Evaluation of the effects of various sound pressure levels on the level of serum aldosterone concentration in rats

Evaluating the effect of heat stress on cognitive performance of petrochemical workers: A field study

Introduction

Heat stress disrupts blood hormones and reduces workers' cognitive performance. To further shed light on the dysfunction of heat stress, the present study aimed to evaluate its effect on cognitive performance of petrochemical workers.

Materials and methods

This descriptive-analytical cross-sectional study was conducted in 2020 in one of the Iranian petrochemical companies. Participants were divided into 2 case groups and 1 control group. They worked 12 h and their shift entialed one week working day and one week working night. According to the ISO 7243 standard, the heat stress index of employees was measured at the beginning, in the middle and at the end of the shift separately. Continuous Performance Test (CPT) and N-back cognitive performance tests were performed at the beginning, in the middle and at the end of the shift to determine the level of cognitive performance. The data were analyzed using SPSS software version 20 and the significance level was set at 0.05.

Results

Comparison of the results in the continuous performance test showed significant differences between the three groups with regard to the omission error and response time at the end of the shift. Moreover, according to the working memory test, participants reaction time during the shift significantly increased. Besides, average correct responses significantly reduced during the shift. Finally, the heat stress throughout the shift had a significant effect on the commission error and the response time of individuals.

Conclusion

Heat stress affects people's cognitive performance in such a way that it can decrease their cognitive performance by increasing the commission error and response time and reducing the average correct response of site operators, generally reducing the cognitive performance of people at the end of the shift.

Predicting and classifying hearing loss in sailors working on speed vessels using neural networks: a field study

 Noise-induced hearing loss (NIHL) is one of the main risk factors affecting people's health and wellbeing in the workplace. Analysing NIHL and consequently controlling the causing factors can significantly affect the improvement of working environments. Methods: One hundred and twelve male sailors participated in this study. They were classified into three groups depending on occupational noise exposure: (A) none, i.e., sound pressure level (SPL) lower than 70dBA, (B) exposed to SPL in the range of 70-85dBA, and (C) exposed to SPL exceeding 80dBA. In a first phase, hearing loss shaping risk factors were identified and analysed, including hearing loss in different frequencies, age, work experience, sound pressure level (SPL), marital status, and systolic and diastolic blood pressure. Then, neural networks were trained to predict the hearing loss changes of personnel and used to determine the weight of hearing loss factors. Finally, the accuracy of predicting models was calculated relying on Bayesian statistics. Results and conclusion: In the present study using neural networks, five models were developed. Their accuracy ranged from 92% to 100%. The frequencies of 4000Hz and 2000Hz showed the strongest association with the hearing loss of the sailors. Also, including systolic and diastolic blood pressure did not have any impact on predicted hearing loss, indicating that SPL was poorly correlated with extra-auditory effects.

Occupational stress assessment of health care workers (HCWs) facing COVID-19 patients in Kerman province hospitals in Iran

BACKGROUND

The health care workers (HCWs) at the frontline of fighting COVID-19 are at higher risk for mental health problems, including stress, anxiety, depression, and insomnia. This study aimed at assess the status of occupational stress in the three occupational groups of nurses, physicians and hospital cleaning crew facing COVID-19 patients in hospitals of Kerman province in Iran.

METHODOLOGY

This cross-sectional descriptive analytical study was performed on 290 medical staffs including nurses, physicians and cleaning crew facing COVID-19 patients working in different hospitals in Iran in 2020. Demographic information form and occupational Stress Questionnaire (HSE tool indicator) were used to collect data. The health and safety executive (HSE) questionnaire has 35 questions and 7 areas, which was developed in the 1990s by the UK Health and Safety Institute to measure occupational stress.

RESULTS

The mean score of total dimensions among HCWs was 2.93. Communications, Manager support, Changes and Demand factors with scores of 2.76, 2.77, 2.83 and 2.87 had the greatest impact on participants' stress levels, respectively. Also, Colleague support factor with a score of 3.38 had the least effect on stress levels. Also, according to the results, 87% of nurses, 79% of cleaning crew and 67% of physicians had a partial to high levels of stress that, on average, 77.5% of the HCWs participating in this study had at least a small amount of stress.

CONCLUSIONS

The mean stress score among the participants of the present study was between high stress level and moderate stress level. Factors such as communications, manager support, change and demand had the greatest impact on employee stress levels. Therefore, by improving the communication between people working in hospitals, increasing managers' support for staff, and reducing workplace demands such as reducing workload and improving workplace environment, the stress level of staff in hospitals during the outbreak of COVID-19 can be reduced.

Weighing and modelling factors influencing serum cortisol and melatonin concentration among workers that are exposed to various sound pressure levels using neural network algorithm: An empirical study

BACKGROUND

Noise is one of the most common harmful agents in the workplace. Exposure to excessive noise can lead to complications such as cardiovascular disorders, disturbance of body hormones' rhythm and hearing loss. This study aimed at weighing and modelling factors influencing serum cortisol and melatonin concentrations of workers that are exposed to various sound pressure levels using neural network algorithm.

METHODOLOGY

A case-control design was adopted in the current research. The required data were collected from 75 industrial and mining firm staff members. They were assigned to three groups with equal sample sizes (25 workers). In developing the conceptual model in regard to variables that may affect workers' serum cortisol and melatonin concentration, SPL, age, weight, and height were included. The influence of SPL on serum cortisol concentration as assessed in the three shifts. Moreover, radioimmunoassay (RIA) was utilized to assess serum cortisol and melatonin concentrations. Neural network algorithm was subsequently exploited to weigh and model predictor factors. IBM SPSS Modeler 18.0 was the software program used for data analysis.

RESULTS

The average cortisol concentration values for administrative, condensing, and pelletizing units respectively were 10.24 ± 2.35, 12.15 ± 3.46, and 14.91 ± 4.16μ g d l . On the other hand, the average melatonin concentration values for administrative, condensing, and pelletizing units respectively were 37 ± 12.52, 34 ± 13.15, and 27 ± 9.54μ g d l . According to the results of the developed model for cortisol, SPL3 (32%) and age (5%) respectively had the highest and lowest impact. On the other hand, considering the model developed for melatonin, height (27%) and SPL1 (10%) were the most and least influential factors in that order. The accuracy rates of the model were also found to be 95% for cortisol and 97% for melatonin.

CONCLUSION

Comparing cortisol concentrations during various shifts revealed a significant reduction (from the beginning to the end of the shift) in all the three groups. Further, the rise of SPL would result in higher secretion of cortisol. Moreover, in all the three groups, the average serum melatonin concentration went up from the beginning to the middle of the shift and then declined to the end of the shift. Considering the accuracy rates of the models developed to predict hormones, neural network algorithm is a suitable and powerful tool for weighing and modelling factors influencing serum cortisol and melatonin concentrations.

Sound and Vibration as Research Variables in Terrestrial Vertebrate Models

Sound and vibration have been shown to alter animal behavior and induce physiological changes as well as to cause effects at the cellular and molecular level. For these reasons, both environmental factors have a considerable potential to alter research outcomes when the outcome of the study is dependent on the animal existing in a normal or predictable biological state. Determining the specific levels of sound or vibration that will alter research is complex, as species will respond to different frequencies and have varying frequencies where they are most sensitive. In consideration of the potential of these factors to alter research, a thorough review of the literature and the conditions that likely exist in the research facility should occur specific to each research study. This review will summarize the fundamental physical properties of sound and vibration in relation to deriving maximal level standards, consider the sources of exposure, review the effects on animals, and discuss means by which the adverse effects of these factors can be mitigated.

Predicting and Weighting the Factors Affecting Workers' Hearing Loss Based on Audiometric Data Using C5 Algorithm

Introduction:

With the extensively spread of industrialization in the world, noise exposure is becoming more prevalent in the industrial settings. The most important and definite harmful effects of sound include hearing loss, both permanent and temporary.

Objective:

This study was designed aimed to use the C5 algorithm to determine the weight of factors affecting the workers’ hearing loss based on the audiometric data.

Methods:

This cross-sectional, descriptive, analytical study was conducted in 2018 in a mining industry in southeastern Iran. In this study, workers were divided into three exposed groups with different sound pressure levels (one control group and two case groups). Audiometry was conducted for each group of 50 persons; hence, the total number of subjects was 150. The stages of this study include: 1) selecting factors (predictive) to check and weigh them; 2) conducting the audiometry for both ears; 3) calculating the permanent hearing loss in each ear and permanent hearing loss of both ears; 4) classifying the types of hearing loss; and 5) investigating and determining the weight of factors affecting the hearing loss and their classification based on the C5 algorithm and determining the error and accuracy rate of each model. To assess and determine the factors affecting the hearing loss of workers, the C5 algorithm and IBM SPSS Modeler 18.0 were used. SPSS V.18 was used to analyze the linear regression and paired t-test tests, too.

Results:

The results showed that in the first model (SPL <70 dBA), the 8KHz frequency with the weight of 31% had the highest effect, the factors of work experience and the frequency of 250Hz each with the weight of 3%, had the least effect, and the accuracy of the model was 100%. In the second model (SPL 70–80 dBA) the frequency of 8KHz with the weight of 21% had the highest effect, the frequency of 250Hz and the working experience each had the lowest effect with the weight of 7% and the accuracy of the model was calculated as 100%. In the third model (SPL >85 dBA), the 4KHz frequency with the weight of 31% had the highest effect, and the work experience with a weight of 1% had the lowest effect, and the accuracy of the model was 94%. In the fourth model, the 4KHz frequency with the weight of 22% had the highest effect and 250Hz and age each with the weight of 8% had the lowest effects; the accuracy of this model was calculated to be 99.05%.

Conclusions:

During investigating and determining the weight of the factors affecting hearing loss by the C5 algorithm, the high weight and effect of the 4KHz frequency were predicted in hearing loss changes. Considering the high accuracy obtained in this modeling, this algorithm is a suitable and powerful tool for predicting and modeling the hearing loss.

The Effect of Occupational Noise Exposure on Serum Cortisol Concentration of Night-shift Industrial Workers: A Field Study

Background

In both developed and developing countries, noise is regarded as the most common occupational hazard in various industries. The present study aimed to examine the effect of sound pressure level (SPL) on serum cortisol concentration in three different times during the night shift.

Methods

This case–control study was conducted among 75 workers of an industrial and mining firm in 2017. The participants were assigned to one of the three groups (one control and two case groups), with an equal number of workers (25 participants) in each group. Following the ISO 9612 standard, dosimetry was adopted to evaluate equivalent SPL using a TES-1345 dosimeter. The influence of SPL on serum cortisol concentration was measured during the night shift. The serum cortisol concentration was measured using a radioimmunoassay (RIA) test in the laboratory. Repeated measure analysis of variance and linear mixed models were used with α = 0.05.

Results

The results indicated a downward trend in the serum cortisol concentration of the three groups during the night shift. Both SPL and exposure time significantly affected cortisol concentration (p < 0.0001, p < 0.0001). Conversely, age and body mass index had no significant influence on cortisol concentration (p = 0.360, p = 0.62).

Conclusion

Based on the obtained results, increasing SPL will lead to enhancement of serum cortisol concentration. Given that cortisol concentration varies while workers are exposed to different SPLs, this hormone can be used as a biomarker to study the effect of noise-induced stress.