Balancing Efficiencies & Tradeoffs: Evaluating EMG Exposure Assessment for Low Back Injury Risk Factors in Heavy Industry Catherine Trask 2008

 Back injury is a prevalent and expensive problem, particularly in heavy industry ‘Solving’ Back Injury

 How should exposure be measured?  For what duration?  Who should be measured?  How many times should they be measured? Thesis Objectives

Thesis Chapters  Chapters 4 and 5  Chapter 6  Chapter 7  How should exposure be measured?  For what duration?  Who should be measured?  How many times should they be measured?

Thesis Chapters  Chapters 1  Chapter 2 and 3  Chapters 4 and 5  Chapter 6  Chapter 7  Introduction to exposure assessment  Introduction to methods  How should exposure be measured?  For what duration?  Who should be measured?  How many times should they be measured?

Introduction to Exposure Assessment

Available Exposure Assessment Methods Direct Measure using electronic devices Observation by trained experts Self-report by the workers

Continuum of Methods High-resolution – lots of detail Objective Expensive Few people Short time Wider scope – ‘big picture’ Subjective Inexpensive More people Longer time Direct Measure Observation Self-report

Data Collection

Worker Recruitment  Contacted workers in heavy industry with accepted back injury claims  Contacted employer to gain access to the worksite  Recruited co-workers at each worksite  126 individuals  Repeated measures  223 measurement days

The Measurement Day Direct Measure by electronic devices Observation by trained experts Self-report by the workers  Measured all methods concurrently  Full shift

BackInjury Manual Materials Handling Risk Factors for Back Injury: Self-Report Working Postures  Asked for the amount of time in each activity  Used pictographs for most questions Self-report

BackInjury Manual Materials Handling Risk Factors for Back Injury: Observation Working Postures  ‘Snapshots’ of 15 variables at 1 minute intervals  Full-shift, excluding breaks Observation

BackInjury Manual Materials Handling Risk Factors for Back Injury: Direct Measurement Working Postures Inclinometer Whole body vibration Seat pad accelerometer Mean 90 th % Cumulative RCM EMG Back muscle activity

Chapter 4: Measuring low back injury risk factors in challenging work environments: an evaluation of cost and feasibility A version of this chapter has been published. Trask, C., Teschke, K., Village, J., Chow, Y., Johnson, P., Luong, N., and Koehoorn, M. (2007). Evaluating methods to measure low back injury risk factors in challenging work environments. American Journal of Industrial Medicine 50(9):

Cost and Feasibility  Success rate = successful measurement/ attempted measurement  Cost ($CDN) per successful measurement

Measurement Success Rates

Measurement Costs (per successful measurement)

Conclusions  Inverse relationship between cost and feasibility  Industrial environments are demanding on mechanical equipment  Cold, dusty, wet, explosive,  Rough handling/vibration  Consider costs and feasibility when planning field work!

A version of this chapter has been submitted for publication. Trask, C., Teschke, K., Morrison, J., Village, J., Johnson, P., Koehoorn, M. (2008) Predicting Exposure for Mean, 90th Percentile, and Cumulative EMG Activity in Heavy Industry. Submitted February 2008 to: Applied Ergonomics. Chapter 5: Predicting exposure for mean, 90th percentile, and cumulative EMG activity in heavy industry

Modeling determinants of exposure %RC = β1(observed variable 1) + β2(observed variable 2) + β3(observed variable 3)… Low Back EMG Observation or self report

Observation-based Model VariableMean EMG (in %RC) β (slope)p Intercept (average for all subjects)19.8 Standing (% time)0.115<0.001* Trunk position >60 o (% time) * kg load in hands (% time)0.910<0.001* kg load in hands (% time)

Self-report Model VariableMean EMG (in %RC) Β (slope)p Intercept (average of subjects)33.4 Sitting (% time) * Industry Construction industry * Forestry industry * Wood product industry Warehousing industry Transportation industry0Reference

Model Performance  Self-report based model  Observation based model

Conclusion Is this enough to conduct injury research?  Chemical exposure studies often predict 30-60%  Many studies using self-report and observation have found a relationship with back injury in the past  Epidemiology often uses categorical exposure variables, not continuous variables  One can predict some of the variability in EMG by asking a few questions or observing a few exposures  Tradeoff is in measuring more individuals, more times

A version of this chapter has been accepted for publication. Trask, C., Koehoorn, M., Village, J., Johnson, P., Teschke, K. (2008) How long is long enough? Evaluating sampling durations for low-back EMG assessment. Journal of Occupational and Environmental Hygiene. Submission number: JOEH R1. Chapter 6: How long is long enough? Selecting efficient sampling durations for low-back EMG assessment

Sampling Duration Rationale  Direct measurements were made for a whole shift  Do you really need to measure a whole shift?  How much information is lost if you measure a portion of the shift?

Selecting sampling durations  Compared 7 different sampling durations of the same work shift:  Whole shift (5.5 to 7.5 hours)  4 hours  2 hours  1 hour  10 minute  2 minute  2 shifts  Re-sampled post hoc  Randomized start time

Sampling durations Whole shift 4 hour 1 hour 2 hour Red = left back muscles Green = right back muscles

Absolute error between sampling durations

Conclusion  8% error for 4-hour and 14% error for 2-hour durations: reasonable estimates  1 hour or less produces very large errors  Balance cost with data precision and sample size  Shorter duration but more workers measured

A version of this chapter has been submitted for publication. Trask, C., Teschke, K., Morrison, J., Koehoorn, M. (2007) Optimizing Sampling Strategies: Components of Low-Back EMG Variability in Five Heavy Industries. Submitted February 2008 to: Occupational and Environmental Medicine. Submission number: OEM/2008/ Chapter 7: Optimizing sampling strategies: components of low-back EMG variability in five heavy industries

 How many individuals?  How many repeats?  (How) should we group measurements? Components of Variability Rationale  Grouping schemes make for less attenuation of an exposure-response relationship  Attenuation can be estimated based on the exposure data, even when the response is not measured

Sample Exposure-Response Relationship Back injury outcome = intercept + β1(exposure variable 1) Response Exposure

Grouping Schemes  No grouping  Job title  Company  Industry  Post hoc ranking of industry/job title groups

Percentage of true E-R by grouping scheme

Workers per group (k) required for 95% of true E-R Grouping strategy Number of groups 25% repeats 50% repeats 100% repeats Grouping by Industry Grouping by Company Grouping by Job Post hoc Grouping

Conclusion  The post hoc grouping scheme was the most efficient grouping scheme  Lowest estimated attenuation  Lowest number of measurements required  Measurement and recruitment challenges mean one should aim for a larger number of measurements  Attenuation isn't everything when selecting a sampling strategy – want to choose sample size to be robust

Summary There are always tradeoffs in exposure assessment  Lots of decisions to make!  How you ‘tip the scales’ toward more samples or more precision depends on the purpose of the study and the characteristics of the population  Contribution is in the ways of framing these questions and starting to quantify the answers

Acknowledgements Participating Workers and Worksites WorkSafe BC Michael Smith Foundation for Health Research CIHR Bridge Fellowship Program Mieke Koehoorn Kay Teschke Jim Morrison Kevin Hong Nancy Luong Melissa Knott James Cooper Judy Village Pete Johnson Jim Ploger Yat Chow

Questions