A CROSS-SECTIONAL STUDY ON MUSCULOSKELETAL DISORDER PREVALENCE SYMPTOMS AT VIDEO DISPLAY TERMINAL (VDT) WORK- STATIONS.

Transcription

1 A CROSS-SECTIONAL STUDY ON MUSCULOSKELETAL DISORDER PREVALENCE SYMPTOMS AT VIDEO DISPLAY TERMINAL (VDT) WORK- STATIONS. Dr.SUMATHY MUNIAMUTHU Associate Professor Department of Mechanical Engineering Vel Tech Engg., College, Chennai 62. Tamilnadu, India Dr.R.RAJU Associate Professor Department of Industrial Engineering Anna University, Chennai Abstract: To identify the relationships between ergonomic workplace practices and Musculoskeletal Disorder prevalence, a framework has been devised and the different critical factors have been found that are responsible for the prevalence of Musculoskeletal Disorder (MSD) among Video Display Terminal (VDT) workplaces. In this study an instrument to measure MSD prevalence in the computer workplaces has been designed and validated. The model being conceived for computer workplaces has to be tested and validated before it is used for analyzing the various issues relating to the MSD prevalence among VDT users. The valid responses were collected from 80 design engineers, 138 systems engineers, 108 managers and 84 data entry personnel. The respondents are also from various age groups, income groups and qualification groups. SPSS version 15.0 was used for all statistical computations. Descriptive statistics like mean and standard deviations were computed. Pearson s moment Correlation analysis, Simple linear regression analysis, Multiple Regression analysis, Step wise Regression analysis were performed on variables under study. It can be seen from the results of the descriptive statistics that the level of MSD variables varies from to in a five point Likert s scale and the MSD Prevalence Level found to be and Job Prevention is found to be Demographic analysis has proved that the data obtained are from the true respondents. Descriptive statistics have shown the various levels of the MSD variables under study. With this background, the model developed in the study has hypothesized that there exists significant negative correlation between the identified MSD risk factors and MSD prevalence. It is concluded that the variables Equipment Setup, Work Environment, Psychosocial Personal Aspect, Rest Break Frequency and Assumed Posture have significant negative correlation with the variable MSD Prevalence level and the variables Equipment Design, Equipment Layout and Psychosocial Work Aspect have non-significant negative correlation with MSD Prevalence level. Among the nine MSD causing risk factors, a variable called Provision of Training have non-significant positive correlation with MSD prevalence level. The results of correlation analysis show that the increase in MSD causing risk factors leading to decrease in MSD prevalence level. Since the variable, Provision of Training shows positive correlation, it has been omitted for further analysis in this study. The eight critical risk factors are significant predictors of MSD Prevalence Level (R 2 = 0.149) and explains 14.9% of the variability of the MSD Prevalence Level. To identify the most significant predictors among the eight critical risk factors responsible for MSD Prevalence Level, a step-wise regression analysis was carried out. The results of stepwise regression identified two most significant factors are Psychosocial Personal Aspect and Assumed Posture. Keywords: Ergonomics, Musculoskeletal Disorder, Video display users. ISSN : Vol. 4 No.04 April

2 I. Introduction In an exploratory investigation of the relationships between ergonomic workplace practices and Musculoskeletal Disorder (MSD) prevalence, a framework has been devised and found that different critical factors are responsible for the prevalence of MSD among Video Display Terminal (VDT) workplaces. In the present study an instrument (Appendix 1) to measure MSD prevalence in the computer workplaces has been designed and validated. The model being conceived for computer workplaces has to be tested and validated before it is used for analyzing the various issues relating to the MSD prevalence among VDT users. II Methodology of final survey The aim of the study is to design a MSD prevalence model with the identified constructs, suitable for testing the MSD prevalence among VDT users and to analyze the critical factors influencing MSD prevalence among VDT users. Before conducting the study, the validation of questionnaire was carried out for its validity and reliability. The validated survey instrument has been to solicit the respondents like Design Engineers, Systems Engineers, Managers, Data Entry Personnels working in Production, Service and Software industries in order to get the perceptions of the respondents. The study was limited to industries in the state of Tamilnadu in South India. A multidimensional multi-item scaling technique has been developed. The study is mainly descriptive in nature with a view to validating the MSD prevalence variables under study. III Sampling Design A sampling design is a definite plan for obtaining a sample from a given population. A sampling criteria adopted in this study was a stratified random sampling procedure with answers from the professionals who would compose the universe to be researched. A total of about 600 questionnaires were distributed to the VDT users of production, service industries. Out of total 600 questionnaires distributed, 427 were collected with the response rate of 71.16% of the respondents. Out of 427, only 410 samples were found to contain complete information and so were valid for analysis. The data, which had incomplete information, have been treated as invalid and not used for the study. The valid responses were collected from 80 design engineers, 138 systems engineers, 108 managers and 84 data entry personnel. The respondents are also from various age groups, income groups and qualification groups. SPSS version 15.0 was used for all statistical computations. Descriptive statistics like mean and standard deviations were computed. Pearson s moment Correlation analysis, Simple linear regression analysis, Multiple Regression analysis, Step wise Regression analysis, t-test and one way ANOVA were performed on variables under study. IV Results and Discussion A.Demographic Analysis Respondents were selected based on stratified random sampling method. Data collected from 410 respondents were analyzed for achieving the research objectives. Before taking up the testing of various hypotheses, demographic analysis was taken up to check the representativeness of the sample used for the data collection in the study. Activities of the organization, age of the respondents, educational background and type of the organizations were used for conducting demographic analysis. Table 1 shows the type of the industry. On considering the type of the organizations used for conducting demographic analysis, about 22.7% of the respondents belong to production type of industry, 27.6% of the respondents belong to service type of industry and 49.8% of respondents belong to software industry. This shows majority of respondents come from software industry. The graphical representation of type of industry is shown in Figure 1. Table 1. Type of Industry Industry Frequency % Production Service software ISSN : Vol. 4 No.04 April

3 Figure 1 - Graphical Representation of Type of Industry Table 2 shows the income grouping of the respondents. While taking income of the respondents for demographics, about 32% of the respondents belong to income less than Rs 2.50 lakhs per annum. About 45% respondents belong to the income group of above Rs 2.50 lakhs to 3.5 lakhs per annum. Finally about 22% of the respondents belong to the income group of above Rs 3.5 lakhs per annum. The income group indicates that the respondents adequately represented from low income group to high income group. The graphical representation of income of the respondents is shown in Figure 2. Table 2 Income of the respondents Figure 2 - Graphical Representation of Income of the Respondents Table 3 shows the respondents educational qualification. About 18% of the respondents are diploma holders, 29% of the respondents are engineering graduates, 14% of the respondents are science and humanities graduates, 14% of the respondents are post graduates in engineering and finally about 25% of the respondents are postgraduates in other than engineering. The Educational Qualification of the respondents is shown in Figure 3. ISSN : Vol. 4 No.04 April

4 Table 3 Educational qualification of the respondents Educational Qualification Frequency Percent Diploma UG-Tech UG-SCH PG-Tech PG-others Figure 3 - Bar Chart for Educational Qualification of the Respondents population. As a whole it is concluded that the respondents in this survey adequately represented the intended B.Descriptive Statistics Data collected through the structured questionnaire have been processed using SPSS package. The composite score for each variable along with their standard deviation obtained through the package is shown in Table 4. Table 4 Descriptive statistics of MSD prevalence variables Variable (MSD prevalence) Mean Standard Deviation Equipment Design Equipment Setup Equipment Layout Provision of Training Work Environment Psychosocial Work Aspect Psychosocial Personal Aspect Rest Break Frequency Assumed Posture MSD Prevalence Level Job Prevention ISSN : Vol. 4 No.04 April

5 It can be seen from the results of the descriptive statistics that the level of MSD variables varies from to in a five point Likert s scale and the MSD Prevalence Level found to be and Job Prevention is found to be The value of standard deviation for all the scores are close to zero, revealing that the variability of responses is very low and the score obtained are consistent. C.Correlation between MSD Risk Factors and the MSD Prevalence In the previous sections the demographic analysis and the descriptive statistics have been discussed. Demographic analysis has proved that the data obtained are from the true respondents. Descriptive statistics have shown the various levels of the MSD variables under study. With this background, the model developed in the study has hypothesized that there exists significant negative correlation between the identified MSD risk factors and MSD prevalence. In order to test the above said relationship the following hypotheses are proposed for testing: H 1 : There is a significant negative correlation between the level of Equipment Design and the degree of MSD prevalence. H 2 : There is a significant negative correlation between the level of Equipment Setup and the degree of MSD prevalence. H 3 : There is a significant negative correlation between the level of Equipment Layout and the degree of MSD prevalence. H 4 : There is a significant negative correlation between the level of Provision of Training and the degree of MSD prevalence. H 5 : There is a significant negative correlation between the level of Work Environment and the degree of MSD prevalence. H 6 : There is a significant negative correlation between the level of Psychosocial Work Aspect and the degree of MSD prevalence. H 7 : There is a significant negative correlation between the level of Psychosocial Personal Aspect and the degree of MSD prevalence. H 8 : There is a significant negative correlation between the level of Rest Break Frequency and the degree of MSD prevalence. H 9 : There is a significant negative correlation between the level of Assumed Posture and the degree of MSD prevalence. D.Testing of hypotheses (H 1 H 9 ) All the directional hypotheses (H 1 H 9 ) have been converted into null and alternate hypotheses for testing. Pearson s correlation analysis has been employed for testing these hypotheses. The Table 5 presents the correlations between the MSD critical risk factors and the MSD Prevalence Level. The MSD critical risk factors are Equipment Design, Equipment Setup, Equipment Layout, Provision of Training, Work Environment, Psychosocial Work Aspect, Psychosocial Personal Aspect, Rest Break Frequency and Assumed Posture. ISSN : Vol. 4 No.04 April

6 Table 5 Result of Correlation analysis. ** correlation is significant at the 0.01 level (2 tailed) * correlation is significant at the 0.05 level (2 tailed) From the Table 5, it is seen that the correlation co-efficient between the Equipment Design and the MSD prevalence level is with p- value of Even though the p value was found to be non-significant, there exists the negative correlation between the Equipment Design and the MSD prevalence level partially supporting the hypothesis (H 1 ). The Equipment Setup shows a negative significant relationship with the degree of MSD prevalence level (H 2 ) and the correlation co-efficient of with p < 0.01 as expected. The correlation co-efficient between the Equipment Layout and the MSD Prevalence Level is found to be with p value of Even though the p-value is non-significant, there exists a negative correlation between Equipment Layout and the MSD Prevalence Level (H 3 ). The correlation between the Provision of Training and the MSD prevalence level (H 4 ) is found to be with p-value of revealing non significant relationship between them. The correlation co-efficient of Work Environment and the MSD prevalence level indicate that there exists high negative correlation between the Work Environment and the MSD prevalence level (r = , p = 0.05) for the VDT users (H 5 ). The correlation co-efficient between the Psychosocial Work Aspect and the degree of MSD prevalence level is found to be with p value of Even though the p value is non-significant, there exists a negative correlation between the level of Psychosocial Work Aspect and the degree of MSD prevalence level (H 6 ). The correlation between the Psychosocial Personal Aspect and the degree of MSD prevalence (H 7 ) is found to be significant at 0.01 (r = ). The correlation result shows that there exists negative correlation between the level of Rest Break Frequency and the degree of MSD prevalence level (H 8 ) and the correlation is significant at 0.01 (r = ). There exists a negative correlation between the level of Assumed Posture and the degree of MSD prevalence level (r = ) and the result is significant at 0.01(H 9 ). As a whole, the correlation results explain that the MSD variables, except Provision of Training, have negative correlations with the MSD prevalence level. In other words, lesser the level of MSD variables such as Equipment Design, Equipment Setup, Equipment Layout, Work Environment, Psychosocial Work Aspect, Psychosocial Personal Aspect, Rest Break Frequency and Assumed Posture higher the degree of MSD prevalence. Based on the results and discussion, the variable Provision of Training is omitted for further analysis. As a whole, it is concluded that the variables Equipment Setup, Work Environment, Psychosocial Personal Aspect, Rest Break Frequency and Assumed Posture have significant negative correlation with the variable MSD Prevalence level and the variables Equipment Design, Equipment Layout and Psychosocial Work Aspect have non-significant negative correlation with MSD Prevalence level. Among the nine MSD causing risk factors, a variable called Provision of Training have non-significant positive correlation with MSD prevalence level. The results of correlation analysis show that the increase in MSD causing risk factors leading to decrease in MSD prevalence level. Since the variable, Provision of Training shows positive correlation, it has ISSN : Vol. 4 No.04 April

7 been omitted for further analysis in this study. As a result of testing of hypotheses (H 1 H 9 ), the MSD causing risk factors are reduced to eight from nine independent factors. E.Intercorrelations among the eight independent variables in predicting the MSD prevalence level As the correlation matrix (Table 5) does not predict the degree of intercorrelations among the eight independent variables in predicting a dependent variable MSD prevalence level, further analysis is carried out using the multiple regression analysis to investigate the relationship between the eight critical MSD risk factors (after omitting the Provision of Training) and the MSD prevalence level. In order to establish the extent of MSD risk factors to predict the level of MSD prevalence, the following two hypotheses are formulated. H 10 : MSD Prevalence Level variables namely, Equipment Design, Equipment Setup, Equipment Layout, Work Environment, Psychosocial Work Aspect, Psychosocial Personal Aspect, Rest Break Frequency and Assumed Posture together predict the MSD Prevalence Level in Indian computer based industries. H 11 : From among the eight MSD variables under study, one or more form as a subset and significantly predict the MSD Prevalence Level. The directional hypotheses (H 10 and H 11 ) have been converted into null and alternate hypotheses for testing. A multiple regression analysis is conducted to test the hypothesis H 10 and the results are depicted in Table 6. Here, the MSD causing variables namely Equipment Design, Equipment Setup, Equipment Layout, Work Environment, Psychosocial Work Aspect, Psychosocial Personal Aspect, Rest Break Frequency and Assumed Posture are considered as independent variables and the level of MSD prevalence is treated as dependent variable. Table 6 Regression Model for MSD variables Model R R-Square Adjusted R-Square Standard error F Sig. As can be seen from the Table 6 that the multiple regression co-efficient of the MSD prevalence level and the eight critical risk factors is found to be F-value of the regression equation is found to be and it is significant at level. This value indicates that the eight critical risk factors considered together have degree of association with the MSD prevalence level thereby supporting the Hypothesis H 10. Results presented suggest that the eight critical risk factors are significant predictors of MSD Prevalence Level (R 2 = 0.149) and explains 14.9% of the variability of the MSD Prevalence Level. The standard error of indicates that the line fits the data. The regression model with R 2 = and F value of leads to the rejection of null hypothesis, R 2 = 0, and we can conclude that the regression model is quiet effective. Table 7 Multiple Regression Co-efficients of MSD prevalence level Model Un-standardized Coefficients Standard Co-efficients t Sig. (Constant) 1 B Std. Error Beta Equipment Design Equipment Setup Equipment Layout Work Environment Psychosocial Work Aspect Psychosocial Personal Aspect Rest Break Frequency Assumed Posture ISSN : Vol. 4 No.04 April

8 Table 7 presents the un-standardised and standardized co-efficients of predictor variables along with the t-values of all the variables. It basically shows the relationship between the predictor (MSD variables) variables and the criterion (MSD Prevalence Level) variables. Each co-efficient shows the relative contribution of independent variables into the regression equation. It is also observed that Psychosocial Work Aspect (p < 0.05), Psychosocial Personal Aspect (p < 0.001), Rest Break Frequency (p < 0.05) and Assumed Posture (p < 0.001) are significant predictors of MSD prevalence level. F.Testing of Hypotheses (H 11 ) To test H 11 and to identify the most significant predictors among the eight critical risk factors responsible for MSD Prevalence Level, a step-wise regression analysis was carried out. The stepwise model is useful when there are relatively large numbers of independent variables for inclusion in the function. By sequentially determining the next best discriminating variable at each step, variables that are not useful are eliminated. The reduced set typically is as good as, and sometimes better than the complete set of variables. The stepwise regression is used in this study in order to mitigate any potential bias from multicollinearity among the eight variables. For conducting the stepwise regression analysis, MSD Prevalence Level was specified as the dependent variable. Independent variables in the stepwise regression were Equipment Design, Equipment Setup, Equipment Layout, Work Environment, Psychosocial Work Aspect, Psychosocial Personal Aspect, Rest Break Frequency and Assumed Posture. The P IN (Probability of F to enter) and P OUT (Probability of F to remove) stipulated for the stepwise multiple regression analysis were 0.05 and 0.10 respectively. Psychosocial Personal Aspect was the first variable to enter the regression equation with Beta-in value of The co-efficient of Multiple determination (R 2 ) value, in the first step of regression analysis was found to be and the F-value was found to be (p < 0.001). Assumed Posture was the second variable to enter regression equation with Beta-in-value of Value of R 2 in the second step of regression analysis was found to be and the F value was found to be (p < 0.001). Thus, two out of eight variables entered the regression equation in the stepwise multiple regression analysis. After the second step, the multiple R value and the co-efficient of multiple determination (R 2 ) values that were obtained in this study are and respectively. As Tables and show, the results of stepwise regression identified two most significant factors pertinent to the dependent variable: 1) Psychosocial Personal Aspect and 2) Assumed Posture. Table 8 Summary of Step-wise Multiple Regression for MSD Prevalence Level Step Variable entered Multiple R R Square Increment in R Sq DF F Sig 1 Psychosocial Personal Aspect , Assumed Posture , Table 9 Step-wise Multiple Regression Co-efficient for MSD Prevalence Level VARIABLES IN THE EQUATION: Variable B SE BETA t Sig Psychosocial Personal Aspect Assumed Posture VARIABLES NOT IN THE EQUATION: Variable BETA IN PARTIAL t Sig Equipment Design Equipment Setup Equipment Layout Work Environment Psychosocial Work Aspect Rest Break Frequency Theoretically, a multicollinearity problem may happen when the predictor variables are not independent. Incorrect estimation of the regression co-efficients may be due to the strong correlation between predictor variables. In this case, two predictor variables, Psychosocial Personal Aspect, Assumed Posture which have strong correlation with other variables, form a reduced set of variables. Psychosocial Personal Aspect is the most significant predictor of MSD Prevalence Level (R 2 = 0.084) and explains 8.4% of the variability of MSD Prevalence Level. The variable, Assumed Posture is the second important predictor variable and this explains ISSN : Vol. 4 No.04 April

9 3% of the total variance of the MSD Prevalence Level. Thus, these two variables are considered as strong predictors of MSD Prevalence Level among VDT users and thereby supporting the hypotheses H 11. V. Research Findings To predict the statistical significance of the independent variables on the dependent variable, this study has utilized various methods including Pearson s correlation technique, Simple Regression and Multiple Regression. The study critically examined MSD Prevalence Level issues among VDT users from the VDT user s perspectives. The results of correlation analysis show that the increase in MSD critical risks factors leading to decrease in MSD prevalence level. However, among the nine MSD causing risk factors, a variable called Provision of Training has positive correlation with MSD prevalence level and therefore it is deleted for further analysis in this study. The MSD causing risk factors considered in this study, after omitting Provision of Training are Equipment Design, Equipment setup, Equipment Layout, Work Environment, Psychosocial Work Aspect, Psychosocial Personal Aspect, Rest Break Frequency and Assumed Posture. The result of multiple regression analysis shows that the eight independent variables jointly predict the dependent variable MSD prevalence level. The result of step-wise regression analysis identified the most significant predictors of MSD prevalence level, which are Psychosocial Personal Aspect and Assumed Posture. VI. Recommendations The findings of the study examined the relationships between the prevalence of self - reported musculoskeletal disorders attributed to work and Equipment Design, Equipment Setup, Equipment Layout, Work Environment, Psychosocial Work Aspect, Psychosocial Personal Aspect, Rest Break frequency and Assumed Posture factors in a general population of Video Display Terminal (VDT) workers. The study has brought to limelight, while designing a preventive intervention program aiming at reducing the occurrence of Musculoskeletal Disorders (MSD) symptoms in the VDT users environment should address the above mentioned eight factors. VII Limitations of the study Limitation of the study concerns the use of a single source of data i.e. a questionnaire survey. Therefore, the problem of common method variance is a possibility. Validity and reliability are closely related. Reliability is a function of consistency of the shots, while validity is a function of shots being arranged around the bull s eye. Thus, an instrument that is valid is always reliable; an instrument that is not valid may or may not be reliable; an instrument that is not reliable is never valid. Because we cannot have validity without reliability. In social science study, it is necessary for the researcher to make tradeoffs between explanatory power and the scope of a research project. Although this study attempts to reasonably infer the causal relationships from the treatment to dependent variables, the ambiguity about the direction of causal influence are still regarded as potential threats to internal validity. Use of questionnaires which rely on symptoms reporting can overestimate the magnitude of the problem as presence of musculoskeletal disorders. The presence of symptoms alone may therefore be an unstable predictor of musculoskeletal disorders in a working population (Gerr et al, 1996). However medical examination is essential to establish a clinical diagnosis. VIII Scope for Future Research A generalized model can also be developed in order to suit any type of industries involving VDT work to test the prevalence of MSD. The responses can be collected from the VDT users all over India and the results made can infer the perception of VDT workers of whole India. This work can be extended to the laptop users by the future researchers. The role of teamwork in reducing stress and improving musculoskeletal health can be studied. The influence of individual factors like sleeping hours and leisure time on MSD prevalence can be studied as a further research. A main health problem associated with VDT work i.e. eye strain can also be included in the future study. To improve our understanding of the etiology of MSD symptoms among VDT users, the best way forward for the future research might be to combine multidisciplinary research efforts of observational (field based) and experimental research. The experimental research may include Visual Analogue scale (VAS) for measuring the factors. Electromyography (EMG) measurements can be done to record the muscle load to predict the musculoskeletal symptoms in various body regions. The standardized clinical examination can also be included by the future researches to record the level of MSD prevalence among VDT users. ISSN : Vol. 4 No.04 April

10 REFERENCES [1] Ali Aydenxz and Savas Gursoy, (2008) Upper Extremity Musculoskeletal Disorders among Computer Users, Turk J Med Sci, Vol. 38(3), pp [2] Andersen, J. H., Kaergaard, A., Mikkelsen, S, Jensen, (2003) U., Frost, P. and Bonde, J. Risk factors in the onset of neck/shoulder pain in a prospective study of workers in industrial and service companies,. Occupational Environmental Medicine, Vol. 60, pp [3] Andy, P. F., (2005) Discovering statistics using SPSS Edition 2, Sage Publications, pp [4] Armstrong, T. J., Buckle, P., Lawrence, J. F., Hagberg,M., Jonsson, B., Kilbom, Kuorinka, I. A. A., Silverstein,B. A., Sjogaard, G. and Viikari Juntura, E. R. A, (1993) A conceptual model for work-related neck and upper-limb musculoskeletal disorders, Scandinavian Journal of Work, Environment and Health, Vol19, pp ,. [5] Bergqvist, U., Wolgast, E., Nilsson, B., Voss, M. (1995) Musculoskeletal disorders among visual display terminal workers: individual, ergonomic, and work organizational factors. Ergonomics; Vol. 38, pp [6] Bernard, B. P. (1997) Musculoskeletal disorders and workplace factors. A critical review of epidemiologic evidence for workrelated musculoskeletal disorders of the neck, upper extremity, and low back, NIOSH. [7] Bongers, P. M., Winter, D. E., Kompier, C. R., M. A. J. and Hildebrandt, V. H. (1993) Psychosocial factors at work and musculoskeletal disease. Scandinavian Journal of Work Environment and Health, Vol. 19, pp [8] Buckle, P. (1997) Upper limb disorders and work: the importance of physical and psychosocial factors, Journal of Psychosomatic Research, Vol. 43, pp [9] Dinesh Bhanderi, S. K., Choundhary, Lata Parmar and Vikas Doshi, (2008) A Study of Occurrence of Musculoskeletal Discomfort in Computer Operators, Indian Journal of Community Medicine, Vol. 33, No.1. [10] Eltayeb, S., Bart Staal, J., Amar, A. H., Salwa, S. A. and Rob A de Bie, (2008) Complaints of the arm, neck and shoulder among computer office workers in Sudan: a prevalence study with validation of an Arabic risk factors questionnaire, Environmental Health, Vol. 7, pp. 33. [11] Gerr, F., Marcus, M., Ensor, C., Kleinbaum, D., Cohen, S., Edwards, A., Gentry, E., Ortiz, D. and Monieth, C. (2002) A Prospective Study of Computer Users: Study Design and Incidence of Musculoskeletal Symptoms and Disorders, American Journal of Industrial Medicine Vol. 41, pp [12] Karlqvist, L., EwaWigaeus Tornqvist, Mats Hagberg, Maud Hagman and Allan Toomingas, (2002) Self-reported working conditions of VDU operators and associations with musculoskeletal symptoms: a cross-sectional study focusing on gender differences, International Journal of Industrial Ergonomics, Vol. 30, pp [13] Klussmann, A., Hansjuergen Gebhardt, Falk Liebers and Monika A Rieger, (2008) Musculoskeletal symptoms of the upper extremities and the neck: A cross-sectional study on prevalence and symptom-predicting factors at visual display terminal (VDT) workstations, BMC Musculoskeletal Disorders, Vol. 9, pp. 96,. [14] Marcus, M. and Gerr, F. (1996). Upper extremity musculoskeletal symptoms among female office workers: associations with video display terminal use and occupational psychosocial stressors, American Journal of Industrial Medicine, Vol. 29, pp ISSN : Vol. 4 No.04 April