Study of musculoskeletal disorders risk factors and discomfort in sculptors in the north of Mexico.

BACKGROUND: The sculpting craft must adopt awkward postures that lead to musculoskeletal disorders (MSDs). OBJECTIVE: This study investigated the prevalence of musculoskeletal discomfort (MD) and its associations with postural risk factors, demographics, and work characteristics among sculptors. They were determined the differences between MDs during the weeks of the study. METHODS: A longitudinal study was conducted; MD was investigated using the Cornell Musculoskeletal Discomfort Questionnaire (CMDQ). Posture was assessed using the Rapid Upper Limb Assessment method (RULA). Multivariate logistic regression (MLR) models analyzed associations with different factors. ANOVA was used to test for differences in MD prevalence. RESULTS: The analysis included 585 responses by body region. The prevalence of MD was high in the lower and upper limbs among sculptors (67.6%), with the lower back, upper arm, neck, and knees being the four most affected regions. Gender (female) (OR = 2.15), marital status (married) (OR = 1.80), health risk (obesity), the dual of a secondary job (OR = 1.94), job stress (OR = 2.10), duration of work (OR = 2.01), and difficulty keeping up with work (OR = 2.00) were significant predictors contributing to the occurrence of MD in different body regions. Only shoulder MD prevalence showed significant differences between study weeks. CONCLUSIONS: Sculptors suffer from MD. Demographic and work characteristic factors influence MD prevalence. Postural training, improved adaptation of work organization, and intervention guidance on ergonomic risks may reduce the prevalence of MD and the risk of MSDs in this population.

Cognitive Analyses for Interface Design Using Dual N-Back Tasks for Mental Workload (MWL) Evaluation.

In the manufacturing environments of today, human-machine systems are constituted with complex and advanced technology, which demands workers' considerable mental workload. This work aims to design and evaluate a Graphical User Interface developed to induce mental workload based on Dual N-Back tasks for further analysis of human performance. This study's contribution lies in developing proper cognitive analyses of the graphical user interface, identifying human error when the Dual N-Back tasks are presented in an interface, and seeking better user-system interaction. Hierarchical task analysis and the Task Analysis Method for Error Identification were used for the cognitive analysis. Ten subjects participated voluntarily in the study, answering the NASA-TLX questionnaire at the end of the task. The NASA-TLX results determined the subjective participants' mental workload proving that the subjects were induced to different levels of mental workload (Low, Medium, and High) based on the ANOVA statistical results using the mean scores obtained and cognitive analysis identified redesign opportunities for graphical user interface improvement.