APJMR-2015-3-180-Predictive-Models-of-Work-Related-Musculoskeletal-Disorders.pdf

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Asia Pacific Journal of Multidisciplinary Research, Vol. 3, No. 1, February 2015 _______________________________________________________________________________________________________________ Predictive Models of Work-Related Musculoskeletal Disorders (WMSDs) Among Sewing Machine Operators in the Garments Industry CARLOS IGNACIO P. LUGAY and AURA C. MATIAS, PhD Graduate School, University of Santo Toma
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  Asia Pacific Journal of Multidisciplinary Research, Vol. 3, No. 1, February 2015 _______________________________________________________________________________________________________________ 56 P-ISSN 2350-7756 | E-ISSN 2350-8442 | www.apjmr.com Predictive Models of Work-Related Musculoskeletal Disorders (WMSDs) Among Sewing Machine Operators in the Garments Industry CARLOS IGNACIO P. LUGAY and AURA C. MATIAS, PhD Graduate School, University of Santo Tomas, España, Manila, PHILIPPINES caloylugay@yahoo.com  Date Received: December 16, 2014; Dare Revised: February 11, 2015 Abstract :   The Philippine garments industry has been a driving force in the country’s economy, with apparel manufacturing firms catering to the local and global markets and providing employment opportunities  for skilled Filipinos. Tight competition from neighboring  Asian countries however, has made the industry’s  situation difficult to flourish, especially in the wake of the Association of Southeast Asian Nations (ASEAN) 2015 Integration. To assist the industry, this research examined one of the more common problems among sewing machine operators, termed as Work-related Musculoskeletal Disorders (WMSDs). These disorders are reflective in the  frequency and severity of the pain experienced by the sewers while accomplishing their tasks. The causes of these disorders were identified and were correlated with the frequency and severity of pain in various body areas of the operator. To forecast pain from WMSDs among the operators, mathematical models were developed to predict the combined frequency and severity of the pain  from WMSDs. Loss time or “unofficial breaktimes” due to pain from WMSDs was likewise forecasted to determine its effects on the firm’s production capacity. Both these predictive models were developed in order to assist garment companies in anticipating better the effects of WMSDs and loss time in their operations.  Moreover, ergonomic interventions were suggested to minimize pain from WMSDs, with the expectation of increased productivity of the operators and improved quality of their outputs. Keywords: Work-related Musculoskeletal Disorders, Risk Factors, Ergonomic Interventions, Severity and  Frequency of Pain, Predictive Models INTRODUCTION Musculoskeletal Disorders (MSDs) are broadly categorized as joint diseases, physical disability, spinal disorders and conditions resulting from trauma, according to the European Commission[1]. They are  prevalent in any workforce as workers engage themselves in diverse activities. From the sedentary work-style in the office, prolonged sitting in the garment industry, to the strenuous activities in construction sites, transportation and shoe sectors, MSD is one of the main health problems faced by every employee today. MSD is characterized by an inability to perform activities in the work place due to repetitive use of movement or maintenance of awkward postures which cause fatigue, muscle weakness, swelling and decline in work performance. Other risk factors include repetition and dynamic forces which lead to work-related injuries and diseases including MSD, thus the term work-related musculoskeletal disorders or WMSDs. Other than the above causes of WMSD, there are others that are considered noteworthy: the characteristics of work environment and practices, and the inherent and unique characteristics of the workers[2].Aside from the aforementioned risk factors, other possible causes of WMSDs in the various industries have been mentioned in an article by the Canadian Center for Occupational Health and Safety[3].To mention some of them, which have also been said in other articles, these are physical factors, such as repetitiveness of task and its pace of work, force of movements of workers, vibration in the workplace; environmental risk factors such as temperature in the workplace; and  psycho-social issues such as communications flow in the organization, control in one’s work, monotony of work and support from peer and management.  Asia Pacific Journal of Multidisciplinary Research, Vol. 3, No. 1, February 2015 _______________________________________________________________________________________________________________ 57 P-ISSN 2350-7756 | E-ISSN 2350-8442 | www.apjmr.com Furthermore, in an article entitled Musculoskeletal Disorders in Great Britain,[4] said article mentioned that age, gender and workplace size could have significant effects on musculoskeletal disorders. The article further showed various statistics on the  prevalence of these disorders on the different body areas In a study by Tokuc, [5] whose research was made in textile firms, which have similarities in the garments industry, he mentioned several worker risk factors which can have a significanteffect on work-related musculoskeletal disorders. These are gender, age, height, educational background, marital status, employment period, working hours, physical exercises and even smoking. In today’s world of tight competition, form ation of trade blocks, integration of economies and the like, opportunities for improvement should always be considered by business firms. This study has seen an opportunity to improve the sector by analyzing WMSDs among the sewing machine operators in the said sector. To be more specific, this study is highly significant due to the following: 1. Considering the competitiveness in the industry, especially with the ASEAN 2015 integration, productivity studies geared toward the same industry would be highly relevant. Given that cost is one major factor in the area of competitiveness, this study is very relevant as it could decrease the costs brought about by WMSDs. 2. One of the two mathematical models (which are  both predictive in nature) developed by this study could be used as a tool to forecast the pain level due to WMSDs affecting the sewing machine operator. The  prediction of this pain level (which incorporates severity and frequency of pain) would be very helpful for management to individually assess their operators and carry out strategies to reduce the pain theoperator experience. Similarly, the same model would be utilized to determine the variables or risk factors which bring about pain from WMSDs. Management of apparel companies could therefore use these information to develop action plans to address the concerned risk factors causing pain. 3. The second model developed by this study would forecast the break times brought about by the  pain from WMSDs. These break times are assumed to take place whenever the operator stops working and instead rests, does body stretching or simply walks around to relieve himself/herself of the pain from WMSDs. These breaktimes data would be very important for the compan y’s production planners since the said planners would be able to know in advance, on an individual and group bases the unproductive time of the operators and thus, can incorporate these information on the company’s  production capacity. The early determination of the firm’s production capacity will lead to better planning of manpower, machines, materials and other resources, thereby delighting customers in terms of costs, quality and quantity. OBJECTIVES OF THE STUDY Based on literature reviews, WMSDs, in various severities and frequencies, have indeed been experienced by many workers and in almost all industries. Thus, the questions for this study do not argue anymore the existence of WMSDs in the garments industry, but rather how said disorders affect the operators. The questions being raised by this research, which are parallel to the objectives of this  project, have been segregated based on the Primary and Secondary Questions, and then further divided  based on the two models presented. Primary Questions: a. Model 1 : What are the variables which cause pain from WMSDs among sewing machine operators in the garments industry? Model 2 : Does the variable “Pain Level” from WMSD have a relationship with “Breaktimes” expended by the operators due to WMSD’s?   b. What mathematical model could be developed to  predict the “Pain Level” due to WMSDs and the “Breaktimes” expended by the operator to relieve himself/herself of the pain from WMSDs. Secondary Question a.   What are the ergonomic interventions that could be s uggested to avoid or minimize the “Pain Level” from WMSDs among sewing machine operators and thus improve productivity of operators and quality of outputs? METHODS Cronbach’s Alpha   Output from the SPSS software for Cronbach’s is 0.70, which is the minimum number of acceptability to prove internal reliability for the survey questions, specifically those which have to do with Psycho-Social Factors. Research Design The research design that this study utilized is the Causal type. This project makes an in - depth analysis  Asia Pacific Journal of Multidisciplinary Research, Vol. 3, No. 1, February 2015 _______________________________________________________________________________________________________________ 58 P-ISSN 2350-7756 | E-ISSN 2350-8442 | www.apjmr.com of the “cause” and “effect” of the variables considered in this study. “Causes” of pain from WMSDs, based on literature, may be any or all of the following: psych-social risk factors, worker risk factors, environmental risk factors and phys ical factors. The “effect” would be the pain brought about by WMSDs experienced by the operators. Another “cause” and “effect” procedure, which this study similarly analyzed, has to do with the pain from WMSDs as the “cause” and the break times as the “effect”. In as much as operators may stop from working to relieve themselves of the pain from WMSDs, this work stoppage will bring about loss or unproductive time, which will affect the company’s  productivity. For this study, two mathematical models or equations were developed. One would be a predictive model to determine the “Pain Level” (which includes severity and frequency) to be experienced by the sewing machine operator. Before the process of developing this model, the different variables which may have an effect on the “Pain Level” were be identified and measured accordingly. After measurements have been gathered, the data for both dependent and independent variable were subjected to the Multiple Regression statistical technique. The outputs of this software were: the significant variables causing the pain and after a series of tests, the mathematical model to forecast the “P ain Level ” . The process of developing the second model is  basically the same as that of the first, except that a Linear Regression statistical tool was utilized, instead of a Multiple Regression technique used for the first model. The variable “Pain Level” which was u sed in the first model as the dependent variable has become the independent variable instead for this second model. The dependent variable for this second model is thus the “Breaktimes”, which has been described earlier. Figure 1 is the Theoretical Framework of this study, illustrating the flow on how this project would be carried out. Figure1. Theoretical Frameworks for Model Numbers 1 and 2 Participants This study was conducted in small and medium garment enterprises (SME) in Metro Manila, whose description, based on the Magna Carta for SmallEnterprises,”  [8], are those which have assets,excluding land, amounting to P20 million pesos and below. As for the sample size for this research, the equation came from Elder[11] who used the same equation for a study which she did for the International Labor Organization.Based on a 95% confidence level and a 5% error, the minimum sample size required for this study is 73 operators. Before administering the survey questionnaire, a screener questionnaire was first answered by the respondents. The objective of this screener questionnaire is to determine if the respondent’s musculoskeletal disorder, if any, is work-related. If the musculoskeletal disorder is not work-related, the operator concerned should be replaced by another one. If the respondent does not experience any WMSD, said operator was still considered a part of the study. Considering the possibility that not all operators, who were given survey questionnaire, are qualified (based on the Screener Questionnaire mentioned above) for this study or that operators will deliberately not answer the survey questionnaire, this study distributed the questionnaire to a total of 123 operators. After collating and analyzing all accomplished questionnaires (main questionnaire and screener survey), a total of 93 operators were considered for this study, which is above the 73 operators required. Sewing machine operators who are involved in this study are those who are using industrial sewing machines. Though 5 organizations were visited for this study, operators from two firms, one located in Tondo, Manila, while the other in PotreroMalabon, were considered as the respondents for this study.  Asia Pacific Journal of Multidisciplinary Research, Vol. 3, No. 1, February 2015 _______________________________________________________________________________________________________________ 59 P-ISSN 2350-7756 | E-ISSN 2350-8442 | www.apjmr.com Variables Under Consideration The following are the major categories and the specific number of the independent variables (IVs) used in this study a. Psycho-social Risk Factors  –   10 IVs  b. Environmental Risk Factors  –   3 IVs c. Physical Risk Factors - 6 IVs d. Worker Risk Factors  –   15 IVs The following are the response or dependent variables used in this research: Frequency Rate, Severity Rate,Combined Frequency and Severity Rate or “PainLevel” , and “Breaktimes”   RESULTS AND DISCUSSION Regression Model 1 After performing Stepwise Regression on 1 dependent variable (Pain Level) and 34 independent variables, only 5 independent variables came out to be significant, with its p-value less than the set level of significance (alpha) of 0.05 These 5 significant variables are: Type of Break time Preferred, Gender, Degree of Difficulty of Sewing Task, Empowerment and Company policies Comparing the level of significance (alpha), which is equal to 0.05 and ANOVA’s p -value or “Sig.”, which is less 0.001 this research can conclude that the first Regression Model 1 (Equation 1), which is, ŷ  = -31.681+0.236x 30 +15.467x 15 +13.788x 31 +5.619x 6 -6.722x 23 (Equation 1) Where: ŷ  = Estimate of Pain Level from WMSD x 30  = Independent Variable, Type of Breaktime Preferred x 15  = Independent Variable, Gender x 31  = Independent Variable, Degree of Difficulty of Sewing Task x 6  = Independent Variable, Empowerment x 23  = Independent Variable, Company Policies i s useful in predicting “Pain Level”. For this model as well, the Coefficient of Determination (R-square) is 30.2%. This means that 30.2% of the variability in the “Pain Level” could be explained by the variability in the 5 significant independent variables. Improvements in the Model 1 The R-square for the Regression Model 1 is 0.302. This value can be improved by checking further the relationship between the dependent variable, “Pain Level” and each of the 5 independent variables identified earlier. Upon analysis, improvement in R-square was achieved by transforming into square the values of the variables, “Empowerment” and “Company Policies”, and consequently having new variables called “Empowerment - square” and “Company Policies - square”. Through these transformations, the R-square improved to 32.2% as seen from Table 3. Table 3. Model Summary for Model 1 Checking for Outliers As part of the process of finalizing the mathematical model, outliers which are deemed to have undue influence on the regression line, were analyzed and if necessary, removed from the set of observations. This is to be done to have a regression model that is a representative of the data. One of the common methods used to check for outlier influence is called Leverage. This is a measure of the distance of an observation/respondent from the center of the data. If the distance is quite far, the observation/respondent might have a potential influence on the regression model and therefore, should be removed. Employee no. 28, 31, 73, 21, 107 were removed after using the Leverage technique. Cook’s Distance is also another measure, whose objective is the same as that of Leverage. For an observation/respondent to be included in the data set, Its Cook’s Distance value should not be greater than 1.0. For this paper, no respondent in the data set has a value greater than 1.0. (Please see Table 4.) Table 4. Cook’s Distance from SPSS    N Minimum Maximum Mean Std. Deviation Cook's Distance 93 .00000 .42071 .0138086 .04535654 Valid N (listwise) 93 Model Summary  Model R R Square Adjusted R Square Std. Error of the Estimate 1 .568 a  .322 .283 16.838 a. Predictors: (Constant), Gender, Type Of Breaktime, empower_square, copol_square, Degree Of Difficulty Of Sewing Tasks
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