| 1. |
- Jackson, Jennie, et al.
(författare)
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Statistical precision of categorical PATH observations of trunk posture
- 2012
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Ingår i: Work. - : IOS Press. - 1051-9815 .- 1875-9270. ; 41:Suppl. 1, s. 5519-5521
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Tidskriftsartikel (refereegranskat)abstract
- Background: Field studies assessing biomechanical occupational exposures frequently utilize direct observation. PATH (Postures, Activities, Tools, and Handling) is a tool for systematically observing occupational exposures during non-cyclic or long, irregular-cycle jobs. While PATH has been used in many studies, its statistical performance under different data collection strategies has not yet been investigated. The purpose of the current study was to examine this issue. Methods: Data from labourers performing the four tasks comprising a ‘Jacking Pit Construction’ operation was extracted from a previously collected data set. Using a probability based re-sampling bootstrap approach, categorical trunk posture exposure data was compared across nine simulated data collection strategies. Results/Conclusion: At the operational level, dispersion curves showed consistent trends of increased precision with increased sizes of the data set and curves tended to intersect at the expected value seen in the parent data set. At the task level, curves did not always follow the predicted pattern, highlighting the potential pitfalls of using PATH for infrequent tasks and the striking effect that individual workers can have on group exposure estimates of such tasks.
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| 2. |
- Mathiassen, Svend Erik, et al.
(författare)
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Statistical performance of observational work sampling for assessment of categorical exposure variables : A simulation approach illustrated using PATH data
- 2014
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Ingår i: Annals of Occupational Hygiene. - : Oxford University Press (OUP). - 0003-4878 .- 1475-3162. ; 58:3, s. 294-316
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Tidskriftsartikel (refereegranskat)abstract
- Objectives. Observational work sampling is often used in occupational studies to assess categorical biomechanical exposures and occurrence of specific work tasks. The statistical performance of data obtained by work sampling is, however, not well understood, impeding informed measurement strategy design. The purpose of this study was to develop a procedure for assessing the statistical properties of work sampling strategies evaluating categorical exposure variables, and to illustrate the usefulness of this procedure to examine bias and precision of exposure estimates from samples of different sizes.Methods. From a parent data set of observations on 10 construction workers performing a single operation, the probabilities were determined for each worker of performing four component tasks and working in four mutually exclusive trunk posture categories (neutral, mild flexion, severe flexion, twisted). Using these probabilities, 5000 simulated data sets were created via probability-based re-sampling for each of six sampling strategies, ranging from 300 to 4500 observations. For each strategy, mean exposure and exposure variability metrics were calculated at both the operation- and task-levels and, for each of these, bias and precision were assessed across the 5000 simulations.Results. Estimates of exposure variability were substantially more uncertain at all sample sizes than estimates of mean exposures and task proportions. Estimates at small sample sizes were also biased. With only 600 samples, proportions of the different tasks and of working with a neutral trunk posture (the most common) were within 10% of the true target value in at least 80% of all the simulated data sets; rarer exposures required at least 1500 samples. For most task-level mean exposure variables and for all operation- and task-level estimates of exposure variability, performance was low, even with 4500 samples. In general, the precision of mean exposure estimates did not depend on the exposure variability between workers.Conclusions. The suggested probability-based simulation approach proved to be versatile and generally suitable for assessing bias and precision of data collection strategies using work sampling to estimate categorical data. The approach can be used in both real and hypothetical scenarios, in ergonomics as well as in other areas of occupational epidemiology and intervention research. The reported statistical properties associated with sample size are likely widely relevant to studies using work sampling to assess categorical variables.
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