title: Predicting Directly Measured Trunk and Upper Arm Postures in Paper Mill Work From Administrative Data, Workers' Ratings and Posture Observations.
abstract: A cost-efficient approach for assessing working postures could be to build statistical models for predicting results of direct measurements from cheaper data, and apply these models to samples in which only the latter data are available. The present study aimed to build and assess the performance of statistical models predicting inclinometer-assessed trunk and arm posture among paper mill workers. Separate models were built using administrative data, workers' ratings of their exposure, and observations of the work from video recordings as predictors.
  
  
  
  Trunk and upper arm postures were measured using inclinometry on 28 paper mill workers during three work shifts each. Simultaneously, the workers were video filmed, and their postures were assessed by observation of the videos afterwards. Workers' ratings of exposure, and administrative data on staff and production during the shifts were also collected. Linear mixed models were fitted for predicting inclinometer-assessed exposure variables (median trunk and upper arm angle, proportion of time with neutral trunk and upper arm posture, and frequency of periods in neutral trunk and upper arm inclination) from administrative data, workers' ratings, and observations, respectively. Performance was evaluated in terms of Akaike information criterion, proportion of variance explained (R2), and standard error (SE) of the model estimate. For models performing well, validity was assessed by bootstrap resampling.
  
  
  
  Models based on administrative data performed poorly (R2 ≤ 15%) and would not be useful for assessing posture in this population. Models using workers' ratings of exposure performed slightly better (8% ≤ R2 ≤ 27% for trunk posture; 14% ≤ R2 ≤ 36% for arm posture). The best model was obtained when using observational data for predicting frequency of periods with neutral arm inclination. It explained 56% of the variance in the postural exposure, and its SE was 5.6. Bootstrap validation of this model showed similar expected performance in other samples (5th-95th percentile: R2 = 45-63%; SE = 5.1-6.2).
  
  
  
  Observational data had a better ability to predict inclinometer-assessed upper arm exposures than workers' ratings or administrative data. However, observational measurements are typically more expensive to obtain. The results encourage analyses of the cost-efficiency of modeling based on administrative data, workers' ratings, and observation, compared to the performance and cost of measuring exposure directly.
authors.affiliation: Centre for Musculoskeletal Research, Department of Occupational and Public Health Sciences, University of Gävle, Gävle SE-801 76, Sweden.
authors.forename: Marina
authors.initials: M
authors.lastname: Heiden
authors.affiliation: Centre for Musculoskeletal Research, Department of Occupational and Public Health Sciences, University of Gävle, Gävle SE-801 76, Sweden.|Division of Occupational and Environmental Medicine, UConn Health, Farmington, CT 06030, USA.
authors.forename: Jennifer
authors.initials: J
authors.lastname: Garza
authors.affiliation: Centre for Musculoskeletal Research, Department of Occupational and Public Health Sciences, University of Gävle, Gävle SE-801 76, Sweden.|Canadian Centre for Health and Safety in Agriculture, College of Medicine, University of Saskatchewan, Saskatoon S7N OW8, Canada.
authors.forename: Catherine
authors.initials: C
authors.lastname: Trask
authors.affiliation: Centre for Musculoskeletal Research, Department of Occupational and Public Health Sciences, University of Gävle, Gävle SE-801 76, Sweden.
authors.forename: Svend Erik
authors.initials: SE
authors.lastname: Mathiassen
mesh_terms: D001132:Arm / Q000502:physiology*; D001696:Biomechanical Phenomena; D003625:Data Collection / Q000379:methods; D006801:Humans; D015233:Models, Statistical; D016273:Occupational Exposure / Q000706:statistics & numerical data; D009790:Occupations; D010209:Paper; D011187:Posture / Q000502:physiology*; D011795:Surveys and Questionnaires; D013647:Task Performance and Analysis; D060726:Torso / Q000502:physiology*
keywords: exposure assessment; musculoskeletal epidemiology; statistical modeling