Volume 14, Issue 3 (10-2024)
J Health Saf Work 2024, 14(3): 482-502 |
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Zare S, Esmaeili R, Zandsalimi F. Modeling and Weighting of Factors Affecting Sustained Attention and Working Memory of Workers Exposed to Different Sound Pressure Levels using Deep Learning and Random Forest Algorithms: A Case Study of a Steel Industry. J Health Saf Work 2024; 14 (3) :482-502
URL: http://jhsw.tums.ac.ir/article-1-7032-en.html
URL: http://jhsw.tums.ac.ir/article-1-7032-en.html
1- Department of Occupational Health Engineering and Safety at Work, Faculty of Public Health, Kerman University of Medical Sciences, Kerman, Iran
2- Student Research Committee, Department of Occupational Health and Safety Engineering, School of Health, Isfahan University of Medical Sciences, Isfahan, Iran ,rezaesmaeili794@yahoo.com
3- Department of Occupational Health and Safety Engineering, Faculty of Health, Kurdistan University of Medical Science, Sanandaj, Iran
2- Student Research Committee, Department of Occupational Health and Safety Engineering, School of Health, Isfahan University of Medical Sciences, Isfahan, Iran ,
3- Department of Occupational Health and Safety Engineering, Faculty of Health, Kurdistan University of Medical Science, Sanandaj, Iran
Abstract: (303 Views)
Introduction: Cognitive functions play a vital role in how tasks are performed; for this, temporary cognitive and mental dysfunctions could lead to grave consequences, especially when an accurate and prompt response is required. Attention and reaction time to noise are among the most effective exogenous factors on the brain processing mechanism. This study aimed to measure the sustained attention of workers in the steel industry exposed to different sound pressure levels.
Material and Methods: The study was conducted in 4 general stages, including 1- Selecting predictive orientation variables (age, work history, different sound pressure levels); 2- Conducting the Cognitive Performance Test (CPT); 3 Conducting N-BACK Cognitive Performance Test and 4- Modeling cognitive performance changes using model precision methods.
Results: Continuous Performance Test (CPT) results indicated that all three groups’ omission error, commission error, and response time were affected by shift time. All three components increased significantly as the shift ended, decreasing individuals’ cognitive function. Also, the higher noise impact in modeling CPT and N-Back tests indicated reduced workers’ concentration.
Conclusion: These study findings suggested that greater noise weight obtained in test modeling in three-time intervals, i.e., in the beginning, middle, and end of the shift, affected the continuous performance components of the CPT and working memory performance of the N-back test, including workers’ response time and reaction time, with workers’ rate of error increasing and their focus decreasing during the shift.
Material and Methods: The study was conducted in 4 general stages, including 1- Selecting predictive orientation variables (age, work history, different sound pressure levels); 2- Conducting the Cognitive Performance Test (CPT); 3 Conducting N-BACK Cognitive Performance Test and 4- Modeling cognitive performance changes using model precision methods.
Results: Continuous Performance Test (CPT) results indicated that all three groups’ omission error, commission error, and response time were affected by shift time. All three components increased significantly as the shift ended, decreasing individuals’ cognitive function. Also, the higher noise impact in modeling CPT and N-Back tests indicated reduced workers’ concentration.
Conclusion: These study findings suggested that greater noise weight obtained in test modeling in three-time intervals, i.e., in the beginning, middle, and end of the shift, affected the continuous performance components of the CPT and working memory performance of the N-back test, including workers’ response time and reaction time, with workers’ rate of error increasing and their focus decreasing during the shift.
Type of Study: Research |
Received: 2024/10/9 | Accepted: 2024/10/1 | Published: 2024/10/1
Received: 2024/10/9 | Accepted: 2024/10/1 | Published: 2024/10/1
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