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  • 1.
    Garza, Jennifer
    et al.
    University of Gävle, Faculty of Health and Occupational Studies, Department of Occupational and Public Health Sciences, Occupational health science. University of Gävle, Centre for Musculoskeletal Research.
    Mathiassen, Svend Erik
    University of Gävle, Faculty of Health and Occupational Studies, Department of Occupational and Public Health Sciences, Occupational health science. University of Gävle, Centre for Musculoskeletal Research.
    Heiden, Marina
    University of Gävle, Faculty of Health and Occupational Studies, Department of Occupational and Public Health Sciences, Occupational health science. University of Gävle, Centre for Musculoskeletal Research.
    Reliability of using observations when assessing different posture variables2016Conference paper (Refereed)
    Abstract [en]

    Background. Working in extreme postures has been identified as a risk factor for musculoskeletal symptoms. Directly measuring work postures is considered to be the most accurate approach for assessing these exposures, but it is often not feasible to directly measure posture due to time or budget constraints. Alternatively, direct measurements of postures can be predicted based on observations of workers’ postures. Since observers are known to differ in posture ratings, it may, however, be necessary to develop calibration procedures for each specific observer.

    Methods. Arm and back postures of a random sample of 28 paper mill workers were measured via inclinometry and also were assessed by three observers from videos. Linear models with participant number and observer as random effects were resolved to assess whether or not observed postures were associated with the corresponding inclinometer values and if the effect of observer on slope and intercept was significant (p<0.05). The variance explained by these models was compared to the variance explained by corresponding linear models yet with observer entered as a fixed effect (i.e. allowing different slopes and intercepts for different observers).

    Results. For all postures, the variance explained was similar when using observer as a fixed compared to a random effect (R-squared ranging from 0.41 to 0.56 for observer as fixed or random effect). Throughout, participant was the major source of variance.

    Discussion. Our findings of similar amounts of variance explained when using observer as a fixed compared to a random effect for all postures indicates that calibration models developed for each individual observer may not necessarily perform better than a general calibration applying to any observer. Since posture observations explained only a small proportion of directly measured posture variance, observation may not be very useful in this setting

  • 2.
    Heiden, Marina
    et al.
    University of Gävle, Faculty of Health and Occupational Studies, Department of Occupational and Public Health Sciences, Occupational health science. University of Gävle, Centre for Musculoskeletal Research.
    Garza, Jennifer
    University of Gävle, Faculty of Health and Occupational Studies, Department of Occupational and Public Health Sciences, Occupational health science. University of Gävle, Centre for Musculoskeletal Research.
    Trask, Catherine
    University of Gävle, Faculty of Health and Occupational Studies, Department of Occupational and Public Health Sciences, Occupational health science. University of Gävle, Centre for Musculoskeletal Research.
    Mathiassen, Svend Erik
    University of Gävle, Faculty of Health and Occupational Studies, Department of Occupational and Public Health Sciences, Occupational health science. University of Gävle, Centre for Musculoskeletal Research.
    Cost-efficient assessment of variation in arm posture during paper mill work2016Conference paper (Refereed)
    Abstract [en]

    Background. Arm posture is a recognized risk factor for occupational upper extremity musculoskeletal disorders and thus often assessed in research and practice. Posture assessment methods differ in cost, feasibility and, perhaps, bias. An attractive approach could be to build statistical models for predicting results of expensive direct measurements of arm posture from cheaper or more accessible data, and apply them to large samples in which only the latter data are available. We aimed to build and assess the performance of such prediction models in a random sample of paper mill workers.

    Methods. 28 workers were recruited to the study, and their upper arm postures were measured during three full work shifts using inclinometers. Simultaneously, the workers were video filmed, and their arm posture and gross body posture were assessed by observing the video afterwards. Models for predicting the inclinometer-assessed duration (proportion of time) and frequency (number/min) of periods spent in neutral right arm posture (<20°) were fitted using subject and observer as random factors, measured shift (1, 2 or 3) as fixed factor, and either observed time in neutral right arm angle or observed gross body posture as predictor.

    Results. For the proportion of time spent in neutral arm posture, the best performance was achieved by using observed gross body posture as predictor (explained variance: R2=26%; standard error: SE=9.8). For the frequency of periods spent in neutral arm posture, the corresponding model fit was R2=60% and SE=5.6. Bootstrap resample validation of the latter model showed an expected performance in other samples of R2=59-60% and SE=5.5-5.6 (5th-95th percentile).

    Discussion. Surprisingly, we found that observed gross body posture was a better predictor of variation in arm posture than observed arm angles. The findings suggest that arm posture during paper mill work can be cost-efficiently assessed using simple observations.

  • 3.
    Heiden, Marina
    et al.
    University of Gävle, Faculty of Health and Occupational Studies, Department of Occupational and Public Health Sciences, Occupational health science. University of Gävle, Centre for Musculoskeletal Research.
    Garza, Jennifer
    University of Gävle, Faculty of Health and Occupational Studies, Department of Occupational and Public Health Sciences, Occupational health science. University of Gävle, Centre for Musculoskeletal Research. Division of Occupational and Environmental Medicine, University of Connecticut Health Center, Farmington, USA.
    Trask, Catherine
    University of Gävle, Faculty of Health and Occupational Studies, Department of Occupational and Public Health Sciences, Occupational health science. University of Gävle, Centre for Musculoskeletal Research. Canadian Centre for Health and Safety in Agriculture, College of Medicine, University of Saskatchewan, Saskatoon, Saskatchewan, Canada.
    Mathiassen, Svend Erik
    University of Gävle, Faculty of Health and Occupational Studies, Department of Occupational and Public Health Sciences, Occupational health science. University of Gävle, Centre for Musculoskeletal Research.
    Predicting directly measured trunk and upper arm postures in paper mill work from administrative data, workers’ ratings and posture observations2017In: Annals of Work Exposures & Health, ISSN 2398-7308, Vol. 61, no 2, p. 207-217Article in journal (Refereed)
    Abstract [en]

    Introduction: A cost-efficient alternative to measuring working postures directly could be to build statistical models for predicting results of such 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.

    Methods: 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 of the model estimate (SE). For models performing well, validity was assessed by bootstrap resampling.

    Results: 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).

    Conclusions: Observational data had a better ability to predict inclinometer-assessed upper arm exposures than workers’ ratings or administrative data, but they are typically more expensive to obtain. The results encourage comparisons of the cost-efficiency of modeling based on administrative data, workers’ ratings, and observation.

  • 4.
    Heiden, Marina
    et al.
    University of Gävle, Faculty of Health and Occupational Studies, Department of Occupational and Public Health Sciences, Occupational health science. University of Gävle, Centre for Musculoskeletal Research.
    Mathiassen, Svend Erik
    University of Gävle, Faculty of Health and Occupational Studies, Department of Occupational and Public Health Sciences, Occupational health science. University of Gävle, Centre for Musculoskeletal Research.
    Garza, Jennifer
    University of Gävle, Faculty of Health and Occupational Studies, Department of Occupational and Public Health Sciences, Occupational health science. University of Gävle, Centre for Musculoskeletal Research. Division of Occupational and Environmental Medicine, UConn Health, Farmington, CT, United States .
    Liv, Per
    University of Gävle, Faculty of Health and Occupational Studies, Department of Occupational and Public Health Sciences, Occupational health science. University of Gävle, Centre for Musculoskeletal Research. Centre for Research and Development, Uppsala University/County Council of Gävleborg.
    Wahlström, Jens
    Department of Public Health and Clinical Medicine, Occupational and Environmental Medicine, Umeå University, Umeå, Sweden.
    A comparison of two strategies for building an exposure prediction model2016In: Annals of Occupational Hygiene, ISSN 0003-4878, E-ISSN 1475-3162, Vol. 60, no 1, p. 74-89Article in journal (Refereed)
    Abstract [en]

    Cost-efficient assessments of job exposures in large populations may be obtained from models in which “true” exposures assessed by expensive measurement methods are estimated from easily accessible and cheap predictors. Typically, the models are built on the basis of a validation study comprising “true” exposure data as well as an extensive collection of candidate predictors from questionnaires or company data, which cannot all be included in the models due to restrictions in the degrees of freedom available for modeling. In these situations, predictors need to be selected using procedures that can identify the best possible subset of predictors among the candidates. The present study compares two strategies for selecting a set of predictor variables. One strategy relies on stepwise hypothesis testing of associations between predictors and exposure, while the other uses cluster analysis to reduce the number of predictors without relying on empirical information about the measured exposure. Both strategies were applied to the same dataset on biomechanical exposure and candidate predictors among computer users, and they were compared in terms of identified predictors of exposure as well as the resulting model fit using bootstrapped resamples of the original data. The identified predictors were, to a large part, different between the two strategies, and the initial model fit was better for the stepwise testing strategy than for the clustering approach. Internal validation of the models using bootstrap resampling with fixed predictors revealed an equally reduced model fit in resampled datasets for both strategies. However, when predictor selection was incorporated in the validation procedure for the stepwise testing strategy, the model fit was reduced to the extent that both strategies showed similar model fit. Thus, the two strategies would both be expected to perform poorly with respect to predicting biomechanical exposure in other samples of computer users.

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