Effect of Body Mass Index on work related musculoskeletal discomfort and occupational stress of computer workers in a developed ergonomic setup

<p>Abstract</p> <p>Background</p> <p>Work urgency, accuracy and demands compel the computer professionals to spend longer hours before computers without giving importance to their health, especially body weight. Increase of body weight leads to improper Body Mass Index (BMI) may aggravate work related musculoskeletal discomfort and occupational-psychosocial stress. The objective of the study was to find out the effect of BMI on work related musculoskeletal discomforts and occupational stress of computer workers in a developed ergonomic setup.</p> <p>Methods</p> <p>A descriptive inferential study has been taken to analyze the effect of BMI on work related musculoskeletal discomfort and occupational-psychosocial stress. A total of 100 computer workers, aged 25-35 years randomly selected on convenience from software and BPO companies in Bangalore city, India for the participation in this study. BMI was calculated by taking the ratio of the subject's height (in meter) and weight (in kilogram). Work related musculoskeletal discomfort and occupational stress of the subjects was assessed by Cornell University's musculoskeletal discomfort questionnaire (CMDQ) and occupational stress index (OSI) respectively as well as a relationship was checked with their BMI.</p> <p>Results</p> <p>A significant association (p < 0.001) was seen among high BMI subjects with their increase scores of musculoskeletal discomfort and occupational stress.</p> <p>Conclusion</p> <p>From this study, it has been concluded that, there is a significant effect of BMI in increasing of work related musculoskeletal discomfort and occupational-psychosocial stress among computer workers in a developed ergonomic setup.</p

Design and evaluation of a computer system operated by the workforce for the collection of perceived musculoskeletal discomfort: A tool for surveillance.

Identification of musculoskeletal problems in industry at an early stage presumably allows for an early control, safer environment and a healthier workforce; all of which can have a significant impact on the overall cost to employers. Unfortunately, there are numerous difficulties in collecting all relevant information with existing data collection methods. DAS (Discomfort Assessment System), a graphical computer system was designed and developed for the collection of perceived musculoskeletal discomfort from the workforce, presumably non-computer users. DAS was evaluated for user performance via a GOMS (Goals, Operators, Methods and Selection rules) model. Empirical testing of the GOMS model developed for DAS revealed a bias in the model. Nevertheless, the prediction error of a revised GOMS model proved to be superior to an analysis of variance in reducing the error variance of the execution time data. User acceptability, user performance and system performance were assessed via a laboratory experiment and two field studies. Results indicated that DAS was easy to learn and easy to use. The implementation of DAS in a work setting confirmed its effectiveness for the collection of musculoskeletal discomfort information. With the information collected by DAS we were able to: (1) determine the number of participating employees who were experiencing some kind of musculoskeletal problems, (2) determine the tasks or jobs that caused more discomfort, (3) determine the muscle groups mostly affected by different tasks, and (4) determine the patterns of discomfort that occurred with time. The sensitivity and specificity of the discomfort data collected by DAS were greater than 80%. Furthermore, the data collected by DAS in a discomfort survey was used to develop a methodology for the analysis of the discomfort data which identified employees with existing or developing musculoskeletal problems. Although not validated, this methodology may be appropriate for the analysis of discomfort data in general. The implementation of DAS at an occupational rehabilitation center showed an alternative application of DAS. This study showed how the data collected by DAS could be used to analize the temporal patterns of discomforts among rehabilitation patients for: (1) managing patient's graded treatment, (2) measuring the effectiveness of different treatment strategies, and (3) monitoring patient's recovery during treatment.Ph.D.Industrial and Operations EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/105440/1/9124096.pdfDescription of 9124096.pdf : Restricted to UM users only