An Analysis of Work-Related Musculoskeletal Disorders Among Butchers in Kano Metropolis, Nigeria

Magnitude of work-related musculoskeletal disorders and its associated factors among Ethiopian nurses: a facility based cross-sectional study

INTRODUCTION

Work-related musculoskeletal disorders have a greater negative impact on nurses' well-being, both physically and emotionally. Because of this issue, nurses take excessive leave, are absent from work, quit their profession, and have a lower quality of life. This leads to an increase in human and economic losses to health care institutions. However, there is a paucity of information about work-related musculoskeletal disorders among nurses in the study area.

OBJECTIVE

This study aimed to assess the magnitude of work-related musculoskeletal disorders and its factors associated among nurses in Hospitals of West Shoa Zone, Central Ethiopia, 2021.

MATERIALS AND METHODS

A cross-sectional facility-based study was conducted with 406 systematically selected nurses from March 2021 to June 2021. A simple random sampling was used to select study subjects. Data were gathered through face-to-face interviews. The data has been cleaned and checked, entered Epi Data 3.1, and exported into Statistical Package for Social Sciences software version 25 for further analysis. Bivariable and multiple logistic regressions were used to determine the factors associated with outcome variable. The level of statistical significance was reported to be less than 0.05.

RESULTS

The prevalence of work-related musculoskeletal disorders was 73.8% and the highest report 247 (62.2%) was seen in the lower back which was followed by the neck 182 (45.8%) and elbow 126 (31.7%), while the lowest affected body part was ankle 46(11.6%). Working in the medical ward (COR = 5.91, 95%CI: 2.53-13.79), surgical ward (COR = 9.63, 95%CI: 3.64-15.51), and operation room theater (COR = 6.02, 95%CI: 2.37-15.27) had higher risk to work-related musculoskeletal discomfort as compared to those who were worked in outpatient department. Nurses those experienced bending or twisting back as a problem during work 2.33 times more likely to have higher risk WMSDs as compared to those who didn't perceive it with (COR 2.33, 95% CI: 1.47-3.71).

CONCLUSION

In this study, the prevalence of work-related musculoskeletal disorders among nurses was high. Working in room/ward, working in malposition, have no on job training to prevent work-related musculoskeletal disorders and bending or twisting back during work were identified as associated factors. Since work-related musculoskeletal disorders are preventable, educating the staff about the use of proper body mechanics and modifying the work environment is essential.

Gait Characteristics and Fatigue Profiles When Standing on Surfaces with Different Hardness: Gait Analysis and Machine Learning Algorithms

Simple Summary

The purpose of this study was to explore if an anti-fatigue soft mat could improve the gait performance after standing for long periods and to examine if a machine-learning algorithm could evaluate fatigue state objectively. Compared with standing directly on the hard ground, using an anti-fatigue mat could reduce the negative effect of standing for a long time (4 h). The machine-learning algorithm demonstrated moderate accuracy in measuring fatigue. The accuracy of gait parameters used to consider a non-fatigued state following the use of an anti-fatigue mat was higher than that of the fatigue state. The results may indicate that it is beneficial to use anti-fatigue mats when standing for long periods, and it is feasible to use gait parameters and machine-learning algorithms to detect fatigue.

Abstract

Background: Longtime standing may cause fatigue and discomfort in the lower extremities, leading to an increased risk of falls and related musculoskeletal diseases. Therefore, preventive interventions and fatigue detection are crucial. This study aims to explore whether anti-fatigue mats can improve gait parameters following long periods of standing and try to use machine learning algorithms to identify the fatigue states of standing workers objectively. Methods: Eighteen healthy young subjects were recruited to stand on anti-fatigue mats and hard ground to work 4 h, including 10 min rest. The portable gait analyzer collected walking speed, stride length, gait frequency, single support time/double support time, swing work, and leg fall intensity. A Paired sample t-test was used to compare the difference of gait parameters without standing intervention and standing on two different hardness planes for 4 h. An independent sample t-test was used to analyze the difference between males and females. The K-nearest neighbor (KNN) classification algorithm was performed, the subject’s gait characteristics were divided into non-fatigued and fatigue groups. The gait parameters selection and the error rate of fatigue detection were analyzed. Results: When gender differences were not considered, the intensity of leg falling after standing on the hard ground for 4 h was significantly lower than prior to the intervention (p < 0.05). When considering the gender, the stride length and leg falling strength of female subjects standing on the ground for 4 h were significantly lower than those before the intervention (p < 0.05), and the leg falling strength after standing on the mat for 4 h was significantly lower than that recorded before the standing intervention (p < 0.05). The leg falling strength of male subjects standing on the ground for 4 h was significantly lower than before the intervention (p < 0.05). After standing on the ground for 4 h, female subjects’ walking speed and stride length were significantly lower than those of male subjects (p < 0.05). In addition, the accuracy of testing gait parameters to predict fatigue was medium (75%). After standing on the mat was divided into fatigue, the correct rate was 38.9%, and when it was divided into the non-intervention state, the correct rate was 44.4%. Conclusion: The results show that the discomfort and fatigue caused by standing for 4 h could lead to the gait parameters variation, especially in females. The use of anti-fatigue mats may improve the negative influence caused by standing for a long period. The results of the KNN classification algorithm showed that gait parameters could be identified after fatigue, and the use of an anti-fatigue mat could improve the negative effect of standing for a long time. The accuracy of the prediction results in this study was moderate. For future studies, researchers need to optimize the algorithm and include more factors to improve the prediction accuracy.

Assessment of musculoskeletal disorders among employees working office workplaces in the manufacturing sector

BACKGROUND

The number of studies investigating the relationship between office employees and the development of musculoskeletal disorders (MSDs) is limited. Only a few focused on assessing workplace ergonomics of office employees.

OBJECTIVE

This study aims to investigate the risk of MSDs and associated factors in the office workplaces of a large-size manufacturing company.

METHODS

Data were gathered from 208 office employees from a manufacturing company via Rapid Office Strain Assessment (ROSA) and Cornell questionnaire. The collected data were analyzed to examine the relationship between ergonomic risk levels and MSDs reported by the office staff.

RESULTS

The mean ROSA final score is 3.52 (std. dev. = 0.71), chair section is 3.30, monitor and telephone section is 2.18, and mouse and keyboard section is 2.69. Thirty-nine of 208 (18.8%) office workplaces are at risk. The most important factor to raise the risk level is found to be the height and depth of the chair. The Cornell questionnaire results highlight that the highest discomfort severity is related to the neck (32.26%), lower back (23.23%) and upper back (22.26%). The discomforts in the neck (r = 0.362), upper back (r = 0.404) and lower back (r = 0.368) are moderately and positively correlated according to the final ROSA score. The results indicate that the highest risked departments are Accounting, Sub Industry, Production Planning, and Manufacturing Management.

CONCLUSION

The parameters associated with the chair section should be considered a priority for reducing and eliminating MSDs among office employees.

Prevalence of musculoskeletal disorders amongst flower farm workers in Kenya

Background

Work-related musculoskeletal disorders (WMSDs) are a global public concern for health and social-care systems, as well as individuals. They are the second-most prevalent cause of disability globally.

Objectives

The primary objective was to determine the prevalence of WMSDs amongst flower farm workers. The secondary objective was to determine the association between the socio-demographic characteristics and the presence of WMSDs in the previous 12-month period.

Method

A cross-sectional descriptive study was conducted. A sample of 270 participants was drawn from 897 farm workers. Quantitative data related to musculoskeletal disorders were collected using the Nordic Musculoskeletal Questionnaire (NMQ). Descriptive statistics were undertaken using frequencies and percentages. Inferential statistics were analysed using a chi-squared test (X²) based on an alpha level of p < 0.05.

Results

A total of 184 (68.1%) respondents reported musculoskeletal discomfort. Amongst the 184 respondents, 178 were performing general farm work. Most 103 (38.1%) of the WMSDs were reported in the lower back. There was a strong association between job designation as a general worker (p = 0.016), an older age (p = 0.027) and having worked for a long time as a farm worker (p = 0.041) and WMSDs.

Conclusion

Flower farm workers in Kenya were found to be heavily burdened by WMSDs. Furthermore, the job designation, older age, as well as having worked for a long time, predisposes workers to the risk of developing WMSDs.

Clinical implications

The high prevalence of WMSDs necessitates policy reform in the flower farm industry.