Method for quantitatively assessing physical risk factors during variable noncyclic work

Anton, Dan M; Cook, Thomad M; Rosecrance, John; Merlino, Linda

doi:10.5271/sjweh.742

Cited by 24 publications

(16 citation statements)

References 47 publications

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“…Thus the results may not be applicable to repetitive work with different exposure and cycle time features. Highly variable noncyclic work is a characteristic of many industries with high rates for upper-extremity musculoskeletal disorders, such as construction and agriculture (23,48,58,59). In such cases, work is not machine-paced and exposure to physical risk factors has little or no periodicity.…”

Section: Discussionmentioning

confidence: 99%

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Bootstrap exploration of the duration of surface electromyography sampling in relation to the precision of exposure estimation

Fethke¹,

Anton²,

Cavanaugh³

et al. 2007

Scand J Work Environ Health

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Section: Discussionmentioning

confidence: 99%

“…In addition, conventional electromyographic (EMG) systems are either not portable or use limited-range telemetry, requiring the subject to be near the data collection and storage location. Newer portable systems are available (21)(22)(23)), but they have been used in only a limited number of field studies.…”

mentioning

confidence: 99%

Bootstrap exploration of the duration of surface electromyography sampling in relation to the precision of exposure estimation

Fethke¹,

Anton²,

Cavanaugh³

et al. 2007

Scand J Work Environ Health

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“…Ignoring this fact may mask possible contrasts between different exposure groups. In occupational literature using conventional EVA, exposure level categories have not been constructed with this purpose in mind [12,13,29,41], probably due to a concern for exploring exposure at suspected more hazardous levels rather than to optimize statistical performance [17]. However, a data-driven optimal exposure classification may reveal subtle changes in an exposure pattern, for instance due to an intervention, that will be left undetected by conventional methods [42].…”

Section: Discussionmentioning

confidence: 99%

“…A variety of metrics have been suggested to assess differences in EVA results between groups or conditions and to extract what is believed to be important properties of exposure. Some approaches are based on an aggregation of EVA cells below or above a certain threshold in exposure level and/or sequence duration [12,14,17], while others derive variables describing the centroid or standard deviation of the EVA cells [13,16,18,19], or suggest statistical analysis procedures using principal component analysis of the EVA marginal distribution [15] and hierarchical regression of exposure level, frequency and duration simultaneously [20]. …”

Section: Introductionmentioning

confidence: 99%

Cluster-based exposure variation analysis

Samani

Mathiassen

Madeleine

2013

BMC Med Res Methodol

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BackgroundStatic posture, repetitive movements and lack of physical variation are known risk factors for work-related musculoskeletal disorders, and thus needs to be properly assessed in occupational studies. The aims of this study were (i) to investigate the effectiveness of a conventional exposure variation analysis (EVA) in discriminating exposure time lines and (ii) to compare it with a new cluster-based method for analysis of exposure variation.MethodsFor this purpose, we simulated a repeated cyclic exposure varying within each cycle between “low” and “high” exposure levels in a “near” or “far” range, and with “low” or “high” velocities (exposure change rates). The duration of each cycle was also manipulated by selecting a “small” or “large” standard deviation of the cycle time. Theses parameters reflected three dimensions of exposure variation, i.e. range, frequency and temporal similarity.Each simulation trace included two realizations of 100 concatenated cycles with either low (ρ = 0.1), medium (ρ = 0.5) or high (ρ = 0.9) correlation between the realizations. These traces were analyzed by conventional EVA, and a novel cluster-based EVA (C-EVA). Principal component analysis (PCA) was applied on the marginal distributions of 1) the EVA of each of the realizations (univariate approach), 2) a combination of the EVA of both realizations (multivariate approach) and 3) C-EVA. The least number of principal components describing more than 90% of variability in each case was selected and the projection of marginal distributions along the selected principal component was calculated. A linear classifier was then applied to these projections to discriminate between the simulated exposure patterns, and the accuracy of classified realizations was determined.ResultsC-EVA classified exposures more correctly than univariate and multivariate EVA approaches; classification accuracy was 49%, 47% and 52% for EVA (univariate and multivariate), and C-EVA, respectively (p < 0.001). All three methods performed poorly in discriminating exposure patterns differing with respect to the variability in cycle time duration.ConclusionWhile C-EVA had a higher accuracy than conventional EVA, both failed to detect differences in temporal similarity. The data-driven optimality of data reduction and the capability of handling multiple exposure time lines in a single analysis are the advantages of the C-EVA.

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“…However, analyzing and interpreting direct measurement results is time intensive and requires considerable technical expertise. Furthermore, the cost of instrumentation and software required to perform direct measures can be prohibitively expensive (Anton et al, 2003; David, 2005). Observational methods are frequently employed in industry because they cost less and are more time efficient than direct measures, and are generally more accurate and reliable than self-reports (Ebersole and Armstrong, 2002; Garg and Kapellusch, 2011; Kilbom, 1994; Takala et al, 2010).…”

Section: Introductionmentioning

confidence: 99%

Inter-rater reliability of cyclic and non-cyclic task assessment using the hand activity level in appliance manufacturing

Paulsen

Schwatka

Gober

et al. 2014

International Journal of Industrial Ergonomics

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This study evaluated the inter-rater reliability of the American Conference of Governmental Industrial Hygienists (ACGIH®) hand activity level (HAL), an observational ergonomic assessment method used to estimate physical exposure to repetitive exertions during task performance. Video recordings of 858 cyclic and non-cyclic appliance manufacturing tasks were assessed by sixteen pairs of raters using the HAL visual-analog scale. A weighted Pearson Product Moment-Correlation Coefficient was used to evaluate the agreement between the HAL scores recorded by each rater pair, and the mean weighted correlation coefficients for cyclic and non-cyclic tasks were calculated. Results indicated that the HAL is a reliable exposure assessment method for cyclic (r̄-barw = 0.69) and non-cyclic work tasks (r̄-barw = 0.68). When the two reliability scores were compared using a two-sample Student's t-test, no significant difference in reliability (p = 0.63) between these work task categories was found. This study demonstrated that the HAL may be a useful measure of exposure to repetitive exertions during cyclic and non-cyclic tasks. Relevance to industry Exposure to hazardous levels of repetitive action during non-cyclic task completion has traditionally been difficult to assess using simple observational techniques. The present study suggests that ergonomists could use the HAL to reliably and easily evaluate exposures associated with some non-cyclic work tasks.

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Method for quantitatively assessing physical risk factors during variable noncyclic work

Cited by 24 publications

References 47 publications

Bootstrap exploration of the duration of surface electromyography sampling in relation to the precision of exposure estimation

Bootstrap exploration of the duration of surface electromyography sampling in relation to the precision of exposure estimation

Cluster-based exposure variation analysis

Inter-rater reliability of cyclic and non-cyclic task assessment using the hand activity level in appliance manufacturing

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