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.

Comparison of univariate and multivariate anthropometric accommodation of the northwest Mexico population.

ABSTRCTErgonomic workstation design is crucial to prevent work-related musculoskeletal disorders. Many researchers have proposed multivariate analysis for human accommodation. However, no multivariate anthropometric analysis exists for the Mexican population. This study compares common multivariate human accommodation approaches (e.g. principal component and archetypal analyses) and clustering techniques (e.g. k-means and Ward's algorithm) with the classical percentile-based univariate accommodation approach, using the Chi-squared goodness-of-fit test and the McNemar's test. The theoretical accommodation percentage obtained by multivariate approaches was higher than those obtained by the percentile univariate approach considering the central 98% data. k-means and archetypal analysis obtained similar and the highest accommodation values, followed by Ward's algorithm and principal component analysis. The study findings can be deployed to assess the design of workstations in Mexico, such as electronic components assembly and crew designs, and the effects of different anthropometric measurements in human accommodation. Practitioner summary: Products and workplaces design are commonly based on the classical univariate approach, using the extreme percentiles. In this study, multivariate approaches were tested on dimensions for sitting workstations, and results showed a bigger accommodation level in comparison to the univariate 1%-99% approaches. Abbreviations: RHM: representative human model; DHM: digital human model; PCA: principal component analysis; AA: archetypal analysis (AA); PCs: principal components; FA: factor analysis; RSS: residual sum of squares; SSE: sum of squared estimated errors; WA: Ward's algorithm; DBI: Davies-Bouldin index; CHI: Calinski-Harabaz index; SI: silhouette index; SH: sitting height; EHS: eye height, sitting; AHS: acromial height, sitting; PH: popliteal height; KHS: knee height, sitting; BPL: buttock-popliteal length; BKL: buttock-knee length; FGR: functional grip reach; AD: anthropometric dimension; E: expected; A: achieved.