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Exposure assessment
Optimising sampling strategies: components of low-back EMG variability in five heavy industries
  1. Catherine M Trask1,2,
  2. Kay Teschke1,3,
  3. Jim Morrison4,
  4. Peter Johnson5,
  5. Mieke Koehoorn1,3
  1. 1School of Environmental Health, University of British Columbia, Vancouver, British Columbia, Canada
  2. 2CBF, Centre for Musculoskeletal Research, University of Gävle, Gävle, Sweden
  3. 3School of Population and Public Health, University of British Columbia, Vancouver, British Columbia, Canada
  4. 4Simon Fraser University School of Kinesiology, Burnaby, British Columbia, Canada
  5. 5Department of Environmental and Occupational Health Sciences, University of Washington, Seattle, Washington, USA
  1. Correspondence to Catherine M Trask, CBF, Centre for Musculoskeletal Research, University of Gävle, SE – 801 76 Gävle, Sweden; cmtrask{at}gmail.com

Abstract

Background Direct/ measurement of work activities is costly, so researchers need to distribute resources efficiently to elucidate the relationships between exposures and back injury.

Methods This study used data from full-shift electromyography (EMG; N=133) to develop three exposure metrics: mean, 90th percentile and cumulative EMG. For each metric, the components of variance were calculated between- and within-subject, and between-group for four different grouping schemes: grouping by industry (construction, forestry, transportation, warehousing and wood products), by company, by job and by quintiles based on exposures ranked by jobs within industries. Attenuation and precision of simulated exposure–response relationships were calculated for each grouping scheme to determine efficient sampling strategies.

Results As expected, grouping based on exposure quintiles had the highest between-group variances and lowest attenuation, demonstrating the lowest possible attenuation with this data.

Conclusion There is potential for grouping schemes to reduce attenuation, but precision losses should be considered and whenever possible empirical data should be employed to select potential exposure grouping schemes.

  • Exposure assessment
  • back disorders
  • statistics
  • ergonomics

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Footnotes

  • Funding The authors would like to thank WorkSafeBC, the Michael Smith Foundation for Health Research, and the Canadian Institutes for Health Research Strategic Training Program Bridging Public Health, Engineering, and Policy Research for financial support. Dr Koehoorn was supported in part by a Scholar Award from the Michael Smith Foundation for Health Research.

  • Competing interests None.

  • Ethics approval This study was conducted with the approval of the University of British Columbia Behavioural Research Ethics Board.

  • Provenance and peer review Not commissioned; externally peer reviewed.

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