Cleaning in the 21st Century: The musculoskeletal disorders associated with the centuries-old occupation - A literature review

A systematic review and meta-analysis of musculoskeletal disorders among firefighters

INTRODUCTION

Due to the nature of their job, firefighters face many ergonomic risk factors and are exposed to several musculoskeletal disorders (MSDs). The aim of this systematic review and meta-analysis was to investigate the prevalence of MSDs among firefighters.

MATERIALS AND METHODS

This study was conducted based on preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines. The literature search was conducted in the databases of PubMed, Scopus, Web of Science, Science Direct, SID, ISC and Google Scholar without applying a time limitation until the end of July 2022. The random effects model was used for meta-analysis, and the I2 index was used to check heterogeneity among studies. The data were analyzed using STATA version 14 software.

RESULTS

The primary literature searches in the databases identified 1,291 articles, 26 of which were entered the meta-analysis stage after screening, study selection, and quality assessment, including 36,342 firefighters. Based on the results of a meta-analysis, the overall prevalence of MSDs among firefighters was 46.39% (95% CI: 32.45-60.32, I2 = 99.7%, P < 0.001). The prevalence of MSDs was also investigated at neck (18.36%), low back (34.32%), shoulder (18.68%), hands (7.43%), upper back (10.15%), knee (19.97%) and foot (6.66%).

CONCLUSION

Considering the relatively high prevalence of MSDs among firefighters and their higher exposition to specific job-related risk factors of MSDs compared to other professions, it is suggested to provide firefighters with appropriate training, implement ergonomic interventions, and regularly screen them to reduce MSDs.

Use of a job exposure matrix to predict the risk of work disability in individuals with back pain: An inception cohort study

BACKGROUND

Low back pain has a high economic burden in Germany due to back pain-related sick leave, disability pensions, and health care utilization. Work-related factors can predict disabling back pain. Job exposure matrices can be used to consider job demands and occupational characteristics in routine data analysis.

OBJECTIVE

This longitudinal analysis tested whether rehabilitation utilization due to musculoskeletal disorders is associated with occupation-linked job exposures in employees with back pain.

METHODS

Data from a German cohort study were used, including employees aged 45 to 59 years with self-reported back pain in the last three months. Individuals' job titles were assessed in the baseline survey in 2017 and matched with parameters of aggregated job exposure indices. Administrative data from the German Pension Insurance were used to extract information on rehabilitation utilization. Proportional hazard models tested the associations.

RESULTS

We considered data of 6,569 participants (mean age: 52.3 years; 57.7% women). During follow-up, with a maximum of 21 months, 296 individuals (4.5%) utilized medical rehabilitation due to musculoskeletal disorders. Adjusted analyses showed that high physical (HR = 2.87; 95% CI 1.74; 4.75) and overall (HR = 2.34; 95% CI 1.44; 3.80) job exposures were associated with a higher risk of rehabilitation utilization.

CONCLUSION

Individuals with back pain working in occupations with high physical job exposures have a higher risk for rehabilitation utilization. To prevent work disability in individuals with back pain, occupational groups with high physical job exposures should be actively informed about tailored intervention options.

Vision-based dirt distribution mapping using deep learning

Cleaning is a fundamental routine task in human life that is now handed over to leading-edge technologies such as robotics and artificial intelligence. Various floor-cleaning robots have been developed with different cleaning functionalities, such as vacuuming and scrubbing. However, failures can occur when a robot tries to clean an incompatible dirt type. These situations will not only reduce the efficiency of the robot but also impose severe damage to the robots. Therefore, developing effective methods to classify the cleaning tasks performed in different regions and assign them to the respective cleaning agent has become a trending research domain. This article proposes a vision-based system that employs YOLOv5 and DeepSORT algorithms to detect and classify dirt to create a dirt distribution map that indicates the regions to be assigned for different cleaning requirements. This map would be useful for a collaborative cleaning framework for deploying each cleaning robot to its respective region to achieve an uninterrupted and energy-efficient operation. The proposed method can be executed with any mobile robot and on any surface and dirt, achieving high accuracy of 81.0%, for dirt indication in the dirt distribution map.

Comparative Ergonomic Study Examining the Work-Related Musculoskeletal Disorder Symptoms of Taiwanese and Thai Workers in a Tape Manufacturing Factory

This study surveyed 114 Taiwanese and 57 Thai workers in a tape manufacturing factory in Taiwan and evaluated their symptoms of work-related musculoskeletal disorder (WMSD) and associated risk factors by using the revised Nordic Musculoskeletal Questionnaire. Task-appropriate biomechanical and body load assessment tools were also employed to examine biomechanical and body load during four specified daily tasks. The results indicated that the prevalence of discomfort symptoms in any body part within one year was 81.6% for the Taiwanese workers and 72.3% for the Thai workers. The body part in which the Taiwanese workers most frequently experienced discomfort was the shoulders (57.0%), followed by the lower back (47.4%), the neck (43.9%), and the knees (36.8%); where the Thai workers most frequently experienced discomfort was the hands or wrists (42.1%), followed by the shoulders (36.8%) and the buttocks or thighs (31.6%). These locations of discomfort were associated with task characteristics. Heavy-material handling (>20 kg) more than 20 times per day was the most significant risk factor for WMSDs for both groups, and this task must thus be urgently improved. We also suggest that providing wrist braces for Thai workers may assist in alleviating their hand and wrist discomfort. The biomechanical assessment results indicated that the compression forces acting on the workers' lower backs exceeded the Action Limit standard; administrative controls must thus be instituted for two heavy-material handling tasks. In the factory, some tasks and workers' movements when completing these tasks must be assessed and improved immediately by using appropriate tools. Although the Thai workers were engaged in more physically demanding tasks, their WMSDs were milder than those of the Taiwanese workers. The results of the study can serve as references for the prevention and reduction of WMSDs in local and foreign workers in similar industries.