved relevance and fidelity. Evaluated on a 100-question benchmark spanning chronic toxicity, occupational hazards, and exposure-specific risks, our LLM-RAG system consistently outperforms baseline models in correctness, faithfulness, and domain-specific helpfulness. Beyond answering queries, our approach demonstrates how integrated knowledge curation and tailored prompting can enable trustworthy and scalable AI reasoning for public and environmental health protection.
4 Apr 2026
Read appraisal →Advanced science (Weinheim, Baden-Wurttemberg, Germany)
A Wearable Brain-Computer Interface for Mitigating Car Sickness via Attention Shifting
Car sickness, an enormous vehicular travel challenge, affects a significant proportion of the population. Pharmacological interventions are limited by adverse side effects, and effective nonpharmacological alternatives remain to be identified. Here, we introduce a novel attention-shifting method based on a closed-loop, artificial intelligence (AI)-driven, wearable mindfulness brain-computer interface (BCI) to alleviate car sickness. As the user performs an attentional task, i.e., focusing on breathing as in mindfulness, with a wearable headband, the BCI collects and analyzes electroencephalography (EEG) data via a convolutional neural network to assess the user's mindfulness state and provide real-time audiovisual feedback. This approach might sustainedly shift the user's attention from physiological discomfort toward the BCI-based mindfulness practices, thereby mitigating car sickness symptoms. The efficacy of the proposed method was rigorously evaluated in two real-world experiments, namely, short and long car rides, with a large cohort of more than 100 participants susceptible to car sickness. Remarkably, over 83% of the participants rated the BCI-based attention shifting as effective, with significant reductions in car sickness severity, particularly in individuals with severe symptoms. Furthermore, EEG data analysis revealed a neurobiological signature of car sickness, which provided mechanistic insights into the efficacy of the BCI-based attention shifting for alleviating car sickness. This study proposes a wearable, nonpharmacological intervention for car sickness, validated in a relatively large-scale study involving over 100 participants in real-world car riding. These findings, derived from a between-cohort comparison, support the potential of this approach to improve the travel experience for car sickness sufferers and represent a novel practical application of BCI technology.
4 Apr 2026
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