Comparative Assessment of Upper Limbs Musculoskeletal Disorders by Rapid Upper Limb Assessment Among Computer Users of Zahedan Universities

1. Background

Along with the use of computers to speed up processes, and save the time, energy and resources, employees' health problems are increasing day by day (1, 2). The main problem in computer related jobs like working with video display terminal (VDT) and video display ultimate (VDU) is cumulative trauma disorders (CTDs) (3). CTDs are a chronic-type of work-related musculoskeletal disorders (WMSDs) caused by exposure to mechanical (ergonomic) risk factors over a long period in the workplace (4). Muscles, bones, ligaments, tendons, tendon sheaths, nerves, and blood vessels are damaged in this type of injury. Some common injuries of this type include tendonitis, tenosynovitis (inflammation of the tendon and its sheath), rotator cuff tendinitis, bicipital tenosynovitis, lateral epicondylitis (tennis elbow), medial epicondylitis (golfers elbow), carpal tunnel syndrome (CTS), cubital tunnel syndrome, thoracic outlet syndrome (TOS), radial tunnel syndrome, pronator teres syndrome, ganglionic cyst, De Quervain syndrome, Guyon's canal syndrome, trigger finger and vibration syndrome (4, 5). Although an acute type of WMSDs such as bunny, hygroma, bursitis and occupational cramp, which develop during a short-term exposure to ergonomic risk factors, has no significant value as occupational health problems (5), upper limbs musculoskeletal disorders (ULMSDs) are significant as the most adverse effect of working with computers and their direct and indirect treatment costs (6). As a result of previous studies, 20%-25% of total costs spent for medical cares, sick leaves, retirements, and pensions in the countries of Northern Europe in 1991 were related to these disorders. It is also estimated that approximately £ 1.25 billion spend on ULMSDs in the UK annually (7). Different studies have shown that approximately 10% of occupational injuries and disorders are associated with the musculoskeletal system (8). Different hypotheses can be accounted for the explanation of the occurrence of musculoskeletal disorders (7, 8). Around the fourth decade of life, muscle strength declines gradually, which is more in women (9). Moreover, along with increased age, the weight of the adipose tissue and subsequently muscles and bones density decrease, and consequently the muscle power is also dwindled (10). Human muscle strength continues to be grown in early adulthood, but in the middle to later ages it declines (9, 10). With increasing age, stretching-mechanical resistance of the bones, muscles, connective tissues, and the joints connectivity are significantly decreased (10, 11). But in most cases, there are factors (beyond the genetic factors and aging), so-called "ergonomic risk factors" due to assigned tasks and jobs, which are involved in inducing the WMSDs (12). The term "work-related musculoskeletal disorders" (WMSDs) implies musculoskeletal disorders that occur by ergonomic risk factors present in assigned tasks and job duties and their influences, which are more than the physiological, anatomical and biomechanical capabilities of the body (13).

Based on the statistics published by the World Health Organization (WHO; 1995), about 58% of the population of older than 10 years in the world spend their time on working (8). This workload leads to $ 21.6 trillion saving in the production and causes the survival of the socioeconomic improvement in the world (8). In a study conducted on 188 women workers in garment industry, it was found that 60% of participants suffered from carpal tunnel syndrome, which is related to their age and job experience (7). Considering the high prevalence of WMSDs and large compensation paid to the injured workers, the prevention and control of these disorders are extremely important, so that the attention of many researchers have been turned to this problem. The best strategy for the prevention and management of WMSDs is using ergonomic risk assessment tools for the evaluation of risk factors causing such disorders in early stage (5, 12). Ergonomic risk factors assessment techniques are semiquantitative or quantitative tools based on the epidemiological, biomechanical, anatomical and physiological studies for the evaluation of workload related to the ergonomic risk factors (task variables) associated with the jobs or tasks that can lead to WMSDs in the long run (4, 5). Ergonomic risk factors assessment tools are divided into four categories; observational methods (Pen-paper based, or computer aided and videotaping observational methods), direct or instrumental methods, self-reporting methods, and psychophysical methods, which each of them has the strengths and weaknesses and different performance for a specific job. In another classification, these methods are generally divided into two categories; whole body techniques and upper limbs techniques (5, 12). Unfortunately, none of these methods is standard, and they only give a prediction and perspective of inducing WMSDs in the near future with respect to the present condition (12). Since the computer users at Medical Sciences University communicate with medical experts, they could obtain much ergonomic information. Therefore, it is predicted that, the general background knowledge, frequency distribution of pain and inappropriate work posture of the users of these two groups differ.

2. Objectives

Because of the high prevalence of complaints regarding the pain signs and symptoms of WMSDs among the computer users of Zahedan Universities, the present study aimed to comparatively evaluate and estimate the prevalence of upper limbs musculoskeletal disorders (ULMSDs) by Nordic musculoskeletal questionnaire and rapid upper limbs assessment (RULA) among computer users of Technical-Engineering and Medical Sciences universities in Zahedan, southeast of Iran.

3. Patients and Methods

3.1. Study Design and Sample Size

This cross-sectional study was conducted on 107 computer users of Zahedan Universities (65 users from Technical-Engineering University and 42 users from Medical Sciences University) that worked for more than 4 hours/day and lack any special diseases affecting ULMSDs. Previous studies regarding the evaluation of musculoskeletal disorders revealed different results regarding the prevalence of ULMSDs. Therefore, considering the lowest percentage of ULMSDs in the neck region (28.1%) and use of the proportions equation (14), the number of required sample size was obtained as follows:

We used , a combination of four methods for data gathering, including direct observation or walking-talking through (to view and analyze the occupational job processes, working conditions and job duties), interviews (to collect the demographic data); nordic musculoskeletal questionnaire (NMQ; to determine the prevalence of pain signs and symptoms of ULMSDs); and rapid upper limb assessment (RULA; to determine the exposure rate of computer users to task variables of inducing ULMSDs, assess the potential risk of such disorders, as well as providing the ergonomic control solutions to improve and modify the work conditions and reduce the prevalence rate of ULMSDs with the aim of eliminating, reducing or minimizing the existing ergonomic risk factors).

3.2. Nordic Musculoskeletal Questionnaire (NMQ)

Nordic musculoskeletal questionnaire (NMQ) as a standardized questionnaire was used to determine the prevalence of ULMSDs signs and pain symptoms in the studied population (15). NMQ was designed and introduced by Kuorinka et al. from Occupational Health Institute of Scandinavian countries and nowadays, it is accepted as a standardized musculoskeletal questionnaire (15, 16). This questionnaire is used for collecting the demographic information such as participants' age, sex, height, weight, job type, and the presence or absence of pain in various body regions (16). To eliminate the effect of the confounding variables, all subjects who had fractures and complications in different body organs due to the accident or suffered musculoskeletal disorders or pains prior to starting this job, were excluded.

3.4. Data Analysis

After data collection, analyses of NMQ and RULA grand score results were carried out using SPSS V21 through different statistical tests. Some statistical tests used for comparison were as follows: 1-way ANOVA to compare the results of RULA grand scores between different jobs, independent-samples t-test to compare the results of RULA grand scores of two groups, and dichotomous variables paired-samples t-test to compare the results of RULA grand scores between right and left hands, and chi-square test (x²) to seek the relation between different qualitative variables. Finally, charts and graphs were drawn using Excel package (14).

4. Results

Participants in this study were 107 computer users (including 65 people from Technical-Engineering University and 42 people from Medical Sciences University). Table 1 presents the statistical measures of central and dispersion tendency, including mean, standard deviation, minimum, maximum, and range of age, weight and height of the participants in this study. As it is shown in the table, mean and standard deviation of the age, weight, and height of the computer users are 33.84 ± 7.27 y, 65.75 ± 11.97 kg, and 163.5 ± 8.5 cm, respectively.

Table 2 shows the distribution of the pain prevalence in various parts of the body upper limbs. It is seen that there was a significant relation between shoulder pain over the last 12 months and hand posture, so that the most percentage pain (27.3%) in left-handed participants was related to the left shoulder and the highest percentage pain (100%) in right-handed computer users belonged to the their right elbow (P < 0.05). The results of the data analysis using chi-square test shows that there were significant relations between the elbow pains in the last 12 months and gender and hand posture (P < 0.05). Also the highest percentage pain in men (75%) and in women (66.7%) was related to their right elbow. Based on the results of Table 2, the highest percentage of pain prevalence was related to the low back region (72%), neck (68.2%), hand and wrist (51.4%), and shoulder 65.27%. The results showed that there were significant correlations between the shoulder or knee pain over the past 12 months and university type (Technical-Engineering and Medical Sciences universities). This means that 50% of the computer users of Technical-Engineering University had a pain in the knees, while only 29.8% of computer users of Medical Sciences suffered the pain in this region.

The results of the present study revealed that, 12 months before this study, the pain in the knees of the computer users of both Technical-Engineering and Medical Sciences universities was 50% and 25%, respectively. The pain in their shoulders was 60.04% and 39.3%, and the pain in their feet and ankles was 81.3% and 18.8%, respectively. The differences in the pain frequency of the computer users of the two universities was statistically significant (P < 0.05), i.e. the pain frequency distribution in the knees, right and left shoulders of the computer users in Technical-Engineering university were more than that of the Medical University users. Moreover, the findings of the study indicated that, although the pain frequency in the back, waist, thighs, and elbows of the computer users of Technical-Engineering University was higher, these differences were not statistically significant (P > 0.05). Table 3 demonstrated the frequency distribution of the computer users across RULA grand score and university. As this Table, presents, the highest frequency distribution of computer users in Technical-Engineering University belonged to the risk levels 3 (61.6%), 4 (32.3%), and 2 (6.2%), respectively. The most frequency distribution of participants in the study in Medical Sciences University belonged to the risk levels 3 (57.1%), 2 (23.8%), and 4 (19%), respectively. The highest total frequency distribution of computer users of Zahedan, regardless of their university were related to the risk levels 3 (59.8%), 4 (27.1.8%), and 2 (13.1%), respectively. Also, mean RULA grand score in computer users of Technical-Engineering University and Medical Sciences University were 5.97 ± 0.9 and 5.36 ± 1, respectively. Mean RULA grand score of computer users of Zahedan Universities was found 5.7 ± 1. Chi-square revealed a significant difference between RULA action levels of Technical-Engineering and Medical Sciences Universities (P < 0.05). Therefore, most of the computer users of Technical-Engineering University (38.5%) had risk levels of 3 or higher. While, most of the computer users of Medical Sciences University had risk levels of 2 or lower. More than 32.3% of the computer users of Technical-Engineering University and 19% of the computer users of Medical Sciences University were posed at risk levels higher than 4 (RULA score more than 7) i.e. extremely high risk for ULMSDs.

Table 4 shows the frequency distribution of the computer users across neck pain prevalence and RULA risk levels. There was a significant relation between neck pain prevalence and RULA risk levels (P < 0.05). The highest prevalence of neck pain belonged to RULA risk levels 3 (36.44%), 4 (14.95%), and 2 (6.54%).

Table 5 shows the frequency distribution of the computer users across low back pain prevalence and RULA risk levels. There was a significant relationship between low back pain prevalence and RULA risk levels (P < 0.05). The highest prevalence of low back pain belonged to RULA risk levels 3 (28.04%), 4 (6.54%), and 2 (4.67%).

Table 6 shows the frequency distribution of the computer users across knees pain prevalence and RULA risk levels. There was a significant relationship between knees pain prevalence and RULA risk levels (P < 0.05). The highest value of knees pain prevalence were related to RULA risk levels 3 (37.38%), 4 (13.08%), and 2 (2.8%).