hig.sePublications
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard-cite-them-right
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • sv-SE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • de-DE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Predicting directly measured trunk and upper arm postures in paper mill work from administrative data, workers’ ratings and posture observations
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.ORCID iD: 0000-0002-5055-0698
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.
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.
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.ORCID iD: 0000-0003-1443-6211
2017 (English)In: Annals of Work Exposures & Health, ISSN 2398-7308, Vol. 61, no 2, 207-217 p.Article in journal (Refereed) Published
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.

Place, publisher, year, edition, pages
2017. Vol. 61, no 2, 207-217 p.
Keyword [en]
exposure assessment, statistical modeling, musculoskeletal epidemiology
National Category
Environmental Health and Occupational Health
Identifiers
URN: urn:nbn:se:hig:diva-21448DOI: 10.1093/annweh/wxw026PubMedID: 28395353OAI: oai:DiVA.org:hig-21448DiVA: diva2:924763
Funder
Forte, Swedish Research Council for Health, Working Life and Welfare, 2010-0748Forte, Swedish Research Council for Health, Working Life and Welfare, 2009-1761
Available from: 2016-04-29 Created: 2016-04-29 Last updated: 2017-06-30Bibliographically approved

Open Access in DiVA

No full text

Other links

Publisher's full textPubMed

Search in DiVA

By author/editor
Heiden, MarinaGarza, JenniferMathiassen, Svend Erik
By organisation
Occupational health scienceCentre for Musculoskeletal Research
Environmental Health and Occupational Health

Search outside of DiVA

GoogleGoogle Scholar

Altmetric score

Total: 292 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard-cite-them-right
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • sv-SE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • de-DE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf