International Journal of Occupational Safety and Ergonomics最新文献

Objectives. Playing a wind instrument requires activation of the orofacial musculature. There is a relationship between the temporomandibular joint, the cervical musculature and the posture of the head. Few musicians report receiving specific information about oral health related to instrumental practice. This study aimed to analyse the effects of a preventive exercise programme on posture, range of motion, myofascial trigger points, muscle activation and craniocervical pain in wind players. Methods. Participants were university woodwind and brass performance students. The experimental group (14 females and 15 males aged 21.4 ± 1.7 years) performed an exercise programme aimed at improving the mobility and musculature of the cervical spine and temporomandibular joint. The control group (11 females and 9 males aged 23.7 ± 4.9 years) received no intervention. Both groups underwent a physical assessment and questionnaire before and after the 12-week intervention. Results. The experimental group experienced increased sagittal head angle and cervical flexion-extension mobility and decreased their cervical pain and trapezius activation at rest. In addition, the prevalence of pain in the last month as well as neck pain decreased. Conclusions. With the implementation of a specific exercise programme, wind players improved their cervical mobility, with less pain and more relaxed upper trapezius muscles in rest.Trial registration: ClinicalTrials.gov identifier: NCT05277649.

Objectives. Ensuring occupational safety and well-being in high-risk industries necessitates addressing gender-based challenges that impact workforce ergonomics, productivity and retention. This study validates the workplace gender challenge questionnaire (WGCQ), an instrument designed to assess gender-related occupational challenges encountered by female officers in the maritime industry. Methods. Study 1 examined the construct validity of the WGCQ through exploratory factor analysis. Study 2 confirmed the factor structure using confirmatory factor analysis and tested the questionnaire's criterion-related validity by correlating its dimensions with the job-related emotional exhaustion scale. Results. Three key dimensions of gender-based occupational challenges were identified: gender inequality, work-family conflict and feeling of inadequacy - all of which are critical determinants of workplace safety, job strain and ergonomic well-being. The results further demonstrated that gender-based disparities in occupational environments contribute to psychosocial stressors, which may increase emotional exhaustion, work-related fatigue and ergonomic inefficiencies. Conclusion. The validated WGCQ provides a standardized framework for assessing these challenges, supporting the development of industry-specific interventions aimed at promoting inclusive workplace policies, ergonomic improvements and occupational health strategies. This instrument can be adapted for use in other male-dominated industries where gender disparities impact workplace safety, job performance and employee well-being.

Objectives: Unsafe behaviors among construction workers are a leading cause of site accidents and injuries. While previous research has examined these behaviors in isolation, the complex causal mechanisms underlying their occurrence remain insufficiently explored. This study introduces a conceptual model that captures the multiple configurational impacts of individual, organizational and environmental factors contributing to unsafe behaviors.

Methods: Drawing on empirical data from 40 construction accident case studies, the research employs fuzzy-set qualitative comparative analysis (fsQCA) to examine the impact of different combinations of factors on behavioral outcomes.

Results and conclusions: The results reveal two key insights: no single factor is a necessary condition for unsafe behaviors - instead, six sub-conditions (e.g., physical condition, psychological condition, personal traits, organizational management, workplace design and physical environment) interact in various configurations to precipitate unsafe actions; and (b) unsafe behaviors emerge from the synergistic interplay of these factors, illustrating a 'multiple paths to the same outcome' pattern and demonstrating causal asymmetry. The identified causal pathways offer practical implications for improving construction safety management by enhancing understanding of the multi-dimensional drivers of unsafe behaviors.

This study evaluates the low-stress mechanical properties of cut protective fabrics made from filament twisted core-sheath yarns. Using a six-end satin weave for its tight structure and reduced interlaces, hybrid woven fabrics were produced with stainless steel/glass filament cores and high-performance polyethylene (HPPE)/polyester sheaths at varying linear densities (98, 74, 59, 49 and 39 Tex). Twelve fabric samples with a uniform areal weight of 200 g/m² were woven and tested through the Kawabata evaluation system for tensile, shear, bending, compression, surface friction and roughness properties. The results showed that fabrics with lower core material content exhibited enhanced tensile strain, linearity and compression resilience, while those with higher core content demonstrated superior tensile strength, shear, bending, compression and surface characteristics. The findings highlight that both core material proportion and yarn structure critically affect the sensory comfort and low-stress mechanical performance of cut protective fabrics.

Fatigue is a major determinant of motorcycle accidents, posing a critical threat to road safety by impairing riders' psychophysiological performance and increasing the crash risk. This study introduces a real-time fatigue monitoring system specifically designed for motorcycle riders, integrating physiological signal acquisition with machine learning classification. Heart rate variability (HRV) and galvanic skin response (GSR) sensors, established biomarkers of fatigue, were employed to continuously collect physiological data. The acquired signals underwent pre-processing to minimize noise and artefacts, followed by extraction of key features, including time-domain HRV indices and GSR conductance levels. These features were classified using a random forest algorithm, selected for robustness and accuracy in high-dimensional data contexts. The system discriminates fatigued from non-fatigued states in real time and delivers multimodal alerts to promote timely rest. Validation through laboratory and field trials demonstrated 84% accuracy, underscoring the potential of physiological monitoring with machine learning to enhance motorcycle rider safety.

This study was conducted to determine the prevalence and risk factors of work-related musculoskeletal disorders (MSDs) among midwives working in delivery rooms in Turkey. The study design was cross-sectional and descriptive. Data were collected using the introductory information form, visual analog scale and Nordic musculoskeletal questionnaires. The survey was completed between May and December 2022 by 386 midwives. All of the midwives had experienced at least one MSD symptom within the previous 12 months, with 24.9% taking sick leave due to MSDs. According to the participants, the most strenuous activities for the musculoskeletal system were assisting with births and suturing (53.4%), assisting with breastfeeding (50.3%) and positioning the pregnant woman during midwifery care (48.4%). Those midwives mentioning the latter activity experienced all MSDs (p ≥ 0.05). There was a significant positive association between working hours, job satisfaction, job stress and MSD.

This literature review examined the role of ambient intelligence (AmI) in occupational safety and health (OSH), focusing on its effectiveness across different workplaces, target groups and OSH hazards. The review aims to: identify occupational domains using AmI; assess the groups benefiting from them; evaluate the hazards addressed; and analyse the perceived benefits and limitations of AmI in OSH. Findings reveal that AmI enhances OSH through real-time monitoring, personalized feedback and proactive hazard prevention, addressing risks such as poor air quality, temperature shifts, noise exposure, toxic gases and stress-related conditions, particularly in high-risk sectors like oil and mining and in office settings. Although AmI improves safety awareness, challenges persist - including wearable discomfort, low tech literacy, privacy concerns and implementation difficulties. The review underscores the need for research to adapt AmI for vulnerable workers, refine user-friendly designs and balance automation with user control to create safer, human-centric workplaces.

Objectives. Healthcare facilities provide semi-solid nutrients to children requiring enteral tube feeding. The introduction of a new injection device (NEW) with a different syringe-to-tube connector geometry from a pre-existing device (PRE) results in increased workload, and an increase in nurses developing hand pain is a concern. A simulated experiment was conducted to determine the impact on upper extremity strain due to the difference between NEW and PRE. Methods. Twelve nurses performed two injection tasks for one portion of semi-solid nutrients into a dummy gastrostomy using the NEW and PRE. Surface electromyograms were measured on the upper extremity. Task time was divided into sucking and extruding manipulations. Task time and mean myoelectric potentials were calculated for each manipulation and statistically compared between the NEW and PRE. Forces required to manipulate the syringe for semi-solid nutrient injection were estimated using fluid dynamic models. Results. Task time was significantly longer with the NEW. Myoelectric potentials for sucking manipulation were significantly higher with the NEW. Sucking force with the NEW was approximately twice that of the PRE. Conclusion. Sucking with the NEW placed a high strain on the caregiver's hand/wrist. The NEW is less appropriate for enteral tube feeding of semi-solid nutrients.

Objectives. Following the first academic year, deck cadets experience increased psychosocial stressors during onboard training. As their adaptation affects readiness for high-stakes decisions and career sustainability, identifying protective resources and risk behaviors is crucial. This study examines the association between positivity and life satisfaction, testing whether doomscrolling - compulsive scrolling through negative news - mediates this association. Methods. A cross-sectional design was employed, utilizing validated scales among 380 deck cadets. Structural equation modeling was conducted to analyze the direct and indirect effects of positivity and doomscrolling on life satisfaction. Model selection relied on a nested χ² difference test, and indirect effects were estimated using bias-corrected bootstrapping with 5000 samples and a 95% confidence interval. Results. Positivity was positively associated with life satisfaction and negatively associated with doomscrolling, while doomscrolling was negatively associated with life satisfaction. The bootstrapped indirect effect of positivity on life satisfaction via doomscrolling was significant, supporting a partial mediation model with a retained direct path. Conclusions. Doomscrolling acts as a behavioral pathway that associates positivity with life satisfaction during onboard training. Addressing a gap in maritime psychology, the study provides the basis for cadet-focused resilience training and digital media literacy practices designed to reduce maladaptive news consumption habits.

Objective. This study evaluated the predictive performance of machine learning and deep learning models in estimating manual strength in men and women using anthropometric variables. Methods. Anthropometric and strength data were collected from 382 participants from the economically active population of Campeche, Mexico. Predictive models implemented included linear regression, random forest, AdaBoost, extreme gradient boosting, TabNet, TabPFN and a custom convolutional neural network. Their performance was assessed using the mean absolute error, mean squared error and explained variance score. Additionally, SHAP (SHapley Additive exPlanations) analysis was conducted to interpret feature importance across models. Results. Deep learning models such as TabNet and TabPFN demonstrated superior prediction accuracy for torque strength, capturing complex non-linear interactions. Linear regression exhibited better generalization, particularly for grip strength prediction. SHAP analysis consistently identified palmar length and elbow-to-fingertip length as the most influential anthropometric predictors. Ensemble methods like random forest and AdaBoost performed well on training data but showed a tendency to overfit. Conclusions. Although advanced models enhanced performance in specific tasks, linear regression remained the most robust for generalization. Feature importance analysis confirmed the biomechanical relevance of the selected predictors. Future applications should balance model complexity with the need for interpretability, depending on ergonomic objectives.