Predictors of Musculoskeletal Symptoms and Improvement of Health Outcomes via Computer Software
Abstract
According to the Bureau of Labor Statistics, musculoskeletal disorders (MSDs) account for 30 percent of overall Nonfatal Occupational Injuries and Illnesses. Every year employers spend 20 billion dollars in direct workers’ compensation costs and five times as much in indirect costs. Risk factors from computer work have been contributed to MSDs from repetitive movements, awkward static postures, and working for long hours without rest breaks. The problem remains to find a valid method to evaluate and reduce MSDs while performing computer work. Two studies on Remedy RSI Guard data and OES data (subjective) were conducted utilizing a computer desktop software that objectively measures computer behaviors of participants to determine if any predictors variables from the OES data demonstrate musculoskeletal discomfort, which can lead to MSDs.
Study 1 (Remedy Data) had 13,672 participants from an oil and gas company who had Remedy RSIGuard® on their computer for 1-year. This software was collected continuously during the workday, monitoring participants’ computer activities, work patterns, and behaviors. This Remedy data was used to compare the OES seven-question body part discomfort survey by regression analysis. However, many of the odds ratio results were 1.0, indicating no association between body part discomfort and the predictor variables.
Study 2 (OES Data) involved the same participants as the Remedy RSIGuard® data. The OES questionnaire was collected at the beginning of the study and compared to the OES seven body part discomfort questions using regression analysis. Document Holder and Stress predictor variables had associations in all seven regression analyses when compared to the body part discomfort questions. The variable Breaks had associations in five regression analyses. The variables Bitrifocals and Properly Working Equipment were found to have associations in four regression analyses.
Overall, the subjective data from the OES data resulted in several variables that predicted musculoskeletal symptoms, whereas only a few predictor variables from the objectively measured Remedy data predicted musculoskeletal symptoms. However, the conundrum of determining the most effective way of recognizing discomfort before MSDs occur is still under study.
Citation
Bridges, Linda Arnett (2022). Predictors of Musculoskeletal Symptoms and Improvement of Health Outcomes via Computer Software. Doctoral dissertation, Texas A&M University. Available electronically from https : / /hdl .handle .net /1969 .1 /197127.