Background: Musculoskeletal symptoms (MSS) are among the most common occupational health problems worldwide and disproportionately affect agricultural workers because of the physical demands of their work. However, evidence on MSS prevalence among farmers in Ecuador remains limited. This study aimed to estimate the prevalence of MSS and examine its association with selected sociodemographic and occupational factors among agricultural workers in rural communities in Loja, Ecuador. Methods: A descriptive, analytical, cross-sectional study was conducted among 103 farmers using the Standardized Nordic Musculoskeletal Questionnaire (NMQ). Descriptive statistics were used to estimate the 12-month prevalence of MSS, and multivariate logistic regression analyses were conducted to assess associations with sociodemographic and occupational variables. Results: A high prevalence of MSS was observed, particularly in the lower back (78.6%), knees (64.1%), and neck (58.3%). Overweight was significantly associated with lower back pain and marginally associated with neck pain. No significant associations were found between MSS prevalence and occupational exposure variables, such as working posture or weekly working hours. Conclusions: MSS are highly prevalent among agricultural workers in rural Ecuador. These findings highlight the need for preventive occupational health strategies to reduce musculoskeletal risk in agricultural settings.
Background: Nurses’ Intention to Leave (ITL) threatens care continuity and organizational stability, increasing workload, stress, and burnout. Understanding ITL and Anticipated Turnover determinants is essential to promote retention. Objectives: This scoping review aimed to identify factors influencing nurses’ ITL and Anticipated Turnover in Italian hospital settings and to highlight mechanisms supporting workforce sustainability. Methods: A comprehensive search of PubMed, Scopus, CINAHL, Web of Science, PsycINFO, and Cochrane Library included English and Italian primary studies (2014–2025) examining qualitative or quantitative determinants of ITL or turnover among hospital nurses in Italy. Results: Thirteen studies met inclusion criteria. Determinants included leadership quality, organizational support, affective commitment, resource availability, workplace climate, and personal experiences of leaving. These were grouped into management and contextual domains.Discussion: Multilevel strategies enhancing leadership, support, and teamwork are crucial to strengthen nurses’ wellbeing and retention. Further longitudinal and intervention studies are needed to guide targeted retention efforts.
Leptospirosis is a re-emerging zoonosis responsible for approximately 1 million human infections and 59,000 deaths annually worldwide. In Europe, notification rates have increased by 5% per year since 2010, a trend compounded by climate change. The study was performed to document a fatal occupational case acquired through an under-recognised environmental exposure pathway, and to establish what regional surveillance data can and cannot support regarding its burden and exposure profile. We report a fatal case of occupational leptospirosis in a 56-year-old male beef cattle farmer in Lombardy (Po Valley, Italy) and describe the subsequent epidemiological investigation. Regional surveillance data, veterinary notifications, ECDC data, and an analysis of occupational and climate-related exposure pathways were reviewed. The index case was caused by a strain of serogroup Javanica (microscopic agglutination test titre 1:3,200 against serovar Poi), absent from standard regional animal serological screening panels, probably acquired through contaminated irrigation water. Among 16 confirmed leptospirosis cases in Lombardy over 22 months, 3 (18.8%) died and 1 (6.3%) had permanent disability (severe-outcome rate 25.0%, likely an underestimate given surveillance failures). Twelve of 16 cases (75.0%) had an agricultural-environmental exposure nexus. Leptospirosis risk in irrigated agriculture extends beyond livestock contact to environmental contamination of surface water by wildlife reservoirs. Climate change is expected to expand the disease’s European distribution and extend the transmission season. A One Health approach integrating occupational medicine, veterinary surveillance, engineering controls, and climate-adaptive prevention is needed.
Background: Precision manual work is linked to sustained postural exposure and a higher risk of work-related musculoskeletal disorders. The extent to which working posture influences functional performance via postural stability and perceived control remains underexplored in occupational settings. This study examined the effects of seated and standing working postures on physical load, mental workload, and functional task performance during occupational precision sewing. Methods: A within-subject crossover experiment was conducted with 26 experienced sewing machine operators. Participants performed standardized sewing tasks in seated and standing postures. Outcomes included perceived physical effort (Borg CR-10), postural risk (RULA), mental workload (NASA-TLX), execution time, sewing quality, and qualitative perceptions of control and stability. Results: Standing posture was associated with higher lower-limb effort (p=0.010), increased postural risk (χ²=60.24, p<0.001), and higher overall mental workload (p=0.033). Sewing quality was moderately reduced in the standing condition, while execution time did not differ (p>0.05). Qualitative findings indicated that standing increased weight-bearing discomfort and postural regulation demands, whereas seated posture supported greater perceived control for precise stitching. Conclusions: Working posture influenced functional performance primarily through stability and perceived control mechanisms. Sustained standing during precision work may increase musculoskeletal risk and compromise task accuracy. Well-adjusted seated workstations should be prioritized for occupational precision sewing tasks. Posture alternation strategies should be carefully managed to balance musculoskeletal load and performance quality.
Background: While AI has been widely adopted in medical, social, and industrial fields, its use in ergonomic risk assessment of occupational movements is limited. Existing automated activity recognition methods often rely on controlled conditions or extra sensors, reducing their practicality. This study aims to develop an AI-driven tool to digitize the Occupational Repetitive Actions (OCRA) risk assessment by automatically identifying occupational actions from standard video recordings in real manufacturing settings. The goal is to help ergonomists conduct risk assessments more broadly and support workers’ health. Methods: Video data were collected from 37 workstations across multiple manufacturing facilities, comprising 869 action clips representing 12 distinct occupational activities. Two-dimensional skeletal pose extraction was performed using MMAction2 to anonymize workers and reduce environmental confounding factors. Graph Convolutional Network architectures (ST-GCN and Two-Stream Adaptive GCN) were trained and validated using stratified dataset partitioning (67.3% training, 24.7% validation, 8.0% test). Results: The proposed model achieved approximately 60% classification accuracy, substantially outperforming the frequency-based (12%) and duration-based (24%) baselines. The performance evaluation used multiple metrics, including Mean Average Precision, F1-score, Jaccard/IoU, Dice-Sørensen coefficient, and Hausdorff distance. Conclusions: This work demonstrates the technical feasibility of AI-driven, markerless motion capture systems for digitalizing musculoskeletal disorder risk assessment in real-world occupational settings. The principal innovation is the successful implementation of action recognition from standard video recordings captured in uncontrolled manufacturing environments, which transcends spatial constraints and eliminates the need for specialized sensing equipment. Despite current limitations in dataset size and classification accuracy, this study lays a foundation for developing deployable platforms to support real-time ergonomic assessment and proactive prevention of work-related musculoskeletal disorders.
