RT Journal Article
SR Electronic
T1 Optimising sampling strategies: components of low-back EMG variability in five heavy industries
JF Occupational and Environmental Medicine
JO Occup Environ Med
FD BMJ Publishing Group Ltd
SP 853
OP 860
DO 10.1136/oem.2010.055541
VO 67
IS 12
A1 Catherine M Trask
A1 Kay Teschke
A1 Jim Morrison
A1 Peter Johnson
A1 Mieke Koehoorn
YR 2010
UL http://oem.bmj.com/content/67/12/853.abstract
AB 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.