Design of strategies to assess lumbar posture during work

Abstract

Quantitative characterization of postural load on the back should describe exposure patterns among workers and factors affecting these exposure patterns. This article presents general guidelines for designing appropriate measurement strategies; how to obtain detailed data with an applicable measurement method, what sampling strategy should be applied, which frequency and duration of measurements are required, and how differences between workers, shifts and tasks are addressed. Formulae are available Io evaluate the best trade-off between the reliability of a measurement device and repeated measurements and between the number of workers to be monitored and the number of measurements per worker. More difficult questions relate to collective assessments versus individual assessments and duration versus frequency of measurements. Methods are described for statistical modelling of data on trunk flexion and rotation. A two-way analysis of variance was applied to assess the principal sources of variation in back load among workers in the woodworking industry. A bootstrapping technique was used to evaluate the minimum number of measurements required to arrive at an unbiased estimate of the average exposure to trunk flexion in two occupational groups. It is advisable to conduct a small pilot study to determine the essential features of the measurement strategy before undertaking a large study comprising many workers and many work situations. Relevance to industry In order to reduce the occurrence of work-related musculoskeletal disorders in occupational populations, quantitative assessment of work-related risk factors is needed. This article presents a framework for the design of exposure assessment strategies, using physical load due to trunk posture as an example. General guidelines are presented to accommodate optimal measurement of physical load at work.

Cost-efficient assessment of biomechanical exposure in occupational groups, exemplified by posture observation and inclinometry

“…"Zero" between-worker components of variance have been reported previously in observation studies of lumbar posture (47), and they may be an artifact associated with the algorithms used for distributing the overall variance in data between different sources. Thus, if within-worker variability is very large, little or no variability will be "left" for the between-worker component, especially if this component is, truly, small (48).…”

“…Observation has been identified as a more versatile method than direct measurements; if assessment of multiple types of exposure is desired, a single observation protocol might more easily assess several of these aspects (27). Because of this, observation is uniquely suited for screening; checklists and screening instruments can be used to identify potentially harmful situations or particularly exposed body parts worthy of more in-depth analysis using a direct measurement method (47). In contrast, a single inclinometer can deliver only data on postural angle of the body part on which it is mounted and derivatives thereof, such as movement velocities.…”

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

“…Still, formulations of the magnitude of different exposure variance components are dependent at least partially on an adequate precision of the individual measurements on which they are based. While previous studies have used bootstrapping to explore several exposure assessment issues (28,35,37,38,48), this is the first study using the technique to examine the effect of sampling duration on the precision of an individual EMG measurement. Overall exposure variability in occupational epidemiology has been broadly partitioned into betweenparticipant and within-participant components (49).…”

“…The chief advantage of the bootstrap procedure is that assumptions about the distribution of the population data (eg, normality) are not required in order to make inferences about the parameter of interest (35). The bootstrap procedure begins with an observed parent data set of sample size N, from which a population parameter q is to be estimated with the statistic q.…”

Systematic evaluation of observational methods assessing biomechanical exposures at work

“…Most jobs and tasks show daily or seasonal variability in biomechanical exposures, and, therefore, the sampling strategy -in terms of the number of subjects and measurements per subject -is decisive for the precision and credibility of the obtained result (148)(149)(150)(151)(152)(153)(154)(155)(156). Some of the methods included in the present selection are based on sampling at fixed-time intervals (eg, OWAS, PATH), while others apply continuous observation for longer periods (eg, PEO, TRAC).…”