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Showing 4 results for Sound

Zahra Zamanian, Bahram Kouhnavard , Bahram Maleki, Fatemeh Ashrafi, Leyla Ahmadvand, Parisa Azad,
Volume 3, Issue 2 (9-2015)
Abstract

Introduction: As a healthcare providing establishment, hospitals can be affected by sources of noise pollution which then impose negative effects on the health and comfort of patients and personnel. The present study was conducted to determine the relationship between sound annoyance and general health in the personnel of university affiliated and non-university affiliated hospitals in Shiraz.

Materials and Methods: The present descriptive analytical study was conducted in four university affiliated and non-university affiliated hospitals in Shiraz with a sample size of 300 using a predetermined equation and according to the results of previous studies. The instruments used in the study included the General Health Questionnaire (GHQ), the Noise Annoyance Scale (NAS) and an audiometer (CEL-440 model).

Findings: The present study found a small inverse relationship between the total general health score in the hospital personnel and the sound pressure level at university-affiliated hospitals (r=-0.103) and a direct positive relationship at non-university affiliated hospitals (r=0.274), which were not statistically significant (P≤0.05). The relationship of somatic symptoms, anxiety, insomnia, social dysfunction and depression to sound pressure was inverse and small (r=-0.195 and P≥0.05). However, there was a direct positive relationship between general health and sound annoyance (r=0.266 and P≤0.05).Conclusion: According to the results obtained, noise levels are higher than the acceptable exposure limit in university-affiliated hospitals, which affect both the patients' and the personnel's general health, necessitating the adoption of control measures.


Payam Heydari, Sakineh Varmazyar, Shahzad Sabeti, Somayeh Jafari, Sajjad Ataei,
Volume 3, Issue 4 (3-2016)
Abstract

Introduction: Listening to music during physical activity reduces the perceived stress on body. Hence, this study aimed to evaluate the effects of music genre and sound pressure level on calorie consumption and physical performance of students during treadmill tests.

Methods: In this cross-sectional study, 27 students were randomly selected. A demographic questionnaire, Borg Rating of Perceived Exertion Scale (RPE 6-20), and the Physical Activity Readiness Questionnaire (PAR-Q) were used for data collection. During flat and sloped treadmill running (five minutes at 6 km/hour), two pieces of music with fast and slow tempo (200 and 70 beat per minute) were also played at two sound pressure levels (60 and 80 dB). During the exercise, the students’ heart rate and calorie consumption were measured by a calorimeter (Beurer PM100). At each stage, the physical performance of the participants was estimated using the relevant equation.

Results: The students had the lowest physical performance (61.48) when no music was played and the highest physical performance (92.48) with slow, loud music. The mean heart rate, calorie consumption, and perceived exertion were maximized without music and minimized when slow, loud music was played.

Conclusion: Playing soft music at high sound pressure levels can promote physical performance during exercise by decreasing heart rate, calorie consumption, and perceived exertion.


Majid Lashgari, Mohammadreza Arab,
Volume 6, Issue 3 (10-2018)
Abstract

Background & Objectives: Sound as a detrimental factor in working environments can create annoying conditions for people in addition to physical problems. Therefore, in addition to evaluating quantitative parameters such as pressure levels, it is absolutely necessary to study the quality parameters of the sound in the work environment. 
Methods: In this descriptive-analytic research, the sound of 285 MF tractor was recorded. Then, the EEG of five drivers were recorded in the pre-driving state and then when driving with the tractor in four different engine speed. The psychoacoustic annoyance model was used to assess the annoyance of tractor drivers. Then means were compared with Duncan comparison test at 5% probability level and the correlation between psychoacoustic acoustic and alpha and beta bands was determined.
Results: The results of ANOVA showed that different levels of engine speed on psychoacoustic annoyance were significant at 1% probability level. The results also showed a decrease in the amplitude of the alpha band, as well as an increase in the beta band amplitude due to increased engine speed. Regression results showed that there is a high correlation between the two alpha and beta bands and the psychoacoustic annoyance, so that the detection coefficient was 0.966 and 0.998, respectively, for the two bands alpha and beta. 
Conclusion: This study showed that changes in the quality parameters of the sound and consequently the resulting annoyance caused the amplitude changes in both the alpha and beta bands. So, it can be concluded that the psychoacoustic annoyance is a good indicator of brain activity.

 

Majid Lashgari, Mohammadreza Arab, Mohsen Nadjafi, Ali Maleki,
Volume 9, Issue 2 (10-2021)
Abstract

Background & Objectives: Due to the sound caused by various machines and tools in different agriculture sectors, occupational safety and health should be continuously evaluated. Indeed, the harmful effects of sound can be better reduced when the effects of sound on people's health and performance are fully known.
Methods: In this study, a garden tractor was used. Sixteen volunteers were exposed to the sound of the tractor, and their EEG was recorded at four different engine speeds. Then, Higuchi and Katz methods were used to calculate the fractal dimension of sound signals as well as brain signals.
Results: The results showed that by increasing engine speed, the values ​​of the fractal dimension in both Higuchi and Katz methods increased. The results also showed an increase in the fractal dimension of brain signals due to an increase in engine speed. The regression results also showed a high correlation between the two brain signals and the sound. The coefficient of explanation was 0.896 and 0.859 in Higuchi and Katz methods, respectively.
Conclusion: This study showed that people's reactions, when exposed to sound, can be predicted using the fractal dimension. Therefore, it is possible to estimate the characteristics of brain signals without recording them, which are often costly and time-consuming.


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