Hamed Yarmohammadi, Seyed Hassan Niksima, Soudabeh Yarmohammadi, Alireza Khammar, Hossein Marioryad, Mohsen Poursadeqiyan,
Volume 9, Issue 3 (9-2019)
Abstract
Introduction: Work-related musculoskeletal disorders (WMSDs) are any disorders or injuries to the musculoskeletal system due to working procedure or conditions. WMSDs is one of the main causes of occupational injuries and disability in advanced and developing countries. The present study was conducted to evaluate the prevalence of musculoskeletal disorders in drivers in order to achieve complete results with high statistical power, using meta-analysis method.
Material and Methods: This study is a systematic review and meta-analysis. In this study, the articles extracted from national and international databases, including Scientific Information Database (SID), Science Direct, PubMed (using the word MESH), Pre Quest, Scopus, Google Scholar, Iran Medix, SID, and MedLib. The main keywords for the search were Prevalence، Musculoskeletal and Drivers. The time for selecting articles was from 2000 to 2017. Data were analyzed using meta-analysis (random effect model). I2 and Q indexes were used to calculate heterogeneity. All statistical analysis was performed using STATA 14 software.
Results: In this study, 22 articles were entered into the meta-analysis process. The sample size was 7706 people with mean of 350 people in each study. The prevalence of musculoskeletal disorders in different organs of drivers was as follows: 26.19% (CI: 38-14.30), 18.07% (CI: 25.99-10.16), and 5.75% (CI: 8.27-3.22) in neck, shoulder, wrist / hand, and elbow respectively. The highest prevalence was related to low back pain 41.63% (confindence Interval (CI): 33.09-50.17), and the lowest prevalence was related to elbow disorder 7.45% with (CI: 95.46-9.43). The significance level was set at 0.05.
Conclusion: The results of this study show that the prevalence of pains in the back, neck, and shoulder are high among drivers. Also, due to the high prevalence of predicting the incidence of impaired driving. in order to control and reduce these disorders, appropriate design of seats and equipment of vehicles, conducting periodic examinations of drivers,performing proper exercise, and considering adequate rest time in working hours are recommended. Ergonomics and occupational health education programs are also recommended to reduce the risk of developing musculoskeletal disorders associated with driving.
Naser Nik Afshar, Mostafa Kamali, Elham Aklaghi Pirposhteh, Hesamedin Askai Majabadi, Nasir Amanat, Mohsen Poursadeqiyan,
Volume 13, Issue 1 (3-2023)
Abstract
Introduction: In recent years, driver’s drowsiness has been one of the leading causes of road accidents, which can lead to physical injuries, death, and significant economic losses. Statistics show that an efficient system is needed to detect the driver’s drowsiness, that gives the necessary warning before an unfortunate event occurs. Therefore, this review study was conducted to investigate the studies on driver’s drowsiness sensors and to present a combination of diagnostic methods and an efficient model design.
Material and Methods: This narrative review study was conducted through a systematic search using “driver” and “drowsiness detection” as search keywords in indexing databases including Scopus, PubMed, and Web of Sciences. The search encompassed the latest related research conducted in this field from 2010 to September 2020. The reference lists were also reviewed to find further studies.
Results: In general, researchers evaluate driver’s drowsiness using three methods including vehicle-based measurement, behavioural measurement, and physiological measurement. The details and how these measurements are made make a big difference to the existing systems. In this study, which is a narrative review, the three mentioned measurements were examined using sensors and also the advantages and limitations of each were discussed. Real and simulated driving conditions were also compared. In addition, different ways to detect drowsiness in the laboratory were examined. Finally, after an analytical comparison of the methods of diagnosing drowsiness, a diagram was presented based on which an efficient and combined model was developed.
Conclusion: Taking into account the limitations of each of the methods, we need a combination of behavioural, performance, and other measures to have an efficient drowsiness diagnosing model. Such model must be tested using simulations and in real world situations.
