Abstract
Background:
Work-related musculoskeletal symptoms (WMSs) are common injuries or pains that primarily affect various body structures. It is difficult to estimate the burden of WMSs in developing countries such as Ethiopia due to a lack of evidence, particularly among university academic staff. There is a universal and rapidly growing need for information about WMSs, as this is the main challenge to public health and economic burden. The purpose of this study was to determine the magnitude of work-related musculoskeletal symptoms and their associated factors among academic staff in Ethiopian universities.
Methods:
From February 2 to March 24, 2021, a web-based cross-sectional study was conducted among 422 academic staff members working in Ethiopian universities. A structured and self-administered Google Form questionnaire was sent and shared with the academic staff via their email addresses, Facebook, and Telegram accounts. Using a p-value of <0.05 and a 95% confidence interval, multivariable logistic regression was used to identify factors associated with the outcome variable.
Results:
Around 321 (77.2%) of the 416 participants were reported to have work-related musculoskeletal symptoms at least in one part of the body (95% CI: 73.1, 81.5%), with 28.1% reporting lower back pain. Respondents working in second-stage universities (AOR = 7.35, 95% CI 3.21, 16.79), being 44 years old or older (AOR = 7.89, 95% CI 2.10, 21.57), having a Ph.D. (AOR = 7.09, 95% CI 1.50, 17.93), engaging in physical activity (AOR = 3.32, 95% CI 1.43, 7.74), and working on a computer (AOR = 6.89, 95% CI 2. 0.72, 19.15) were the factors associated with work-related musculoskeletal symptoms.
Conclusion:
Almost three-quarters of academic staff reported work-related musculoskeletal symptoms in this survey. Factors such as university establishment stage, age, educational status, physical activity, and frequent computer use were found to be significantly associated with work-related musculoskeletal symptoms.
Keywords: Academic, Factors, Musculoskeletal, Staff, Universities
Introduction
Work-related musculoskeletal symptoms (WMSs) are common injuries that primarily affect body structures like tendons, muscles, joints, ligaments, nerves, and bones.1,2 Musculoskeletal symptoms of affected body parts include discomfort, pain, and swelling, which can lead to disability. Static body positions, repetitive movements, prolonged muscular contractions, and weight lifting are the most common collective causes of these work-related musculoskeletal symptoms.3,4 According to the World Health Organization, physical inactivity is the “new smoking” an encumbered bomb of ill-health on the border of explosion. Poor workplace and physical health can result in corporate losses due to lower productivity and absenteeism, as well as increased health-care costs.5,6
Academic jobs require constant computer use, including typing and sitting, which increases the risk of musculoskeletal problems due to computer use is an integral part of training, particularly in universities.7 Musculoskeletal symptoms are a growing public health concern that affects both young and old people aged 20 to 80.8 -12 The magnitude of WMSs varies depending on the study populations as well as across countries.13 A recent survey conducted in several countries found that the prevalence of work-related musculoskeletal symptoms ranged from 55% to 85.7% among computer users, office workers, and academic personnel.14 -17
Individuals are living longer lives with a higher prevalence of non-communicable diseases and injuries such as musculoskeletal symptoms around the world.18 According to the Global Burden of Disease survey conducted in 2010 and 2016; musculoskeletal pain was the leading cause of physical injuries and disabilities, ranking sixth among the top ten diseases in terms of global burden of disease.18 -20 The musculoskeletal disorder is well-known as one of the most expensive symptoms among the working population, with devastating economic, societal, and public health consequences around the world. Approximately 9.5 million working days were lost due to musculoskeletal symptoms at work.13,21
The lifetime incidence of lower back pain ranged from 58% to 80%, affecting 80% of people in developed countries.21 The multifactorial risk factors for musculoskeletal symptoms in adult workforces worldwide were prolonged awkward posture and computer use, some degree of the whole body vibration, and participation in repetitive activities.13,22 -26 On the other hand, Age, gender, unstable health conditions, service years, poor ergonomic practice at work, and job stress were the most common associated factors with musculoskeletal symptoms among academic staff.27 -31
It is difficult to estimate the burden of WMSs in developing countries such as Ethiopia due to a lack of evidence, particularly among university academic staff.32,33 As a result; the purpose of this study was to determine the magnitude of work-related musculoskeletal symptoms and associated factors among academic staff in Ethiopian universities.
Methods
Study designs and settings
A web-based cross-sectional survey was carried out among academic staff working in Ethiopian universities from February 02, 2021 to March 24, 2021. Ethiopia is a huge country, with diverse nations and nationalities, with more than 90 ethnic and linguistic groups and a total population of around 117 million people. Ethiopia has ten administrative regions, with rural areas housing more than 80% of the population. Ethiopia currently has 45 universities, with 8, 14, and 23 being first-, second-, and third-generation universities, respectively. Ethiopian universities are estimated to have 32 000 academic staff.34,35
Population and eligibility
All Ethiopian university academic staff made up the source and study population. During the data collection period, the study included all academic staff that used email or other social media. Academic staff with inactive email addresses and other social media accounts were excluded from the study, with a few exceptions. On the other hand, academic staff with a history of musculoskeletal system injuries, spinal surgery, or major surgery in any part of the body, congenital anomalies such as spine and limb anomalies, and pregnant women, was excluded from the study.
Sample size determination and sampling techniques
The sample size was determined using a single population proportion formula (n = Z2a/2(p(1-p)/d2)). Considering a 50% proportion (P), a 95% level of confidence (Za/2), a 5% margin of error (d), and a 10% non-response rate, we lastly attained a sample size of 422. The snowball sampling technique was used to access academic staff that was using email and other social media like Facebook and Telegram. We considered non-discriminative exponential snowball sampling technique, in which the first subject is recruited and then provides multiple referrals. Each new referral generates more data for the next referral, and so on, until the desired sample size is reached.
Data collection tool and procedures
First, the questionnaires were prepared in English using Google form by reviewing former studies.36 -39 The data collection tool includes socio-demographic, ergonomic practice during computer use, and work-related musculoskeletal symptoms items/questions (supplementary file 1). Data was collected through a pretested, structured, web-based, and self-administered questionnaire. The prepared Google form link was shared with the Academic staff working in Ethiopian universities through their email addresses, Facebook, and Telegram accounts. The online Google form link is found at https://docs.google.com/forms/d/e/1FAIpQLScQQZYjOqil9bbePpbpYfoAUbQmUcxnSvv4jKNBK8202hnQJw/viewform?usp=sf_link.
Data processing and analysis
The Google form responses were entered into an excel spreadsheet and then exported to STATA version 14. The descriptive analysis made use of a frequency table, cross-tabulation, and charts. To identify factors associated with work-related musculoskeletal symptoms, a binary logistic regression model was used with all required assumptions checked. The data’s normality was checked, Hosmer and Lemeshow’s model fitness test (0.847) was used, and the multicollinearity of independent variables was tested using the variance inflation factor (VIF < 10). Candidates for multivariable binary logistic regression can be variables with a p-value of <0.20 in the bivariable logistic regression analysis. In this regard, a p-value of <0.05 with a 95% confidence level was considered statistically significant for all variables.
Operational definitions
A work-related musculoskeletal symptom (WMSs) is a self-reported pain or discomfort in at least one of the body sites in the past 12 months. The body sites include the neck, shoulder, upper back, lower back, /leg, ankle, foot, elbow, and wrist/hand.40 The presence of WMSs was coded as “yes” if WMSs were reported and “no” if WMSs symptoms had not been reported.
Academic staffs are instructors who work in academic settings (teaching, learning, research, and community service).41
Physical activity: Perform any type of physical activity per week more frequently when respondents select at least one of the two response options (always or often) and less frequently when respondents select one of the three response options (sometimes, rarely, or never).
Seating position with bending back: Seating up right with bending back occurs more frequently when respondents choose at least one of the two response options (always or often) and less frequently when respondents choose one of the three response options (sometimes, rarely, or never).
Results
Socio-demographic characteristics
A total of 416 respondents have accomplished the online survey questionnaire with a response rate of 98.6%. Of these participants, 301 (72.4%) were males, and 227 (54.6%) of them were within the age category of 24 to 33 years old. Concerning educational status, around 317 (76.2%) of respondents attained up to a second degree or masters, and 150 (36.1%) of them had 1 to 5 service years. One hundred sixty-seven respondents (40.1%) walked on foot to reach their working institution (Table 1).
Table 1.
Socio-demographic characteristics of Academic staff in Ethiopian Universities, 2021 (n = 416).
| Variables | N (%) | Presence of WMSs in past 12 months | |
|---|---|---|---|
| Yes (%) | No (%) | ||
| Regions | |||
| Harari | 3 (0.7) | 3 (100) | 0 |
| Sidama Zone | 4 (1.0) | 3 (75.0) | 1 (25.0) |
| Gambella | 9 (2.2) | 7 (77.8) | 2 (22.2) |
| Somali | 10 (2.4) | 7 (70.0) | 3 (30.0) |
| Dire-Dawa | 12 (2.9) | 10 (83.3) | 2 (16.7) |
| Tigrai | 20 (4.8) | 13 (65.0) | 7 (35.0) |
| Benishangul-Gumuz | 21 (5.0) | 12 (57.1) | 9 (42.9) |
| SNNP | 56 (13.5) | 41 (73.2) | 15 (26.8) |
| Oromia | 144 (34.6) | 117 (81.3) | 27 (18.7) |
| Stage of university | |||
| First-generation | 133 (32.0) | 104(78.2) | 29 (21.8) |
| Second generation | 219 (52.6) | 174 (79.5) | 45 (20.5) |
| Third generation | 64 (15.4) | 43 (67.2) | 21 (32.8) |
| Sex | |||
| Male | 301 (72.4) | 236 (78.4) | 65 (21.6) |
| Female | 115 (27.6) | 85 (73.9) | 30 (26.1) |
| Educational status | |||
| First degree (BSc or BA) | 52 (12.5) | 35 (67.3) | 17 (32.7) |
| Second degree (Master) | 317 (76.2) | 247 (77.9) | 70 (22.1) |
| Third-degree (Ph.D.) | 47 (11.3) | 39 (83.0) | 8 (17.0) |
| Age of respondents | |||
| 24-33 year | 227 (54.6) | 164 (72.2) | 63 (27.8) |
| 34-43 year | 162 (38.9) | 135 (83.3) | 27 (16.7) |
| 44 and above years | 27 (6.5) | 22 (81.5) | 5 (18.5) |
| Service years | |||
| 1-5 service year | 150 (36.1) | 115 (76.7) | 35 (23.3) |
| 6-10 service year | 129 (31.0) | 95 (73.6) | 34 (26.4) |
| 11-15 service year | 102 (24.5) | 84 (82.4) | 18 (17.6) |
| 16 and above service year | 35 (8.4) | 27 (77.1) | 8 (22.9) |
| Means of Transportation | |||
| Public transportation | 123 (29.6) | 109 (88.6) | 14 (11.4) |
| Institution own transportation | 126 (30.3) | 95 (75.4) | 31 (24.6) |
| Walking on foot | 167 (40.1) | 117 (70.1) | 50 (29.9) |
SNNP- Southern, Nation and Nationality People
Ergonomic practices during computer use
In this study, 209 respondents (50.2%) reported that the chairs used at the workplace do not have foot and hand rests. About 145 (34.9%) of the respondents frequently perform physical exercise per week. One hundred seventy-nine respondents (43%) were working frequently on their computers, and 83.4% of them were taking regular breaks of 20 to 60 minutes per day. Regarding the regular seating position, around 159 (38.2%) of respondents reported the regular sitting position with a bent back (Table 2, Figures 1 and 2).
Table 2.
Ergonomic practices during computer use among Academic staff in Ethiopian Universities, 2021 (n = 416).
| Items/questions | Respondent’s n (%) | Presence of WMSs in past 12 month | |
|---|---|---|---|
| Yes (%) | No (%) | ||
| Laptop is carried/hanged at | |||
| Both side (shoulder) | 126 (30.3) | 100 (79.4) | 26 (20.6) |
| One side(shoulder) | 252 (60.6) | 190 (75.4) | 62 (24.6) |
| One hand | 38 (9.1) | 31 (81.6) | 7 (18.4) |
| Presence of workplace safety guideline | |||
| Yes | 158 (38.0) | 123 (77.8) | 35 (22.2) |
| No | 211 (50.7) | 160 (75.8) | 51 (24.2) |
| I don’t know | 47 (11.3) | 38 (80.9) | 9 (19.1) |
| Physical exercise per week | |||
| Always | 33 (7.9) | 31 (93.9) | 2 (6.1) |
| Often | 145 (34.9) | 115 (79.3) | 30 (20.7) |
| Sometimes | 127 (30.5) | 95 (74.8) | 32 (25.2) |
| Rarely | 78 (18.8) | 61 (78.2) | 17 (21.8) |
| Never | 33 (7.9) | 19 (57.6) | 14 (42.4) |
| Frequency of work on your computer? | |||
| Rarely | 22 (5.3) | 14 (63.6) | 8 (36.4) |
| Sometimes | 57 (13.7) | 38 (66.7) | 19 (33.3) |
| Often | 179 (43.0) | 141 (78.8) | 38 (21.2) |
| Always | 158 (38.0) | 134 (84.8) | 24 (15.2) |
| Frequency of lower back supported properly when you sitting | |||
| Never | 15 (3.6) | 9 (60.0) | 6 (40.0) |
| Rarely | 68 (16.3) | 55 (75.3) | 18 (24.7) |
| Sometimes | 127 (30.5) | 108 (75.5) | 35 (24.5) |
| Often | 135 (32.5) | 116 (79.5) | 30 (20.5) |
| Always | 64 (15.4) | 33 (84.6) | 6 (15.4) |
| Average time of standing in hours per day | |||
| 1-3 h | 310 (74.5) | 240 (77.4) | 70 (22.6) |
| 4-6 h | 106 (25.5) | 81 (76.4) | 25 (23.6) |
| Average time of sitting in hours per day | |||
| 1-4 h | 169 (40.6) | 133 (78.7) | 36 (21.3) |
| 5-9 h | 217 (52.2) | 162 (74.7) | 55 (25.3) |
| 10 and above hours | 30 (7.2) | 26 (86.7) | 4 (13.3) |
| Health break per day | |||
| 20-60 min | 347 (83.4) | 267 (76.9) | 80 (23.1) |
| 61-120 min | 69 (16.6) | 54 (78.3) | 15 (21.7) |
Figure 1.
Shows the types of chairs used at workplace reported by academic staff in Ethiopian Universities, 2021.
Figure 2.
Shows the frequency seating position with bending back reported by academic staff working in Ethiopian Universities.
Prevalence of work-related musculoskeletal symptoms (WMSs)
Approximately 321 (77.2%; 95% CI: 73.1, 81.5%) of the total study participants were reported to have work-related musculoskeletal symptoms at least in one part of the body. Upper back pain (23.1%) and lower back pain (28.1%) were the most commonly reported musculoskeletal symptoms by respondents. Three hundred fifty-eight (38.0%) of the respondents reported mild work-related musculoskeletal symptoms (Figures 3 and 4).
Figure 3.
Shows musculoskeletal symptoms reported by academic staff working in Ethiopian Universities, 2021.
Figure 4.
Shows the severity of musculoskeletal symptoms reported by academic staff working in Ethiopian universities, 2021.
Factors associated with work-related musculoskeletal symptoms
In this study, working at the second established university, age, educational status, service years, physical exercise, workplace safety guidelines, and frequent working on the computer were factors associated with a work-related musculoskeletal disorder in multivariable logistic regression. Respondents working in second-stage universities were nearly 7 times more likely to develop work-related musculoskeletal symptoms (WMSs) than those working in first-stage universities (AOR = 7.35, 95% CI: 3.21, 16.79). The odds of developing WMSs among Ph.D. attained academic staff were 7 times higher in relation to their counterparts (AOR = 7.09, 95% CI 1.50, 17.93). Respondents 44 years old and older were nearly 8 times more likely to develop WMSs than younger respondents (AOR = 7.89, 95% CI: 2.10, 21.57). In terms of service years, academic staff with 16 or more years of experience were 88% less likely to develop WMSs than their counterparts (AOR = 0.12, 95% CI 0.02, 0.71). The odds of having WMSs were 3.32 times more likely among respondents less frequently performing physical exercise per week than their counterparts (AOR = 3.32, 95% CI 1.43, 7.74). Respondents who worked on their computers frequently were nearly 7 times more likely to develop WMSs than those who worked on their computers less frequently (AOR = 6.89, 95% CI 2.72, 19.15). The odds of having WMSs were nearly 4 times higher among academic staff who did not know about the presence of workplace safety guidelines in their institution than their counterparts (AOR = 4.35, 95% CI 1.83, 9.13) (Table 3).
Table 3.
Bivariable and multivariable logistic regression analysis for musculoskeletal disorder among academic staff in Ethiopian Universities, 2021 (n = 416).
| Variables | Presence of WMSs | COR 95% CI | P-value | AOR 95% CI | P-value | |
|---|---|---|---|---|---|---|
| Yes (%) | No (%) | |||||
| University generation | ||||||
| First-generation | 104 (78.2) | 29 (21.8) | 1 | 1 | ||
| Second generation | 174 (79.5) | 45 (20.5) | 6.37 (3.09, 13.15) | .012 | 7.35 (3.21,16.79) | .001 |
| Third generation | 43 (67.2) | 21 (32.8) | 1.85 (0.71, 4.80) | .135 | 2.68 (0.93, 7.71) | |
| Educational status | ||||||
| First degree (BSc or BA) | 35 (67.3) | 17 (32.7) | 1 | 1 | ||
| Second degree (Master) | 247 (77.9) | 70 (22.1) | 2.92 (1.23, 6.93) | .003 | 2.60 (0.97, 6.98) | .009 |
| Third degree (PhD) | 39 (83) | 8 (17) | 6.20 (1.75, 12.18) | <.001 | 7.09 (1.50,17.93) | <.001 |
| Age | ||||||
| 24-33 year | 164 (72.2) | 63 (27.8) | 1 | 1 | ||
| 34-43 year | 135 (83.3) | 27 (16.7) | 4.27 (2.16, 8.44) | .023 | 4.07 (1.60,10.35) | .001 |
| 44 and above years | 22 (81.5) | 5 (18.5) | 2.28 (0.63, 8.20) | .014 | 7.89 (2.10,21.57) | .006 |
| Service years | ||||||
| 1-5 service year | 115 (76.7) | 35 (23.3) | 1 | 1 | ||
| 6-10 service year | 95 (73.6) | 34 (26.4) | 0.87 (0.42, 1.84) | .153 | 0.42 (0.18, 0.98) | .232 |
| 11-15 service year | 84 (82.4) | 18 (17.6) | 2.44 (1.03, 5.77) | .042 | 0.51 (0.17, 1.51) | .065 |
| 16 and above service year | 27 (77.1) | 8 (22.9) | 1.01 (0.33, 3.08) | .345 | 0.12 (0.02, 0.71) | .0027 |
| Types of the computer used | ||||||
| Laptop only | 197 (76.4) | 61 (23.6) | 1 | |||
| Laptop and desktop | 121 (79.1) | 32 (20.9) | 0.81 (0.44, 1.50) | .345 | ||
| Desk top only | 3 (60) | 2 (40) | 0.15 (0.06, 0.46) | .025 | ||
| Presence of workplace safety guideline | ||||||
| Yes | 123 (77.8) | 35 (22.2) | 1 | 1 | ||
| No | 160 (75.8) | 51 (24.2) | 0.57 (0.30, 1.84) | .136 | 1.10 (0.47, 2.56) | .426 |
| I don’t know | 38 (80.9) | 9 (19.1) | 1.75 (0.56, 5.47) | .213 | 4.35 (1.83, 9.13) | .002 |
| Average time of standing in hours per day | ||||||
| 1-3 h | 240 (77.4) | 70 (22.6) | 1 | |||
| 4-6 h | 81 (76.4) | 25 (23.6) | 3.56 (0.27, 7.01) | .234 | ||
| Seating position with bending back | ||||||
| Less frequently | 157 (71.7) | 62 (28.3) | 1 | |||
| More frequently | 164 (83.2) | 33 (16.8) | 3.01 (1.60, 5.70)* | .042 | ||
| Frequency of work on your computer? | ||||||
| Less frequently | 46 (58.2) | 33 (41.8) | 1 | 1 | ||
| More frequently | 275 (81.6) | 62 (18.4) | 6.74 (3.15, 14.45) | .03 | 6.89 (2.72, 19.15) | .002 |
| Physical exercise per week | ||||||
| Less frequently | 175 (73.5) | 63 (26.5) | 3.23 (1.72, 6.10) | .035 | 3.32 (1.43, 7.74) | .009 |
| More frequently | 146 (82) | 32 (18) | 1 | 1 | ||
*p-value <0.05 and 95% CI for A, does not include 1.
Discussion
Work-related musculoskeletal symptoms (WMSs) are a significant cause of work-related injury and disability in developed and developing countries. A poor working environment and the absence of effective work-related injury prevention programs give rise to a very high rate of WMSs in developing countries like Ethiopia.42 This study aimed to assess the magnitude of WMSs and associated factors among academic staff in Ethiopian universities. The current study found that 77.2% of respondents had a work-related musculoskeletal symptom (WMSs) at least in one part of the body, which is slightly higher than study findings in Mekelle University, Ethiopia (65.2%43), Nigeria (71.7%30), and a little bit lower than study finding in Malaysia (78.9%28), Nepal (80.7%).44 On the other hand, the finding in this study was relatively lower when compared to the findings in Ireland (85%31) and Brazil (85.7%7). Possible explanations for this variation include differences in study year, sample size, workload, assessment tools, and operational definition of WMSs. The most common body part pains or discomforts reported by respondents in this study were upper (23%) and lower back (28%), respectively. This could be because academic staff uses computers in their daily activities, resulting in a prolonged sedentary working habit that exposes them to such WMSs. The findings of this study were lower than those of a study conducted at Mekelle University in Ethiopia, where upper back (neck) pain was 41.5% and lower back pain was 40.3%43; in the other university environment, lower and upper back pain were 47 and 59%, respectively.27 This variation could be explained by respondent characteristics such as prolonged sitting with a bent back and standing position, which cause pain in the upper and lower back. Previous research has shown that prolonged sitting causes quick stiffness of the lumbar area, which bending the lumbar area may alter the passive painfulness of the lumbar spine or the lower back predisposes the lumbar area to injury, and that prolonged sitting without breaks causes firmness of the disk height at the 4 to 5 level.45 -47 Of respondents who reported the presence of WMSs, around 38.0% and 35.1% of the respondents were found to have mild and moderate pains or discomfort. This finding could be based on each study participant’s personal perceptions of mild and moderate pain for the reported musculoskeletal symptoms.
Several important variables were examined in this study using variable and multivariable logistic regression to identify factors associated with the outcome variable. In the final model, factors associated with work-related musculoskeletal symptoms included age, educational status, service years, physical exercise, the presence of workplace safety guidelines, and frequent computer use. Academic staff working in the universities established at the second stage was more likely to develop WMSs than those working in the first established universities. This observed dissimilarity might be due to differences in the implementation of workplace safety guidelines; the most senior universities may have proper infrastructure, services, and furniture (ergonomically recommended chairs and tables) that enable academic staff to minimize WMSs. The respondents within the age category of 44 years old and above were more likely to develop WMSs than younger respondents. This study’s findings were consistent with the findings in Nigeria.48 This might be due to age-related variations that affect the functional capacities of adults over time, which in turn lead to an increase in work-related musculoskeletal symptoms.1 In terms of service years, Academic staff with 16 or more years of experience were less likely to develop WMSs than their counterparts. On the contrary, a study conducted in Nigeria48 found that as service years increased, the likelihood of developing a work-related musculoskeletal disorder increased. This discrepancy might be due to the fact that the data is self-reported, in which case it may be hidden, and they may recover from WMSs before the intended study period. The respondents frequently working on their computers were more likely to develop WMSs when compared to those working on their computers less frequently. This finding was comparable with the study findings at Mekelle University, Ethiopia,43 Brazil,7 and Malaysia.28 This might be due to the fact that working for an elongated period without shifting positions may predispose Academic staff to micro-trauma and soft tissue damage.31 On the other hand, the odds of having WMSs were more likely among respondents who perform physical exercise per week less frequently than their counterparts. This finding was in line with a study done at Mekelle University, Ethiopia,43 the Republic of Ireland,31 and Saudi Arabia.41 This might be due to regular exercise being able to create stronger muscles that improve muscle action against workload stress and pain during tiresome work.13 Moreover, it might be due to the fact that sedentary life can lead to poor muscular strength and also expose the muscles to muscular spasms and tiredness, which can probably escalate the risk of WMSs.41
Limitations of the study
Some of the limitations of this study were the presence of musculoskeletal symptoms determined through a questionnaire-based approach or self-reported pain that may differ from their real situation. The respondents were limited to social media and internet users. The study findings might not reveal the whole country’s actual condition due to the under-representation of certain universities. Another limitation of the study related to the lack of physical examination to confirm work-related musculoskeletal symptoms and all findings of this study was not compared with previous findings, due to limited literatures. Despite these limitations, the study provides relative data for policymakers and health care planners. Future studies should consider longitudinal and experimental study designs to offer deeper insights into work-related musculoskeletal symptoms.
Conclusion
In this survey, nearly three-quarters of academic staff reported having work-related musculoskeletal symptoms at least in one part of the body. Lower and upper back pains were the most commonly reported WMSs. In this study, factors associated with work-related musculoskeletal symptoms included age, educational status, service years, physical exercise, the presence of workplace safety guidelines, and frequent computer use. As a result, it is preferable to establish a workplace safety policy and provide ergonomically appropriate furniture (chairs, tables) to all academic staff. Improving poor ergonomic practices through proper training, reducing long periods of sedentary work without a break, and engaging in regular physical activity per week would be critical in addressing the problem.
Supplemental Material
Supplemental material, sj-docx-1-ehi-10.1177_11786302221131690 for Work-Related Musculoskeletal Symptoms and Associated Factors Among Academic Staff in Ethiopian Universities by Demisu Zenbaba, Biniyam Sahiledengle, Diriba Dibaba, Tilahun Tufa, Ayele Mamo and Daniel Atlaw in Environmental Health Insights
Acknowledgments
We would like to thank all the study participants and Madda Walabu University for their helpful participation in this study.
Footnotes
Funding: The author(s) received no financial support for the research, authorship, and/or publication of this article.
The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
Author Contributions: DZ: Conceptualizes the study, designs the study and data curation, performs the analysis, writes and approves the final manuscript. BS, DD, TT, DA and AM: contributed to the analysis, reviewed the manuscript critically, and approved the final manuscript. Before submission, all authors read and approved the final manuscript.
Ethical Approval and Consent to Participate: The online survey was conducted according to the Helsinki declaration after ethical clearance was obtained from the Ethical Review Board of the Madda Walabu University Goba referral hospital. Participants were informed to fill the online self-administered questionnaire voluntarily with a full right not to respond to all or any of the questions. The online survey has no personal identifier, so that anonymity was kept. The first online pages for the self-administered questionnaire could be accessed by the participants and have information to go ahead to the next page if they fully consent to take part.
Consent for publication: Not applicable.
ORCID iD: Daniel Atlaw 
https://orcid.org/0000-0002-2968-4958
Supplemental material: Supplemental material for this article is available online.
References
- 1. Executive HaS. Work-related musculoskeletal symptoms in Great Britain (WRMSDs); Annual Statistics Available at 2018(Accessed March 5, 2019), http://wwwhsegovuk/statistics/causdis/musculoskeletal/indexhtm.
- 2. Vieira ER, Albuquerque-Oliveira PR, Barbosa-Branco A. Work disability benefits due to musculoskeletal disorders among Brazilian private sector workers. BMJ Open. 2011;1:e000003. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3. Putz-Anderson V, Bernard BP, Burt SE, Cole LL, Fairfield-Estill C, Fine LJ, et al. Musculoskeletal Symptoms and Workplace Factors. National Institute for Occupational Safety and Health (NIOSH); 1997:104. [Google Scholar]
- 4. Ortiz-Hernández L, Tamez-González S, Martínez-Alcántara S, Méndez-Ramírez I. Computer use increases the risk of musculoskeletal disorders among newspaper office workers. Med Res Arch. 2003;34:331-342. [DOI] [PubMed] [Google Scholar]
- 5. Moghaddam GK, Lowe CR. Physical Activity. Health and Wellness Measurement Approach for, Mobile Healthcare. Springer; 2019:13-49. [Google Scholar]
- 6. Baddeley B, Sornalingam S, Cooper M. Sitting is the new smoking: where do we stand? Br J Gen Pract. 2016;66:258-258. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7. Lima Júnior JPD, Silva TFAD. Analysis of musculoskeletal disorders symptoms in professors of the University of Pernambuco – Petrolina Campus. Rev Dor. 2014;15:276-280. [Google Scholar]
- 8. Oha K, Animägi L, Pääsuke M, Coggon D, Merisalu E. Individual and work-related risk factors for musculoskeletal pain: a cross-sectional study among Estonian computer users. BMC musculoskeletal symptoms. 2014;15:181-185. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9. Khan SA, Chew KY. Effect of working characteristics and taught ergonomics on the prevalence of musculoskeletal disorders amongst dental students.. BMC musculoskeletal symptoms. 2013;14:118-8. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10. Aminian O, Banafsheh Alemohammad Z, Sadeghniiat-Haghighi K. Musculoskeletal disorders in female dentists and pharmacists: a cross-sectional study. Acta Med Iran. 2012;50:635-640. [PubMed] [Google Scholar]
- 11. Alshagga MA, Nimer AR, Yan LP, Ibrahim IA, Al-Ghamdi SS, Radman Al-Dubai SA. Prevalence and factors associated with neck, shoulder, and low back pains among medical students in a Malaysian Medical College. BMC Res Notes. 2013;6:244-247. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12. Oksanen AM, Laimi K, Löyttyniemi E, Kunttu K. Trends of weekly musculoskeletal pain from 2000 to 2012: National study of Finnish university students. Eur J Pain. 2014;18:1316-1322. [DOI] [PubMed] [Google Scholar]
- 13. Duthey B. Background paper 6.24 low back pain. Priority medicines for Europe and the world. Global Burden of Disease (2010),(March). 2013. Mar 15:1-29. [Google Scholar]
- 14. Morris LD, Daniels KJ, Ganguli B, Louw QA. An update on the prevalence of low back pain in Africa: a systematic review and meta-analyses. BMC Musculoskeletal symptoms. 2018;19:196-204. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15. Chim JM. Musculoskeletal symptoms among office employees in Hong Kong and best practice office ergonomics solutions, In proceedings of the eighth international conference on prevention of work-related musculoskeletal symptoms, Busan, Korea 2013. (pp. 330-331). [Google Scholar]
- 16. Wu S, He L, Li J, Wang J, Wang S. Visual display terminal use increases the prevalence and risk of work-related musculoskeletal disorders among Chinese office workers: a cross-sectional study. J Occup Health. 2012;54:34-43. [DOI] [PubMed] [Google Scholar]
- 17. Chim JM. Ergonomics for the prevention of musculoskeletal disorders of computer users in Hong Kong, Singapore and Japan. J Ergonomics S. 2015;s4:2. [Google Scholar]
- 18. Briggs AM, Woolf AD, Dreinhöfer K, et al. Reducing the global burden of musculoskeletal conditions. Bull World Health Organ. 2018;96:366-368. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19. EU-OSHA. European Agency for Safety and Health at Work. Annual Report Available at http://wwwoshaeuropaeu/en/tools-andpublicationscom Accessed 2017(December 31, 2018.).
- 20. Hoy D, March L, Brooks P, et al. The global burden of low back pain: estimates from the Global Burden of disease 2010 study. Ann Rheum Dis. 2014;73:968-974. published online ahead of print March 24. doi: 10.1136/annrheumdis-2013-204428. 10. [DOI] [PubMed] [Google Scholar]
- 21. Duffield SJ, Ellis BM, Goodson N, et al. The contribution of musculoskeletal disorders in multimorbidity: implications for practice and policy. Best Pract Res Clin Rheumatol. 2017;31:129-144. [DOI] [PubMed] [Google Scholar]
- 22. Hoy D, Bain C, Williams G, et al. A systematic review of the global prevalence of low back pain. Arthritis Rheum. 2012;64:2028-2037. [DOI] [PubMed] [Google Scholar]
- 23. Kamper SJ, Apeldoorn AT, Chiarotto A, et al. Multidisciplinary biopsychosocial rehabilitation for chronic low back pain: Cochrane systematic review and meta-analysis. BMJ. 2015;350:h444. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24. Executive HaS. Work-related Musculoskeletal Disorder (WRMSDs) Statistics. 2015.wwwhsegovuk/statistics/lfs/swit3w12xlsx
- 25. Bernstein IA, Malik Q, Carville S, Ward S. Low back pain and sciatica: summary of NICE guidance. BMJ. 2017;356:i6748. [DOI] [PubMed] [Google Scholar]
- 26. World Health Organization t. Global Recommendations on Physical Activity for Health. World Health Organization; 2010. [PubMed] [Google Scholar]
- 27. James C, James D, Nie V, et al. Musculoskeletal discomfort and use of computers in the university environment. Appl Ergon. 2018;69:128-135. [DOI] [PubMed] [Google Scholar]
- 28. Karwan M, Azuhairi A, Hayati K. Predictors of upper limb symptoms among public university workers in Malaysia. International Journal of Public Health and Clinical Sciences. 2015;2:133-150. [Google Scholar]
- 29. Oha K, Viljasoo V, Merisalu E. Prevalence of musculoskeletal symptoms, assessment of parameters of muscle tone and health status among office workers. Agron Res. 2010;8:192-200. [Google Scholar]
- 30. Ojoawo A, Akinola A, Awotidebe T. Prevalence of work related musculoskeleal pain among academic and non academic staff of A Nigerian University. Gülhane Tip Dergisi. 2017;58:341. [Google Scholar]
- 31. Collins JD, O'Sullivan LW. Musculoskeletal disorder prevalence and psychosocial risk exposures by age and gender in a cohort of office based employees in two academic institutions. Int J Ind Ergon. 2015;46:85-97. [Google Scholar]
- 32. Naude B, Mudzi W, Mamabolo M, Becker P. Low back pain among hospital employees in Gauteng, South Africa: point prevalence and associated factors. Occup Health Southern Africa. 2009;15:24-30. [Google Scholar]
- 33. Sultan-Taïeb H, Parent-Lamarche A, Gaillard A, et al. Economic evaluations of ergonomic interventions preventing work-related musculoskeletal disorders: a systematic review of organizational-level interventions. BMC Public Health. 2017;17:935-1013. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 34. Salmi J, Sursock A, Olefir A. Improving the performance of Ethiopian universities in science and technology. World Bank; 2017. [Google Scholar]
- 35. Worldometer. The estimated total population in Ethiopia. www.Worldometers.info. 2021.
- 36. Cho C-Y, Hwang Y-S, Cherng R-J. Musculoskeletal symptoms and associated risk factors among office workers with high workload computer use. J Manip Physiol Ther. 2012;35:534-540. [DOI] [PubMed] [Google Scholar]
- 37. Obembe AO, Johnson OE, Tanimowo TO, Onigbinde AT, Emechete AA. Musculoskeletal pain among undergraduate laptop users in a Nigerian University. J Back Musculoskelet Rehabil. 2013;26:389-395. [DOI] [PubMed] [Google Scholar]
- 38. Ayanniyi O, Ukpai B, Adeniyi A. Differences in prevalence of self-reported musculoskeletal symptoms among computer and non-computer users in a Nigerian population: a cross-sectional study. BMC Musculoskeletal Symptoms. 2010;11:177-179. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 39. Dagne D, Abebe SM, Getachew A. Work-related musculoskeletal disorders and associated factors among bank workers in Addis Ababa, Ethiopia: a cross-sectional study. Environ Health Prev Med. 2020;25:33-38. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 40. Aghilinejad M, Choobineh AR, Sadeghi Z, Nouri MK, Bahrami Ahmadi A. Prevalence of musculoskeletal disorders among Iranian steel workers. Iran Red Crescent Med J. 2012;14:198-203. [PMC free article] [PubMed] [Google Scholar]
- 41. Sirajudeen MS, Alaidarous M, Waly M, Alqahtani M. Work-related musculoskeletal symptoms among faculty members of the college of applied medical sciences, Majmaah university, Saudi Arabia: a cross-sectional study. International journal of health sciences. 2018;12:18. [PMC free article] [PubMed] [Google Scholar]
- 42. Vos T, Abajobir AA, Abate KH, Abbafati C, Abbas KM, Abd-Allah F, et al. Global, regional, and national incidence, prevalence, and years lived with disability for 328 diseases and injuries for 195 countries, 1990–2016: a systematic analysis for the Global Burden of disease study 2016. The Lancet. 2017;390:1211-1259. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 43. Meaza H, Temesgen MH, Redae G, Hailemariam TT, Alamer A. Prevalence of musculoskeletal pain among academic staff of Mekelle University, Ethiopia. Clinical Medicine Insights: Arthritis and Musculoskeletal Symptoms. 2020;13:1179544120974671. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 44. Das A, Shah S, Adhikari TB, et al. Computer vision syndrome, musculoskeletal, and stress-related problems among visual display terminal users in Nepal. PLoS One. 2022;17:e0268356. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 45. Mohan V, Justine M, Jagannathan M, Aminudin SB, Johari SHB. Preliminary study of the patterns and physical risk factors of work-related musculoskeletal disorders among academicians in a higher learning institute. J Orthop Sci. 2015;20:410-417. [DOI] [PubMed] [Google Scholar]
- 46. Rugelj D. Low back pain and other work-related musculoskeletal problems among physiotherapists. Appl Ergon. 2003;34:635-639. [DOI] [PubMed] [Google Scholar]
- 47. Andersen LL, Clausen T, Burr H, Holtermann A. Threshold of musculoskeletal pain intensity for increased risk of long-term sickness absence among female healthcare workers in eldercare. PLoS One. 2012;7:e41287. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 48. Omokhodion FO, Sanya AO. Risk factors for low back pain among office workers in Ibadan, Southwest Nigeria. Occup Med. 2003;53:287-289. [DOI] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Supplemental material, sj-docx-1-ehi-10.1177_11786302221131690 for Work-Related Musculoskeletal Symptoms and Associated Factors Among Academic Staff in Ethiopian Universities by Demisu Zenbaba, Biniyam Sahiledengle, Diriba Dibaba, Tilahun Tufa, Ayele Mamo and Daniel Atlaw in Environmental Health Insights