A PRELIMINARY ASSESSMENT OF PHYSICAL WORK EXPOSURES AMONG
ELECTRONIC WASTE WORKERS AT AGBOGBLOSHIE, ACCRA GHANA
Authors: Augustine A. Acquah1,*, Clive D'Souza2, Bernard J. Martin2, John Arko-Mensah1, Paul
K. Botwe1, Prudence Tettey1, Duah Dwomoh1, Afua Amoabeng Nti1, Lawrencia Kwarteng1,
Sylvia Takyi1, 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.
* Corresponding Author: Augustine A. Acquah, Department of Biological, Environmental and
Occupational Health Sciences, P.O. Box LG 13, School of Public Health, CHS, University of
Ghana; email: firstname.lastname@example.org
Acquah, A. A., D'Souza, C., Martin, B. J., Arko-Mensah, J., Botwe, P., Tettey, P., Dwomoh, D.,
Nti, A. A., Kwarteng, L., Tyaki, S., Quaki, I., Robins, T., & Fobil, J. N. (2021). A Preliminary
Assessment of Physical Work Exposures among Electronic Waste Workers at Agbogbloshie,
Accra Ghana. International Journal of Industrial Ergonomics, 82 (2021), 103096. DOI:
This study examined physical work exposures associated with informal e-waste recycling
at the largest dumpsite in Africa.
Data on self-reported work activity (n = 163), pedometry (n = 42) and from observations
at the e-waste site were analyzed.
Duration of work-related sitting, standing and walking differed significantly between
collectors, dismantlers, and burners.
E-waste collectors (91%), dismantlers (66%) and burners (94%) reported manual material
handling on ≥5 days per week.
Exposure assessment methods adapted to environmental and socioeconomic conditions of
informal e-waste recycling are needed.
Occupational exposure associated with unstructured, informal e-waste recycling has received very
limited attention. This study aimed to quantify the occupational physical exposures among
informal e-waste workers at the largest e-waste site in Africa.
A cross-sectional field survey of 163 male e-waste workers was conducted using a self-report
occupational physical activity questionnaire, along with direct work observations, and pedometer
estimates of walking activity for a subset of workers (n = 42).
Results indicated significant differences in self-reported 7-day work exposures among the three
main e-waste job categories, namely, collectors (n = 70), dismantlers (n = 73) and burners (n =
20). Prolonged walking, sitting and standing on five or more days in the workweek was frequently
reported by collectors (87%), dismantlers (82%) and burners (60%), respectively. Nearly 90% of
collectors and burners and 60% of dismantlers reported lifting and carrying on five or more days
in the workweek.
The exposure combinations identified suggest a risk for musculoskeletal disorders (MSDs).
Findings call attention to the need for research examining potential associations between physical
exposures and MSDs affecting e-waste workers in Agbogbloshie. The high exposure variability
both between and within workers has implications for future exposure assessments conducted in
unregulated, informal work settings.
Keywords: E-waste; informal recycling; e-waste collection; physical activity exposure; OPAQ;
Electrical and electronic waste (e-waste) poses a new global health challenge (Bakhiyi et al., 2018;
Perkins et al., 2014). Rapid technological advances and high demand for new electronic and
electrical equipment has led to accelerated obsolescence and a shorter lifespan for modern-day
electronic appliances, devices and gadgets (Shamim and Mursheda, 2015). Consequently,
managing and recycling the sheer volume of discarded e-waste has created a global environmental
and occupational health problem. Each year, large volumes of e-waste from Europe and North
America get shipped to developing countries such as Ghana, Nigeria and South Africa (Maphosa
and Maphosa, 2020; Oteng-Ababio, 2012). While a small fraction of these electronics get put to
second-hand use, the majority (estimated over 80%) are either unusable or very close to their end-
of-life and end up in dumpsites (Maphosa and Maphosa, 2020). Subsequently, sustainable
management of these waste in a manner that is environmentally and occupationally safe has
become a challenge (Adanu et al. 2020; Bakhiyi et al., 2018).
E-waste recycling activities in middle- and low-income countries is particularly challenging due
to the lack of appropriate recycling infrastructure and policy (Maphosa and Maphosa, 2020). Safe
work methods and equipment required for efficient extraction of re-usable and/or valuable
constituents from old, discarded electronic appliances and devices are lacking (Zhang and Xu,
2016). The recycling process is almost exclusively manual, informal, unregulated and conducted
by low-skilled workers, with little or no attention to occupational health and safety practices such
as the use of personal protective equipment or properly designed workstations. Manual e-waste
recycling is labour-intensive and has become an emerging global health problem due to the health
risks it presents (Perkins et al., 2014). Prior studies describe manual waste collection as requiring
varying levels of manual material handling (MMH) combined with long periods of sitting and/or
standing in non-neutral postures, and/or walking in unfavourable outdoor environmental
conditions (Emmatty and Panicker, 2019; Kuijer et al., 2010). These forms of physical activities
are likely to have adverse effects on the health of workers (Kwon et al., 2011), and more
particularly when performed under harsh environmental conditions (Kong et al., 2018). Studies
specific to informal e-waste recycling work at worksites in China (Chi et al., 2011), India (Wath
et al., 2011), Brazil (Gutberlet and Baeder, 2008), and Nigeria (Ohajinwa et al., 2018) collectively
suggest diverse socioeconomic realities and work conditions across locations, while highlighting
the pervasive problem of informal e-waste recycling faced by many countries around the world.
1.1. Background on E-waste Recycling at Agbogbloshie, Ghana.
Over the last 20 years, Agbogbloshie in Accra, Ghana, has become one of the largest dumping
grounds for electronic waste in the world (Heacock et al., 2016), making it one of the most polluted
places on earth (Bernhardt and Gysi, 2013; Oteng-Ababio, 2012). E-waste workers at this site are
among the poorest and the most vulnerable members of the urban populations in Ghana (Akormedi
et al., 2013; Amankwaa, 2013). The site is occupied mainly by migrants from the northern part of
Ghana. Among them are farmers who have travelled to Southern Ghana including Agbogbloshie
to look for alternate sources of income. The primary goal of e-waste recycling at Agbogbloshie is
to recover valuable scrap metals such as copper, gold, iron and aluminium for sale (Acquah et al.,
2019; Akormedi et al., 2013). The major processes consist of scavenging and collecting e-waste
items, then manually dismantling irreparable and/or non-functional items, and finally open-air
burning of insulated wires and other components that cannot be dismantled in order to extract
valuable metals. Engaging in informal e-waste recycling at Agbogbloshie is known to adversely
affect the health of workers due to high exposure to toxic chemicals (Basu et al., 2016; Feldt et al.,
2014; Srigboh et al., 2016; Wittsiepe et al., 2015), elevated noise (Akormedi et al., 2013; Burns et
al., 2016; Carlson and Krystin, 2016), and harsh environmental conditions (Akormedi et al., 2013;
Burns et al., 2016; Yu et al., 2017).
Prior studies on the physical work conditions and work-related physical activity exposures of e-
waste workers at Agbogbloshie have only provided qualitative interview-based descriptions
(Acquah et al., 2019; Akormedi et al., 2013; Yu et al., 2017). These studies offer evocative
summaries of the physical work conditions and various work-related illnesses and injuries. For
example, a recent qualitative study by Acquah et al., (2019) reported both acute injuries (e.g.,
burns, lacerations) and chronic health issues such as musculoskeletal pains, coughs, and headaches
across the three main categories of e-waste workers: collectors, dismantlers and burners. The
adverse work-related health effects were compounded by the lack of use of personal protective
equipment, low level of health risk awareness, and by evidence of psychosocial stressors
associated with e-waste recycling. The latter includes psychological demands, poor social support,
low income, and limited opportunities for other types of gainful employment (Acquah et al., 2019;
Akormedi et al., 2013; Yu et al., 2017). However, reliable quantitative data on the nature of the
physical exposures at the Agbogbloshie work site is lacking. Hence, we undertook an initial step
to quantifying these physical work exposures in order to understand the magnitude of the risk of
developing work-related musculoskeletal disorders (MSDs) towards the longer-term goal of
developing appropriate ergonomic interventions adapted to the local context.
1.2 Occupational Exposure Assessment in Informal Work
Ergonomics studies on occupational physical exposures among low-skilled workers who engage
in non-structured, informal work such as waste collection and sorting is relatively scarce (Emmatty
and Panicker, 2019; Todd, 2009). To date, most studies on occupational exposures to injury risk
factors have largely focused on formal industrial settings such as manufacturing (Bao et al., 2015;
Dickerson et al., 2018; Lavender et al., 1999; Marshall et al., 2000; Mossa et al., 2016; Silverstein
et al., 1987), construction (Buchholz et al., 1996; Parida and Ray, 2012), agriculture (Kong et al.,
2018) healthcare (Czuba et al., 2012; Janowitz et al., 2006; Punnett and Bergqvist, 1999; Robertson
et al., 2012; Stucke and Menzel, 2007) or office work (Armstrong et al., 1994; Wærsted et al.,
2010), to cite only a few. The main focus of these physical work exposure assessments include
quantifying the magnitude of the exposure (e.g., intensity of force exertions, weight of loads
carried, non-neutral postures), the repetitions involved, and the duration of the exposure (Andreas
and Grooten, 2018; Chiasson et al., 2012; Li and Buckle, 1999; Takala et al., 2010). These physical
exposures are predisposing factors to the development of work-related MSDs (Eatough et al.,
2012; Kuorinka et al., 1995; Marras, 2008) and potentially interact with a range of other
organisational, psychosocial and individual factors (Bongers et al., 2002; Kuorinka et al., 1995;
Oakman et al., 2014, Bongers et al., 2002; Jaffar et al., 2011) to cause or aggravate MSDs.
A critical aspect of selecting a suitable method for physical exposure assessment is the structure
or regularity of the job content. Structured work environments such as manufacturing assembly
lines are more straightforward to characterize based on a limited amount of exposure data. In
contrast, exposure assessment in non-repetitive manual work (i.e., where the intensity, repetitions,
and duration of the tasks vary over time and between workers) is challenging because the
assessments may need to be performed across multiple workers and over long durations in order
to develop a representative profile of the exposure. Thus, assessment of physical work exposures
in informal and unstructured work settings often require a preliminary job analysis (Acquah et al.,
2019) and use of multiple measurement methods in order to understand their variability (Neitzel
et al., 2013). Methods for quantifying physical work exposures include self-reported
questionnaires, observational methods, direct measurements (Burdorf and van der Beek, 1999; Li
and Buckle, 1999) and biomechanical analyses involving specific tools such as the NIOSH lifting
guide or the University of Michigan’s 3D Static Strength Prediction Program (Chaffin et al., 2006).
Direct measurements and biomechanical analyses provide more accurate results; however, these
methods tend to be expensive (Juul-Kristensen et al., 2001) and their application to non-repetitive
work settings can be challenging (Chaffin et al., 2006). Compared to direct measurements,
assessments relying on observational methods and self-reported questionnaires are more cost-
effective and easier to implement in applied settings, and thus are still widely used despite their
subjective nature and lower accuracy and precision (Takala et al., 2010).
1.3 Study Aims
The primary aim of this study was to quantify the occupational physical activity (OPA) exposures
of e-waste workers engaged in informal e-waste recycling at Agbogbloshie, and to compare these
exposures among the three main e-waste job categories, namely, collectors, dismantlers and
2.1. Study Site and Population
The Agbogbloshie e-waste site is about 0.5km2 (Laskaris et al., 2019). It is located close to the
central business district of Accra (Oteng-Ababio, 2012) near the Agbogbloshie food market, and
along the banks of the Korle lagoon and Odaw river (Davis et al., 2019). The workforce largely
consists of itinerant workers who collect scrap metal and e-waste items, dismantle items to extract
valuable constituents and burn items that cannot be dismantled (Davis et al., 2019). The workers
are almost exclusively males, primarily young adults and occasionally minors (< 18 years old).
2.2. Study Design
A cross-sectional study involving the use of a questionnaire, supplementary field observations,
and pedometry measurements was conducted between August to October 2018. Direct field
observations were conducted on random days throughout the study period in order to contextualize
and supplement the data obtained from questionnaires and direct field measurements. E-waste
workers were recruited for the study by word of mouth. They were recruited from different
locations of the site in an attempt to obtain a diverse sample in terms of job category (i.e. collecting,
dismantling and burning). Following a verbal description of the study objectives and methods, a
self-selected sample of 163 male e-waste workers agreed to participate in the study. Written
informed consent was obtained from all adult participants. For minors, written informed consent
was obtained from the adult relatives they work with or their immediate adult work supervisors.
These work supervisors typically served as guardians for the minors while they were at the e-waste
The study was approved by the College of Health Sciences Ethical Review Committee at the
University of Ghana, Accra. Participants were compensated 30 Ghana Cedis (approximately 5.26
US dollars) after data collection was completed.
2.3. Assessment Tools
2.3.1. Demographic Questionnaire: A brief questionnaire was developed to obtain information
about demographic characteristics (e.g., age, gender), primary job category, main work activities
performed, and work history of participants (e.g. number of years and/or months having worked
in e-waste recycling, average number of days and hours of work per week). A team of 5 researchers
serving as interviewers fluent in English as well as Dagbani, which is the local dialect spoken by
e-waste workers, administered the questionnaire and recorded participant responses on paper. In
addition to the questionnaire, participants’ weight, stature, and stride length were measured and
used later to calibrate the pedometers used in the measurement of cumulative step counts. Stride
length was computed using procedures recommended by the device manufacturer, namely, by
measuring the total distance walked for 10 steps (i.e., measured from the heel of the foot taking
the first step to the toe of the foot taking the last step) and dividing the distance by 10 (Omron,
2.3.2. Modified Occupational Physical Activity Questionnaire (OPAQ): The OPAQ (Reis et
al., 2005), which is a seven-item survey questionnaire, was used to collect information on the
average time spent per week in work-related sitting or standing, walking, and in performing heavy
labour activities such as lifting, carrying, pushing and pulling. For the purposes of this study, we
collectively refer to lifting, carrying, pulling and pushing activities as MMH in place of the less
common term ‘heavy labour activities’ used in the OPAQ. Modelled on questions from the OPAQ
and Quick Exposure Checklist (QEC; Stanton et al., 2004), participants were also asked to indicate
the maximum weight handled in the workweek during MMH on an ordinal scale as either light
(5kg or less), moderate (6-10kg), heavy (11-20kg), or very heavy (>20kg). Participants were
considered quite adept at estimating weight since they often weigh e-waste items to determine its
financial value (i.e., price when buying or selling) as part of their typical workflow. A pilot study
was conducted in December 2017 to determine the feasibility of using the OPAQ, specifically, to
determine whether participants understood the questions and could respond appropriately to obtain
the desired information. Based on the results and feedback from 15 e-waste workers, three
modifications to the OPAQ were implemented. First, since workers indicated that their estimation
of the proportion of time spent sitting, standing and walking or performing MMH activities was
not reliable (poor estimation of exact time), the questionnaire was modified to obtain a binary
response (Yes/No) to whether each activity was performed for (1) at least 1 hour continuously
during the workday, and (2) a total of 4 hours in a workday. Second, the frequencies of work-
related standing and walking were assessed separately as opposed to the original OPAQ which
assesses standing and walking together. Third, the assessment of MMH activities (labelled “heavy
labour” in the original OPAQ), which form the core component of e-waste recycling, was assessed
by asking participants to rate on an ordinal scale how often they performed lifting, carrying, and
pushing and/or pulling activities within a workweek. The modified version of the OPAQ
(Appendix-A) was administered to the participants, their verbal responses were documented on
the paper questionnaire by the researcher, and subsequently coded into Stata V15 for analysis.
When responding to the questionnaire, participants were instructed to use the full workweek prior
to when the questionnaire was presented as the reference period (i.e., a 7-day period starting
2.3.3. Pedometer measurements: From the 163 participants, a random subset of 47 participants
were provided a waist belt instrumented with a pedometer (Omron HJ-321) at the start of their
workday and collected at the end of the workday for two consecutive workdays randomly selected
in the study period. The average readings from both workdays were used in the analysis. Ten
pedometers were acquired for the study; however, 5 pedometers were not returned by participants
early in the study. Thus, data collection was limited to using 5 pedometers. This also caused a
decrease in sample size from 47 down to 42 of 163 participants equipped with a pedometer on two
consecutive workdays. Overall, pedometry data obtained from 9 collectors, 27 burners and 6
dismantlers were used in data analysis. The pedometers provided data about total steps count,
aerobic step counts, average steps per minute, distance walked, and energy expenditure
(kilocalories; kcal) over an entire workday. Aerobic steps were recorded whenever a minimum of
60 steps were taken within a 10-minute period (Duchečková and Forejt, 2014). The Omron
pedometer has been validated against other well-known physical activity monitors (Battenberg et
al., 2017; Kendall et al., 2019) and has an accuracy greater than 90% for measuring step counts
(Battenberg et al., 2017). This specific pedometer model has been used widely in other studies that
monitored physical activity (Olzenak & Byrne, 2017; Owoeye et al., 2016; Sampaio et al., 2016;
Yusoff et al., 2018).
2.4. Statistical Analysis
All measures were analysed using the Stata V15 software package (StataCorp LLC, TX).
Descriptive statistics were used to summarise demographic variables and the proportion of workers
who performed different types of OPA. Separate one-way ANOVA tests were used to examine
statistical differences in age, years on the job, days worked per week, and hours worked per day
across the three primary job categories (i.e., collectors, dismantlers, and burners). Separate Chi-
square (χ2) tests were conducted to examine differences in the proportion of participants by primary
job category that reported long durations of sitting, standing, and walking postures, frequent MMH
activities of lifting, carrying, pushing and/or pulling, and maximum load handled, which were
assessed on ordinal rating scales. Significant main effects of job category were further analysed
using Chi-square pairwise tests with a Bonferroni adjustment for multiple comparisons (p < 0.05).
Pedometer measurements (total and aerobic step counts, steps per minute, distance walked, and
energy expenditure) were not normally distributed, and thus medians and interquartile ranges
(IQR) were reported in order to reduce the influence of data outliers. Separate non-parametric
Kruskal-Wallis tests were used to examine significant differences across the three e-waste job
categories for each of the five pedometer measures. Qualitative data obtained from direct
observations were used to supplement and contextualize the quantitative results where possible.
3.1. Participant Demographics
The age of participants ranged from 11 years to 43 years. Of the 163 participants, 25 (15.3%) were
minors while 6 (3.6%) did not know their age. The majority of participants (63.1%) were in the
18-29 years age range. Only 6 participants (3.8%) out of those knowing their age were older than
40 years. Work experience represented as the number of years worked in e-waste recycling ranged
from 1 week to 25 years with an average ± standard deviation (SD) of 6.48 ± 5.44 years.
Participants worked for at least 2 days in a week but the majority reported working at least 6 days
(88%) or 7 days (54%) per week. The mean ± SD reported work duration per day was 9.95 ± 2.43
hours and ranged from 2 hours to 14 hours depending on the availability of work and the type of
e-waste recycling activity performed. Direct field observation data revealed an average ± SD work
duration of 6.26 ± 2.63 hours per day with a range of 2 to 12 hours per day with intermittent breaks
that varied considerably from 10 minutes to about 4 hours such as when waiting for recycling
products from other e-waste workers (i.e., from collectors to dismantlers, or from dismantlers to
Based on the primary job performed, 70 (42.9%) of the participants were categorized as collectors,
73 (44.8%) as dismantlers, and 20 (12.3%) as burners. A few of these participants also reported
performing tasks associated with a secondary job category. For instance, 9 collectors (5.5%)
engaged in occasional dismantling of e-waste, 3 collectors (1.8%) and 2 dismantlers (1.2%) also
reported performing burning-related tasks. Due to the small number of such cases, only the primary
job category was used in the subsequent statistical data analysis.
Table 1 summarises the age distribution, the number of years of experience in e-waste work,
number of workdays per week and hours of work per day stratified by the three e-waste job
categories. Collectors had the broadest age range (11 - 43 years). Notably, all the minors in the
study were collectors. The average age differed significantly by job category (p = 0.004). The
mean age was significantly higher for dismantlers (26.7 ± 6.6 years) compared to collectors (23.4
± 6.2; p = 0.007) and burners (22.8 ± 3.9; p = 0.055). Years of work experience in e-waste recycling
also differed significantly by the primary job category (p = 0.013). Years of e-waste work
experience was significantly higher among dismantlers (7.9 ± 5.4) compared to collectors (5.3 ±
5.7; p = 0.014) and burners (5.5 ± 3.2; p = 0.176). There was no significant difference in the
average number of workdays per week (p = 0.292) or average hours worked per day (p = 0.277)
among the three categories of e-waste workers (Table 1).
Table 1 – Participant primary job category and demographic characteristics
Mean ± SD
Age in years
11 - 43
23.4 ± 6.2
F = 5.77, p = 0.004 *
18 - 42
26.7 ± 6.6
18 - 30
22.8 ± 3.9
Years of work
at e-waste site
0.02 (1wk) - 22
5.3 ± 5.7
F = 4.45, p = 0.013 *
1 - 25
7.9 ± 5.4
1 - 13
5.5 ± 3.2
4 - 7
6.3 ± 0.7
F = 1.24, p = 0.292
2 - 7
6.1 ± 1.0
2 - 7
6.0 ± 1.1
No. of working
hours per day
6 - 14
10.3 ± 1.8
F = 1.30, p = 0.277
4 - 14
9.8 ± 2.9
2 - 12
9.4 ± 2.7
* indicates significance at p < 0.05
3.2. Occupational Physical Exposures of E-waste Workers
3.2.1. Sitting, standing and walking
Direct field observations indicated that all of the participants performed their work while either
sitting, standing or walking during the workday. The corresponding proportions are detailed after
the following contextual description to contrast with what is usually assumed in regulated
industrial work. Dismantlers were observed usually sitting on a very low stool or a dismantled
appliance such as an old cathode ray tube television or microwave oven. Hence, sitting was more
of a squatting posture such that the included angles at the knees and hips were less than 90 degrees.
Observations also revealed a diverse range of sitting durations between dismantlers. When not
walking to search for and collect items, the collectors’ mode of sitting varied widely. It involved
sitting on their collection cart that corresponded to a sit-stand posture with the included angle at
the hips and knees exceeding 90 degrees. Collectors also occasionally sat on the ground or on a
piece of log or rock along the route travelled in search of e-waste. A considerable amount of sitting
was observed during workers idle time. Dismantlers and burners took rest breaks seated under a
shed after they exhausted their stock of items to dismantle or burn and waited for supplies.
Collectors were also observed sitting to rest at random intervals and for varying durations while in
search of e-waste in nearby communities. For all workers, standing and walking were done on
uneven surfaces. These surfaces were soft and muddy during rainy periods or hard and bumpy
during the dry season. Walking performed by dismantlers was primarily for transporting insulated
components, cables, and wires to the burners for the extraction of metal, e.g., gold, copper,
aluminium. Due to toxic fumes generated during open-air burning this task was done at short
distances away from the sites where dismantling and other ancillary tasks of weighing and selling
of e-waste products were performed (Laskaris et al., 2019; Nti et al., 2020; Takyi et al., 2020). In
addition to burning insulated copper wire for dismantlers at a fee, some burners also spent time in
extreme torso flexion picking and gathering leftover pieces of metal scrap littered across the
burning sites for sale. This was observed more commonly among entry-level burners.
Table 2 summarizes the proportion of collectors, dismantlers and burners who reported engaging
in sitting, standing or walking for 1 hour or more continuously in a workday, and separately for a
total of 4 hours or more in the workday during the previous workweek. Sitting continuously for ≥
1 hour in a workday was reported mostly by dismantlers (91.8%) while standing continuously for
≥ 1 hour during a workday was reported mostly by burners (95%). Chi-square test of proportions
indicated no statistically significant differences in the proportion of participants across the three
job categories who reported sitting (p = 0.119) or standing (p = 0.070) continuously for ≥ 1 hour
in a workday. However, the proportion of participants who reported ≥ 1 hour of continuous
walking differed significantly (p = 0.018) across job categories. Post hoc comparisons revealed
that the proportion of participants reporting continuous walking ≥ 1 hour in a workday was
significantly higher for collectors (92.9%) compared to dismantlers (78.1%; p = 0.038) but not
significantly different from burners (75.0%).
The proportion of participants that spent ≥ 4 hours per workday sitting, standing and walking
differed significantly by job category (p < 0.001) for each of the three postures (Table 2). The
proportion of participants that reported sitting for ≥ 4 hours was significantly higher for both
dismantlers (82.2%) and burners (65.0%) compared to collectors (30.0%; p < 0.001 and p = 0.013,
respectively). The difference in proportions between dismantlers and burners was not statistically
significant (p = 0.204). The proportion of participants that reported standing for ≥ 4 hours was also
significantly higher among dismantlers (49.3%) and burners (60.0%) compared to collectors
(18.6%; p < 0.001 and p = 0.001, respectively). In contrast, walking for a total of ≥ 4 hours was
reported at a higher proportion by collectors (88.5%) compared to both dismantlers (54.8%; p <
0.001) and burners (65%; p = 0.038). Differences in proportions between dismantlers and burners
for both prolonged standing and walking were not statistically significant (p > 0.05).
Table 2: Proportion of e-waste workers stratified by job category engaged in work-related
sitting, standing and walking activities.
Job Category (n)
Proportion of participants
Chi-square statistic and
for 1 hour during a
χ2 = 4.487, p = 0.106,
Fisher’s exact p = 0.119
for 1 hour during a
χ2 = 4.881, p = 0.087,
Fisher’s exact p = 0.070
for 1 hour during a
χ2 = 7.211, p = 0.027,
Fisher’s exact p = 0.018*
Sitting for a total of
4 hours during a day’s
χ2 = 40.376, p = 0.001,
Fisher’s exact p = 0.001*
Standing for a total of
4 hours during a
χ2 = 19.384, p = 0.001,
Fisher’s exact p = 0.001*
Walking for a total of
4 hours during a
χ2 = 19.961, p = 0.001,
Fisher’s exact p = 0.001*
* indicates significant main effects at p < 0.05
3.2.2. Manual Material Handling Activities
Manual material handling activities identified by direct observations included lifting and carrying
of loads as well as pushing and pulling hand-drawn carts used for transporting e-waste.
Descriptions of observations are followed by quantitative results. The frequency and magnitude
of the load handled differed between tasks and job categories. Loads handled by collectors
included the force to tow or move the hand-drawn collection cart, which is a function of the design
of the cart used, the load on the cart, and the terrain (Jung et al., 2005). The weight of the cart and
the items collected varied from day to day as a function of items identified for recycling. Collectors
also lifted and carried items when loading and unloading the cart. All 25 (15.3%) minors in the
study self-identified as collectors. These minors were observed walking behind their supervisors,
typically an older or senior worker, pushing the hand-drawn cart from the rear as the senior worker
pulled the cart from the front. It is typical for younger entry-level workers arriving at the e-waste
site to assist other workers until they gained some experience and accumulated enough capital
prior to working independently. Loads handled by dismantlers mainly consisted of the weight of
items being dismantled and occasionally the weight of the wheelbarrow used to convey items such
as insulated metal components and wires to the burning site for metal recovery. Manual
dismantling also involved repetitive forceful exertions in non-neutral postures using tools such as
hammers, chisel and screwdrivers. The load handled by burners was mainly from the weight of
components (e.g., insulated cables, wires) being burnt. Lifting and carrying among burners
involved using a long metal rod to lift and flip wires/cables during burning. This was usually done
with the trunk in slight flexion. Occasionally, burners would lift, carry and lower items from a
wheelbarrow onto the ground for burning and this involved moderate to severe forward flexion of
the trunk for short intervals.
Table 3 summarizes the proportion of participants by job category based on their self-reported
frequency of performing the three different MMH activities, namely, lifting, carrying, and
pushing-pulling in the previous workweek. Chi-square test of proportions indicated statistically
significant differences across job categories for each of the three MMH activities (p < 0.001). In
term of lifting activities, a high proportion of collectors (91.2%) and burners (94.1%) reported
performing lifts on ≥ 5 days in a week compared to dismantlers (65.8%), with the difference
between collectors and dismantlers being statistically significant (p = 0.001). In contrast, the
proportion of dismantlers that reported lifting activities 3-4 times a week (24.7%) was significantly
higher than collectors (4.4%; p = 0.001). Carrying activities on ≥ 5 days per week were performed
mostly by collectors (91.2%) compared to burners (82.4%) and dismantlers (62.5%), with the
difference between collectors and dismantlers being statistically significant (p < 0.001). However,
the proportion of participants that reported carrying on 3-4 days a weeks was significantly higher
among dismantlers (22.5%) compared to collectors (2.9%; p = 0.001). Pushing and/or pulling of a
hand-drawn cart on ≥ 5 days per week was significantly more frequent among collectors (68.2%)
compared to dismantlers (26.4%; p < 0.001) and burners (26.3%; p = 0.003). The proportion of
burners that reported infrequent or no pushing/pulling activities (63.2%) was significantly higher
than the proportion of collectors (28.8%; p = 0.018).
Table 3: Self-reported frequency of manual material handling activities related to e-waste
work performed by participants and stratified by job category within a workweek.
≥ 5 days
χ2 = 20.524,
p = 0.002,
p = 0.001*
χ2 = 26.656,
p < 0.001,
p < 0.001*
χ2 = 37.053,
p < 0.001,
p < 0.001*
* indicates significant main effects at p < 0.05
Table 4 summarizes the number and proportion of participants stratified by their self-reported level
of maximum weight handled during the prior workweek coded on an ordinal scale from light (≤5
kg) to very heavy (>20 kg). Over 86% of all study participants reported handling an object
weighing heavier than 20 kg (i.e., labelled ‘Very heavy’). Results from the Chi-square test of
proportions indicated a statistically significant difference among job categories in the level of
maximum weight handled (p = 0.011). The proportion of participants that reported handling
weights heavier than 20 kg was significantly higher for collectors (95.6%) compared to dismantlers
(81.9%, p = 0.034) and burners (68.4%, p = 0.002). The proportion of participants that reported
handling moderate weights between 6 – 10 kg was significantly higher for burners (15.8%)
compared to collectors (1.5%, p = 0.025). None of the other paired comparisons within weight
category were statistically significant.
Table 4: Number and proportion of participants reporting different categories of maximum
weight handled within a workweek, stratified by job category.
Job Category (n)
Maximum weight handled, n (%)
statistic & p-value
(< 5 kg)
(6 – 10 kg)
(10 – 20 kg)
(> 20 kg)
χ2 = 15.686, p =
exact p = 0.011*
* indicates significant main effects at p < 0.05
3.2.3. Pedometer Measurements
Table 5 summarizes the median and IQR values for regular steps, aerobic steps, steps per minute,
distance covered and energy expenditure (kcal) among the subset of collectors, dismantlers and
burners. Kruskal-Wallis tests indicated no statistically significant differences across the three job
categories for any of the five pedometer measures (p > 0.05). The small sample sizes for collectors
(n = 9) and burners (n = 6) may have contributed to reduced statistical power in detecting any
differences. The median number of regular and aerobic steps were slightly higher for collectors
than burners and dismantlers (Table 5), while the median aerobic step count was lowest (zero) for
dismantlers. The median distance walked by participants in each category was 5.4 km for
collectors, 4.9 km for burners and 3.2 km for dismantlers, respectively. The median calorie
expenditure from walking was marginally higher for burners (190 kcal) compared to collectors
(170 kcal) and dismantlers (122 kcal), however the small sample sizes for collectors and burners
make their respective median estimates relatively unstable.
Table 5: Pedometer measurements obtained from a sub-set of study participants (n = 42)
averaged over 2 workdays and stratified by job category.
χ2 = 3.688,
p = 0.158
χ2 = 3.526,
p = 0.172
χ2 = 2.859,
p = 0.240
χ2 = 4.049,
p = 0.132
χ2 = 3.334,
p = 0.189
This study quantified the OPA exposures of e-waste workers at Agbogbloshie with an emphasis
on differences among the three main categories of workers, namely, collectors, dismantlers, and
burners. Notably, the results indicated that the type and level of self-reported exposures vary
substantially between and within worker categories. Despite its preliminary and cross-sectional
nature, this study is only one of few conducted at Agbogbloshie, Ghana that focuses on the physical
demands of informal e-waste recycling. Prior exposure studies were mostly descriptive accounts
(Acquah et al., 2019; Akormedi et al., 2013; Yu et al., 2017), which although insightful, failed to
provide meaningful quantitative information about the source and magnitude of physical activity
exposures. The latter is necessary to enable comparisons with other work settings and/or other
informal e-waste recycling sites in developing countries, to estimate the potential for developing
MSDs, and to guide the design and implementation of tailored ergonomics interventions (e.g.,
engineering controls, health and safety policy, worker training). More broadly, the study adds to
the growing knowledge-base documenting exposure of e-waste workers at Agbogbloshie to
various occupational hazards such as toxic chemicals, air pollutants, and noise (Akormedi et al.,
2013; Basu et al., 2016; Srigboh et al., 2016; Wittsiepe et al., 2015; Yu et al., 2017).
Our results corroborate qualitative findings by Akormedi et al. (2013) and Yu et al. (2017) about
e-waste worker characteristics, the challenging work environment, and the physically strenuous
work conditions prevailing at Agbogbloshie. We discuss the ergonomics implications of these
4.1. E-waste Workers
Participants in this study were mostly collectors (43%) and dismantlers (45%), while burners
comprised only 12% of the study sample. The latter may have reduced statistical power in pair-
wise comparisons of proportions involving burners for some of the exposure variables, e.g.,
between collectors and burners for continuous walking ≥ 1 hour. Differences in job content and
financial prospects may explain the smaller number of burners at Agbogbloshie and in our study
sample. Akormedi et al. (2013) reported that burners earned substantially less income per day
compared to collectors and dismantlers (i.e., USD 16 compared to USD 26 and USD 52
respectively, on a good day). The reliance on dismantlers to provide items for burning and the high
exposure to smoke and toxic fumes from open air burning made this task less appealing to workers
(Acquah et al., 2019; Akormedi et al., 2013). Workers at this site did not have assigned roles or
designations. On few occasions, participants reported performing e-waste recycling activities other
than their primary job. For example, a dismantler who did not have enough items to dismantle may
temporarily assume the role of collector and walk/travel into the community in search of more e-
waste items to dismantle. Likewise, a burner who did not have items to burn may assume a
dismantling role by helping other dismantlers to disassemble their items for a small fee or in
exchange for some of the extracted metal (e.g., copper wires). Since such secondary activities were
infrequent and with limited impact on exposure estimates in the present context, these were not
distinguished or differentiated in the present study. However, the potential for fluid roles wherein
worker change their main activities (and hence related exposures) on their own over time may have
implications for prospective longitudinal studies.
Our study also confirmed prior reports that e-waste recycling at Agbogbloshie is performed mainly
by men (Akormedi et al., 2013). The absence of women has been attributed to the high physical
demands of manual e-waste recycling and a preference for less strenuous supportive roles such as
vending food and water to workers (Ahlvin, 2012). The worker population at this site was also
relatively young (mean age of 24.8 years) and included 25 minors (15.3%). All minors in the study
sample were collectors and assisted older workers in scavenging and gathering e-waste items. The
substantial proportion of minors working at the site is particularly concerning since Ghana is a
signatory to multiple international instruments that prohibit child labour (UNICEF, 2019). Our
finding suggests broader concerns about poorly enforced legislation and policies prohibiting child
labour. UNICEF-Ghana estimates that about 21% of children in Ghana aged between 5 to17 years
were involved in child labour and 14% were engaged in hazardous forms of labour (UNICEF,
2019). However, the problem of child labour is not unique to Agbogbloshie but plagues waste
picking/collecting in many developing countries around the world (ILO, 2004).
The experience level of workers recruited for this study varied from as little as 7 days to as high
as 22 years. Experience in e-waste recycling work for the study cohort was least among collectors
(5.3 ± 5.7 years) and highest among dismantlers (7.9 ± 5.4 years). Prior studies suggest that low-
skilled migrants from northern Ghana seeking a means of livelihood were often drawn to e-waste
recycling at Agbogbloshie and often start out as collectors (Akormedi et al., 2013). These
collectors overtime progress to more technical and lucrative roles of e-waste recycling such as
dismantling. Collecting of e-waste requires extensive walking (e.g., over 75% of collectors
performed over 10,000 steps daily) with a low prospect of obtaining e-waste items on any day, as
observed in this study.
E-waste recycling was performed every day of the week. Most participants worked six days per
week and rested either on Fridays or Sundays. Akormedi et al. (2013) suggested that rest days may
correspond to workers religious affiliation with Muslims more likely to take Fridays off while
Christians took Sundays off. Furthermore, e-waste workers reported to spend between 2 and 14
hours a day performing various recycling tasks, with a computed average of about 9 hours per day.
This finding corroborates prior reports of a typical 10-12 hours of work per day among workers at
Agbogbloshie (Akormedi et al. (2013). Variability in the number of hours and work distribution
can be explained by our observations. We noticed that the workday duration depended on the
recycling task being performed and the availability of work to be done. For example, a dismantler
who had few items to dismantle, could complete the task in about two hours and would be idle for
the rest of the day until a new batch of items were available for dismantling from the collectors.
On the other hand, collectors would spend longer time wandering in search of e-waste. Burners
were also engaged in less active work times as burning of a pile of copper wires took under 20
minutes and required occasional manipulation during burning despite continuous standing and
stepping/walking. Some burners kept busy by gathering potentially valuable metal scrap and
remnants littered about the burning sites. Overall, the lack of temporal structure and definitive
roles presents methodological challenges when comparing exposures across time or between work
sites and work domains (e.g., manufacturing, office work).
4.2. Occupational Physical Exposures
Participants’ exposures to sitting, standing, walking and MMH activities differed substantially by
the primary job category. Particularly concerning were the high proportion of workers that reported
exposure durations ≥4 hours in the workday (e.g., prolonged sitting, standing, walking) and
frequent exposure to lifting and carrying on ≥ 5 days per week. The health implications of these
specific combinations of exposures and job category are discussed in relation to prior research.
However, we advise caution for direct comparisons with activity questionnaires from other work
settings since most studies base their exposures on an 8-hour workday and/or a 5-day workweek
(e.g. Reis et al. 2005).
4.2.1. Sitting: Prolonged sitting was most frequent among dismantlers (Table 2), who assumed
largely deviated postures from sitting on a low stool or non-functional appliance, with excessive
forward flexion and twisting of the trunk while performing their task. Prolonged sitting
(Hoogendoorn et al., 1999) and in non-neutral postures (Roffey et al., 2010a) have been associated
with chronic low back pain. Biomechanical studies have also shown possible adverse effects on
spinal structures from sustained non-neutral trunk postures (Chaffin et al., 2006). Sitting for long
periods at work changes the activation patterns of a number of weight-bearing muscles, which in
the long term affects the curvature of the back resulting in back pain (Beach et al., 2005; Callaghan
& McGill, 2001). Prolonged sitting is also associated with lower bone mineral density due to a
limited physical stress (see Chaffin et al. 2006) and osteoporosis (Kolbe-Alexander et al., 2004).
Thus, prolonged sitting among e-waste dismantlers could increase their risk of work-related low
back pain and disorders, as suggested by the high prevalence of low back disorders among informal
waste collection and processing workers (Emmatty and Panicker, 2019; Ohajinwa et al., 2018).
4.2.2. Standing: Standing at work may be advantageous to the worker in that it provides a large
degree of freedom and ensures a wide range of mobility in the lower limb thereby increasing
efficiency and productivity (Halim and Rahman Omar, 2011). That notwithstanding, prolonged
standing may also lead to muscle fatigue and discomfort (Garcia et al., 2020, 2018, 2016, 2015),
chronic venous insufficiency and other occupational injuries (Garcia et al., 2020, 2018, 2016,
2015; Lafond et al., 2009; Madeleine et al., 1997; Tomei et al., 1999). E-waste recycling activities
such as burning involve a considerable amount of standing (see Table 2) and significant
associations between prolonged standing and work-related low back, lower leg and shoulder MSDs
have been pointed out (Chandrasakaran et al., 2003; Musa et al., 2000). Prolonged standing
transfers the load of the upper body to the lower parts resulting in low back pain (Halim and
Rahman Omar, 2011) and pain in the feet (Messing and Kilbom, 2001). Furthermore, standing
during burning of e-waste is compounded with forward flexion and twisting of the trunk pose
further harm to the low back. Bending and twisting of the spine during work is associated with low
back pain (Chaffin et al., 2006; Marras, 2008; Marras et al., 1998; Wai et al., 2010a).
Reducing the time spent in standing could help decrease the risk of adverse health effect. For
example, the use of cable strippers to extract copper from insulated wires instead of burning could
reduce work-related standing in addition to alleviating some of the health risks to burners from
smoke inhalation and environmental contamination from open-air burning. However, such
interventions had limited success at Agbogbloshie (Adanu et al., 2020; Little, 2016) for
sociocultural reasons that fall outside the scope of the present study.
4.2.3. Walking: Prolonged walking (i.e., walking for hours without taking a break) increases
energy expenditure (Patton et al., 1991) and induces fatigue (Morrison et al., 2016; Pinto Pereira
and Gonçalves, 2011; Walsh et al., 2018; Yoshino et al., 2004) which may increase the risk of
physical discomfort, pain and MSDs. The associated risks may be exacerbated by walking over
uneven, unpaved terrain, in a harsh outdoor environment including hot and humid climate, and/or
with poor air quality due to toxic fumes from open burning and sewage (Akormedi et al., 2013;
Yu et al., 2017). Prolonged walking was most prominent for collectors (88.5%). Numerous studies
have reported an association between prolonged walking and low back pain (Garcia et al., 2016;
Roffey et al., 2010). Thus, e-waste recycling activities that involve long hours of walking such as
when collecting e-waste is likely to pose serious health risks to workers. Walking for prolonged
periods subjects the spine to prolonged biomechanical loading with detrimental effects on spinal
structures (Callaghan and McGill, 2001). Furthermore, collectors often walk these long distances
while pulling a hand-drawn cart which exerts additional compressive and shear stresses on the
spine and the shoulder as predicted by biomechanical analyses (see Chaffin et al. 2006 for review).
Appropriate measures to reduce the adverse effects of these health risks are necessary.
Pedometry generally provides more accurate and reliable estimates of physical activity exposures
than self-report questionnaires (Sitthipornvorakul et al., 2014; Takala et al., 2010). However, direct
measurements are challenging in non-repetitive work settings such as the unregulated, informal e-
waste recycling work investigated in this study. Furthermore, statistical comparisons between self-
reported walking and pedometer measurements were not considered meaningful here since the
former assessed the perceived frequency of continuous walking during the workweek preceding
the administration of the questionnaire, while the pedometers measured the effective walking on
2 consecutive but randomly selected workdays of the study. The high day-to-day variability in
physical activity exposure discussed above further diminishes the validity of direct comparisons
between the pedometry and self-reported data. For instance, some workers were observed
performing secondary e-waste recycling tasks that differ from their primary job during periods of
low work volume.
Non-parametric statistical analysis indicated no significant differences between pedometer
measurements of the three e-waste job categories. Thus, these data need to be interpreted with
caution. However, trends in self-report-based walking and pedometer measurements among job
categories provide useful insights that might have implications for future research at
Agbogbloshie. Median pedometer steps and distances walked were higher for collectors than
burners and dismantlers. Aerobic steps count and cadence (steps/min) were also slightly higher for
collectors than burners and dismantlers (see Table 5). These trends were compatible with the self-
reported questionnaire data and were not surprising considering that collectors travelled long
distances between the e-waste scrapyard and adjacent communities. In contrast, the pedometer-
based total step counts for dismantlers and burners were higher than expected based on trends in
the observations and self-reported data. Compared to burners, the aerobic step counts for
dismantlers and burners were also low suggesting multiple short bouts of walking. About 50% of
dismantlers recorded no aerobic steps likely due to the predominance of sitting or standing in place
to perform their task. On a few occasions, when dismantlers had few parts to work with, they were
observed walking to neighbouring communities in search of e-waste instead of waiting for
collectors to return with e-waste items. Similarly, burners were observed switching to dismantling
tasks for short durations to earn some income while waiting for new items to burn. This may
explain the relatively high pedometer readings on some days which increased the median step
counts (i.e., 5360 steps for dismantlers and 8964 steps for burners) among these otherwise more
sedentary groups. It is also possible that the many short bouts of walking caused burners and
dismantlers to underreport their frequency of continuous walking and thus not adequately captured
by the questionnaire. While these initial study findings may not be decisive, it does provide
evidence that frequent exposure to prolonged sitting, standing and walking is present at levels that
should raise concern. Further systematic study is needed to quantify the variability in these
exposures both between and within workers over time.
4.2.4. Manual Material Handling Activities: E-waste recycling at Agbogbloshie involved
different types and intensities of MMH activities. However, the present study only quantified the
self-reported frequency of performing MMH activities in a workweek. The frequency of MMH
activities differed both between and within job categories. Lifting and carrying were notably more
frequent for burners and collectors than dismantlers, with 80 to 90% of burners and collectors
performing these activities on ≥ 5 days per week. Although the frequency of lifting and carrying
was lower for dismantlers than collectors or burners, they reported a high intensity of load handling
suggesting interactions between hand load frequency, intensity, and possibly duration across job
categories. Understandably, pulling and pushing activities was more prevalent among collectors
by definition of their job. However, not all collectors operated carts and instead walked with a
cloth sack to carry e-waste items. The 68% of collectors that reported performing pushing and/or
pulling on 5 or more days in the preceding workweek was in sharp contrast to the nearly 29% of
collectors who reported no pushing and/or pulling in the week. This reinforces the high variability
in work exposures among collectors, with much of this variability potentially related to the choice
of MMH equipment and from success or failure in locating items for recycling on a given day.
MMH activities may be a source of work-related MSD among e-waste workers. Although not
exactly e-waste recycling, manual collection and handling of solid waste performed in informal
settings is associated with a high prevalence of shoulder and low back MSDs (Abou-Elwafa et al.,
2012; Kuijer et al., 2010; Mehrdad et al., 2008). Additionally, there is strong evidence in the
ergonomics literature that identifies MMH including lifting, carrying, pushing and pulling as a
leading cause of work-related shoulder and low back disorders (Hoogendoorn et al., 1999;
Hoozemans et al., 2002; Roffey et al., 2010; Wai et al., 2010b). Hoozemans et al. (2002) have also
reported a dose-response relationship between pushing and/or pulling and shoulder complaints
among industry workers. Although some of these physical activities may not be strenuous when
considered on their own, their repetition and combination with extreme postures contribute to the
development of MSDs, as widely acknowledged in previous studies (Chaffin et al., 2006; Fan et
al., 2014; Latko et al., 1999). Collectively these studies suggest that the extent of MMH activities
performed could predispose e-waste workers at Agbogbloshie to developing work-related MSDs;
thus, calling for additional research to examine associations between specific physical work
exposures and MSDs in this worker population.
4.3. Study Limitations
The unregulated nature of e-waste recycling at Agbogbloshie made it difficult to employ sampling
strategies used in regular industrial work. However, attempts were made to sample participants at
different locations within the e-waste site and on different days to obtain a representative sample.
This study had a modest sample size but was comparable to other survey studies conducted at
Agbogbloshie (n ~ 142 to 180; Adanu et al, 2020; Laskari et al., 2019). Another limitation was
that the accuracy of the self-reported data relies on the ability of participants to recall the duration
and frequency of their typical exposures to the different work activities. This is particularly
challenging in unregulated unstructured work, wherein the exposures vary considerably among
workers, and for the same worker within days and between days. For example, some participants
did not have a response to questions about MMH activities, which reduced the effective sample
size in Tables 3 and 4.
Bias in workers responses to the questions could also stem from self-perceptions about their work
activity exposures as well as low literacy, nuances in local dialects, and differences in
comprehension and interpretation of the questionnaire. To minimize this effect, the present study
employed professional research staff who were familiar with English and the local language as to
conduct the field interviews. The format of the self-report questionnaire responses, whether
continuous, ordinal or categorical, was also important to consider because of the low literacy and
language diversity. During the pilot phase of the study, participants found it difficult to accurately
estimate their proportion of time spent performing the different OPAs. Thus, a categorical scheme
was employed in the modified OPAQ. For example, the time spent in sitting, standing and walking
was presented in two ways: (1) one-hour of continuous activity, and (2) a total of 4 hours of activity
during the workday (Appendix A). As an initial step, this format to the questionnaire helped reduce
participant’s wavering or indecision about the responses and shorter response times. Although
this format made it relatively easy for workers to provide information on their OPA exposures, a
more reliable direct form of quantification of these OPA levels could be explored in future studies.
This study was also limited by its inability to continuously observe workers that were instrumented
with pedometers in order to document the activities they performed which could help explain the
trends in pedometer measurements. There could be a small possibility of some participants with
pedometers being involved in additional activities other than their primary job. However, our
observations of other workers suggested this was infrequent and as such was not considered in our
statistical data analysis.
4.4. Methodological Implications
From a methodological perspective, the current study draws attention to the need for new,
validated methods to measure physical work exposures among workers engaged in informal e-
waste recycling. Due to the challenges of studying informal work settings, in this study we opted
for a multi-method approach, namely, direct observations, self-report questionnaires, and
pedometry. The modified version of the OPAQ was appropriate in capturing information about
some of the most common physical activity exposures among e-waste workers in a short period of
time. However certain quantitative exposure data were missed. First, information about the
frequency of force exertions associated with using hand tools (e.g., hammers, chisels,
screwdrivers), which is typical among dismantlers, was not assessed in the OPAQ. Second,
information about whole-body postures (e.g., standing, seated, squat, stooped) associated with
MMH activities such as lifting, pushing and/or pulling, and magnitude of hand force exertions
during tool use was not obtained. Additional research is needed to quantify the relationships
between the MMH activities performed (type, magnitude or intensity, duration, and frequency)
and the postures used during those activities specific to work methods in low-resource informal
Direct instrumentation-based methods have advantages in terms of accuracy and reliability
(Chaffin et al., 2006; Neitzel et al., 2013), however, the variable nature of informal e-waste
recycling at Agbogbloshie presented some challenges. Pedometers were time and cost-prohibitive
to use and as such not all participants in the study were instrumented with pedometers.
Unfortunately, the pedometers were also perceived by participants as valuable and inexplicably
would go missing during data collection. Five pedometers were not returned in the early stage of
the study, which further limited data collection from more workers. These realities and trade-offs
in field research can limit the ability to fully capture the variability in exposures between workers
and within workers over time. Simple pocket pedometers were used for this study due to their low
cost. Future studies could consider using GPS-enabled activity monitors (i.e., as a way to
potentially track the devices and minimize the risk of loss or theft) and could measure additional
physiological data (e.g., heart rate) to provide better information about the physical workload
experienced. However, regardless of choice of direct instrumentation, this approach would not
capture the sociotechnical and environmental interactions and economic constraints on workers
that influence their choice of job category, work methods and equipment used, and consequently
their work-related exposures.
To understand these realities facing low-skilled, low-income workers in an unregulated, informal
work setting, qualitative direct observations in this study proved just as valuable as the quantitative
questionnaire data. As such, development of a direct observation technique to systematically
sample work content and quantify physical work exposures among e-waste workers may be most
ideal. Direct observation and quantification of workers physical exposures have proven successful
in other non-repetitive work settings such as construction (e.g., the Posture Activity and Tools
Handled (PATH) work-sampling based approach by Buchholz et al., (1996)) and could provide a
template for similar approaches suited to unregulated, informal e-waste recycling. More broadly,
our study findings provide important lessons and motivation for future research to examine
physical activity exposures and associated work-related injury prevalence in the informal e-waste
This study contributes to the limited literature on work-related activity exposures and work
conditions of e-waste workers at the largest e-waste dumpsite in sub-Saharan Africa. While prior
studies at Agbogbloshie emphasized exposures to toxic chemicals, poor air quality, and noise, this
initial study draws unique attention to the physical work exposures associated with informal e-
waste recycling. E-waste recycling involves varying levels of sitting, standing, walking and a range
of MMH activities that collectively may be detrimental to the musculoskeletal health of the worker.
Self-reported work exposures examined in this study differed substantially between the three
primary e-waste job categories. Long hours of sitting were common among dismantlers while
burners and collectors were more likely to be engaged in prolonged standing and walking
respectively. All job categories had a high proportion of workers performing manual material
handling activities on five or more days in the week. Dismantlers and burners engaged in frequent
lifting and carrying tasks while pushing and/or pulling were most frequent among collectors.
Frequent exposure to these physically demanding activities may compound the musculoskeletal
health effects of prolonged sitting, standing and walking.
Achieving proper balance between sitting, standing, walking and performing MMH activities in
informal e-waste work may help reduce its potential negative health effects. However, the
development of appropriate and context adapted ergonomics interventions relies on accurate and
reliable quantitative information about work exposures and associated work-related injuries. Our
study highlights the need for further occupational safety and ergonomics research in the informal
e-waste recycling sector. Specifically, more objective and reliable assessment methods that can
account for the between and within worker variability in exposures inherent to informal e-waste
work are required. Future studies also need to consider the local economic realities and social
context encountered in an unregulated, low resource and multi-ethnic work environment.
The study was supported by the 1⁄2 West Africa-Michigan CHARTER in GEOHealth with funding
from the United States National Institutes of Health/Fogarty International Center (US NIH/FIC)
(paired grant no 1U2RTW010110-01/5U01TW010101) and Canada’s International Development
Research Center (IDRC) (grant no. 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.
CRediT authorship contribution statement
Augustine A. Acquah: Conceptualization, Methodology, Investigation, Formal analysis, Writing
- original draft, Writing – review & editing, Resources. Clive D'Souza: Conceptualization,
Methodology, Writing - original draft, Writing - review & editing, Supervision, Validation.
Bernard Martin: Conceptualization, Methodology, Writing - original draft, Writing - review &
editing, Supervision, Validation. John Arko-Mensah: Conceptualization, Investigation, Writing
- review & editing, Supervision. Paul K. Botwe: Writing - review & editing, Supervision.
Prudence Tettey: Writing - review & editing, Supervision. Duah Dwomoh: Formal analysis,
Writing - review & editing. Afua Amoabeng Nti: Investigation, Writing - review & editing.
Lawrencia Kwarteng: Investigation, Writing - review & editing. Sylvia Takyi: Investigation,
Writing - review & editing. Isabella A. Quakyi: Conceptualization, Writing - review & editing,
Supervision. Thomas G. Robins: Writing - review & editing, Resources, Supervision, Funding
acquisition. Julius N. Fobil: Conceptualization, Investigation, Resources, Writing - review &
editing, Supervision, Funding acquisition.
Declaration of competing interests
The authors declare that they have no known competing financial interests or personal
relationships that could have appeared to influence the work reported in this paper.
We acknowledge all workers at the Agbogbloshie e-waste site for their cooperation and dedicated
contribution towards this study. We also acknowledge the hard work of our field team at the
University of Ghana School of Public Health who assisted in organising field visits and conducting
Appendix – A: Modified version of the OPAQ used for data collection
Q1. In the last workweek, did you do the following during your work schedule?
* Sitting activities
Prolonged sitting (4 hours or more per day)
Sitting continuously for 1 hour or more
* Standing activities
Prolonged standing (4 hours or more per day)
Standing continuously for 1 hour or more
Prolonged walking (4 hours or more per day)
Walking for 1 hour or more
* Manual material handling activities
Q2. In the last work week, how often did you do the following during your work schedule?
1-2 days last
3-4 days last
5 or more days
Pushing or pulling a cart
Q3. If you did any of the above, what is the maximum weight you usually handle while
performing these tasks?
Light (5kg or less)
Heavy (11-20 kg)
Very heavy (>20kg)
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