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Trunk and upper arm postures in paper mill work
Applied Ergonomics 76 (2019) 90–96
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Trunk and upper arm postures in paper mill work Marina Heiden , Camilla Zetterberg, Svend Erik Mathiassen
T
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Centre for Musculoskeletal Research, Department of Occupational and Public Health Sciences, University of Gävle, SE-801 76, Gävle, Sweden
ARTICLE INFO
ABSTRACT
Keywords: Exposure Inclinometry Observation
The aim of this study was to assess postures and movements of the trunk and upper arm during paper mill work, and to determine the extent to which they differ depending on method of assessment. For each of 28 paper mill workers, postures and movements were assessed during three full shifts using inclinometer registration and observation from video. Summary metrics for each shift, e.g., 10th, 50th, and 90th posture percentile, were averaged across shifts and across workers. In addition, the standard deviation between workers, and the standard deviation between shifts within worker were computed. The results showed that trunk and arm postures during paper mill work were similar to other occupations involving manual materials handling, but the velocities of arm movements were lower. While postures determined by inclinometry and observation were similar on a group level, substantial differences were found between results obtained by the two methods for individual workers, particularly for extreme postures. Thus, measurements by either method on individuals or small groups should be interpreted with caution.
1. Introduction Paper production is an important industrial branch, serving the demands for all kinds of paper products used in everyday life. Studies have shown that workers involved in the production of paper are exposed to dust, chemicals, gases, noise, heat and humidity (Jungbauer et al., 2005; Kauppinen et al., 2002; Korhonen et al., 2004; Neitzel et al., 2016). Most of these exposures vary considerably among workers, which presents challenges to research on health risks of working in the paper production industry. To date, studies on health effects of pulp and paper mill work have mostly focused on respiratory function and cancer. Several studies have reported increased risk of respiratory problems among these workers, and the risk has been related to, for example, gassings and microorganisms (Glindmeyer et al., 2003; Hoffman et al., 2004; Murgia et al., 2011; Sikkeland et al., 2007). In addition to the previously mentioned exposures in paper mill work, the workers perform heavy lifting/pushing/pulling, and work with their arms above shoulder height, often in constrained spaces along the paper machines (Inoue and Harada, 2002). Although investigated only in few studies, the prevalence of musculoskeletal symptoms has been estimated to be about 60% among paper mill workers, and the symptoms are mainly located in the shoulders and lower back (Coluci et al., 2012; Inoue and Harada, 2002). Leino-Arjas et al. (2002) showed in a nationwide study in Finland that the hospitalization rate for back disorders among workers manufacturing pulp,
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paper and paper products was higher than the Finnish population average. In order to identify targets for interventions to reduce musculoskeletal disorders, and thus effectively reduce exposure to injury risk factors in the workplace, thorough documentation of the workers’ biomechanical exposures is needed. Methods for assessing biomechanical exposures in occupational settings differ in cost, feasibility, and resulting information (Heiden et al., 2017; Trask et al., 2007, 2014). Postures and movements can be measured using equipment that is attached to the worker, but certain movements, such as twisting of the back, may not be captured by the measurement devices. They may also be sensitive to the climate conditions during data collection. Observations, on the other hand, may provide information about postures and movements, but also about complex exposures beyond the reach of direct measurements. However, they require trained observers to be present onsite or inspect recorded documentation of the work, typically on video. Previous research on the agreement between postural exposures assessed by inclinometry and observation in occupational settings has shown that they may provide different results. Teschke et al. (2009) found that observations explained 30–61% of the variance in inclinometer recordings, and recommended the use of both measures to gain as much information as possible. Burdorf et al. (1992) showed significant correlations between the methods, but large differences in exposure estimates at an individual level. In a study of upper arm postures among hairdressers, Rezagholi et al. (2012) found good
Corresponding author. E-mail addresses: [email protected] (M. Heiden), [email protected] (C. Zetterberg), [email protected] (S.E. Mathiassen).
https://doi.org/10.1016/j.apergo.2018.12.004 Received 2 May 2018; Received in revised form 1 October 2018; Accepted 8 December 2018 0003-6870/ © 2018 Elsevier Ltd. All rights reserved.
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agreement between observations and inclinometry at the group level for median inclination, while results for time spent in extreme and neutral postures differed considerably between the methods. To better understand the extent to which postures and movements assessed by observations and inclinometry agree, as a basis for recommending either one or both, studies are needed in which both methods are used to obtain the same postural exposures. The aim of this study was to assess postures and movements of the trunk and upper arm during paper mill work. Further, we aimed to determine the extent to which the postural exposures differ depending on the method of assessment.
Three observers, who had not participated in the data collection at the paper mill, were recruited to process the video recordings. They were given standardized training in posture evaluation from still frames using custom software (Trask et al., 2013). During the training, the observers first worked together to ensure consistent interpretation of the images, and then individually while getting feedback on “true” postures and the estimates of the other observers. Subsequently, each observer analyzed the video recordings from all 84 shifts, in randomized order. The software program presented still frames from the videos at 135s intervals, starting after the equipment setup and reference angle determination, and ending just before equipment removal. For each still frame, the observers estimated the worker's trunk inclination (−180−180°) and left and right upper arm inclination (0–180° regardless of direction) in degrees relative to the vertical. These estimates were obtained with the aid of a posture matching manikin on the computer screen (Jackson et al., 2016; Trask et al., 2013).
2. Material and methods 2.1. Participants Twenty-eight workers (2 women and 26 men) were recruited at random among 55 workers at a Swedish paper mill. They satisfied the inclusion criterion of working full-time without modified duties, and their work tasks mainly consisted of operating paper machines, paper sampling, transporting and packaging paper rolls, and monitoring/ controlling production. The mean age of the participants was 45 years (range 21–59 years), and they had worked at the paper mill for, on average, 25 years (range 1–36 years). All participants were informed about the study verbally and in writing, and signed an informed consent to participate. The study was approved by the Regional Ethical Review Board in Uppsala (2011/026).
2.3. Data processing The inclinometer files were exported from the VitaMove data collection software, downsampled to 20 Hz, and converted to fit analysis software developed at Lund University, Sweden (Hansson et al., 2001, 2006). To avoid incorrect recordings of extreme trunk extensions due to reclining trunk postures, we used an algorithm to identify and replace extreme extension angles by 0° (trunk) and 10° (upper arm) (Trask et al., 2013). A custom Excel macro was used to calculate the following exposure metrics for trunk forward projection and upper arm inclination during each shift: 10th, 50th, 90th and 99th percentile, 10th – 90th percentile range, proportion of time in neutral (< 20°) and extreme (> 60°) angles, frequency of periods in neutral (counts/min), 10th, 50th, 90th and 99th percentile of movement velocity, proportion of time at low (< 5°/s) and high (> 90°/s) velocities, and proportion of time at “rest” (< 20° and < 5°/s) (Kazmierczak et al., 2005; Wahlström et al., 2010, 2016). Since arm inclination angles measured by inclinometry may be biased (Jackson et al., 2015), we also calculated all arm inclination variables on the basis of adjusted inclinometer recordings as proposed in the “calibration” procedure developed by Jackson et al. (2015) and implemented by Wahlström et al. (2016). Similarly to the inclinometer recordings, observational data of trunk and arm inclination during reclining postures were replaced by 0° for trunk and 10° for upper arm. Reclining postures were identified as observed sitting posture, and estimated trunk angle < -20°. In total, 15 940 still frames from the videos were analyzed by each observer. The average number of frames per shift was 191 (range 60–287). For each observer and shift, the 10th, 50th, 90th and 99th percentile of trunk and upper arm inclination was calculated, as well as the 10th – 90th percentile range, and the proportion of time spent in neutral (< 20°) and extreme (> 60°) angles.
2.2. Study design and measurements Each worker's trunk and upper arm posture was assessed during three full work shifts using inclinometer registration and observation from video. At the paper mill, the workers rotated between five different shift types: weekday morning shifts (8 h), weekday afternoon shifts (7 h), weekday night shifts (9 h), weekend day shifts (12 h), and weekend night shifts (12 h). Measurements were scheduled so that each worker was monitored on three different shift types. At least two workers were monitored for each of the ten combinations of three shift types among five, yielding in total 15–18 measured shifts per shift type. After each shift, the workers reported the proportion of time spent on different work tasks during the shift. Tasks were defined in collaboration with employer and union representatives. Since the present study focused on postures and movements in manual work, shifts where the worker reported having spent more than 50% of the time surveying production on a computer screen were excluded from further analyses. 2.2.1. Inclinometer measurements Trunk and upper arm postures relative to the line of gravity were measured at 32 Hz using the VitaMove triaxial accelerometer system (2 M Engineering, Veldhoven, The Netherlands). To measure trunk posture, an accelerometer was positioned between the medial borders of the scapulae, and to measure arm posture, one accelerometer was positioned on each upper arm over the flattest lateral portion of the deltoid muscle (Wahlström et al., 2016). A zero inclination reference position was determined for each arm by recording inclination with the subject seated, leaning to the side with the arm hanging down vertically while holding a weight of 1 kg in the hand. A neutral trunk reference position was recorded with the subject standing upright (Wahlström et al., 2016). At the end of the shift, the equipment was removed and data were downloaded to hard drives.
2.4. Statistical analysis All analyses were performed in IBM SPSS Statistics 22.0 for Windows (IBM Corp., Armonk, NY, USA). Posture and movement data were summarized as means across shifts and across workers, standard deviation between workers, and standard deviation between shifts within worker. Standard deviations were obtained from a one-way random effects model with worker and shift within worker as random effects, using restricted maximum likelihood estimation. The association between observed and inclinometer-assessed arm postures was determined using Pearson's correlation and linear regression for both adjusted and unadjusted inclinometer data. In statistical tests of differences in slope, p = 0.05 was chosen as the level of significance. Model assumptions of normally distributed residuals with homogenous variance were checked using standard graphical procedures.
2.2.2. Observations Throughout the work shifts, the workers were video recorded. Each worker was followed by a camera operator, aiming to capture the worker's trunk and upper arm postures during all work tasks. After the shift, the video recordings were stored on hard drives. 91
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3. Results
Table 1 Trunk postures and movements for paper mill workers. Mean across shifts and workers, standard deviation between workers (SDBW), and standard deviation between shifts within worker (SDWW) for inclinometry (n = 62) and observation (n = 66).
In 18 of the 84 measured shifts, the worker reported having spent more than 50% of the time surveying production on a computer screen. Hence, they were excluded from the analyses. These 18 excluded shifts included 5 weekday morning shifts, 5 weekday afternoon shifts, 2 weekday night shifts, 3 weekend day shifts, and 3 weekend night shifts, and they were unevenly distributed among the participants (4 workers had all their shifts excluded, 2 workers had 2 of their shifts excluded, and 2 workers had one of their shifts excluded). Among the 66 remaining shifts, inclinometer recordings of trunk and upper arm postures were successfully obtained from 62 (trunk), 60 (right arm), and 61 (left arm) shifts. Observations were available from all 66 shifts. The mean duration of a shift recording was 7 h and 30 min, i.e. 450 min (range 207–627 min). Shifts could differ considerably in the occurrence of different tasks, according to the workers’ self-reports. Some tasks were rarely performed in the shifts, while others could occur for between 0% and 60% of a shift.
Exposure
Posture 10th percentile, ° 50th percentile, ° 90th percentile, ° 99th percentile, °
3.1. Trunk postures and movements
Percentile range (10th – 90th), °
During the shifts, the workers spent the majority of time in a neutral trunk posture (i.e., < 20° of trunk flexion; Table 1). Trunk inclination > 60° occurred only for about 1% of the shift (1.3% for inclinometry and 1.0% for observation). The 90th percentile of trunk inclination was 29.2° according to inclinometer recordings, and 18.7° according to observations. The median velocity of trunk movement was 6.1°/s (Table 1). For inclinometer recordings, the variability within workers was greater than or equal to the variability between workers (Table 1). This was also the case for most of the observations.
Time in neutral (< 20°), % Time in extreme (> 60°), % Frequency of periods (> 3 s) in a neutral posture, min−1 Movement velocity 10th percentile, ° s−1
3.2. Upper arm postures and movements
50th percentile, ° s−1
Postures and movements of the right and left arm were similar (Table 2). On average, the workers spent about 5% of the time with their arms above 60° (unadjusted inclinometer recordings: 4.7% for right arm and 4.1% for left arm; observations: 7.6% for right arm and 5.8% for left arm). The 90th percentile of right arm inclination was 44.6° according to unadjusted inclinometer recordings, and 48.4° according to observations. The corresponding estimates for left arm were 45.6° and 43.6°, respectively. Approximately 38% of the shift was spent with arm inclination < 20° and movement velocity < 5°/s (unadjusted inclinometer recordings: 38.1% for right arm and 37.3% for left arm). The median velocity of arm movement was 3.3°/s for right and 3.4°/s left arm (Table 2). For most variables, the within-worker standard deviation was larger than the between-worker standard deviation (Table 2), and both were larger than 10% of the mean value. For the 10th percentile of arm movement velocity (right and left arm) and the proportion of time in extreme left arm inclination (> 60°), standard deviations were larger than the mean value of the group (Table 2).
90th percentile, ° s−1 99th percentile, ° s−1 Time at low velocities (< 5° s−1), % Time at high velocities (> 90° s−1), % Posture and movement Time at rest (> 20° and 5° s−1), %
Trunk forward projection Inclinometry
Observation
Mean SDBW SDWW Mean SDBW SDWW Mean SDBW SDWW Mean SDBW SDWW Mean SDBW SDWW Mean SDBW SDWW Mean SDBW SDWW Mean SDBW SDWW
−5.9 2.9 4.1 4.8 3.4 3.4 29.2 3.9 6.4 60.2 5.1 10.5 35.1 5.2 6.0 79.3 5.9 7.8 1.3 0.7 0.7 1.6 0.3 0.4
−4.7 2.4 2.9 2.6 3.1 2.2 18.7 3.1 4.3 52.1 7.1 15.6 23.4 0.5 4.5 70.3 4.3 6.8 1.0 0.5 0.8
Mean SDBW SDWW Mean SDBW SDWW Mean SDBW SDWW Mean SDBW SDWW Mean SDBW SDWW Mean SDBW SDWW
0.3 0.3 0.4 6.1 1.2 2.5 45.3 4.9 12.9 136.8 19.6 24.7 46.5 3.4 11.4 3.3 1.1 1.2
Mean SDBW SDWW
41.3 3.8 12.9
registrations and observations of selected upper arm inclination variables, for both adjusted and unadjusted inclinometer data. Pearson's correlation between inclinometer registrations and observations ranged between −0.29 and 0.70 for trunk posture variables, and between −0.11 and 0.54 for unadjusted upper arm posture variables. Generally, the arms were less elevated according to the observations than recorded by the inclinometers. This was reflected in particularly large differences between observation and inclinometry data for large values of median arm inclination and time in extreme, and for small values of time in neutral. Associations between inclinometer registrations and observations did not differ significantly between adjusted and unadjusted recordings of median arm inclination and time in neutral (tests for differences in slope: median right arm p = 0.811; median left arm: p = 0.695; neutral right arm: p = 0.953; neutral left arm: p = 0.979). For time in extreme (i.e., arm inclination > 60°), inclinometer results based on unadjusted
3.3. Adjusted versus unadjusted inclinometer data The adjustment of inclinometer recordings from the upper arms generally led to increased values of arm inclination and movement velocity, and thus to lower estimates of time spent in neutral postures and time at low velocities (Table 2). Variability within and between workers increased in most variables when recordings were adjusted. The most pronounced changes occurred in the proportion of time spent with arm inclination above 60° (Table 2). 3.4. Associations between inclinometer and observation Fig. 1 illustrates the linear relationship between inclinometer 92
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Table 2 Upper arm inclination postures and movements for paper mill workers. Mean across shifts and workers, standard deviation between workers (SDBW), and standard deviation between shifts within worker (SDWW) for unadjusted and adjusted inclinometry (right arm: n = 60; left arm: n = 61) and observation (right arm: n = 66; left arm: n = 66). Exposure
Inclinometry
Observation
Unadjusted Right Posture 10th percentile, ° 50th percentile, ° 90th percentile, ° 99th percentile, ° Percentile range (10th – 90th), ° Time in neutral (< 20°) % Time in extreme (> 60°) % Frequency of periods (> 3 s) in a neutral posture, min−1 Movement velocity 10th percentile, ° s−1 50th percentile, ° s−1 90th percentile, ° s−1 99th percentile, ° s−1 Time at low velocities (< 5° s−1), % Time at high velocities (> 90° s−1), % Posture and movement Time at rest (> 20° and 5° s−1), %
Adjusted Left
Right
Left
8.5 0.8 1.8 18.5 3.6 3.1 48.4 6.0 8.0 113.0 10.2 26.4 39.9 5.5 8.4 56.4 6.7 7.8 7.6 2.7 3.3
8.3 1.0 1.5 17.8 3.7 2.7 43.6 5.4 6.6 82.9 0.0 45.3 35.2 5.2 7.0 59.2 7.1 6.8 5.8 2.0 2.9
Left
Mean SDBW SDWW Mean SDBW SDWW Mean SDBW SDWW Mean SDBW SDWW Mean SDBW SDWW Mean SDBW SDWW Mean SDBW SDWW Mean SDBW SDWW
9.9 0.0 1.3 21.6 9.8 7.4 44.6 10.0 10.7 64.5 6.7 12.3 34.7 10.0 10.9 51.1 12.7 17.4 4.7 2.4 4.3 0.7 0.0 0.3
10.1 0.2 1.7 21.6 10.2 6.6 45.6 10.4 10.4 65.7 8.2 11.6 35.5 10.2 10.2 50.5 15.2 14.0 4.1 0.3 5.7 0.7 0.3 0.3
10.2 0.0 1.5 23.7 11.5 8.7 50.5 11.6 12.5 73.6 7.9 14.3 40.3 11.2 12.7 48.2 12.1 18.3 8.1 3.8 7.7 0.6 0.1 0.3
10.4 0.0 2.0 23.9 11.8 7.9 51.6 12.0 12.1 75.0 9.6 13.6 41.2 11.7 11.9 46.9 14.1 15.5 9.6 6.4 10.2 0.6 0.2 0.2
Mean SDBW SDWW Mean SDBW SDWW Mean SDBW SDWW Mean SDBW SDWW Mean SDBW SDWW Mean SDBW SDWW
0.2 0.4 0.2 3.3 2.1 1.7 35.2 2.5 15.5 142.3 24.0 33.1 41.2 4.2 12.5 3.1 0.9 1.2
0.3 0.4 0.3 3.4 2.7 2.2 33.8 4.8 15.1 137.8 15.8 39.5 41.9 3.3 12.1 2.9 0.8 1.3
0.3 0.4 0.3 3.7 3.1 2.4 39.9 2.9 17.6 161.1 27.0 37.6 38.7 4.2 12.8 3.8 1.0 1.6
0.3 0.4 0.3 3.8 3.0 2.4 38.4 5.5 17.1 156.2 18.0 44.7 38.7 4.0 13.1 3.7 0.9 1.6
Mean SDBW SDWW
38.1 14.0 20.2
37.3 14.0 19.4
36.5 13.8 20.7
35.3 13.2 20.3
recordings were markedly closer to results obtained by observation than inclinometer results based on adjusted recordings. Statistical tests showed that the difference in slope was significant for the left arm (p = 0.011) but not for the right arm (p = 0.269). The proportion of explained variance in observed postures ranged between 22% and 53% for unadjusted recordings, and between 10% and 52% for adjusted recordings. It should be noted, however, that the regressions are likely influenced considerably by a few extreme values, as suggested by visual inspection of Fig. 1.
Right
investigated exposure variables, within-worker variability was greater than between-worker variability, regardless of assessment method. When inclinometer recordings of arm inclination were adjusted to correct for suspected bias, estimates of arm inclination and velocity increased, as did the variability in most of the exposure metrics. For the 50th percentile of arm inclination and for time in neutral, substantial differences between inclinometer recordings and observations were found for both adjusted and unadjusted inclinometer data, particularly at extreme exposures. For time in extreme arm inclination, however, inclinometer data and observations agreed considerably better for unadjusted than for adjusted data. The 90th percentile of inclinometry-registered trunk inclination during paper mill work appears similar to exposures during materials kitting in car manufacturing (26°; Christmansson et al., 2002) and during baggage handling (34°; Wahlström et al., 2016). It was higher than in poultry processing (16°; Juul-Kristensen et al., 2001) but lower
4. Discussion In the present study, postures and movements of the trunk and upper arm of paper mill workers were assessed using two methods: inclinometry and observation. Observation generally yielded lower estimates of trunk and arm inclination than inclinometry. For most 93
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Fig. 1. Association between inclinometer registrations and observations of median arm inclination, time in neutral (< 20°) and time in extreme (> 60°) arm inclination, for unadjusted (right arm: n = 60; left arm: n = 61) and adjusted inclinometer data (right arm: n = 60; left arm: n = 61).
than in car disassembly (40°; Kazmierczak et al., 2005) and welding (82°; Fethke et al., 2011). Arm inclination angles were similar to those reported during baggage handling (90th percentile: 52°; Wahlström et al., 2016), hairdressing (90th percentile: 49–52°; Wahlström et al., 2010), hospital cleaning (90th percentile: 53–54°; Unge et al., 2007), and rubber manufacturing and mechanical assembly (90th percentile: 50–51°; Nordander et al., 2008). The 90th arm inclination percentile and the time in extreme arm inclination (i.e., > 60°) were lower than in dairy parlor work (61–72°; 13–17%; Douphrate et al., 2012) and car disassembly (72°; 15%; Kazmierczak et al., 2005). Possibly, the similarities in exposures between paper mill work and other occupations, such as baggage handling, reflect the intermittent work performed; in paper mill work, the work pace is dictated by paper machines.
The velocity of trunk movements during paper mill work was comparable to velocities during materials kitting in car manufacturing (median velocity: 11°/s; Christmansson et al., 2002) and baggage handling (median velocity: 8°/s; Wahlström et al., 2016). Median trunk velocity was slightly lower than in poultry processing (16°/s; JuulKristensen et al., 2001) and car disassembly (15°/s; Kazmierczak et al., 2005), while the 90th velocity percentile was substantially lower than in car disassembly (69°/s; Kazmierczak et al., 2005). Median upper arm movement velocity was slightly lower than that reported for baggage handlers (11°/s; Wahlström et al., 2016) and dentists (8–10°/s; Jonker et al., 2009), and the 90th percentile of movement velocity was 20–40°/ s higher in these occupational groups. In meat cutting and hospital cleaning, the median velocity was much higher than in paper mill work 94
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(81–209°/s and 71°/s, respectively; Arvidsson et al., 2012; Unge et al., 2007). One reason for the comparatively low arm movement velocities among paper mill workers could be that they regularly handled heavy and bulky objects, which prevented them from moving fast. For inclinometry-assessed as well as observed posture, withinworker variability was usually greater than between-worker variability. This may reflect the work organization at the paper mill, where the workers regularly rotated tasks. Sizes of within- and between-worker variabilities are important when deciding which measurement strategy to use when investigating occupational exposures, i.e., whether to invest in measuring few workers on many days each, or many workers on few days each (Burdorf, 1995; Mathiassen et al., 2002). On average, the variability in trunk posture percentiles was similar to that of baggage handlers (Wahlström et al., 2016). This was also the case for most movement velocity percentiles. For arm inclination, variabilities of the 10th and 50th percentiles were similar to those in baggage handling (Wahlström et al., 2016) and hairdressing (Wahlström et al., 2010), but for the 90th percentile, the variability within and between workers was about twice as large as that observed in baggage handling. A possible explanation for this difference could be that few tasks required extreme arm inclination, and these tasks were not performed at a regular basis by all workers at the paper mill. Overall, the variability in arm movement velocity percentiles was similar to those reported in baggage handling (Wahlström et al., 2016) and hairdressing (Wahlström et al., 2010). When comparing estimates of average postural exposures in the population obtained by inclinometry and observation, the differences were not exceptional. They correspond to differences between observed and inclinometry-assessed arm inclination in hairdressing (Rezagholi et al., 2012). However, Pearson's correlations between the methods ranged between −0.29 and 0.70 for the different exposure variables when inclinometer recordings were not adjusted. At an individual level, observed median arm inclination angle and time in neutral posture appeared to differ considerably from the corresponding results obtained by inclinometry. Our results are in agreement with findings by Burdorf et al. (1992) in a study of trunk bending, reporting a correlation of 0.57 between direct observations and continuous recordings in an association considerably more flat than the line of identity. Their findings of an increased over-estimation of time spent in non-neutral trunk posture (i.e., > 20°) corresponds to our results showing better agreement between inclinometry and observation in neutral arm inclination (i.e., < 20°) as the level of inclinometer-recorded exposure increases. This non-correspondence between methods stands in remarkable contrast to the very good agreement between observed and “true” arm inclination under ideal viewing conditions in a laboratory (Jackson et al., 2016). Thus, it may be that the association between observation and inclinometry in the present study was distorted due to difficulties in rating postures when these were only partly visible on the video stills (Trask et al., 2015, 2017). Another possible explanation could be that observations of oblique angles are more prone to suffer from systematical error than observations in a plane perpendicular to the observer, even though previous comparisons between observed and “true” angles under such circumstances show disagreements to be notably less than those in our data (Xu et al., 2011). In any case, our results illustrate that observed postures may deviate considerably from postures determined using inclinometers when assessing individuals or small populations, and that it appears necessary to perform a regression calibration in cases where these two approaches need to be comparable. As expected, the adjustment of arm inclinometer recordings led to higher estimates of arm inclination, particularly for the upper percentiles. For these percentiles, the average adjusted estimates corresponded better to the average estimates from observations. A closer look at the associations between inclinometry and observation showed marginal effects of adjustment on median arm inclination and time in neutral (< 20°). However, for time in extreme posture (> 60°), it led to remarkably less correspondence between inclinometer recordings and
observations than that obtained with unadjusted data. Considering that the adjustment equation was developed from data obtained under strictly controlled conditions, this may reflect a tendency of observers to be more conservative in their ratings when judging video stills of real work life situations. The present study offers a comprehensive assessment of trunk and upper arm postures in paper mill work. Since two assessment methods were applied, the results can be compared to other findings based on subjective (i.e., observations) as well as objective measurements (i.e., inclinometer registrations). At the same time, our results highlight important differences between the methods that need to be considered when attempting to estimate “true” postures in individual workers. When adjusting inclinometer recordings to correct for bias in arm inclination angles, the correspondence between inclinometry and observation became weaker for extreme exposures, while exposures close to the group mean became more similar. We emphasize that these results may be specific to the conditions in the studied paper mill work, and that other associations between observations and inclinometry could occur in settings with other postural exposures or different conditions with respect to, for instance, lighting and visibility. 5. Conclusions The present study showed that trunk and upper arm postures during paper mill work were similar to exposures reported in other occupations involving manual materials handling, while arm movement velocities were lower than reported in most previous studies. On a group level, exposures determined by inclinometry recordings and video observations agreed well. However, substantial differences were found between results obtained by the two methods for individual workers, particularly when exposures were high. This suggests that posture data obtained by observation and inclinometry for individuals or small groups should be compared or merged only after proper adjustment for disagreement between the methods, for instance by regression calibration. Funding This work was supported by the Swedish Research Council for Health, Working Life and Welfare [grant numbers 2010-0748, 20091761]; and the University of Gävle. Conflicts of interest Declarations of interest: none. Acknowledgements We would like to thank all involved staff at the paper mill, and we appreciate the efforts made by Mahmoud Rezagholi, Karin Holmkvist, Niklas Lindfors, Magdalena Lindquist, Viktor Lyskov, Pontus Wiitavaara, Lena Liljedahl, Per Gandal and Mikael Forsman in collecting and processing the data. References Arvidsson, I., Balogh, I., Hansson, G.A., Ohlsson, K., Akesson, I., Nordander, C., 2012. Rationalization in meat cutting - consequences on physical workload. Appl. Ergon. 43, 1026–1032. Burdorf, A., 1995. Reducing random measurement error in assessing postural load on the back in epidemiologic surveys. Scand. J. Work. Environ. Health 21, 15–23. Burdorf, A., Derksen, J., Naaktgeboren, B., van Riel, M., 1992. Measurement of trunk bending during work by direct observation and continuous measurement. Appl. Ergon. 23, 263–267. Christmansson, M., L, M., Hansson, G.-Å., Ohlsson, K., Unge Byström, J., Möller, T., Forsman, M., 2002. A case study of a principally new way of materials kitting - an evaluation of time consumption and physical workload. Int. J. Ind. Ergon. 30, 49–65. Coluci, M.Z., Alexandre, N.M., de Freitas Pedrini, T., 2012. Musculoskeletal symptoms
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Applied Ergonomics journal homepage: www.elsevier.com/locate/apergo
Trunk and upper arm postures in paper mill work Marina Heiden , Camilla Zetterberg, Svend Erik Mathiassen
T
∗
Centre for Musculoskeletal Research, Department of Occupational and Public Health Sciences, University of Gävle, SE-801 76, Gävle, Sweden
ARTICLE INFO
ABSTRACT
Keywords: Exposure Inclinometry Observation
The aim of this study was to assess postures and movements of the trunk and upper arm during paper mill work, and to determine the extent to which they differ depending on method of assessment. For each of 28 paper mill workers, postures and movements were assessed during three full shifts using inclinometer registration and observation from video. Summary metrics for each shift, e.g., 10th, 50th, and 90th posture percentile, were averaged across shifts and across workers. In addition, the standard deviation between workers, and the standard deviation between shifts within worker were computed. The results showed that trunk and arm postures during paper mill work were similar to other occupations involving manual materials handling, but the velocities of arm movements were lower. While postures determined by inclinometry and observation were similar on a group level, substantial differences were found between results obtained by the two methods for individual workers, particularly for extreme postures. Thus, measurements by either method on individuals or small groups should be interpreted with caution.
1. Introduction Paper production is an important industrial branch, serving the demands for all kinds of paper products used in everyday life. Studies have shown that workers involved in the production of paper are exposed to dust, chemicals, gases, noise, heat and humidity (Jungbauer et al., 2005; Kauppinen et al., 2002; Korhonen et al., 2004; Neitzel et al., 2016). Most of these exposures vary considerably among workers, which presents challenges to research on health risks of working in the paper production industry. To date, studies on health effects of pulp and paper mill work have mostly focused on respiratory function and cancer. Several studies have reported increased risk of respiratory problems among these workers, and the risk has been related to, for example, gassings and microorganisms (Glindmeyer et al., 2003; Hoffman et al., 2004; Murgia et al., 2011; Sikkeland et al., 2007). In addition to the previously mentioned exposures in paper mill work, the workers perform heavy lifting/pushing/pulling, and work with their arms above shoulder height, often in constrained spaces along the paper machines (Inoue and Harada, 2002). Although investigated only in few studies, the prevalence of musculoskeletal symptoms has been estimated to be about 60% among paper mill workers, and the symptoms are mainly located in the shoulders and lower back (Coluci et al., 2012; Inoue and Harada, 2002). Leino-Arjas et al. (2002) showed in a nationwide study in Finland that the hospitalization rate for back disorders among workers manufacturing pulp,
∗
paper and paper products was higher than the Finnish population average. In order to identify targets for interventions to reduce musculoskeletal disorders, and thus effectively reduce exposure to injury risk factors in the workplace, thorough documentation of the workers’ biomechanical exposures is needed. Methods for assessing biomechanical exposures in occupational settings differ in cost, feasibility, and resulting information (Heiden et al., 2017; Trask et al., 2007, 2014). Postures and movements can be measured using equipment that is attached to the worker, but certain movements, such as twisting of the back, may not be captured by the measurement devices. They may also be sensitive to the climate conditions during data collection. Observations, on the other hand, may provide information about postures and movements, but also about complex exposures beyond the reach of direct measurements. However, they require trained observers to be present onsite or inspect recorded documentation of the work, typically on video. Previous research on the agreement between postural exposures assessed by inclinometry and observation in occupational settings has shown that they may provide different results. Teschke et al. (2009) found that observations explained 30–61% of the variance in inclinometer recordings, and recommended the use of both measures to gain as much information as possible. Burdorf et al. (1992) showed significant correlations between the methods, but large differences in exposure estimates at an individual level. In a study of upper arm postures among hairdressers, Rezagholi et al. (2012) found good
Corresponding author. E-mail addresses: [email protected] (M. Heiden), [email protected] (C. Zetterberg), [email protected] (S.E. Mathiassen).
https://doi.org/10.1016/j.apergo.2018.12.004 Received 2 May 2018; Received in revised form 1 October 2018; Accepted 8 December 2018 0003-6870/ © 2018 Elsevier Ltd. All rights reserved.
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agreement between observations and inclinometry at the group level for median inclination, while results for time spent in extreme and neutral postures differed considerably between the methods. To better understand the extent to which postures and movements assessed by observations and inclinometry agree, as a basis for recommending either one or both, studies are needed in which both methods are used to obtain the same postural exposures. The aim of this study was to assess postures and movements of the trunk and upper arm during paper mill work. Further, we aimed to determine the extent to which the postural exposures differ depending on the method of assessment.
Three observers, who had not participated in the data collection at the paper mill, were recruited to process the video recordings. They were given standardized training in posture evaluation from still frames using custom software (Trask et al., 2013). During the training, the observers first worked together to ensure consistent interpretation of the images, and then individually while getting feedback on “true” postures and the estimates of the other observers. Subsequently, each observer analyzed the video recordings from all 84 shifts, in randomized order. The software program presented still frames from the videos at 135s intervals, starting after the equipment setup and reference angle determination, and ending just before equipment removal. For each still frame, the observers estimated the worker's trunk inclination (−180−180°) and left and right upper arm inclination (0–180° regardless of direction) in degrees relative to the vertical. These estimates were obtained with the aid of a posture matching manikin on the computer screen (Jackson et al., 2016; Trask et al., 2013).
2. Material and methods 2.1. Participants Twenty-eight workers (2 women and 26 men) were recruited at random among 55 workers at a Swedish paper mill. They satisfied the inclusion criterion of working full-time without modified duties, and their work tasks mainly consisted of operating paper machines, paper sampling, transporting and packaging paper rolls, and monitoring/ controlling production. The mean age of the participants was 45 years (range 21–59 years), and they had worked at the paper mill for, on average, 25 years (range 1–36 years). All participants were informed about the study verbally and in writing, and signed an informed consent to participate. The study was approved by the Regional Ethical Review Board in Uppsala (2011/026).
2.3. Data processing The inclinometer files were exported from the VitaMove data collection software, downsampled to 20 Hz, and converted to fit analysis software developed at Lund University, Sweden (Hansson et al., 2001, 2006). To avoid incorrect recordings of extreme trunk extensions due to reclining trunk postures, we used an algorithm to identify and replace extreme extension angles by 0° (trunk) and 10° (upper arm) (Trask et al., 2013). A custom Excel macro was used to calculate the following exposure metrics for trunk forward projection and upper arm inclination during each shift: 10th, 50th, 90th and 99th percentile, 10th – 90th percentile range, proportion of time in neutral (< 20°) and extreme (> 60°) angles, frequency of periods in neutral (counts/min), 10th, 50th, 90th and 99th percentile of movement velocity, proportion of time at low (< 5°/s) and high (> 90°/s) velocities, and proportion of time at “rest” (< 20° and < 5°/s) (Kazmierczak et al., 2005; Wahlström et al., 2010, 2016). Since arm inclination angles measured by inclinometry may be biased (Jackson et al., 2015), we also calculated all arm inclination variables on the basis of adjusted inclinometer recordings as proposed in the “calibration” procedure developed by Jackson et al. (2015) and implemented by Wahlström et al. (2016). Similarly to the inclinometer recordings, observational data of trunk and arm inclination during reclining postures were replaced by 0° for trunk and 10° for upper arm. Reclining postures were identified as observed sitting posture, and estimated trunk angle < -20°. In total, 15 940 still frames from the videos were analyzed by each observer. The average number of frames per shift was 191 (range 60–287). For each observer and shift, the 10th, 50th, 90th and 99th percentile of trunk and upper arm inclination was calculated, as well as the 10th – 90th percentile range, and the proportion of time spent in neutral (< 20°) and extreme (> 60°) angles.
2.2. Study design and measurements Each worker's trunk and upper arm posture was assessed during three full work shifts using inclinometer registration and observation from video. At the paper mill, the workers rotated between five different shift types: weekday morning shifts (8 h), weekday afternoon shifts (7 h), weekday night shifts (9 h), weekend day shifts (12 h), and weekend night shifts (12 h). Measurements were scheduled so that each worker was monitored on three different shift types. At least two workers were monitored for each of the ten combinations of three shift types among five, yielding in total 15–18 measured shifts per shift type. After each shift, the workers reported the proportion of time spent on different work tasks during the shift. Tasks were defined in collaboration with employer and union representatives. Since the present study focused on postures and movements in manual work, shifts where the worker reported having spent more than 50% of the time surveying production on a computer screen were excluded from further analyses. 2.2.1. Inclinometer measurements Trunk and upper arm postures relative to the line of gravity were measured at 32 Hz using the VitaMove triaxial accelerometer system (2 M Engineering, Veldhoven, The Netherlands). To measure trunk posture, an accelerometer was positioned between the medial borders of the scapulae, and to measure arm posture, one accelerometer was positioned on each upper arm over the flattest lateral portion of the deltoid muscle (Wahlström et al., 2016). A zero inclination reference position was determined for each arm by recording inclination with the subject seated, leaning to the side with the arm hanging down vertically while holding a weight of 1 kg in the hand. A neutral trunk reference position was recorded with the subject standing upright (Wahlström et al., 2016). At the end of the shift, the equipment was removed and data were downloaded to hard drives.
2.4. Statistical analysis All analyses were performed in IBM SPSS Statistics 22.0 for Windows (IBM Corp., Armonk, NY, USA). Posture and movement data were summarized as means across shifts and across workers, standard deviation between workers, and standard deviation between shifts within worker. Standard deviations were obtained from a one-way random effects model with worker and shift within worker as random effects, using restricted maximum likelihood estimation. The association between observed and inclinometer-assessed arm postures was determined using Pearson's correlation and linear regression for both adjusted and unadjusted inclinometer data. In statistical tests of differences in slope, p = 0.05 was chosen as the level of significance. Model assumptions of normally distributed residuals with homogenous variance were checked using standard graphical procedures.
2.2.2. Observations Throughout the work shifts, the workers were video recorded. Each worker was followed by a camera operator, aiming to capture the worker's trunk and upper arm postures during all work tasks. After the shift, the video recordings were stored on hard drives. 91
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3. Results
Table 1 Trunk postures and movements for paper mill workers. Mean across shifts and workers, standard deviation between workers (SDBW), and standard deviation between shifts within worker (SDWW) for inclinometry (n = 62) and observation (n = 66).
In 18 of the 84 measured shifts, the worker reported having spent more than 50% of the time surveying production on a computer screen. Hence, they were excluded from the analyses. These 18 excluded shifts included 5 weekday morning shifts, 5 weekday afternoon shifts, 2 weekday night shifts, 3 weekend day shifts, and 3 weekend night shifts, and they were unevenly distributed among the participants (4 workers had all their shifts excluded, 2 workers had 2 of their shifts excluded, and 2 workers had one of their shifts excluded). Among the 66 remaining shifts, inclinometer recordings of trunk and upper arm postures were successfully obtained from 62 (trunk), 60 (right arm), and 61 (left arm) shifts. Observations were available from all 66 shifts. The mean duration of a shift recording was 7 h and 30 min, i.e. 450 min (range 207–627 min). Shifts could differ considerably in the occurrence of different tasks, according to the workers’ self-reports. Some tasks were rarely performed in the shifts, while others could occur for between 0% and 60% of a shift.
Exposure
Posture 10th percentile, ° 50th percentile, ° 90th percentile, ° 99th percentile, °
3.1. Trunk postures and movements
Percentile range (10th – 90th), °
During the shifts, the workers spent the majority of time in a neutral trunk posture (i.e., < 20° of trunk flexion; Table 1). Trunk inclination > 60° occurred only for about 1% of the shift (1.3% for inclinometry and 1.0% for observation). The 90th percentile of trunk inclination was 29.2° according to inclinometer recordings, and 18.7° according to observations. The median velocity of trunk movement was 6.1°/s (Table 1). For inclinometer recordings, the variability within workers was greater than or equal to the variability between workers (Table 1). This was also the case for most of the observations.
Time in neutral (< 20°), % Time in extreme (> 60°), % Frequency of periods (> 3 s) in a neutral posture, min−1 Movement velocity 10th percentile, ° s−1
3.2. Upper arm postures and movements
50th percentile, ° s−1
Postures and movements of the right and left arm were similar (Table 2). On average, the workers spent about 5% of the time with their arms above 60° (unadjusted inclinometer recordings: 4.7% for right arm and 4.1% for left arm; observations: 7.6% for right arm and 5.8% for left arm). The 90th percentile of right arm inclination was 44.6° according to unadjusted inclinometer recordings, and 48.4° according to observations. The corresponding estimates for left arm were 45.6° and 43.6°, respectively. Approximately 38% of the shift was spent with arm inclination < 20° and movement velocity < 5°/s (unadjusted inclinometer recordings: 38.1% for right arm and 37.3% for left arm). The median velocity of arm movement was 3.3°/s for right and 3.4°/s left arm (Table 2). For most variables, the within-worker standard deviation was larger than the between-worker standard deviation (Table 2), and both were larger than 10% of the mean value. For the 10th percentile of arm movement velocity (right and left arm) and the proportion of time in extreme left arm inclination (> 60°), standard deviations were larger than the mean value of the group (Table 2).
90th percentile, ° s−1 99th percentile, ° s−1 Time at low velocities (< 5° s−1), % Time at high velocities (> 90° s−1), % Posture and movement Time at rest (> 20° and 5° s−1), %
Trunk forward projection Inclinometry
Observation
Mean SDBW SDWW Mean SDBW SDWW Mean SDBW SDWW Mean SDBW SDWW Mean SDBW SDWW Mean SDBW SDWW Mean SDBW SDWW Mean SDBW SDWW
−5.9 2.9 4.1 4.8 3.4 3.4 29.2 3.9 6.4 60.2 5.1 10.5 35.1 5.2 6.0 79.3 5.9 7.8 1.3 0.7 0.7 1.6 0.3 0.4
−4.7 2.4 2.9 2.6 3.1 2.2 18.7 3.1 4.3 52.1 7.1 15.6 23.4 0.5 4.5 70.3 4.3 6.8 1.0 0.5 0.8
Mean SDBW SDWW Mean SDBW SDWW Mean SDBW SDWW Mean SDBW SDWW Mean SDBW SDWW Mean SDBW SDWW
0.3 0.3 0.4 6.1 1.2 2.5 45.3 4.9 12.9 136.8 19.6 24.7 46.5 3.4 11.4 3.3 1.1 1.2
Mean SDBW SDWW
41.3 3.8 12.9
registrations and observations of selected upper arm inclination variables, for both adjusted and unadjusted inclinometer data. Pearson's correlation between inclinometer registrations and observations ranged between −0.29 and 0.70 for trunk posture variables, and between −0.11 and 0.54 for unadjusted upper arm posture variables. Generally, the arms were less elevated according to the observations than recorded by the inclinometers. This was reflected in particularly large differences between observation and inclinometry data for large values of median arm inclination and time in extreme, and for small values of time in neutral. Associations between inclinometer registrations and observations did not differ significantly between adjusted and unadjusted recordings of median arm inclination and time in neutral (tests for differences in slope: median right arm p = 0.811; median left arm: p = 0.695; neutral right arm: p = 0.953; neutral left arm: p = 0.979). For time in extreme (i.e., arm inclination > 60°), inclinometer results based on unadjusted
3.3. Adjusted versus unadjusted inclinometer data The adjustment of inclinometer recordings from the upper arms generally led to increased values of arm inclination and movement velocity, and thus to lower estimates of time spent in neutral postures and time at low velocities (Table 2). Variability within and between workers increased in most variables when recordings were adjusted. The most pronounced changes occurred in the proportion of time spent with arm inclination above 60° (Table 2). 3.4. Associations between inclinometer and observation Fig. 1 illustrates the linear relationship between inclinometer 92
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Table 2 Upper arm inclination postures and movements for paper mill workers. Mean across shifts and workers, standard deviation between workers (SDBW), and standard deviation between shifts within worker (SDWW) for unadjusted and adjusted inclinometry (right arm: n = 60; left arm: n = 61) and observation (right arm: n = 66; left arm: n = 66). Exposure
Inclinometry
Observation
Unadjusted Right Posture 10th percentile, ° 50th percentile, ° 90th percentile, ° 99th percentile, ° Percentile range (10th – 90th), ° Time in neutral (< 20°) % Time in extreme (> 60°) % Frequency of periods (> 3 s) in a neutral posture, min−1 Movement velocity 10th percentile, ° s−1 50th percentile, ° s−1 90th percentile, ° s−1 99th percentile, ° s−1 Time at low velocities (< 5° s−1), % Time at high velocities (> 90° s−1), % Posture and movement Time at rest (> 20° and 5° s−1), %
Adjusted Left
Right
Left
8.5 0.8 1.8 18.5 3.6 3.1 48.4 6.0 8.0 113.0 10.2 26.4 39.9 5.5 8.4 56.4 6.7 7.8 7.6 2.7 3.3
8.3 1.0 1.5 17.8 3.7 2.7 43.6 5.4 6.6 82.9 0.0 45.3 35.2 5.2 7.0 59.2 7.1 6.8 5.8 2.0 2.9
Left
Mean SDBW SDWW Mean SDBW SDWW Mean SDBW SDWW Mean SDBW SDWW Mean SDBW SDWW Mean SDBW SDWW Mean SDBW SDWW Mean SDBW SDWW
9.9 0.0 1.3 21.6 9.8 7.4 44.6 10.0 10.7 64.5 6.7 12.3 34.7 10.0 10.9 51.1 12.7 17.4 4.7 2.4 4.3 0.7 0.0 0.3
10.1 0.2 1.7 21.6 10.2 6.6 45.6 10.4 10.4 65.7 8.2 11.6 35.5 10.2 10.2 50.5 15.2 14.0 4.1 0.3 5.7 0.7 0.3 0.3
10.2 0.0 1.5 23.7 11.5 8.7 50.5 11.6 12.5 73.6 7.9 14.3 40.3 11.2 12.7 48.2 12.1 18.3 8.1 3.8 7.7 0.6 0.1 0.3
10.4 0.0 2.0 23.9 11.8 7.9 51.6 12.0 12.1 75.0 9.6 13.6 41.2 11.7 11.9 46.9 14.1 15.5 9.6 6.4 10.2 0.6 0.2 0.2
Mean SDBW SDWW Mean SDBW SDWW Mean SDBW SDWW Mean SDBW SDWW Mean SDBW SDWW Mean SDBW SDWW
0.2 0.4 0.2 3.3 2.1 1.7 35.2 2.5 15.5 142.3 24.0 33.1 41.2 4.2 12.5 3.1 0.9 1.2
0.3 0.4 0.3 3.4 2.7 2.2 33.8 4.8 15.1 137.8 15.8 39.5 41.9 3.3 12.1 2.9 0.8 1.3
0.3 0.4 0.3 3.7 3.1 2.4 39.9 2.9 17.6 161.1 27.0 37.6 38.7 4.2 12.8 3.8 1.0 1.6
0.3 0.4 0.3 3.8 3.0 2.4 38.4 5.5 17.1 156.2 18.0 44.7 38.7 4.0 13.1 3.7 0.9 1.6
Mean SDBW SDWW
38.1 14.0 20.2
37.3 14.0 19.4
36.5 13.8 20.7
35.3 13.2 20.3
recordings were markedly closer to results obtained by observation than inclinometer results based on adjusted recordings. Statistical tests showed that the difference in slope was significant for the left arm (p = 0.011) but not for the right arm (p = 0.269). The proportion of explained variance in observed postures ranged between 22% and 53% for unadjusted recordings, and between 10% and 52% for adjusted recordings. It should be noted, however, that the regressions are likely influenced considerably by a few extreme values, as suggested by visual inspection of Fig. 1.
Right
investigated exposure variables, within-worker variability was greater than between-worker variability, regardless of assessment method. When inclinometer recordings of arm inclination were adjusted to correct for suspected bias, estimates of arm inclination and velocity increased, as did the variability in most of the exposure metrics. For the 50th percentile of arm inclination and for time in neutral, substantial differences between inclinometer recordings and observations were found for both adjusted and unadjusted inclinometer data, particularly at extreme exposures. For time in extreme arm inclination, however, inclinometer data and observations agreed considerably better for unadjusted than for adjusted data. The 90th percentile of inclinometry-registered trunk inclination during paper mill work appears similar to exposures during materials kitting in car manufacturing (26°; Christmansson et al., 2002) and during baggage handling (34°; Wahlström et al., 2016). It was higher than in poultry processing (16°; Juul-Kristensen et al., 2001) but lower
4. Discussion In the present study, postures and movements of the trunk and upper arm of paper mill workers were assessed using two methods: inclinometry and observation. Observation generally yielded lower estimates of trunk and arm inclination than inclinometry. For most 93
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Fig. 1. Association between inclinometer registrations and observations of median arm inclination, time in neutral (< 20°) and time in extreme (> 60°) arm inclination, for unadjusted (right arm: n = 60; left arm: n = 61) and adjusted inclinometer data (right arm: n = 60; left arm: n = 61).
than in car disassembly (40°; Kazmierczak et al., 2005) and welding (82°; Fethke et al., 2011). Arm inclination angles were similar to those reported during baggage handling (90th percentile: 52°; Wahlström et al., 2016), hairdressing (90th percentile: 49–52°; Wahlström et al., 2010), hospital cleaning (90th percentile: 53–54°; Unge et al., 2007), and rubber manufacturing and mechanical assembly (90th percentile: 50–51°; Nordander et al., 2008). The 90th arm inclination percentile and the time in extreme arm inclination (i.e., > 60°) were lower than in dairy parlor work (61–72°; 13–17%; Douphrate et al., 2012) and car disassembly (72°; 15%; Kazmierczak et al., 2005). Possibly, the similarities in exposures between paper mill work and other occupations, such as baggage handling, reflect the intermittent work performed; in paper mill work, the work pace is dictated by paper machines.
The velocity of trunk movements during paper mill work was comparable to velocities during materials kitting in car manufacturing (median velocity: 11°/s; Christmansson et al., 2002) and baggage handling (median velocity: 8°/s; Wahlström et al., 2016). Median trunk velocity was slightly lower than in poultry processing (16°/s; JuulKristensen et al., 2001) and car disassembly (15°/s; Kazmierczak et al., 2005), while the 90th velocity percentile was substantially lower than in car disassembly (69°/s; Kazmierczak et al., 2005). Median upper arm movement velocity was slightly lower than that reported for baggage handlers (11°/s; Wahlström et al., 2016) and dentists (8–10°/s; Jonker et al., 2009), and the 90th percentile of movement velocity was 20–40°/ s higher in these occupational groups. In meat cutting and hospital cleaning, the median velocity was much higher than in paper mill work 94
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(81–209°/s and 71°/s, respectively; Arvidsson et al., 2012; Unge et al., 2007). One reason for the comparatively low arm movement velocities among paper mill workers could be that they regularly handled heavy and bulky objects, which prevented them from moving fast. For inclinometry-assessed as well as observed posture, withinworker variability was usually greater than between-worker variability. This may reflect the work organization at the paper mill, where the workers regularly rotated tasks. Sizes of within- and between-worker variabilities are important when deciding which measurement strategy to use when investigating occupational exposures, i.e., whether to invest in measuring few workers on many days each, or many workers on few days each (Burdorf, 1995; Mathiassen et al., 2002). On average, the variability in trunk posture percentiles was similar to that of baggage handlers (Wahlström et al., 2016). This was also the case for most movement velocity percentiles. For arm inclination, variabilities of the 10th and 50th percentiles were similar to those in baggage handling (Wahlström et al., 2016) and hairdressing (Wahlström et al., 2010), but for the 90th percentile, the variability within and between workers was about twice as large as that observed in baggage handling. A possible explanation for this difference could be that few tasks required extreme arm inclination, and these tasks were not performed at a regular basis by all workers at the paper mill. Overall, the variability in arm movement velocity percentiles was similar to those reported in baggage handling (Wahlström et al., 2016) and hairdressing (Wahlström et al., 2010). When comparing estimates of average postural exposures in the population obtained by inclinometry and observation, the differences were not exceptional. They correspond to differences between observed and inclinometry-assessed arm inclination in hairdressing (Rezagholi et al., 2012). However, Pearson's correlations between the methods ranged between −0.29 and 0.70 for the different exposure variables when inclinometer recordings were not adjusted. At an individual level, observed median arm inclination angle and time in neutral posture appeared to differ considerably from the corresponding results obtained by inclinometry. Our results are in agreement with findings by Burdorf et al. (1992) in a study of trunk bending, reporting a correlation of 0.57 between direct observations and continuous recordings in an association considerably more flat than the line of identity. Their findings of an increased over-estimation of time spent in non-neutral trunk posture (i.e., > 20°) corresponds to our results showing better agreement between inclinometry and observation in neutral arm inclination (i.e., < 20°) as the level of inclinometer-recorded exposure increases. This non-correspondence between methods stands in remarkable contrast to the very good agreement between observed and “true” arm inclination under ideal viewing conditions in a laboratory (Jackson et al., 2016). Thus, it may be that the association between observation and inclinometry in the present study was distorted due to difficulties in rating postures when these were only partly visible on the video stills (Trask et al., 2015, 2017). Another possible explanation could be that observations of oblique angles are more prone to suffer from systematical error than observations in a plane perpendicular to the observer, even though previous comparisons between observed and “true” angles under such circumstances show disagreements to be notably less than those in our data (Xu et al., 2011). In any case, our results illustrate that observed postures may deviate considerably from postures determined using inclinometers when assessing individuals or small populations, and that it appears necessary to perform a regression calibration in cases where these two approaches need to be comparable. As expected, the adjustment of arm inclinometer recordings led to higher estimates of arm inclination, particularly for the upper percentiles. For these percentiles, the average adjusted estimates corresponded better to the average estimates from observations. A closer look at the associations between inclinometry and observation showed marginal effects of adjustment on median arm inclination and time in neutral (< 20°). However, for time in extreme posture (> 60°), it led to remarkably less correspondence between inclinometer recordings and
observations than that obtained with unadjusted data. Considering that the adjustment equation was developed from data obtained under strictly controlled conditions, this may reflect a tendency of observers to be more conservative in their ratings when judging video stills of real work life situations. The present study offers a comprehensive assessment of trunk and upper arm postures in paper mill work. Since two assessment methods were applied, the results can be compared to other findings based on subjective (i.e., observations) as well as objective measurements (i.e., inclinometer registrations). At the same time, our results highlight important differences between the methods that need to be considered when attempting to estimate “true” postures in individual workers. When adjusting inclinometer recordings to correct for bias in arm inclination angles, the correspondence between inclinometry and observation became weaker for extreme exposures, while exposures close to the group mean became more similar. We emphasize that these results may be specific to the conditions in the studied paper mill work, and that other associations between observations and inclinometry could occur in settings with other postural exposures or different conditions with respect to, for instance, lighting and visibility. 5. Conclusions The present study showed that trunk and upper arm postures during paper mill work were similar to exposures reported in other occupations involving manual materials handling, while arm movement velocities were lower than reported in most previous studies. On a group level, exposures determined by inclinometry recordings and video observations agreed well. However, substantial differences were found between results obtained by the two methods for individual workers, particularly when exposures were high. This suggests that posture data obtained by observation and inclinometry for individuals or small groups should be compared or merged only after proper adjustment for disagreement between the methods, for instance by regression calibration. Funding This work was supported by the Swedish Research Council for Health, Working Life and Welfare [grant numbers 2010-0748, 20091761]; and the University of Gävle. Conflicts of interest Declarations of interest: none. Acknowledgements We would like to thank all involved staff at the paper mill, and we appreciate the efforts made by Mahmoud Rezagholi, Karin Holmkvist, Niklas Lindfors, Magdalena Lindquist, Viktor Lyskov, Pontus Wiitavaara, Lena Liljedahl, Per Gandal and Mikael Forsman in collecting and processing the data. References Arvidsson, I., Balogh, I., Hansson, G.A., Ohlsson, K., Akesson, I., Nordander, C., 2012. Rationalization in meat cutting - consequences on physical workload. Appl. Ergon. 43, 1026–1032. Burdorf, A., 1995. Reducing random measurement error in assessing postural load on the back in epidemiologic surveys. Scand. J. Work. Environ. Health 21, 15–23. Burdorf, A., Derksen, J., Naaktgeboren, B., van Riel, M., 1992. Measurement of trunk bending during work by direct observation and continuous measurement. Appl. Ergon. 23, 263–267. Christmansson, M., L, M., Hansson, G.-Å., Ohlsson, K., Unge Byström, J., Möller, T., Forsman, M., 2002. A case study of a principally new way of materials kitting - an evaluation of time consumption and physical workload. Int. J. Ind. Ergon. 30, 49–65. Coluci, M.Z., Alexandre, N.M., de Freitas Pedrini, T., 2012. Musculoskeletal symptoms
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