WOR-2010-0956

39
Work 35 (2010) 39–48
DOI 10.3233/WOR-2009-0956
IOS Press
Biomechanical shoulder loads and postures in
light automotive assembly workers:
Comparison between shoulder pain/no pain
groups
F.A. Seamana, W.J. Alberta,∗ , N.R.E. Weldona, J. Crolla and J.P. Callaghan b
a
b
Faculty of Kinesiology, University of New Brunswick, Fredericton, NB, Canada
Department of Kinesiology, University of Waterloo, Waterloo, ON, Canada
Received 23 July 2008
Accepted 8 January 2009
Abstract. Background: The number of workplace shoulder compensation claims resulting from musculoskeletal disorders
(MSDs) has decreased slightly in recent years, however the median number of days off work remains unchanged, which suggests
an increase in injury severity [25]. Little information is available regarding cumulative shoulder exposures, and there is no
information on their impact on shoulder pain.
Methods: Seventy-nine automotive seat frame assembly workers completed a questionnaire about the prevalence and severity
of shoulder pain and were videotaped performing assembly tasks. 3DMatch, a posture-matching software program, was used
to calculate the peak and cumulative shoulder moments and forces by matching postures seen in the video with predetermined
ranges of posture to be used in the biomechanical model.
Results: Of the 45.6% who reported shoulder pain, there was a mild correlation of pain severity with posterior shear of the
shoulder. There were no significant differences in peak loads between Pain and No Pain groups; however, the No Pain group
experienced significantly more cumulative caudal shear.
Conclusion: Although there was no difference in percent time spent in different flexed postures between pain groups, those
working some jobs may be at an increased risk of developing MSDs based on the amount of time spent in flexed postures, as well
as the peak flexion moment acting on the shoulder.
Keywords: Shoulder pain, cumulative load, peak load, posture, workplace
1. Introduction
Over the past few decades, much of the research on
workplace musculoskeletal disorders has focused on
risk factors for developing low back pain. Researchers
have, with some success, identified work posture [18,
19], task repetition [5,19], and load magnitudes for
∗ Address for correspondence: Wayne J. Albert, 2 Peter Kelly
Drive, Fredericton, NB, Canada E3B 5A3. Tel.: +1 506 447 3254;
Fax: +1 506 453 3511; E-mail: [email protected]
lifting [9,10,15] as variables to control in attempts to
decrease the high incidence of low back pain. Shoulder pain and injury, though, have received much less
attention.
In a recent study of on-site physiotherapy visits by
employees at an automotive plant, the shoulder was
the most commonly treated area of the body and required the highest number of physiotherapy visits as
compared with other parts of the body [20]. In certain
industries, such as manufacturing (i.e., vehicles), nursing, and mining, musculoskeletal disorder (MSD) rates
1051-9815/10/$27.50  2010 – IOS Press and the authors. All rights reserved
40
F.A. Seaman et al. / Shoulder loads and postures in automotive assembly
are up to four times higher than those in the general
population [19]. The United States Bureau of Labor
Statistics reported 141,800 injuries and illnesses in the
vehicle manufacturing industry alone in 2006, 12.4%
of the total annual average employment. Fifty-three
percent of those resulted in days away from work, job
transfer, or job restriction [23]. The Workplace Safety
& Insurance Board (Ontario, Canada) reported 22,614
(25.2%) lost time claims in the manufacturing industry
in 2005; the highest of any other industry [25].
Several studies have identified an association with
increased physical demand and postural demands of the
shoulder in relation to MSD development [18,19]. The
repetitive nature of many upper extremity jobs has also
been reported as a major risk factor, however, to date,
there is little information quantifying the load demands
associated with the repetitive manufacturing tasks and
the associated cumulative loading on the shoulder. Furthermore, the available workplace assessment tools [11,
13,16] which include the shoulder are limited to postural evaluations and are unable to provide biomechanical
loading information.
Cumulative loading has become an accepted risk factor in low back pain development [10,15] and is now
being used to assess the demands of the shoulder [12],
however, only a few biomechanical models have been
developed to calculate loading of the shoulder [2]. The
inconsistency in methodologies to determine shoulder
demands or models has made it difficult to compare
results, and a lack of injury data has made it impossible
to determine a dose-response relationship.
The main purposes of this study were to: 1) document peak and cumulative shoulder loads; 2) determine whether there are differences in peak and cumulative shoulder loads between sexes and pain groups;
3) determine whether there are differences in shoulder
posture between genders and pain groups; 4) assess the
relationship between cumulative shoulder loading and
reported shoulder pain severity; and 5) determine potential risk of injury by comparing peak shoulder loads
with published strength data.
2. Methods
Data collection took place in an automotive seat
frame assembly plant over a two-week period, and involved video data and questionnaire approaches. This
information was used to document the peak and cumulative loads experienced by employees, as well as
the prevalence of reported shoulder pain. Approval to
conduct this research study was obtained from the University of New Brunswick’s Research Ethics Board.
Informed consent was obtained from all participants.
2.1. Participants
Employees from an automotive seat frame assembly
plant were recruited through a meeting with all employees. Employees who were on reduced workload
for any reason were excluded. Of the 110 participants
who agreed to be videotaped and complete the questionnaire, the company released two participants before
data collection began and two withdrew from the study,
leaving 106 participants. Of these, 27 could not be used
in the analysis because of incomplete data sets (one
person did not complete the questionnaire and 26 did
not have complete video data sets), leaving a final count
of 79 participants, 34 males and 45 females (mean age
of 37.1 years ± 11, mean height of 168.7 cm ± 10.3,
mean mass of 75.7 kg ± 16.5).
2.2. Job and task selection
The mass of the seat frame was approximately 8 kg,
depending on the model being built. The jobs that
contributed to the seat frame assembly were separated
into the following groups: Epsilon, GeFit, Welders,
Rewelders, and Train. The Epsilon line attaches the
runners to the bottom of the seat frame and bolts it
to a base. The GeFit line inserts small parts into the
dials used to manually recline the seat. Welders insert
several pieces into an autoweld machine to build the
seat frame. Rewelders check all of the welds on the
seat frame and touch up areas that need reinforcement.
Train Drivers deliver parts to all assembly lines and
cells in the plant, loading and unloading bins from a
trolley with three levels of shelving.
Of the 79 participants who took part in the study with
complete questionnaires and video data, ten jobs that
did not fit into the other 5 groupings (Epsilon (n = 17),
GeFit (n = 17), Welders (n = 17), Rewelders (n =
5), Train Drivers (n = 5)) had only one or two workers who regularly performed the tasks. For statistical
purposes and to maintain participant anonymity, these
jobs were combined into a Miscellaneous group (n =
18) and will not be presented in the job-specific results. Although there were few workers in the Train and
Rewelder job groups, the company wanted information
about these specific jobs, and thus, were analyzed as
separate groups.
F.A. Seaman et al. / Shoulder loads and postures in automotive assembly
2.3. Documenting shoulder loads
Each task was assessed by observing workers at each
station as they performed regular assembly line duties.
Prior to collecting video data, a period of time was
set aside to allow the employee to become comfortable
with the presence of the investigator, and with being
videotaped. Two Panasonic video cameras (Secaucus, New Jersey, USA) were used to collect video at
30 Hz at an appropriate angle to the sagittal plane where
the participant’s actions could be seen clearly [22]. Participants were videotaped for the amount of time it took
to collect at least ten cycles of the task, in order to
choose three clear cycles to be used for analysis [3].
A digital Chatillon force gauge (AMETEK Inc., Paoli,
PA, USA) was used to measure all loads lifted, lowered, pushed and pulled by the participant. If the force
gauge could not be attached to the piece being worked
on, the participant was asked to exert the same amount
of force as they would normally use to complete the
task. This data was recorded and later entered into
a biomechanical model as the three-dimensional hand
forces.
2.4. Documenting shoulder pain
Each participant was asked to fill out a questionnaire
about the onset and severity of any current shoulder
pain, history of shoulder pain, effect of shoulder pain on
employment and activities of daily living, and whether
treatment was sought for shoulder pain. Shoulder pain
was defined as an ache, pain or discomfort in the shoulder region, whether or not it extended to the neck or
down the arms. The pain could have been described
as an aching, stiffness, burning, numbness or tingling
sensation. A representative body diagram was included to help the worker identify the area with which they
had experienced pain. A Visual Analogue Scale (VAS)
was used to assess their perceived severity of pain, with
the scale running from 0–10 cm (no pain to greatest
amount of pain possible). Participants marked an ‘X’
on the line and their VAS score was then determined
based on its position.
2.5. Data processing
Each video file was imported into video capture
software on a computer using Pinnacle Studio version
7.15.1 (Pinnacle Systems, Inc., CA, USA). Jobs were
broken down into tasks and sub-tasks, when needed.
Three clear cycles of each task were chosen and used for
41
analysis. The three cycles chosen for posture matching
were selected based on video quality and ability of the
investigator to clearly see the worker’s positions. If
more than three cycles were of good quality, three cycles were chosen randomly for posture matching. Task
cycles were clipped using the video capture software
and converted into AVI format. Each video clip was
then decimated to 3 Hz [1].
2.6. Posture matching
Each decimated file was imported into the 3DMatch
(University of Waterloo, Waterloo, ON, Canada) posture matching software used to assess cumulative and
peak loads, and postures of the shoulder [2]. 3DMatch
is a custom program used for assessing the participant’s
three-dimensional posture frame by frame and matching observed postures to the categories on the computer
screen. Posture categories (bins) were developed based
on joint range of motion and data from other postural
approaches that have been used [17,24]. The median
values from the posture bins selected are used as inputs in the rigid link quasi-static model to determine
cumulative and peak loading variables (reaction moments for abduction/adduction, internal/external rotation, flexion/extension, and reaction forces for anterior/posterior shear, cranial/caudal shear, and compression/tension) [2,8].
3DMatch also outputs information regarding time
and percent of work cycle spent in flexed and abducted
postures. Each calculation is done separately for the
right and left shoulder. The height and mass of each
participant was entered into 3DMatch in order to develop customized models for each participant. The hand
forces were also entered into the model, specifying the
direction of force (push, pull, or lift).
After each video clip was posture-matched and run
through the model, the three cycles for each participant’s task were averaged to create a mean value. If
there were any sub-tasks, they were added together to
account for each time they occurred during each cycle
of the task. Each employee received a 30-minute lunch
break and two 15-minute breaks which were accounted
for, in addition to the seven hours of work time. Cumulative loading during a full shift was then estimated
by extrapolating the average value of the three cycles.
The number of cycle repetitions performed by workers
during each shift was obtained from the company. This
was used to determine the total time the participants
performed assembly tasks. The remainder of the time
was assessed with the participant in a neutral position,
such as waiting for parts or walking around [4].
42
F.A. Seaman et al. / Shoulder loads and postures in automotive assembly
Table 1
Prevalence (n) of reported shoulder pain by sex
Sex
Right shoulder Left shoulder
Both
Total
only
only
shoulders
Male (n = 34)
1
4
8
13
Female (n = 45)
7
6
10
23
Total (n = 79)
8
10
18
36
2.7. Data analysis
All statistical analyses were performed using SPSS
14.0 for Windows. Statistical significance was set at an
alpha level of 0.05.
Descriptive statistics and a one-way ANOVA were
conducted to assess differences in posture, cumulative
and peak loads between sexes and pain groups (those
who reported having shoulder pain and those who did
not). The Kruskal-Wallis test was used as the nonparametric alternative when the Levene’s test for homogeneity was significant.
A separate analysis was performed solely on the
group of participants who reported shoulder pain. A
Pearson Product Moment correlation was used to check
for any correlations between the severity of reported
shoulder pain and cumulative load variables.
To provide a sense of potential risk, the shoulder peak
loading was compared to published maximum strength
data [6]. For comparative purposes, maximum flexion
at 30 degrees for women in the group of 30–39 years of
age was used as it best corresponds to the average age
of the group in the current study. Moderate potential
risk of injury was determined as a peak flexion moment
between 75% and 100% of maximum strength. High
potential risk of injury was determined as greater than
100% of maximum strength.
attributed to their work, four participants did not believe their shoulder pain was attributed to their work,
and two participants were unsure. The most common
activities reported as making their shoulder pain worse
were: lifting (n = 26), pulling (n = 20), pushing (n =
17), twisting from the shoulder (n = 15), raising the
arm in front (n = 14), overhead motions (n = 14), and
raising the arm to the side (n = 13).
There were no significant differences in the prevalence of reported shoulder pain between sexes (p =
0.996), therefore the sexes were combined for further
analyses between Pain and No Pain groups.
3.2. Posture
On average, the participants spent nearly half of their
shift in mild shoulder flexion; 43.3% and 46.8% for the
right and left shoulder, respectively. They also spent
the majority of the shift in neutral abduction with 96.8%
and 97.3% for the right and left shoulder, respectively. Males spent significantly more time in neutral right
shoulder flexion (p = 0.007), and females spent significantly more time in mild right shoulder flexion (p =
0.003). There were no significant differences in percent
time spent in neutral, mild, and severe flexed postures
between the pain groups.
3.3. Peak load
There were no significant differences in mean peak
moments and forces between the Pain and No Pain
groups (Fig. 1).
3.4. Cumulative loads
3. Results
3.1. Questionnaire results
Of the 79 participants, 45.6% reported shoulder pain,
38.2% of males and 51.1% of females, the majority
of whom reported experiencing shoulder pain in both
shoulders. Table 1 presents the information on prevalence of reported shoulder pain.
The following information refers only to responses
from participants who reported experiencing shoulder
pain (n = 36). The median VAS score for right shoulder pain was 3.5 cm (range from 0.5 to 7.6 cm), and
2.7 cm (range from 0.8 to 8.4 cm) for the left shoulder.
Thirty-one participants believed their shoulder pain was
Figure 2 compares the mean cumulative moments
and forces acting on the shoulder between the Pain and
No Pain groups. The No Pain group experienced significantly more cumulative caudal shear than the Pain
group for both the right (p = 0.009) and left shoulder
(p = 0.009).
3.5. Analysis of pain group
There were no significant correlations between the
right shoulder VAS scores and cumulative load variables. For the left shoulder, a mild correlation was
found between the VAS scores and cumulative posterior
shear (p = 0.023), and caudal shear (p = 0.036).
F.A. Seaman et al. / Shoulder loads and postures in automotive assembly
43
Table 2
Risk of shoulder injury or disorder based on peak and cumulative loads experienced
for each job. Bolded numbers signify high potential risk of injury and moderate risk
of injury are shaded
Peak loads
Caudal
Flexion
shear (N) moment (Nm)
71.6
28.2
Epsilon
(n = 17)
GeFit
(n = 17)
Train
(n = 5)
Welders
(n = 17)
Rewelders
(n = 5)
40
Pain
998.7
122.1
116.1
47.1
1600.9
297.2
52.1
16.3
1162.6
52.2
104.3
35.6
1224.2
162.1
b)
No Pain
45
Pain
40
No Pain
35
20
n
io
n
al
ns
Ex
R
Fl
ot
te
at
ex
io
io
n
n
io
at
ot
rn
te
100
90
80
70
60
50
40
30
20
10
0
Force (N)
Pain
ns
io
n
Te
om
pr
es
si
on
lS
ra
ni
a
C
C
he
ar
r
he
a
al
S
au
d
C
Po
st
er
io
rS
he
ar
he
ar
No Pain
te
rio
rS
An
ns
io
n
Te
om
pr
es
si
on
C
r
he
a
ra
ni
al
S
C
al
S
au
d
C
er
io
rS
he
a
he
a
r
r
r
Po
st
Ex
In
Ex
No Pain
he
a
ct
rn
te
rn
te
te
In
Pain
rS
io
n
io
du
ct
ns
Ab
al
Ex
te
Fl
R
R
al
rn
io
n
ex
io
at
at
ot
du
Ad
io
n
n
io
io
ct
io
ct
du
Ab
d)
100
90
80
70
60
50
40
30
20
10
0
An
te
rio
R
0
n
5
0
n
10
5
n
15
10
du
15
25
Ad
20
30
al
Moment (Nm)
25
Force (N)
c)
14.8
30
n
Moment (Nm)
35
48.9
ot
a)
Cumulative loads
Caudal
Flexion
shear (kN·s) moment (kNm·s)
861.4
99.3
Fig. 1. Mean peak moments of the a) left shoulder and b) right shoulder, and mean peak forces for the c) left shoulder and d) right shoulder for
Pain and No Pain groups. * denotes significance.
3.6. Risk of developing musculoskeletal disorders of
the shoulder
Table 2 illustrates the shoulder peak loading variables that may signify potential risk based on normative strength values from previous research [6]. Table 3
shows the odds ratios for risk of developing shoulder
MSDs, as determined by Punnett et al. [18], specific
to the postures used by the participants in the current
study.
4. Discussion
Forty-five percent of the workers who took part in
the study reported shoulder pain, which is similar to
44
F.A. Seaman et al. / Shoulder loads and postures in automotive assembly
Table 3
Percent time in shoulder flexion and abduction of the right shoulder with corresponding
odds ratios (in brackets) for risk of shoulder disorders based on work by Punnet et al. [18]
Flexion
Mild
53.6
45.2
37.1
19.4
39.2
43.8
Neutral
44.8
54.7
55.4
80.3
60.4
55.3
Epsilon
GeFit
Train
Welders
Rewelders
Miscellaneous
Severe*
0.5 (1.7)
0.1 (1.7)
6.1 (1.7)**
0.2 (1.7)
0.0
1.3 (1.7)
Abduction
Mild
Severe
4.5
0.1 (1.7)
1.4
0.0
6
0.5 (1.7)
1.4
0.4 (1.7)
8.9
0.0
2.4
0.8 (1.7)
Neutral
94.0
98.7
91.8
98.2
90.6
97.2
*The odds ratio for severe flexion of both shoulders at least once per task cycle is 4.0.
**If the Train group had reached 10% of shift in severe flexion, the O.R. increases to 2.8
for the right shoulder and 5.1 for the left shoulder.
Pain
b) 250
No Pain
200
Moment (kNm*s)
150
100
50
100
50
An
t
er
io
rS
io
n
rn
al
Ex
te
ns
io
ex
io
at
ot
R
Fl
n
n
io
at
ot
R
te
io
n
he
ar
on
Te
ns
i
si
pr
es
C
om
he
ar
lS
C
ra
ni
a
al
Sh
e
au
d
on
0
ar
200
0
C
Ex
400
200
he
ar
al
600
Te
ns
400
No Pain
800
da
lS
he
ar
C
ra
ni
al
Sh
ea
C
r
om
pr
es
si
on
600
Pain
1000
C
au
No Pain
er
io
rS
ct
rn
Force (kN*s)
800
Po
st
du
te
In
1200
Pain
rS
he
ar
io
io
ct
du
1400
rS
he
ar
1000
An
te
rio
n
n
n
ns
Ab
al
Ex
te
ex
Fl
R
io
n
io
n
ot
at
io
rn
te
Ex
In
te
rn
al
Ad
R
ot
du
at
ct
io
n
n
io
n
io
ct
du
Ab
d)
n
0
1200
Force (kN*s)
No Pain
150
0
c)
Pain
200
Ad
Moment (kNm*s)
250
Po
st
er
io
a)
Fig. 2. Mean cumulative moments of the a) left shoulder and b) right shoulder, and mean cumulative forces for the c) left shoulder and d) right
shoulder for Pain and No Pain groups. * denotes significance.
the incidence rate of McClellan et al.’s [12] automotive
manufacturing research. The majority of the reported
pain was experienced in both shoulders. This result is
not surprising in that 31 of the 36 who reported shoulder
pain attributed the pain to their work and many of the
jobs analyzed required symmetrical movements (such
as lifting bins) or were equally demanding for both sides
(e.g., retrieving the part with the left hand, working on
it with both hands, and disposing of it with the right
hand along the assembly line). All workers reporting
only left shoulder pain were left-handed. Handedness
was split evenly amongst those reporting only right
shoulder pain.
Although there was no significant difference between
the shoulder pain groups and loading or postural outcome variables, previous research has provided evidence for the increase risk of MSDs when non-neutral
postures are required for a significant percentage of the
cycle time [18]. As well, excessive exposure to peak
and cumulative loads, especially for risk in low back
pain development has been advocated [10,15]. Hughes et al. [6] suggested the maximum strength at 30
F.A. Seaman et al. / Shoulder loads and postures in automotive assembly
degrees of flexion to be 37 Nm for women aged 30–
39 years, 75% of which would be 27.8 Nm. Using this
criteria, job tasks with peak flexion moments falling
within the range of 27.8–37.9 Nm were classified as
moderate risk of shoulder injury development, and job
tasks with peak flexion moments above 37.9 Nm were
classified as high risk. Based on this method, Epsilon
and Rewelders have moderate risk of injury. Although
only males worked as Train Drivers (which explains
the higher peak flexion moments), if a female were to
perform the Train Driver tasks they would have a high
potential risk of injury. The maximum strength at 30
degrees of flexion for males is 55 Nm, 75% of which
is 41.3 Nm. Therefore, males are also at a moderate
potential risk of injury when performing Train Driver
tasks. Since there are no normative tables for caudal
shear or threshold values for cumulative shoulder loading values from which to compare, an estimation of
potential risk is not currently possible.
4.1. Posture
Many studies have shown that increased time spent
by workers in non-neutral postures increases the risk
of developing MSDs and reporting shoulder pain [18].
Comparing the postural demands of the auto manufacturing workers to the established odds ratios suggest a
moderate risk of MSD development (OR = 1.7). On
average, the Train group spent 6.1% of their shift in
severe shoulder flexion, suggesting an OR of 1.7. Since
the train drivers are lifting bins to and from the trolley, the risk of shoulder MSD development increases
by a factor of 4 because both shoulders are put in severe flexion when loading and unloading from the top
shelf. If they spend more than 10% of their work cycle
in severe shoulder flexion it can increase their risk of
developing MSDs by up to six times [18]. Rewelders
spent the most time in mild abduction (8.9%) since
they need to manipulate the seat frame to check the
welds. There are few studies with which to compare
these results, however in a similar study in an assembly
plant of automotive mouldings, McClellan et al. [12]
reported that the workers were in mild shoulder flexion
for 30% of their work shift. Although those workers
spent less cycle time in flexed postures they spent more
time in abducted postures than workers in the current
study. In an assessment of the trunk, neck, shoulder,
and elbow work postures of orthodontists, Newell and
Kumar [14] suggested that increased time spent in static and deviated postures may lead to musculoskeletal
disorders. In the case of the jobs in the current study,
45
workers assumed mild shoulder flexion for most of the
shift. However, the jobs were fairly dynamic whereby
the participants would reach for a part and then return
to a neutral position.
4.2. Peak loads
Unlike the NIOSH action limit for spine compression while lifting, there are no threshold values available for the shoulder beyond assessments of isometric shoulder strength. Based on the complexity of the
shoulder, there are several modes of loading and the
corresponding resulting peak loading variables, none
of which have yet been deemed as the most detrimental to the shoulder joint and surrounding tissues. This
study adds to the limited current knowledge on peak
shoulder loading and provides a database for shoulder
loading in a manufacturing environment.
The reported shoulder loads show large standard deviations. When all jobs are combined for reporting purposes, the large standard deviations are a reflection of
the diversity in the various jobs analyzed. For example,
there is a large discrepancy in peak loads experienced
for the Welders and the Train group. It was thought that
by grouping by job, the variance would be smaller but
individual technique such as posture used to complete
the job, or choices made to complete the task faster (e.g.
carrying more bins or parts) may contribute to large
variations within jobs. Interestingly, the large variance
was unaffected whether the participant level was large
or small in a given group. This suggests that individual
techniques used at each job should be examined more
closely in future investigations.
Although it is critical to document peak moments
that workers experience, it is equally important to know
their association to individual strength in order to move
towards a threshold limit. We did not collect maximum
strength values from our participants, but used normative strength tables from previously published research.
Hughes et al. [6] published normative strength values
for males and females in different age groups. Participants were tested in 30, 60 and 90 degrees of shoulder flexion and abduction with both their dominant and
non-dominant arms. The published strength values for
the age range of 30–39 years for 30 degrees of flexion
and 30 degrees of abduction, were used to estimate the
percentage of maximum strength used by the autoworkers in this study (all jobs combined). Males used approximately 42.2% of their maximum shoulder strength
for the right shoulder, and 37.5% for their left shoulder. For abduction, males used approximately 31.5%
46
F.A. Seaman et al. / Shoulder loads and postures in automotive assembly
of their maximum strength for the right shoulder, and
21.4% for the left shoulder. Females, on the other hand,
used approximately 58.4% of their maximum shoulder
strength for the right shoulder, and 46.0% for the left
shoulder. For abduction, females used approximately 42.7% and 32.2% of their right and left maximum
shoulder strength, respectively. Interestingly, the Train
group (which was all male) used approximately 85.6%
and 89.2% of their maximum shoulder flexion strength
for the right and left shoulder respectively, and 78.2%
and 48.4% of their maximum abduction strength for the
right and left shoulder, respectively. Although not part
of the current study, it would be interesting to know
what percentage of time employees work above 30%
of the maximum strength values, which would speak to
endurance.
4.3. Cumulative loads
As was seen with peak loads, there were large variances in the cumulative loads experienced by the participants. With all jobs combined, the large standard
deviations could be related to the different demands of
the jobs. When separated by job, however, the standard deviations are still large. This may be a result of
differences in work technique. It is possible that the
number of parts each participant was able to produce
will be different. With GeFit, for example, it is estimated that one worker makes 2300 parts per shift. This
number was then used to extrapolate the cumulative
load values of one cycle to a full work shift. The remaining time was deemed to be unplanned rest [4]. It
is possible that the large standard deviations within job
groups are a result of the extrapolation method. Since
some participants had shorter cycle times, they subsequently had longer unplanned rest when extrapolating,
and thus lower cumulative load values. In reality, the
participant probably does not have that much rest time,
but continues making parts and actually has a higher
than average production rate, which would also lead to
higher cumulative loads.
Currently the only published limit with respect to
cumulative loading of relative use for the shoulder is
the ISO 11228-1 standard [7] set at 10,000 kg handled
per day. This standard has been set for manual material
handling activities and represents a limit based on the
load being handled and not the net biomechanical loads
experienced by the low back or shoulder structures.
Using the maximum NIOSH lifting constant of 23 kg
in an ideal lifting situation, this translates to less than
one lift per minute in an 8-hour day. This limit is also
based on the low back and not specifically for shoulder
injury prevention. If the ISO limit were to be considered, the jobs assessed in this automotive plant would
not be deemed risky as the total load handled per day
averaged around 2000 kg. Unfortunately, the ISO limit
neglects to consider static posture, non-neutral postures
and cycle times in its assessment of cumulative load.
These are all aspects that have been shown by previous researchers to have an association with cumulative
loading [9,10,13,15].
Interestingly, the No Pain group experienced greater
cumulative caudal shear than the Pain group. Intuitively, if cumulative loading is suggested as being associated with shoulder pain, then the higher caudal shear
should be experienced by the Pain group. However, it
is possible that the participants who experienced shoulder pain have altered their technique used to complete
their work tasks in order to reduce their pain. By doing
so, they have reduced the caudal shear that is acting on
the shoulder. Since the data was collected post-injury,
we do not have the ability to assess technique changes,
only technique differences between groups. Longitudinal studies would be required to establish changes
in specific technique as well as helping to establish a
shoulder injury model. Finally, with the lack of homogeneity among the job tasks we were not able to
determine specific variables that may have led to the
differences in caudal shear. With a more homogeneous
group, we may have been able to uncover additional
information.
It is possible that shoulder injury is the “response”
variable that must be looked at instead of perceived
pain. Pain is extremely subjective and the determination of a dose-response relationship may be impossible
if it is based on the participants’ report of pain and pain
severity. A model of shoulder injury is needed as one
of the next steps, which should include the mechanism
of injury. Through the development of an injury model,
we may find that cumulative load is not a mechanism of
shoulder injury at all or that it is predominately muscular in nature and thus the cumulative moments are the
only variable of importance.
4.4. Limitations
As with any study, there are limitations. For one,
there was a fairly high attrition rate of 26%, mainly
due to incomplete video data sets. Some of the assembly lines went down during the collection period
resulting in the loss of video collection of participants
F.A. Seaman et al. / Shoulder loads and postures in automotive assembly
at each station on the line. Despite this, there was still a
significant sample size, but some small job groupings.
Only two investigators posture-matched the video
data in order to reduce error. Each investigator posturematched the same participant for all jobs, but did not
posture-match all videos for each job. As a result, any
variance from the actual posture being used in the video
would not necessarily be consistent between the investigators, however there is very little error in posture
matching when performed by a trained observer [21].
When collecting the video data, it was not always
possible to collect at 45 degrees from the sagittal plane
for all participants. Depending on the assembly line
setup, the line of view was often obstructed by machines; therefore investigators were required to find an
alternate position monitoring the participant’s movements. This was not deemed to be an issue since the use
of 3DMatch for posture matching was found to have
strong inter-observer reliability regardless of camera
angle [22].
The hand-held force gauge allowed the use of several different attachments to measure lift, push and pull
forces with less than 0.25% error. Although it is possible that the technique of the participant was altered
by holding the force gauge while simulating the task,
it is not believed that it significantly impacted the technique of the participant based on the specificity of attachments used.
neutral postures. They also suggest that there may
be a protective adaptation in work performance based
on the higher caudal shear in the No Pain group. A
longitudinal study is required to further examine this
relationship.
It is therefore concluded that pain is not a robust
measure within the workplace due to the subjective
nature of its reporting. The lack of a clear relationship
between the biomechanical variables and pain suggest
that other variables (e.g., psychosocial, physical) may
be necessary to explain the variability in the “dose” and
that pain may not be the best indicator of the “response”.
Future research needs to focus on the mechanism of
shoulder injury to better establish the variables of this
relationship and the database of shoulder loading needs
to be expanded to include other industrial tasks.
Acknowledgments
The authors wish to acknowledge the Natural Sciences and Engineering Research Council of Canada and
AUTO21 Network of Centres of Excellence for financial support, as well as the employees and management
at Faurecia Automotive Seating Canada Ltd.
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5. Conclusion
The 3DMatch postural assessment tool was used to
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