OBJECTIVE: Questionnaires for assessment of biomechanical exposure are frequently used in surveillance programs, though few studies have evaluated which key questions are needed. We sought to reduce the number of variables on a surveillance questionnaire by identifying which variables best summarized biomechanical exposure in a survey of the French working population. METHODS: We used data from 2002 to 2003 French experimental network of Upper-limb work-related musculoskeletal disorders (UWMSD), performed on 2,685 subjects in which 37 variables assessing biomechanical exposures were available (divided into four ordinal categories, according to the task frequency or duration). Principal Component Analysis (PCA) with orthogonal rotation was performed on these variables. Variables closely associated with factors issued from PCA were retained, except those highly correlated to another variable (rho > 0.70). In order to study the relevance of the final list of variables, correlations between a score based on retained variables (PCA score) and the exposure score suggested by the SALTSA group were calculated. The associations between the PCA score and the prevalence of UWMSD were also studied. In a final step, we added back to the list a few variables not retained by PCA, because of their established recognition as risk factors. RESULTS: According to the results of the PCA, seven interpretable factors were identified: posture exposures, repetitiveness, handling of heavy loads, distal biomechanical exposures, computer use, forklift operator specific task, and recovery time. About 20 variables strongly correlated with the factors obtained from PCA were retained. The PCA score was strongly correlated both with the SALTSA score and with UWMSD prevalence (P < 0.0001). In the final step, six variables were reintegrated. CONCLUSION: Twenty-six variables of 37 were efficiently selected according to their ability to summarize major biomechanical constraints in a working population, with an approach combining statistical analyses and existing knowledge.
OBJECTIVE: Questionnaires for assessment of biomechanical exposure are frequently used in surveillance programs, though few studies have evaluated which key questions are needed. We sought to reduce the number of variables on a surveillance questionnaire by identifying which variables best summarized biomechanical exposure in a survey of the French working population. METHODS: We used data from 2002 to 2003 French experimental network of Upper-limb work-related musculoskeletal disorders (UWMSD), performed on 2,685 subjects in which 37 variables assessing biomechanical exposures were available (divided into four ordinal categories, according to the task frequency or duration). Principal Component Analysis (PCA) with orthogonal rotation was performed on these variables. Variables closely associated with factors issued from PCA were retained, except those highly correlated to another variable (rho > 0.70). In order to study the relevance of the final list of variables, correlations between a score based on retained variables (PCA score) and the exposure score suggested by the SALTSA group were calculated. The associations between the PCA score and the prevalence of UWMSD were also studied. In a final step, we added back to the list a few variables not retained by PCA, because of their established recognition as risk factors. RESULTS: According to the results of the PCA, seven interpretable factors were identified: posture exposures, repetitiveness, handling of heavy loads, distal biomechanical exposures, computer use, forklift operator specific task, and recovery time. About 20 variables strongly correlated with the factors obtained from PCA were retained. The PCA score was strongly correlated both with the SALTSA score and with UWMSD prevalence (P < 0.0001). In the final step, six variables were reintegrated. CONCLUSION: Twenty-six variables of 37 were efficiently selected according to their ability to summarize major biomechanical constraints in a working population, with an approach combining statistical analyses and existing knowledge.
Authors: M Melchior; Y Roquelaure; B Evanoff; J-F Chastang; C Ha; E Imbernon; M Goldberg; A Leclerc Journal: Occup Environ Med Date: 2006-06-20 Impact factor: 4.402
Authors: G A Hansson; I Balogh; J U Byström; K Ohlsson; C Nordander; P Asterland; S Sjölander; L Rylander; J Winkel; S Skerfving Journal: Scand J Work Environ Health Date: 2001-02 Impact factor: 5.024
Authors: Yiqun Chen; John D Meyer; Jaechul Song; J Corbett McDonald; Nicola Cherry Journal: Scand J Work Environ Health Date: 2002-08 Impact factor: 5.024
Authors: Baptiste Marçon; Willy Ngueyon Sime; Francis Guillemin; Nicolas Hubert; François Lagrange; Céline Huselstein; Jacques Hubert Journal: Res Rep Urol Date: 2019-09-27