Physical workload in various types of work: Part II. Neck, shoulder and upper arm

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Abstract

To explore the correlation between, and the variation in, various measures of exposure to potential risk factors for work-related upper extremity musculoskeletal disorders (UE-WMSDs), physical workload was measured in 43 types of work (713 individuals), using inclinometry for the head and upper arms, and electromyography (EMG) for the trapezius muscles.

Many exposure measures were highly correlated. Head flexion (90th percentile), extension (1st percentile), and movements (50th percentile); arm elevation (99th percentile) and movements (50th percentile); trapezius muscular rest (fraction of time) and peak load (90th percentile), constitute main exposure dimensions. The variations were large: head: flexion 9°–63°, extension −39°–4°, movements 2.3–33 °/s; arm: elevation 49°–124°, movements 3.0–103 °/s; trapezius: muscular rest 0.8%–52% of time, peak load 3.1%–24% of maximal EMG. Even within work categories, e.g. “repetitive industrial”, there were large variations.

Somewhat higher loads were recorded on the right as compared to the left side (differences: arm elevation 2°, arm movements 19%; trapezius peak load 18%), but these were small compared to the differences due to work.

There were high correlations between movements of arm and head (rs = 0.96), as well as arm and wrist (rs = 0.92), and between, on the one hand, trapezius muscular rest and peak load, and on the other, arm and head movements (|rs| = 0.47–0.62), as well as arm elevation (|rs| = 0.54–0.85), which has to be considered when assessing exposure–response relations.

Relevance to industry

Direct measurements provide objective and quantitative data of the main physical risk factors for UE-WMSDs, appropriate for estimating the risk, as well as giving priority to and evaluating interventions.

Introduction

Physical, as well as psychosocial factors have for many years been identified as risk factors for developing work-related musculoskeletal disorders (WMSDs) (ACGIH, 2001, Andersen et al., 2003, Buckle and Devereux, 2002, Howard et al., 2009, Ikuma et al., 2009, Larsman and Hanse, 2009, NIOSH, 1997, NRC, 2001, Östergren et al., 2005, Sluiter et al., 2001). In spite of this knowledge – and unlike the situation for chemical agents, e.g. organic solvents, heavy metals, silica and asbestos, as well as physical factors like noise, vibration and electromagnetic fields – preventive measures have so far only led to little, if any, reduction of WMSDs. One basic reason for this discrepancy is that for ascertaining the exposure to chemicals and the physical factors above, quantitative generic exposure measurement methods have been the obvious choice, while for assessing physical workload questionnaires and observer ratings, usually on categorical scales, have been preferred. Although these methods have been adequate for identifying risk factors, quantitative measures are required for establishing generic exposure–response relations. These in turn would give a fundament for forceful preventive actions, including legislation, as well as input to the patho-mechanistic understanding. The present study focuses on direct technical measurements for assessing the physical workload.

Posture, movements, force, lack of recovery, and combinations of these exposures, have all been identified as risk factors. From technical measurements, performed continuously for hours of work, a virtually infinite number of summary (exposure) measures can be derived. For deriving exposure–response relations using statistical models a minimal number of exposure measures, representing the principal dimensions each displaying a wide range of values and mutually uncorrelated, is optimal. Also, the relation between the load on the right/dominant and the left/non-dominant side is relevant when considering possible side differences in WMSDs.

In a previous study we explored the quantitative physical workload of the wrist and forearm, in occupational work (Hansson et al., 2009). The present study elucidate the physical workload of relevance for neck and shoulder disorders (upper extremity work-related musculoskeletal disorders; UE-WMSDs) by evaluating measurements of head and upper arm postures and movements by inclinometry and trapezius muscle load by electromyography (EMG) in a wide variety of work. The variation of the measurements and the correlations between them were explored, and the parameters needed to characterise the workload were defined. The information on UE-WMSDs (Nordander et al., 2009) and exposure–response relations will be investigated in separate studies.

Section snippets

Material and methods

Direct technical measurements of head and upper arm postures and movements and muscular activity of the trapezius muscles (see below) have been performed at the workplaces for 43 various types of work as well as for breaks (Table 1). The majority of the measurements have been performed in studies focusing on risk of UE-WMSDs or interventions, which are already published. However, no comprehensive evaluation of these or the previously unpublished exposure data has been performed before. Most of

Side differences

For all percentiles and all groups, arm elevation showed similar values for the right and left sides (Fig. 1a); the right arm was only slightly more elevated than the left one, e.g. for the 99th percentile on average 2.0°. Accordingly, the correlation between the sides were high; Spearman's rank-correlation coefficient (rs) was, for the 10th, 50th, 90th and 99th percentiles, 0.88, 0.75, 0.77 and 0.92, respectively.

Arm movements were in general and for all percentiles higher for the right arm

Discussion

All measures showed a wide range of physical exposure among different types of work. This was in general true also within the work categories. Arm movements and trapezius muscular peak load was higher on the right side, while the side differences regarding arm elevation and trapezius muscular rest were smaller. Work with arm support implied high values for the 50th percentile of arm elevation. All movement measures were highly correlated. Only a few of the measures showed low correlations,

Acknowledgements

This study was supported by the Swedish Medical Research Council, the Swedish Council for Work Life and Social Research (including Centre for Medicine and Technology for Work Life and Society at Lund University; METALUND), AFA Insurance, the Medical Faculty of Lund University and the County Councils of Southern Sweden. Ms Anita Ohlsson gave skilful technical assistance. The contributions by Dr Nils Fallentin, Dr Jane Frølund Thomsen, Dr Chris Jensen, Dr Dirk Jonker, Dr Birgit Juul-Kristensen,

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