Development of an observation-based tool for ergonomic exposure assessment in informal electronic waste recycling and other unregulated non-repetitive work

Abstract

Most existing ergonomic assessment tools are intended for routine work. Time- and cost- efficient observational tools for ergonomic assessment of unregulated work are lacking. This paper presents the development of an observation-based tool designed to investigate ergonomic exposures among informal electronic waste workers that could be applied to other unregulated jobs/tasks. Real-time coding of observation is used to estimate the relative duration and intensity of exposure to key work postures, forceful exertions, movements, contact stress and vibration. Time spent in manual material handling activities such as carrying, lifting and pushing/pulling of working carts are also estimated. A preliminary study conducted with 6 e-waste workers showed that the tool can easily be used with minimal training and good inter- observer agreement (i.e., 89% to 100%) for most risk factors assessed. This new assessment tool provides effective and flexible options for quantifying ergonomic exposures among workers engaged in unregulated, highly variable work.

Full text

Development of an observation-based tool for ergonomic
exposure assessment in informal electronic waste recycling and
other unregulated non-repetitive work
Augustine A. Acquah1, Clive D’Souza2, Bernard Martin2, John Arko-Mensah1, Afua Asabea
Nti1, Lawrencia Kwarteng1, Sylvia Takyi1, Paul K. Botwe1, Prudence Tettey1, Duah
Dwomoh1, Isabella A. Quakyi1, Thomas G. Robins3, Julius N. Fobil1
1.Department of Biological Environmental and Occupational Health Sciences, School of Public
Health, University of Ghana, Accra, Ghana.
2.Center for Ergonomics, Department of Industrial and Operations Engineering, University of
Michigan, Ann Arbor, MI, USA.
3.Environmental Health Sciences, School of Public Health, University of Michigan, Ann Arbor, MI,
USA.
Abstract
Most existing ergonomic assessment tools are intended for routine work. Time- and cost-efficient
observational tools for ergonomic assessment of unregulated work are lacking. This paper presents
the development of an observation-based tool designed to investigate ergonomic exposures among
informal electronic waste workers that could be applied to other unregulated jobs/tasks. Real time
coding of observation is used to estimate the relative duration, intensity, and frequency of exposure
to key work postures, forceful exertions, movements, contact stress and vibration. Time spent in
manual material handling activities such as carrying, lifting and pushing/pulling of working carts
are also estimated. A preliminary study conducted with 6 e-waste workers showed that the tool can
easily be used with minimal training and good inter-observer agreement (i.e., 89% to 100%) for
most risk factors assessed. This new assessment tool provides effective and flexible options for
quantifying ergonomic exposures among workers engaged in unregulated, highly variable work.
1. INTRODUCTION
Various tools for estimating ergonomic exposures exist, including observation methods
(Chiasson et al., 2012; Herzog and Buchmeister, 2015), direct methods employing
instrumentation (Winkel and Mathiassen, 1994) and self-reported questionnaires (Spielholz
et al., 2001). These methods have been validated in industrial (Buchholz et al., 1996; Karhu
et al., 1977) and office (Gambo, 2017) settings where tasks are predefined and constitute
primarily a daily routine. Most current assessment methods are time-consuming (Chaffin et
al., 2006; Takala et al., 2010), some are either expensive to implement, require intensive
training of observers in order to effectively use them (Buchholz et al., 1996) or are just not
conducive for use in unregulated work environments wherein the type and intensity of work
performed, even by the same individual, are highly variable within and between days. For
example, a previous study at an informal electronic waste (e-waste) recycling work-site at
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Agbogbloshie in Ghana, reported challenges with compliance to bioinstrumentation
(Acquah et al., 2018). As such, characterising ergonomic exposures in low-resource,
unstructured work settings such as e-waste recycling work in developing countries remains a
challenge for ergonomics practitioners.
Specific to e-waste recycling processes at Agbogbloshie, the rudimentary work practices
present a significant number of ergonomic hazards (Acquah et al., 2019b) and predispose
workers to alarming rates of work-related musculoskeletal disorders (MSDs) (Acquah et al.,
2019a). The latter study revealed a 90% overall prevalence of MSDs among e-waste
workers, including the lower back (65%), knees (39.3%) and shoulders (37.4%).
Quantifying the suspected ergonomic risk factors associated with unregulated work are
important for establishing likely associations between ergonomic exposures and MSDs so as
to tailor ergonomic interventions aimed at addressing specific risk factors and work
conditions in e-waste recycling. Previous studies at Agbogbloshie have attempted to quantify
physical work hazards associated with e-waste recycling using existing assessment tools.
Although some meaningful estimates were achieved through these methods (unpublished
data), the ability to provide reliable estimate of intensity and duration from these data was
very limited.
The present study aimed to address a methodological gap by developing a low-cost
observation-based ergonomic exposure assessment tool that enables easy quantification of
the intensity, duration and frequency of physical exposures encountered in unregulated work
settings such as informal e-waste work. This developed tool was used to estimate exposures
for 2 worker categories on a limited scale in order to assess inter-observer agreement and
validity (i.e., ability to quantify differences in exposures between e-waste dismantlers and
burners).
2. METHODS
A methodology to characterize the key ergonomic exposures in e-waste recycling was
developed based on concepts employed in other observational methods including RULA
(McAtamney and Nigel Corlett, 1993), OWAS (Karhu et al., 1977), PATH (Buchholz et al.,
1996), quick exposure checklist (QEC; Li and Buckle, 1998) and tools used to estimate
ergonomic exposures in other work settings (Gilkey, 2002).
2.1 Tool Development
This study was approved by the College of Health Sciences Ethical Review Committee at
the University of Ghana, Accra. Written informed consent was obtained from all
participants.
2.1.1 Understanding the processes and activities—Initially, multiple field visits,
walk-through observations and worker interviews were conducted in order to fully
understand and appropriately document the processes involved in e-waste recycling. Three
main e-waste recycling activities were previously identified and classified: i.e., collecting,
dismantling and burning of e-waste (Acquah et al., 2019).
Collecting
involved traveling to
different neighbourhoods and nearby residential areas scavenging for end-of-life electronics.
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Dismantling
involved breaking apart e-waste items to separate the different metal
constituents.
Burning
involved open-air burning of insulated components (e.g., copper
cables) to retrieve valuable metals for sale. Work methods for each job process were
adequately described to capture the main components of the tasks involved and the work
tools used.
2.1.2 Development of the coding guide and coding template—Following the
initial phase described above, data coding criteria were developed. The focus of the tool
being developed was to estimate the ergonomic risk factors e-waste workers were exposed
to, the proportion of time each factor was present and the time spent in key manual material
handling tasks. Thus, the tool was designed to assess posture, force, repetition, contact stress
and vibration. In addition, manual material handling activities such as carrying, lifting,
pushing/pulling a cart were also distinguished.
The body segment postures assessed included neck, trunk, lower and upper limbs as outlined
below. For each segment, postures were categorized on an ordinal scale with at most 2 or 3
levels in order to facilitate the pace of coding in real time while obtaining meaningful results
in agreement with the relevant literature.
-
Neck:
Two postures were coded as either neutral or non-neutral. The study opted for a
simple binary classification based on findings by Buchholz et al. (1996) who demonstrated
that adding more neck posture categories reduced inter-observer agreement for the PATH
tool. Unlike OWAS which excludes the neck, this body segment was included since neck
pain was among the top six MSDs reported by e-waste worker (Acquah et al., 2019).
-
Trunk:
Three postures were distinguished and coded as neutral (<20° flexion), moderate
(between 20° and 45°) or severe (>45°) forward flexion and/or lateral bending. The
threshold criteria were adapted from prior studies [i.e., the PATH methodology by Buchholz
et al., (1996), guidelines by NIOSH (2014)]. Lateral bending or twisting were combined
with flexion postures since these postures were observed to often occur in conjunction. The
addition of moderate and severe flexion to trunk postures (although absent in OWAS) was
based on findings by Punnett et al. (1991) who reported increased risk of back disorders
associated with severe vs. mild trunk flexion, twisting and lateral bending.
-
Upper limbs
: Three postures were distinguished and coded as hands/arms below waist
height, below shoulder height but above waist height, and above shoulder height.
-
Lower limbs
: Three postures were categorised as walking, sitting or standing. Walking was
coded as either ordinary walking or walking while pushing/pulling a cart as is usually the
case for e-waste collectors. Standing was either neutral or standing with knees bent >45°.
Sitting was coded in 3 subcategories; sitting with either hips and knees at about 90°, hips
and knees greater than 90° or hips and knees less than 90°.
Other ergonomic risk factors assessed in addition to posture included force exertions and
movement repetition using ordinal categories based on the QEC (Li and Buckle, 2000).
Force was subjectively graded as low (≤1kg), moderate (between 1kg to 4kg) or high (≥4kg).
Repetition was coded as low (≤10x per min), medium (11–20x per min), and high (≥20x per
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min). The developed tool also assessed exposure to contact stress and vibration. These were
coded on a binary scale as either present or absent.
Following initial piloting of the tool and in-depth consultations with experts in the field, the
tool was modified to include common manual material handling activities performed during
e-waste recycling, i.e., carrying, lifting, pushing/pulling of a cart or wheel-barrow. Lifting
and carrying activities were coded as Light (≤5kg), Moderate (6 to 10kg), Heavy (11 to
20kg) and Very Heavy (≥20kg), akin to the QEC (Li and Buckle, 2000). In order to
familiarize observers with estimates of the weight handled by workers, frequently handled
items and work tools identified from field visits were weighed using a weighing scale prior
to conducting structured observational assessments. With respect to pushing/pulling of
wheelbarrow or cart, the focus of the coding was whether the wheelbarrow or cart was
empty or loaded.
To facilitate easy recording of observed data in the absence of hand-held tablets or
computerised devices, a pen and paper-based coding template was designed. Ordinal
categories associated with each of the risk factors assessed were assigned numerical codes
and written in cells juxtaposed to these risk factors. The columns in the template
corresponded to the observation duration/time and the rows corresponded to the ergonomic
risk factor being assessed. Each cell corresponded to 60 seconds of observation. To enhance
speed in data coding, when no changes were observed between two epochs, the preceding
cell was left blank until a change in the risk factor being assessed was observed at which
point the new value was written in the cell corresponding to that time interval.
2.2 Observer Training
Two research assistants (RAs) were trained for two weeks. The first week of the training
focused on familiarizing the RAs with the processes and work methods involved in e-waste
recycling by watching assigned videos of workers performing e-waste recycling and
interpreting the various exposure codes using a coding guide. Next, the RAs were instructed
on use of the newly developed tool to code observations from the videos. During this time,
they were allowed to pause the video when necessary to facilitate the capture/identification
of required details. They were also encouraged to discuss any instances of confusion or
ambiguity with the coding process among themselves. The second week of training was
conducted in the field and focused on coding from direct observations in real time. During
the direct field observations, the observers worked concurrently so that inter-observer
agreement could be established.
2.2.1 Inter-observer agreement—Inter-observer agreement was determined by
comparing coded data for 6 workers including 3 dismantlers and 3 burners. Each worker was
observed by two observers simultaneously for 10 minutes. The inter-observer agreement was
compared for the neck, trunk, upper and lower limb postures as well as force, repetition,
contact stress and vibration.
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2.3 Piloting of the tool
The newly developed tool was piloted on the same 6 workers whose data were used for the
inter-observer agreement. Each worker was observed for a full working day and the
proportion of time they spent in various ergonomic exposures computed.
2.3.1 Procedure for data collection—Workers were approached during the field visits
and were presented the purpose of the study. Six workers at the time of the visit consented to
participate in the pilot study. They were observed from the start of their work shift until
completion. E-waste workers have variable work schedules and work durations which is
usually dependent on availability of raw materials to work with (Acquah et al., 2019b). The
observers also wore a video camera (GoPro Inc.) to obtain a backup of the observed data
thus ensuring the opportunity to review and/or verify missing data later if necessary.
2.3.2 Data processing—The observation data coded on the paper templates by each
observer were entered into MS Excel spreadsheets. Conventional methods were used to
count the frequency of noted observations. The proportion of time spent in various postures,
activities and exposure duration to risk factors were computed and tabulated in Excel.
3. RESULTS
3.1 Inter-observer agreement
Data coded by the two observers for neck posture (dismantlers), upper limbs (burners),
lower limbs (burners and dismantlers) as well as repetition and contact stress (dismantlers)
were in perfect agreement. Table 1 summarizes the percent agreement and kappa statistic for
other areas. Pooled data showed 89.17% to 100% agreement for all risk factors observed.
3.2 Pilot data (exposure profile for 6 workers)
The total observation times were 721 minutes for the 3 burners and 382 minutes for the 3
dismantlers. The proportion of work time corresponding to various ergonomic exposures
differed between burners and dismantlers. Burners spent 65.2% of their work time in neutral
standing (standing with knees straight) and 32.3% sitting with their hips and knees angles
less than 90°. Dismantlers spent 85.1% of their work time sitting (with their hips and knees
less than 90°) and 13.9% in neutral standing. Figure 1 depicts the proportion of working
time spent by burners and dismantlers in different neck, trunk and upper limb postures.
Burners and dismantlers spent the majority of their work time (81.3% and 99.2%
respectively) in non-neutral neck postures. They mostly worked with their arms/hands below
waist level (99.7% for burners and 99.0% for dismantlers). Whereas dismantlers worked
with their trunk in moderate flexion (79.8% of working time), burners worked most of the
time with their trunks severely flexed (76.4% of work time).
Figure 2 summarizes the proportion of time burners and dismantlers were exposed to various
intensities of force and repetition. Burners were exposed to low force exertions for 96.5% of
their work time, while dismantlers were exposed to high force exertion 66.8% of their work
time. However, burners and dismantlers spent most of their work time (73.5% and 84.8%,
respectively) in high repetition tasks (i.e. > 20 movements/min).
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Duration of exposure to contact stress were higher among dismantlers than burners (87.2%
vs. 5.6% of the work time, respectively). Furthermore, exposure duration to hand vibrations
was higher among dismantlers than burners (i.e., 77.8% vs. 2.9% of the work time,
respectively).
4. DISCUSSION
An observation-based method was developed to quantify for the first time and in real time
ergonomic exposures among unregulated type of work such as e-waste recycling in a
developing country. The inter-observer agreement as well as preliminary data coded in real-
time indicates that our tool appears to provide the information necessary to adequately
quantify exposure.
The inter-observer agreement was high for upper and lower limb postures. This was mainly
because lower limb postures walking, sitting and standing were easy to identify and their
corresponding exposure levels were easy to distinguish. Burners mostly worked in a
standing posture and occasionally would sit in between burning activities or while they were
waiting for dismantlers to bring them items to burn. Among burners, standing with severe
trunk flexion (76.4% of work time) was observed when not actively burning as they spent a
substantial amount of time picking up pieces of metal that had fallen to the ground during
the burning of e-waste. Upper limb activities for both burners and dismantlers were often
performed with the arms below the waist height, which was relatively straightforward for the
observers to identify. However, visual coding of trunk postures in real-time every 60s,
particularly discriminating between neutral and slight trunk flexion, was more challenging
and susceptible to misclassification resulting in lower agreement between observers (i.e.,
89.17% agreement, Kappa = 0.69).
Other common coding errors related to presence/absence of contact stress and vibration,
especially for burners. These risk factors were easily identifiable among dismantlers since
their task were performed with high force intensity (66.8% of the work time) using hammers
and chisels while burners predominantly exerted low forces most of the time (96.5%).
Repetition was difficult to estimate since the observers had little time to count in quick
succession the number of hand movements while also discerning other risk factors within
the 60s-time interval. Thus, the observers during the training period had to get familiar with
visual approaches to quickly and easily estimate these counts effectively. As such, some
disparities occurred between observers resulting in the low level of agreement (Kappa =
0.667, with 92.5% agreement). Finally, judging moderate and severe force was also a
challenge for observers.
5. CONCLUSIONS
The newly developed tool was effective in capturing information in real time the relative
duration and intensity of key risk factors. The method adequately estimates time spent in
postures, exerting forceful and repetitive movements as well as indicating whether contact
stress and vibration were absent or present. The tool is relatively easy to use compared to
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other established observation-based tools that are time consuming to evaluate one risk factor
at a time and require a prolonged training period to achieve high inter-observer reliability.
The developed tool is amenable to unregulated work environments since the “low tech” pen
and paper approach can be used in low-resource settings where funds to purchase portable
computing devices and direct measurement instrumentation may be limited. While
advantageous in low-resource settings, the pen and paper approach made the transfer of
coding into spreadsheets tedious and time-consuming. Future studies could explore the tool
for use on portable hand-held devices (e.g., tablet computers). Although the simplification
into a small number of categories (1–3 maximum) allows real time coding, the tool cannot
be used without some training.
ACKNOWLEDGEMENTS
We acknowledge the dedicated effort of Nyamedo N.A. Yeboah, Emmanuel N.A. Odametey, Gifty N. Konadu,
Nana Adjoa Asare and Priscilla Nyale in the data collection stage of this study.
This work was supported by the West Africa-Michigan Charter II in GEOHealth; jointly funded by the US NIH
Fogarty International Center under Award Number U01 TW010103 and by the Canadian International Development
Research Centre under award number 108121-001. Co-authors CD and BM were supported in part by the training
grant T42-OH008455 from the National Institute for Occupational Safety and Health (NIOSH), Centers for Disease
Control and Prevention (CDC). The views expressed in this publication do not necessarily reflect the official
policies of nor endorsement by NIH, NIOSH, CDC, and/or the Canadian and US Governments.
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Figure 1:
Proportion of time (%) spent by burners (n = 3) and dismantlers (n = 3) in different work
postures.
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Figure 2:
Proportion of time (%) exposed to different intensity levels of force and repetition for
burners (n = 3) and dismantlers (n = 3).
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Table 1:
Inter-observer agreement for two trained observers using 10-minute observations each of 6 workers (i.e., 3 burners and 3 dismantlers).
Variable Burners (n = 30 minutes) Dismantlers (n = 30 minutes) Pooled (n = 60 minutes)
Posture Kappa % agreement Kappa % agreement Kappa % agreement
-Neck 0.760 93.33%
** **
0.782 96.67%
-Trunk 0.687 86.67% 0.257 91.67% 0.695 89.17%
-Upper limbs
** **
0.000 96.67% 0.000 98.33%
-Lower limbs
** ** ** **
1.000 100%
Force 0.754 95.00 0.636 86.67% 0.878 94.17%
Repetition 0.610 85%
** **
0.667 92.50%
Contact stress 0.851 93.33%
** **
0.925 96.67%
Vibration 0.000 96.67% 0.651 96.67% 0.933 96.67%
**
Perfect agreement. All coded values were the same for both observers.
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