Iran Occupational Health Journal

fa بررسی تاثیر صدای دفاتر بازاداری بر روی عملکرد شناختی کارکنان THE INVESTIGATION OF OPEN-PLAN OFFICE SOUND’S EFFECT ON STAFF’S COGNITIVE PERFORMANCE HSE HSE پژوهشي Research <div class="WordSection1" dir="RTL">چکیده مقدمه : در محیط باز اداری منابع مختلف صدای فرکانس پائین نظیر صدای ناشی از فعالیت و مکالمه افراد با یکدیگر، آلودگی صوتی ایجاد می کند که عاملی تاثیر گذار بر سلامت ذهنی و عصبی-روانی، جسمانی، کارائی و اثر بخشی کارکنان تحت مواجهه در محیط های باز اداری می باشد. تحقیق پیش رو با هدف بررسی اثرات مواجهه با صدای فرکانس پائین بر عملکرد شناختی کارمندان و نقش تیپ های شخصیتی بر میزان حساسیت صوتی انجام گرفت. روش بررسی : در این مطالعه ضمن تعیین تیپ شخصیتی افراد با استفاده از پرسشنامه آیزنک، افراد شرکت کننده در طرح نسبت به انجام تست استروپ در محل کار خود و اتاق ایزوله پرداختند. در انتها با مقایسه نتایج تست ها، فرضیه های مطالعه مورد بررسی قرار گرفتند. یافته ها : نتایج نشان داد عملکرد شناختی افراد برون گرا و درون گرا قبل و حین مواجهه با صدا متفاوت بوده و بهبود یافته است که بیانگر قانون Yerkes-Dodson در محیط کار می باشد. نتایج حاکی از این می باشد که تمرکز و توجه انتخابی برون گرایان در محل کار بهتر و بالاتر از درون گرایان می باشد. نتیجه گیری : صدای محیط کار اداری روی عملکرد شناختی افراد تاثیر گذار می باشد و تیپ شخصیتی افراد بعنوان یک عامل تعدیل کننده، بر میزان این تاثیر موثر می باشد با توضیح اینکه برون گرایان نسبت به درون گرایان در محل کار عملکرد بهتری خواهند داشت. کلید واژه ها : عملکرد شناختی، دفاتر باز اداری، صدای کم فرکانس، درون گرائی، برون گرائی <div aria-label="شکستن صفحه" class="cke_pagebreak" contenteditable="false" data-cke-display-name="pagebreak" data-cke-pagebreak="1" style="page-break-after:always" title="شکستن صفحه"></div>  </div> <div aria-label="شکستن صفحه" class="cke_pagebreak" data-cke-display-name="pagebreak" data-cke-pagebreak="1" style="page-break-after:always" title="شکستن صفحه" contenteditable="false"></div> <div style="text-align: justify;">INTRODUCTION: Exposure to environmental sounds (noise pollution) is a key global public health concern that has implications for health and well-being (1). According to the report of the World Health Organization (WHO), noise pollution has direct and indirect effects on the psychological and physiological health of people (2). As mechanical, irregular, unpleasant, unwanted and unavoidable waves, sound is an important source of physical and mental stress that is known as the most important physical harmful factor in developed and developing countries and it causes disorders in the hearing system (3, 4). The significant impact of sound on human health is a well-known fact (5). The adverse effects of sound on human health are related to various parameters such as sound type, frequency, intensity, pressure, duration of exposure and personal sensitivity to sound (4). In most researches, the effect of sound level is raised and related to sound frequency or low frequency sound (LFN (little research has been done. Low-frequency sound has a frequency range of 20 to 200 or 10 to 250 Hz, and the equivalent level of their sound exposure varies between 51 and 65 decibels, which is caused by the conversation of employees, the sound of the ventilation system, the sound of the telephone, and the sound of equipment such as printers(5,6). Previous studies showed that in open office offices, LFN mainly affects the cognitive performance and mental workload of employees (4). There are several studies that have investigated the effect of internal factors of office environments such as temperature, indoor air quality, light and sound on people's performance and have come to the conclusion that people's cognitive performance is also affected by the factors of the work environment (6). According to Carroll (1993), cognitive performance refers to abilities related to cognitive tasks such as learning, memory, language, problem solving, and reasoning (7). Although sound characteristics such as sound intensity and frequency are the main cause of harmful effects of sound, it is of particular importance to pay attention to the characteristics and personality traits of people as hosts of effects (8). Studies on the effects of noise on performance indicate that noise may cause some job problems and the number of errors increase work, but these effects depend on the type of sound and the type of work being performed and the personality type of people (5). Considering the mentioned materials and the existing contradictions regarding the effect of sound on the cognitive performance of people, as well as the effect of personality type on the level of said effect, the upcoming research aims to investigate the effects of exposure to low-frequency sound on the cognitive performance of employees of health, paramedical and Nursing of Kashan University of Medical Sciences and the role of personality types on the amount of effects and accepting or rejecting the existence of a logical and direct relationship in this regard. Considering that previous studies on the effect of sound with different frequencies on people's cognitive functions (especially selective attention and interference time) were conducted under controlled conditions and students were used to perform the test, in this study the effect of sound in the workplace it is investigated on the cognitive performance of employees in real working environments. METHODOLOGY: This study was conducted among faculty members and staff of health, paramedical and nursing faculties of Kashan University of Medical Sciences. At first, a letter was sent to the staff and faculty members of paramedical, health and nursing faculties of Kashan University of Medical Sciences through the administrative automation system, and the staff of these three faculties were invited to participate in the study. At the same time, and in order to familiarize all personnel with the research plan, the pamphlet introducing the research plan was also distributed among them. After reading the pamphlet and getting familiar with the objectives of the research project, 66 people volunteered to participate in the study by completing the informed consent form. In order to ensure the health of the auditory and visual systems (full color vision) of the volunteers, audiometry (5) and Ishihara (5,8) tests were performed and 5 volunteers were excluded from the study due to hearing loss. In order to determine the personality type of people (extrovert/introvert), the volunteers were asked to complete the Eysenck personality questionnaire (5). After completing the Eysenck questionnaire by the participants, at first, the answers were checked from the perspective of factor L (reality of the answers), and 18 candidates were excluded from the study due to giving false answers. Then the personality type of the people participating in the project was determined. In order to perform the complex Stroop test (5) in an isolated environment, after 1:30 minutes from the start of work, the volunteers were asked to go to the designated room for the test (isolated room) and perform the Stroop test in that environment. In this stage and the following stages, in order to eliminate interfering factors, the participants were asked to have a full 8-hour sleep the night before the test and to refrain from drinking coffee, chocolate and caffeinated drinks in the hours before the test. Then, in order to determine the effect of the noise of the open-plan office environment on the employees' cognitive performance, on a specific day after 1:30 minutes from the start of work, the employees were asked to perform the Stroop test at their workplace. At this stage, 3 candidates failed to complete the Stroop test and were excluded from the study. Finally, the total number of participants in the research project was 40 people, who were divided into two equal groups of introverts and extroverts according to their personality type. After conducting the Stroop test at the workplace by the employees, by comparing the results obtained from the Stroop test in an isolated environment and the Stroop test at the workplace, the hypotheses of the study were examined. Figure 1 shows the method of conducting this research. <img alt="" id="_x0000_i1025" o:ole="" src="file:///C:UsersIRANICAAppDataLocalTempmsohtmlclip1�1clip_image001.emz" style="width:457.5pt; height:363pt" > Figure 1: Study Method RESULT: According to the frequency composition of sample population, it is clear that based on observations, %35 of the respondents are men and %65 are women. Among the introverts, 12 people (%60) were women and   8 people (%40) were men, and among the extroverts, 14 people (%70) were women and 6 people (%30) were men. The average age of introverts was 40.75 and the average age of extroverts was 42.4. The average score of personality type in extroverts was 76.45 and in introverts was 23.95. Table 1 shows the comparison of average Interference time 1 and 2 / sum of errors 1 and 2 / Interference number 1 and 2 in the population of introverts. As can be seen, the highest average value is related to Interference time 1 – Interference time 2 with a standard deviation of 20.57. The results of the statistical test showed that there is a significant relationship between the Interference number of answer 1 and the Interference number of answer 2. Table 2 shows the comparison of average Interference time 1 and 2 / sum of errors 1 and 2 / Interference number of answer 1 and 2 in the extrovert population. As it can be seen, the highest average value is related to the Interference number of answer 1 – the Interference number of answer 2 with a standard deviation of 48.27. Chart 1 shows well the results of the test in the isolation room and the workplace among the introverts and extroverts population. According to the graph, the test results of the two groups of people at the workplace are close to each other. In fact, it can be said that the performance of extroverts in the workplace is better than the performance of introverts. Table 1: Comparison of average Interference time 1 and 2 / sum of errors 1 and 2 / Interference number of answer 1 and 2 in the population of introverts.</div> <table align="right" class="PlainTable21" dir="rtl" style="border-collapse:collapse; border:none" width="0"> <tbody> <tr> <td colspan="2" rowspan="3" style="border-width: 1px medium medium; border-style: solid none none; border-color: rgb(127, 127, 127) currentcolor currentcolor; width: 100px; padding: 0cm 7px; height: 12px; text-align: justify;"></td> <td colspan="5" style="border-width: 1px medium; border-style: solid none; border-color: rgb(127, 127, 127) currentcolor; width: 302px; padding: 0cm 7px; height: 12px; text-align: justify;"></td> <td rowspan="3" style="border-width: 1px medium medium; border-style: solid none none; border-color: rgb(127, 127, 127) currentcolor currentcolor; width: 44px; padding: 0cm 7px; height: 12px; text-align: justify;">t</td> <td style="border-width: 1px medium; border-style: solid none; border-color: rgb(127, 127, 127) currentcolor; width: 52px; padding: 0cm 7px; height: 12px; text-align: justify;"></td> <td style="border-width: 1px medium; border-style: solid none; border-color: rgb(127, 127, 127) currentcolor; width: 76px; padding: 0cm 7px; height: 12px; text-align: justify;"></td> <td rowspan="3" style="border-width: 1px medium medium; border-style: solid none none; border-color: rgb(127, 127, 127) currentcolor currentcolor; width: 80px; padding: 0cm 7px; height: 12px; text-align: justify;">Sig            (2-tailed)</td> </tr> <tr> <td rowspan="2" style="border-width: medium; border-style: none; border-color: currentcolor; width: 57px; padding: 0cm 7px; height: 43px; text-align: justify;">average</td> <td rowspan="2" style="border-width: medium; border-style: none; border-color: currentcolor; width: 66px; padding: 0cm 7px; height: 43px; text-align: justify;">Standard deviation</td> <td rowspan="2" style="border-width: medium; border-style: none; border-color: currentcolor; width: 71px; padding: 0cm 7px; height: 43px; text-align: justify;">Standard error average</td> <td colspan="2" style="border-width: medium medium 1px; border-style: none none solid; border-color: currentcolor currentcolor rgb(127, 127, 127); width: 107px; padding: 0cm 7px; height: 43px; text-align: justify;">95% Confidence Interval of the Difference</td> <td style="border-width: medium medium 1px; border-style: none none solid; border-color: currentcolor currentcolor rgb(127, 127, 127); width: 52px; padding: 0cm 7px; height: 43px; text-align: justify;">sig</td> <td style="border-width: medium medium 1px; border-style: none none solid; border-color: currentcolor currentcolor rgb(127, 127, 127); width: 76px; padding: 0cm 7px; height: 43px; text-align: justify;">correlation</td> </tr> <tr> <td style="border-width: medium; border-style: none; border-color: currentcolor; width: 52px; padding: 0cm 7px; height: 27px; text-align: justify;">lower</td> <td style="border-width: medium; border-style: none; border-color: currentcolor; width: 56px; padding: 0cm 7px; height: 27px; text-align: justify;">upper</td> <td style="border-width: medium; border-style: none; border-color: currentcolor; width: 52px; padding: 0cm 7px; height: 27px; text-align: justify;"></td> <td style="border-width: medium; border-style: none; border-color: currentcolor; width: 76px; padding: 0cm 7px; height: 27px; text-align: justify;"></td> </tr> <tr> <td colspan="2" style="border-width: 1px medium; border-style: solid none; border-color: rgb(127, 127, 127) currentcolor; width: 100px; padding: 0cm 7px; height: 16px; text-align: justify;">Interference time 1 – Interference time 2</td> <td style="border-width: 1px medium; border-style: solid none; border-color: rgb(127, 127, 127) currentcolor; width: 57px; padding: 0cm 7px; height: 16px; text-align: justify;">7.75</td> <td style="border-width: 1px medium; border-style: solid none; border-color: rgb(127, 127, 127) currentcolor; width: 66px; padding: 0cm 7px; height: 16px; text-align: justify;">20.57</td> <td style="border-width: 1px medium; border-style: solid none; border-color: rgb(127, 127, 127) currentcolor; width: 71px; padding: 0cm 7px; height: 16px; text-align: justify;">4.6</td> <td style="border-width: 1px medium; border-style: solid none; border-color: rgb(127, 127, 127) currentcolor; width: 52px; padding: 0cm 7px; height: 16px; text-align: justify;">1.88-</td> <td style="border-width: 1px medium; border-style: solid none; border-color: rgb(127, 127, 127) currentcolor; width: 56px; padding: 0cm 7px; height: 16px; text-align: justify;">17.38</td> <td style="border-width: 1px medium; border-style: solid none; border-color: rgb(127, 127, 127) currentcolor; width: 44px; padding: 0cm 7px; height: 16px; text-align: justify;">1.68</td> <td style="border-width: 1px medium; border-style: solid none; border-color: rgb(127, 127, 127) currentcolor; width: 52px; padding: 0cm 7px; height: 16px; text-align: justify;">0.001</td> <td style="border-width: 1px medium; border-style: solid none; border-color: rgb(127, 127, 127) currentcolor; width: 76px; padding: 0cm 7px; height: 16px; text-align: justify;">0.66</td> <td style="border-width: 1px medium; border-style: solid none; border-color: rgb(127, 127, 127) currentcolor; width: 80px; padding: 0cm 7px; height: 16px; text-align: justify;">0.1</td> </tr> <tr> <td colspan="2" style="border-width: medium; border-style: none; border-color: currentcolor; width: 100px; padding: 0cm 7px; height: 16px; text-align: justify;">error 1 –  error2</td> <td style="border-width: medium; border-style: none; border-color: currentcolor; width: 57px; padding: 0cm 7px; height: 16px; text-align: justify;">4.9</td> <td style="border-width: medium; border-style: none; border-color: currentcolor; width: 66px; padding: 0cm 7px; height: 16px; text-align: justify;">12.68</td> <td style="border-width: medium; border-style: none; border-color: currentcolor; width: 71px; padding: 0cm 7px; height: 16px; text-align: justify;">2.83</td> <td style="border-width: medium; border-style: none; border-color: currentcolor; width: 52px; padding: 0cm 7px; height: 16px; text-align: justify;">1.03-</td> <td style="border-width: medium; border-style: none; border-color: currentcolor; width: 56px; padding: 0cm 7px; height: 16px; text-align: justify;">10.83</td> <td style="border-width: medium; border-style: none; border-color: currentcolor; width: 44px; padding: 0cm 7px; height: 16px; text-align: justify;">1.72</td> <td style="border-width: medium; border-style: none; border-color: currentcolor; width: 52px; padding: 0cm 7px; height: 16px; text-align: justify;">0.001</td> <td style="border-width: medium; border-style: none; border-color: currentcolor; width: 76px; padding: 0cm 7px; height: 16px; text-align: justify;">0.67</td> <td style="border-width: medium; border-style: none; border-color: currentcolor; width: 80px; padding: 0cm 7px; height: 16px; text-align: justify;">0.1</td> </tr> <tr> <td colspan="2" style="border-width: 1px medium; border-style: solid none; border-color: rgb(127, 127, 127) currentcolor; width: 100px; padding: 0cm 7px; height: 16px; text-align: justify;">Interference number 1 – Interference number 2</td> <td style="border-width: 1px medium; border-style: solid none; border-color: rgb(127, 127, 127) currentcolor; width: 57px; padding: 0cm 7px; height: 16px; text-align: justify;">3.85</td> <td style="border-width: 1px medium; border-style: solid none; border-color: rgb(127, 127, 127) currentcolor; width: 66px; padding: 0cm 7px; height: 16px; text-align: justify;">7.07</td> <td style="border-width: 1px medium; border-style: solid none; border-color: rgb(127, 127, 127) currentcolor; width: 71px; padding: 0cm 7px; height: 16px; text-align: justify;">1.58</td> <td style="border-width: 1px medium; border-style: solid none; border-color: rgb(127, 127, 127) currentcolor; width: 52px; padding: 0cm 7px; height: 16px; text-align: justify;">0.54</td> <td style="border-width: 1px medium; border-style: solid none; border-color: rgb(127, 127, 127) currentcolor; width: 56px; padding: 0cm 7px; height: 16px; text-align: justify;">7.16</td> <td style="border-width: 1px medium; border-style: solid none; border-color: rgb(127, 127, 127) currentcolor; width: 44px; padding: 0cm 7px; height: 16px; text-align: justify;">2.43</td> <td style="border-width: 1px medium; border-style: solid none; border-color: rgb(127, 127, 127) currentcolor; width: 52px; padding: 0cm 7px; height: 16px; text-align: justify;">0.021</td> <td style="border-width: 1px medium; border-style: solid none; border-color: rgb(127, 127, 127) currentcolor; width: 76px; padding: 0cm 7px; height: 16px; text-align: justify;">0.51</td> <td style="border-width: 1px medium; border-style: solid none; border-color: rgb(127, 127, 127) currentcolor; width: 80px; padding: 0cm 7px; height: 16px; text-align: justify;">0.25</td> </tr> <tr> <td nowrap="nowrap" style="border-width: medium medium 1px; border-style: none none solid; border-color: currentcolor currentcolor rgb(127, 127, 127); width: 57px; padding: 0cm 7px; height: 12px; text-align: justify;"></td> <td nowrap="nowrap" style="border-width: medium medium 1px; border-style: none none solid; border-color: currentcolor currentcolor rgb(127, 127, 127); width: 43px; padding: 0cm 7px; height: 12px; text-align: justify;"></td> <td nowrap="nowrap" style="border-width: medium medium 1px; border-style: none none solid; border-color: currentcolor currentcolor rgb(127, 127, 127); width: 57px; padding: 0cm 7px; height: 12px; text-align: justify;"></td> <td nowrap="nowrap" style="border-width: medium medium 1px; border-style: none none solid; border-color: currentcolor currentcolor rgb(127, 127, 127); width: 66px; padding: 0cm 7px; height: 12px; text-align: justify;"></td> <td nowrap="nowrap" style="border-width: medium medium 1px; border-style: none none solid; border-color: currentcolor currentcolor rgb(127, 127, 127); width: 71px; padding: 0cm 7px; height: 12px; text-align: justify;"></td> <td nowrap="nowrap" style="border-width: medium medium 1px; border-style: none none solid; border-color: currentcolor currentcolor rgb(127, 127, 127); width: 52px; padding: 0cm 7px; height: 12px; text-align: justify;"></td> <td nowrap="nowrap" style="border-width: medium medium 1px; border-style: none none solid; border-color: currentcolor currentcolor rgb(127, 127, 127); width: 56px; padding: 0cm 7px; height: 12px; text-align: justify;"></td> <td nowrap="nowrap" style="border-width: medium medium 1px; border-style: none none solid; border-color: currentcolor currentcolor rgb(127, 127, 127); width: 44px; padding: 0cm 7px; height: 12px; text-align: justify;"></td> <td nowrap="nowrap" style="border-width: medium medium 1px; border-style: none none solid; border-color: currentcolor currentcolor rgb(127, 127, 127); width: 52px; padding: 0cm 7px; height: 12px; text-align: justify;"></td> <td nowrap="nowrap" style="border-width: medium medium 1px; border-style: none none solid; border-color: currentcolor currentcolor rgb(127, 127, 127); width: 76px; padding: 0cm 7px; height: 12px; text-align: justify;"></td> <td nowrap="nowrap" style="border-width: medium medium 1px; border-style: none none solid; border-color: currentcolor currentcolor rgb(127, 127, 127); width: 80px; padding: 0cm 7px; height: 12px; text-align: justify;"></td> </tr> <tr> <td style="border-width: medium; border-style: none; border-color: currentcolor; width: 57px; text-align: justify;"></td> <td style="border-width: medium; border-style: none; border-color: currentcolor; width: 43px; text-align: justify;"></td> <td style="border-width: medium; border-style: none; border-color: currentcolor; width: 57px; text-align: justify;"></td> <td style="border-width: medium; border-style: none; border-color: currentcolor; width: 66px; text-align: justify;"></td> <td style="border-width: medium; border-style: none; border-color: currentcolor; width: 71px; text-align: justify;"></td> <td style="border-width: medium; border-style: none; border-color: currentcolor; width: 52px; text-align: justify;"></td> <td style="border-width: medium; border-style: none; border-color: currentcolor; width: 56px; text-align: justify;"></td> <td style="border-width: medium; border-style: none; border-color: currentcolor; width: 44px; text-align: justify;"></td> <td style="border-width: medium; border-style: none; border-color: currentcolor; width: 52px; text-align: justify;"></td> <td style="border-width: medium; border-style: none; border-color: currentcolor; width: 76px; text-align: justify;"></td> <td style="border-width: medium; border-style: none; border-color: currentcolor; width: 80px; text-align: justify;"></td> </tr> </tbody> </table> <div style="text-align: justify;"> Table 2: Comparison of the average Interference number of answer 1 and 2 / Interference time 1 and 2 / total error 1 and 2 in the extrovert population</div> <table align="center" class="PlainTable21" dir="rtl" style="border-collapse:collapse; border:none" width="0"> <tbody> <tr> <td colspan="3" rowspan="3" style="border-width: 1px medium medium; border-style: solid none none; border-color: rgb(127, 127, 127) currentcolor currentcolor; width: 121px; padding: 0cm 7px; height: 14px; text-align: justify;"></td> <td colspan="10" style="border-width: 1px medium; border-style: solid none; border-color: rgb(127, 127, 127) currentcolor; width: 299px; padding: 0cm 7px; height: 14px; text-align: justify;"></td> <td colspan="2" rowspan="3" style="border-width: 1px medium medium; border-style: solid none none; border-color: rgb(127, 127, 127) currentcolor currentcolor; width: 47px; padding: 0cm 7px; height: 14px; text-align: justify;">t </td> <td colspan="2" style="border-width: 1px medium; border-style: solid none; border-color: rgb(127, 127, 127) currentcolor; width: 66px; padding: 0cm 7px; height: 14px; text-align: justify;"></td> <td colspan="2" style="border-width: 1px medium; border-style: solid none; border-color: rgb(127, 127, 127) currentcolor; width: 74px; padding: 0cm 7px; height: 14px; text-align: justify;"></td> <td colspan="2" rowspan="3" style="border-width: 1px medium medium; border-style: solid none none; border-color: rgb(127, 127, 127) currentcolor currentcolor; width: 97px; padding: 0cm 7px; height: 14px; text-align: justify;">   Sig            (2-tailed)</td> </tr> <tr> <td colspan="2" rowspan="2" style="border-width: medium; border-style: none; border-color: currentcolor; width: 57px; padding: 0cm 7px; height: 38px; text-align: justify;">average</td> <td colspan="2" rowspan="2" style="border-width: medium; border-style: none; border-color: currentcolor; width: 66px; padding: 0cm 7px; height: 38px; text-align: justify;">Standard deviation</td> <td colspan="2" rowspan="2" style="border-width: medium; border-style: none; border-color: currentcolor; width: 64px; padding: 0cm 7px; height: 38px; text-align: justify;">Standard error average</td> <td colspan="4" style="border-width: medium medium 1px; border-style: none none solid; border-color: currentcolor currentcolor rgb(127, 127, 127); width: 111px; padding: 0cm 7px; height: 38px; text-align: justify;">95% Confidence Interval of the Difference</td> <td colspan="2" style="border-width: medium medium 1px; border-style: none none solid; border-color: currentcolor currentcolor rgb(127, 127, 127); width: 66px; padding: 0cm 7px; height: 38px; text-align: justify;">sig</td> <td colspan="2" style="border-width: medium medium 1px; border-style: none none solid; border-color: currentcolor currentcolor rgb(127, 127, 127); width: 74px; padding: 0cm 7px; height: 38px; text-align: justify;">correlation</td> </tr> <tr> <td colspan="2" style="border-width: medium; border-style: none; border-color: currentcolor; width: 57px; padding: 0cm 7px; height: 19px; text-align: justify;">lower</td> <td colspan="2" style="border-width: medium; border-style: none; border-color: currentcolor; width: 54px; padding: 0cm 7px; height: 19px; text-align: justify;">upper</td> <td colspan="2" style="border-width: medium; border-style: none; border-color: currentcolor; width: 66px; padding: 0cm 7px; height: 19px; text-align: justify;"></td> <td colspan="2" style="border-width: medium; border-style: none; border-color: currentcolor; width: 74px; padding: 0cm 7px; height: 19px; text-align: justify;"></td> </tr> <tr> <td colspan="3" style="border-width: 1px medium; border-style: solid none; border-color: rgb(127, 127, 127) currentcolor; width: 121px; padding: 0cm 7px; height: 19px; text-align: justify;">Interference time 1 – Interference time 2</td> <td colspan="2" style="border-width: 1px medium; border-style: solid none; border-color: rgb(127, 127, 127) currentcolor; width: 57px; padding: 0cm 7px; height: 19px; text-align: justify;">4.1-</td> <td colspan="2" style="border-width: 1px medium; border-style: solid none; border-color: rgb(127, 127, 127) currentcolor; width: 66px; padding: 0cm 7px; height: 19px; text-align: justify;">53.62</td> <td colspan="2" style="border-width: 1px medium; border-style: solid none; border-color: rgb(127, 127, 127) currentcolor; width: 64px; padding: 0cm 7px; height: 19px; text-align: justify;">11.99</td> <td colspan="2" style="border-width: 1px medium; border-style: solid none; border-color: rgb(127, 127, 127) currentcolor; width: 57px; padding: 0cm 7px; height: 19px; text-align: justify;">29.19-</td> <td colspan="2" style="border-width: 1px medium; border-style: solid none; border-color: rgb(127, 127, 127) currentcolor; width: 54px; padding: 0cm 7px; height: 19px; text-align: justify;">20.99</td> <td colspan="2" style="border-width: 1px medium; border-style: solid none; border-color: rgb(127, 127, 127) currentcolor; width: 47px; padding: 0cm 7px; height: 19px; text-align: justify;">0.34-</td> <td colspan="2" style="border-width: 1px medium; border-style: solid none; border-color: rgb(127, 127, 127) currentcolor; width: 66px; padding: 0cm 7px; height: 19px; text-align: justify;">0.6</td> <td colspan="2" style="border-width: 1px medium; border-style: solid none; border-color: rgb(127, 127, 127) currentcolor; width: 74px; padding: 0cm 7px; height: 19px; text-align: justify;">0.12-</td> <td colspan="2" style="border-width: 1px medium; border-style: solid none; border-color: rgb(127, 127, 127) currentcolor; width: 97px; padding: 0cm 7px; height: 19px; text-align: justify;">0.73</td> </tr> <tr> <td colspan="3" style="border-width: medium; border-style: none; border-color: currentcolor; width: 121px; padding: 0cm 7px; height: 19px; text-align: justify;">error 1 –  error2</td> <td colspan="2" style="border-width: medium; border-style: none; border-color: currentcolor; width: 57px; padding: 0cm 7px; height: 19px; text-align: justify;">12.25</td> <td colspan="2" style="border-width: medium; border-style: none; border-color: currentcolor; width: 66px; padding: 0cm 7px; height: 19px; text-align: justify;">47.52</td> <td colspan="2" style="border-width: medium; border-style: none; border-color: currentcolor; width: 64px; padding: 0cm 7px; height: 19px; text-align: justify;">10.62</td> <td colspan="2" style="border-width: medium; border-style: none; border-color: currentcolor; width: 57px; padding: 0cm 7px; height: 19px; text-align: justify;">9.99-</td> <td colspan="2" style="border-width: medium; border-style: none; border-color: currentcolor; width: 54px; padding: 0cm 7px; height: 19px; text-align: justify;">34.49</td> <td colspan="2" style="border-width: medium; border-style: none; border-color: currentcolor; width: 47px; padding: 0cm 7px; height: 19px; text-align: justify;">1.15</td> <td colspan="2" style="border-width: medium; border-style: none; border-color: currentcolor; width: 66px; padding: 0cm 7px; height: 19px; text-align: justify;">0.002</td> <td colspan="2" style="border-width: medium; border-style: none; border-color: currentcolor; width: 74px; padding: 0cm 7px; height: 19px; text-align: justify;">0.64</td> <td colspan="2" style="border-width: medium; border-style: none; border-color: currentcolor; width: 97px; padding: 0cm 7px; height: 19px; text-align: justify;">0.26</td> </tr> <tr> <td colspan="3" style="border-width: 1px medium; border-style: solid none; border-color: rgb(127, 127, 127) currentcolor; width: 121px; padding: 0cm 7px; height: 19px; text-align: justify;">Interference number 1 – Interference number 2</td> <td colspan="2" style="border-width: 1px medium; border-style: solid none; border-color: rgb(127, 127, 127) currentcolor; width: 57px; padding: 0cm 7px; height: 19px; text-align: justify;">12.55</td> <td colspan="2" style="border-width: 1px medium; border-style: solid none; border-color: rgb(127, 127, 127) currentcolor; width: 66px; padding: 0cm 7px; height: 19px; text-align: justify;">48.27</td> <td colspan="2" style="border-width: 1px medium; border-style: solid none; border-color: rgb(127, 127, 127) currentcolor; width: 64px; padding: 0cm 7px; height: 19px; text-align: justify;">10.79</td> <td colspan="2" style="border-width: 1px medium; border-style: solid none; border-color: rgb(127, 127, 127) currentcolor; width: 57px; padding: 0cm 7px; height: 19px; text-align: justify;">10.04-</td> <td colspan="2" style="border-width: 1px medium; border-style: solid none; border-color: rgb(127, 127, 127) currentcolor; width: 54px; padding: 0cm 7px; height: 19px; text-align: justify;">35.14</td> <td colspan="2" style="border-width: 1px medium; border-style: solid none; border-color: rgb(127, 127, 127) currentcolor; width: 47px; padding: 0cm 7px; height: 19px; text-align: justify;">1.16</td> <td colspan="2" style="border-width: 1px medium; border-style: solid none; border-color: rgb(127, 127, 127) currentcolor; width: 66px; padding: 0cm 7px; height: 19px; text-align: justify;">0.26</td> <td colspan="2" style="border-width: 1px medium; border-style: solid none; border-color: rgb(127, 127, 127) currentcolor; width: 74px; padding: 0cm 7px; height: 19px; text-align: justify;">0.26</td> <td colspan="2" style="border-width: 1px medium; border-style: solid none; border-color: rgb(127, 127, 127) currentcolor; width: 97px; padding: 0cm 7px; height: 19px; text-align: justify;">0.25</td> </tr> <tr> <td style="border-width: medium; border-style: none; border-color: currentcolor; padding: 0cm; width: 13px; text-align: justify;"> </td> <td nowrap="nowrap" style="border-width: medium medium 1px; border-style: none none solid; border-color: currentcolor currentcolor rgb(127, 127, 127); width: 65px; padding: 0cm 7px; height: 14px; text-align: justify;"></td> <td colspan="2" nowrap="nowrap" style="border-width: medium medium 1px; border-style: none none solid; border-color: currentcolor currentcolor rgb(127, 127, 127); width: 82px; padding: 0cm 7px; height: 14px; text-align: justify;"></td> <td colspan="2" nowrap="nowrap" style="border-width: medium medium 1px; border-style: none none solid; border-color: currentcolor currentcolor rgb(127, 127, 127); width: 49px; padding: 0cm 7px; height: 14px; text-align: justify;"></td> <td colspan="2" nowrap="nowrap" style="border-width: medium medium 1px; border-style: none none solid; border-color: currentcolor currentcolor rgb(127, 127, 127); width: 60px; padding: 0cm 7px; height: 14px; text-align: justify;"></td> <td colspan="2" nowrap="nowrap" style="border-width: medium medium 1px; border-style: none none solid; border-color: currentcolor currentcolor rgb(127, 127, 127); width: 62px; padding: 0cm 7px; height: 14px; text-align: justify;"></td> <td colspan="2" nowrap="nowrap" style="border-width: medium medium 1px; border-style: none none solid; border-color: currentcolor currentcolor rgb(127, 127, 127); width: 57px; padding: 0cm 7px; height: 14px; text-align: justify;"></td> <td colspan="2" nowrap="nowrap" style="border-width: medium medium 1px; border-style: none none solid; border-color: 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" style="width:360.75pt; height:216.75pt" > Chart 1: results of the test in the isolation room (1) and the workplace (2) among the introverts and extroverts DISCUSSION: According to diagram 1, the interference time of introverts is longer than extroverts during the test in the isolation room (introverts have responded to the stimulus later and more accurately), and this has caused the number of errors of introverts less than extroverts; Extroverted people have a faster response to the stimulus, and as a result, the number of their errors has also increased, which is in line with Eysenck's theory (5), according to which extroverts act faster than introverts in performing intelligence tests, but Their accuracy is less. It is noteworthy that according to the results of the complex Stroop test at the workplace, the duration of interference of introverts is less than that of extroverts (they responded to the stimulus faster than the time in the isolated room) and the extroverts compared to the time of the test in the room. In isolation, they responded slower to the stimulus, but the number of errors of extroverts was still higher than introverts, which can be a strong reason for the low accuracy of extroverts compared to introverts. According to the results of the present study, the interference time and the number of errors of introverts in the workplace are less than those isolated from extroverts, which are in line with the findings of Jalil Derakhshan et al. (5) in the research of Jalil Derakhshan et al. Introverts performed better after exposure to noise than before exposure to noise, while it was expected that introverts would perform lower than extroverts according to related research. These findings can be explained based on the theory of adaptation; adaptation refers to the gradual adaptation of a person to environmental conditions (5). From the point of view of the interference score factor (the difference between the number of correct responses to the congruent stimulus and the number of correct responses to the incongruent stimulus), which expresses the individual's focus and selective attention, as clearly shown in Figure 1, the performance of extroverts is better than introverts. It is indicative of the higher concentration of extroverts than introverts in the workplace that the results of this study are in line with the results of similar studies. The sound components of attention, tolerance and concentration are reduced. The similar results in this field can be mentioned in Babamiri's research that in introverted people, the problem related to concentration and fatigue in the condition of playing sound is clear compared to the condition without sound(5). CONCLUSION: The results obtained from this research show that the sound of the open-plan office         (low frequency sound) has an effect on cognitive performance and the individual differences of people               (in the present study, personality type) are effective on the amount of this effect in such a way that extroverts at the work place have a higher concentration and selective attention compared to introverts. On the other hand, according to the findings of the current research and the Yerkes & Dodson law, the stimuli of the work environment (presence at an optimal level) can motivate people and increase their performance. During exposure to sound, extroverts have higher cognitive performance components than introverts compared to the time before exposure to sound. The better performance of introverts during the test in the isolation room is an indication of their avoidance of stimulation and their tendency to silence is a natural and genetic issue. CONFLICT OF INTEREST: The authors declare that there are no conflicts of interest regarding the publication of this manuscript.</div> <div style="text-align: justify;"></div> عملکرد شناختی, محیط باز اداری, صدای کم فرکانس, درون گرائی, برون گرائی COGNITIVE PERFORMANE, OPEN OFFICE ENVIRONMENT, LOW FREQUENCY SOUND, INTROVERSION, EXTROVERSION. 88 105 http://ioh.iums.ac.ir/browse.php?a_code=A-10-3599-1&slc_lang=fa&sid=1 Mohammad Ghashghaei محمد قشقائی mohammadghashghaei@yahoo.com 13600319475328460045785 0009-0002-5455-4183 Yes Kashan University of Medical Science دانشگاه علوم پزشکی کاشان Masoud Motalebi Kashani مسعود مطلبی کاشانی motalleby_m@yahoo.com 13600319475328460045786 13600319475328460045786 No Kashan University of Medical Science دانشگاه علوم پزشکی کاشان Ali Asghar Khajevandi علی اصغر خواجه وندی khajevandi.asghar2008@gmail.com 13600319475328460045787 13600319475328460045787 No Kashan University of Medical Science دانشگاه علوم پزشکی کاشان Fatemeh Sadat Asgarian فاطمه سادات عسگریان fatisadat@yahoo.com 13600319475328460045788 13600319475328460045788 No Kashan University of Medical Science دانشگاه علوم پزشکی کاشان