網路遨遊時電腦工作者生理效應關聯之研究

(1)

網路遨遊時電腦工作者生理效應關聯之研究

沈浩宇¹ 沈瑞棋^2,* 朱貽達³

1南榮技術學院企業管理系

2高雄餐旅大學通識教育中心

3雲林科技大學資訊管理系

摘要

隨著資訊科技的進步，電腦與網路快速的發展，許多人在現實社會中無法獲得滿足感，因而轉向虛擬的網路世界中，藉此獲得滿足感，或是在網路中建立自信，許多人因此沉迷上網，長時間的上網、對生理及心理造成負面的影響，例如：肩頸痠痛、眼睛紅腫、疲勞、睡眠品質不佳、失眠、脾氣暴躁、沒有耐心、注意力不集中，嚴重的話可能會導致猝死 (過勞死)。本研究的目的是探討網路遨遊時電腦工作者上午及下午生理效應關聯之研究。研究結果顯示：(1) 受測者在上午及下午上網後 HR 有顯著差異，下午心跳比早上快，因為早上血壓慢慢上升，直到下午血壓值到達最高，而血壓上升時心跳也會加快。(2) 副交感神經活性 (HF%)，交感神經活性，(LF%)，交感副交感平衡性指標 (LF/HF)有顯著差異，早上由於交感神經作用，因此 LF 的值明顯較高，此時人們比較有精神及專注力，下午副交感神經活性 (HF) 的值比較高，因此人們感覺到比較鬆懈或是疲勞。因此早上比較適合做需要腦力思考的事情，如果是上網或是比較不需要思考的事情，適合在下午做。

關鍵詞：網路遨遊，電腦工作者，生理效應。

DIFFERENTIATING PHYSIOLOGICAL EFFECTS OF INTERNET SURFING ON COMPUTER WORKERS

William Hao-Yu Shen¹ Jui-Chi Shen^2,* Yi-Da Chu³

1Department of business administration Nan-Jeon Institute of Technology Tainan City, Taiwan 73746, R.O.C.

2General Education Center

National Kaohsiung University of Hospitality and Tourism Kaohsiung City, Taiwan 81271, R.O.C.

3Department of Information management National Yunlin University of Science & Technology

Yunlin County, Taiwan 64002, R.O.C.

Key Words: computer worker, internet surfing, physiological effects.

ABSTRACT

With the progression of information technology, a greater number of people use the Internet at work. Previous studies focused on the effects of

*通訊作者：沈瑞棋，e-mail: shen@mail.nkuht.edu.tw

Corresponding author: Jui-Chi Shen, e-mail: shen@mail.nkuht.edu.tw

(2)

shyness, anxiety, loneliness, depression, and self-consciousness on the level of Internet use. This study examines the physiological effects of Internet surfing on workers. Participants consisted of 20 university and graduate students. Each participant was measured 4 times in the morning and in the afternoon. Heart rate variability (HRV), blood pressure, and pulse rate were measured as physiological indices. Results showed that (1) Internet surfing in the morning and afternoon caused significant differences in the participants’ heart rate (HR). In the afternoon, heartbeats were faster than in the morning. (2) As HF%, LF%, and LF/HF are significantly different, the value of HF is higher in the afternoon, which causes people to feel more relaxed or fatigued. Therefore, activities that require more thinking should be performed in the morning, and activities that require less thinking should be performed in the afternoon.

I. INTRODUCTION

With the progression of information technology, computers and the Internet are not simply tools for supporting human work, but have become indispensable technological products to people’s daily lives. Combining computers with networks has created various applications, such as finding information on the Internet, listening to music, chatting and making friends on the Internet, and playing online games. Practically anything is accessible through the Internet. In Taiwan, a greater number of people use the Internet. Most consist of young groups and many play online games, with some even being addicted to these games.

Using updated news from the United Nations, the current global Internet population has surpassed 20 billion, with an accurate figure of 20.8 billion. Moreover, the population of mobile phone users has exceeded 50 billion as compared with the year 2000. In the last decade, the population of the Internet and mobile phone users has grown 5 times. The data showed that in the current global population, one out of three people has access to the Internet, and 550 million people use a broadband fixed network and 9.5 million people have mobile Internet (telecommunications network) access [1].

Because of the rapid growth of the Internet, there are questions on its impact, both positive and negative, on society and users. One recurring concern is “Internet addicts,” whose Internet usage has become excessive, out of control, and disruptive to their lives. Reports of Inter- net abuse first appeared in the popular press and cited anecdotal evidence [2-5]. News on deaths from over- working are broadcasted in the mass media, but this also occurs with Internet abuse. There are many rules on working hours, but rules on controlling the use of the

Internet have not been created.

Most people use the Internet without negative conse- quences and benefit from the use. However, some people misuse the Internet, which causes problems in their lives.

Misuse is often characterized by guilt, cravings, and at- tempts to hide, or reduce the time of use online. People who misuse the Internet often use it to alter their moods when they feel depressed, anxious, or isolated [5]. The American Medical Association report [6] considered video game addiction, including addiction to online games, a disorder. Additionally, Griffiths et al. [7] considered Internet addiction to be a form of technological addiction (e.g., computer addiction), and a subset of behavioral ad- dictions (such as compulsive gambling). Any behavior that has the six “core components” of Internet addiction (salience, mood modification, tolerance, withdrawal symptoms, conflict, and relapse) is defined as functionally ad- dictive [7]. Because of the increased awareness that online game addiction is a legitimate concern, the reason and the depth of involvement in these games are important study issues. Previous studies have suggested that indi- vidual psychological characteristics (including personality traits) may cause certain people to overuse the Internet.

These studies focused on the effects of shyness, anxiety, loneliness, depression, and self-consciousness on the level of Internet use [8-11].

Work breaks are often used to combat work-related stress and inactivity. A 10- to 15-min break in the morning and another in the afternoon is standard at many work- places. Because work breaks are so common, what occurs during these intervals can significantly affect overall public health [12].

After an important meeting or course, taking a break or having a cup of coffee or afternoon tea might help retain new information and improve memory. A study con-

(3)

firmed that taking breaks helps strengthen and improve recall in memory, which is as effective as a good night’s sleep. Dr. Lila Davachi, Assistant Professor of Psychol- ogy of the New York University Center for Neural Science, stated that breaks after class help retain information that has immediately been learned. The brain turns off other tasks so that it can concentrate on what it has just learned.

During rest, the brain works; therefore, resting is crucial for memory and cognitive ability. This is difficult to detect, especially because today’s information technology allows us to work non-stop [13].

In recent years, several articles in the international medical literature confirmed that breaks motivate people, with 10 min achieving the best results. To detect and observe the depth of sleeping and the mental state of consciousness, study participants slept for 5 min, 10 min, 20 min, 30 min, and 1 h. This study found that sleeping for 10 min achieved the best mental state. Participants who slept for more than 30 min had a lower mental state [14].

Occupational medicine specialist Dr. Wang stated that because our body tissues have an endurance of 1 h, when engaging in any activity, the duration should be less than 60 min. It is preferable for people who must work long term to take a 10-15 min breaks after working for 45-50 min, and to perform body muscle activities to reduce stress [15].

Static muscular work in awkward postures with or without using force to an external object results in fatigue, discomfort, and musculoskeletal disorders [16]. Surfing on the Internet for a long time is similar to working in awkward postures, and causes the same disorders.

This study examines physiological effects of surfing the Internet and suitable times in using the Internet. Mul- tiple physiological parameters included systolic blood pressure (SYS), diastolic blood pressure (DIA), heart rate (HR), and heart rate variability (HRV) as indicators of sympathetic activity LF%, indicators of parasympathetic activity HF%, and as the sympathetic and parasympathetic balance indices (LF/HF) to measure physiological parameters, respectively.

II. MATERIALS AND METHOD

Online games were used to ensure that participants were online during the experiment. In this study, the experimental environment was at a laboratory. Data were collected at the laboratory because of the relative ease of controlling variables, and unpredictable conditions were

user surfing the web

Physiological Parameters Data Collection

ANSWatch^®

Measurement

Fig. 1 Experimental process

avoidable. Participants were university students and graduate students. There were three parts to the experiment: Part 1 users played online games, in Part 2 instru- ments were measured, and in Part 3 data were collected.

The experimental process is shown in Fig. 1.

1. Participants

In this study, 20 people (13 men and 7 women) were randomly selected, and their basic information, including gender, age, education, and occupation, were obtained before measuring physiological parameters.

2. Apparatus

The experimental apparatus comprised of:

i. Hardware: Computer (AS-M5000), a computer monitor, a timer, headphones, and the ANSWatch^®. ii. Software: SPSS 17.0, ANSWatch^® Manager Pro

data analyzer, and Excel 2007.

3. Procedure

Prior to the experiment, rules were clarified. Par- ticipants were required to sleep 6 to 8 h prior to this experiment. Participants had to fast 1 h before the experiment. The temperature of the laboratory was controlled at 25 ± 2°C. Before measurement, all items were re- moved from the wrist, and participants took a 10-min break. During measurements, participants were not al- lowed to talk or change positions. Physical parameters were measured 4 times in the morning and in the afternoon for each participant. Therefore, participants were measured 8 times in total. Experiments started from 9:30 to

(4)

7.5-min 7.5-min

50-min 10-min

Rest Rest

Surfing on Internet Rest

Measurement Measurement Measurement Measurement Totally 90 minutes

Fig. 2 Diagram of the experimental timing in the morning and afternoon

12:00 AM and from 2:30 to 5:00 PM. Before measurement, participants completed questionnaires and took a 10-min break before measuring physical parameters.

After the first measurement, participants surfed the Inter- net for 50 min. Second measurements were taken after surfing the Internet, followed by a 7.5-min break. Third measurements were taken after the break, followed by another 7.5-min break, and the final measurement. The experimental timing is shown in Fig. 2.

4. Data Collection

Several studies show that the autonomic nervous system and cardiovascular disease [17-19] are significantly correlated. Medical professions in heart disease [20-21], hypertension [22-23], fatigue resulting in sudden death [24-25], and the rate of heart rate variability [26] have been identified to assess the regulatory function of cardiac autonomic nervous system. Therefore, part of the physiological parameters of data collection is to use frequency-domain HRV analysis (frequency-domain methods) [27] that simultaneously measures the sympathetic and parasympathetic nervous system, and uses the ANSWatch^® wrist monitor (Taiwan Scientific Corporation; Taiwan De- partment of Health medical device, product registration number 001525) as a noninvasive device.

ANSWatch^® is used to measure physiological parameters. Measurements take approximately 7 min and output eight physiological parameter indices on the LCD screen, including the HR, SYS, DIA, HRV, HF%, LF%, LF/HF, and the number of irregular heartbeats. After the data are downloaded to the computer, the ANSWatch^® Manager Pro software shows the physiological parameter data and conducts analysis and evaluation.

5. Physiological Parameter Analysis

ANSWatch^® measurement is a blood pressure waveform described as pressure, blood pressure, and peripheral arterial blood pressure. It is converted into a voltage waveform through filters to maximize the process of analysis by calculus to measure HRV and related indica-

Morningn Afternoon 180

160 140 120 100 80 60 40 20 0

Systolic blood pressure

Befor surfing After suring Rest 7.5-min Rest 15-min Mean ± SD, **: p < 0.01, *: p < 0.05, p-value by paired t-test.

Fig. 3 Changes in the average systolic blood pressure in the morning and in the afternoon

tors of autonomic nervous activity precisely. In physiol- ogy, as measured on the ECG signal for the upstream waves indicators, referring to the playing beat of blood the heart of the signal, and the ANSWatch^® is in strict accor- dance with international standards in 1996 HRV, in the measurement of blood pressure signal is the output of blood from the heart of the signal, known as the down- stream targets, both the measured heart rate variability in heart rate are from the medication of the autonomic nervous system.

6. Statistical Analysis

In statistical analysis, paired t tests showed significant parameter changes before and after surfing the Internet, and after resting for 7.5 and 15 min. Furthermore, the relationship of physiological parameters was examined with the Pearson correlation coefficient. Statistical analysis of physiological data was processed with EXCEL 2007 (Microsoft Inc.) and subjective data were processed with SPSS version 17.0.

III. RESULTS

In this study, changes in physiological parameters in the morning, afternoon, and before and after surfing the Internet are evaluated. The statistical results were ana- lyzed.

1. Variation in physiological parameters in the morning and in the afternoon

Figs. 3 to 9 show the result of physiological assess- ments to determine the effects of surfing the Internet in the morning and in the afternoon.

Fig. 3 shows the average systolic blood pressure in

(5)

Morning Afternoom 120

100 80 60 40 20 0

Diastolic blood pressure

Before surfing After surfing Rest 7.5-min Rest 15-min Mean ± SD, **: p < 0.01, *: p < 0.05, p-value by paired t-test.

Fig. 4 Changes in the average diastolic blood pressure in the morning and in the afternoon

120 100 80 60 40 20 0

Heartrate

p = 0.022*

Figu. 5 Change in average heart rate in the morning and afternoon

the morning and in the afternoon. Systolic blood pressure was non-significant in the morning and in the afternoon, “before surfing the Internet,” “after surfing the Internet,” “resting for 7.5 min,” and “resting for 15 min.”

In the morning, the means of “before surfing the Internet,”

“after surfing the Internet,” “resting for 7.5 min,” and

“resting for 15 min” were not significantly different. In the afternoon, the means of “before surfing the Internet,”

“resting for 15 min” were not significantly different.

Fig. 4 shows the average diastolic blood pressure in the morning and in the afternoon. Diastolic blood pressure was non-significant in the morning and in the afternoon “before surfing the Internet,” “after surfing the Internet,” “resting for 7.5 min,” and “resting for 15 min,”

In the morning, the means of “before surfing the Internet,”

“resting for 15 min” were not significantly different. In the afternoon, the means of “before surfing the Internet,”

“resting for 15 min,” were not significant.

500 400 300

100 200

0

Heartrate variability

p = 0.084

Fig. 6 Changes in the average heart rate variability in the morning and in the afternoon

90 80 70 50 60

40 30 20 10 0

HF

p = 0.003**

Fig. 7 Change in average HF in the morning and in the afternoon

Fig. 5 shows the average heart rate in the morning and in the afternoon. Heart rate was significantly lower in the morning “after surfing the Internet,” (p = .022) than in the afternoon. Heart rate was non-significant in the morning and in the afternoon “before surfing the Internet,” “resting for 7.5 min,” and “resting for 15 min,”.

Fig. 6 shows the average heart rate variability in the morning and in the afternoon. Heart rate variability was non-significant in the morning and in the afternoon “before surfing the Internet,” “after surfing the Internet,” and

“resting for 7.5 min”. Heart rate variability after “resting for 15 min” in the morning tended to be higher (p = .084) than in the afternoon. Heart rate variability gradually increased in the morning “before surfing the Internet,”

“resting for 15 min,” but gradually decreased in the afternoon.

Fig. 7 shows the average HF in the morning and in the afternoon. HF was non-significant in the morning and in the afternoon “before surfing the Internet,” “after

(6)

Table 1 Physiological parameters before and after surfing the Internet in the morning

Parameters Before surfing After surfing t value DF ^a p value SYS 115.55 ± 15.11 116.45 ± 13.95 0.218 19 0.830 DIA 75.50 ± 8.97 81.40 ± 9.15 2.369 19 0.029*

HR 82.25 ± 15.31 77.40 ± 14.55 -1.334 19 0.198 HRV 207.70 ± 105.29 246.30 ± 156.42 1.212 19 0.240 HF% 57.40 ± 13.83 53.55 ± 15.00 -1.257 19 0.224 LF% 42.60 ± 13.83 46.40 ± 15.01 1.246 19 0.228 LF/HF 0.91 ± 0.83 1.35 ± 2.31 1.189 19 0.249

a: DF (degree of freedom)

*: p < .05, **: p < .01

90 80 70 50 60

40 30 20 10 0

LF

p = 0.003**

Fig. 8 Changes in the average LF in the morning and in the afternoon

surfing the Internet,” and “resting for 7.5 min.” HF was significantly lower in the morning after “resting for 15 min” (p = .003) than in the afternoon. HF gradually decreased in the morning “before surfing the Internet,” “after surfing the Internet,” “resting for 7.5 min,” and “resting for 15 min,” but gradually increased in the afternoon.

Fig. 8 shows the average LF in the morning and in the afternoon. HF was non-significant in the morning and in the afternoon “before surfing the Internet,” “after surfing the Internet,” and “resting for 7.5 min.” LF was significantly higher in the morning after “resting for 15 min”

(p = .003) than in the afternoon. LF gradually increased in the morning “before surfing the Internet,” “after surfing the Internet,” “resting for 7.5 min,” and “resting for 15 min,” but gradually decreased in the afternoon.

Fig. 9 shows the average LF and HF in the morning and in the afternoon. LF and HF were non-significant in the morning and in the afternoon “before surfing the Internet,” “after surfing the Internet,” and “resting for 7.5 min.” LF and HF were significantly higher in the morning

Morning Afternoom 5

4.5 4 3.5 2.5 3 2 1.5 1 0.5 0

LF/HF

p = 0.003**

Fig. 9 Changes in the average LF and HF in the morning and in the afternoon

after “resting for 15 min” (p = .003) than in the afternoon.

HF gradually decreased in the morning “before surfing the Internet,” “after surfing the Internet,” “resting for 7.5 min,” and “resting for 15 min,” but gradually increased in the afternoon.

2. Variation in physiological parameters when surfing This section studies the variation in physiological parameters in the morning. Tables 1 to 3 show the paired t test results for the physiological parameters of participants in the morning.

In Table 1, differences in physiological parameters before surfing and after surfing the Internet in the morning are examined. DIA values (p = .029) show significant differences. Other physiological parameters (SYS, HR, HRV, HF, LF, and LF and HF) are not significantly different before or after surfing the Internet.

Differences in the physiological parameters after surfing the Internet and resting for 7.5 min in the morning were examined. It shows no significant differences after surfing the Internet and resting for 7.5 min. And, there

(7)

Table 2 Physiological parameters after surfing the Internet and resting for 7.5 min in the afternoon Parameters After surfing Resting for 7.5 min t value DF ^a p value

SYS 122.95 ± 23.55 112.00 ± 18.96 1.407 19 0.176 DIA 79.00 ± 15.17 78.00 ± 7.34 0.245 19 0.809 HR 87.00 ± 18.92 80.55 ± 14.46 1.938 19 0.068 HRV 271.70 ± 124.05 246.85 ± 154.14 0.593 19 0.560 HF% 57.35 ± 9.35 48.85 ± 13.82 2.169 19 0.043*

LF% 42.65 ± 9.35 51.15 ± 13.82 -2.169 19 0.043*

LF/HF 0.80 ± 0.34 1.24 ± 0.78 -2.177 19 0.042*

*: p < .05, **: p < .01

Table 3 Physiological parameters after surfing the Internet and resting for 15 min in the afternoon Parameters After surfing Resting for 15 min t value DF ^a p value

SYS 122.95 ± 23.55 116.45 ± 10.16 1.215 19 0.239 DIA 79.00 ± 15.17 79.40 ± 9.16 -0.145 19 0.886

HR 87.00 ± 18.92 79.45 ± 13.54 2.406 19 0.026*

HRV 271.70 ± 124.05 224.70 ± 133.15 1.439 19 0.167 HF% 57.35 ± 9.35 59.00 ± 14.64 -0.428 19 0.673 LF% 42.65 ± 9.35 40.70 ± 14.06 0.528 19 0.603 LF/HF 0.80 ± 0.34 0.81 ± 0.61 -0.094 19 0.926

*: p < .05, **: p < .01

were no significant differences after surfing the Internet and resting for 15 min.

In this stage, variations in physiological parameters in the afternoon are examined. Tables 5 to 6 show the paired t test results of the physiological parameters of participants in the afternoon. There were no significant differences in the physiological parameters before and after surfing the Internet in the afternoon.

Table 2 shows differences in physiological parameters after surfing the Internet and resting for 7.5 min in the afternoon. In Table 2, significant differences in HF%, LF%, and LF and HF were determined. The HF% decreased significantly, LF% increase significantly, and LF and HF increased significantly. The HR value is near p < .05. These parameters are shown in Table 2.

Table 3 shows differences in physiological parameters after surfing the Internet and resting for 15 min in the afternoon. In Table 3, HR is significantly different. Other physiological parameters are not significantly different after surfing the Internet and resting for 15 min. These parameters are shown in Table 3.

Differences in the physiological parameters before surfing the Internet in the morning and in the afternoon, and differences in physiological parameters before and after surfing the Internet in the morning and in the afternoon were examined. And, the physiological parameters are both not significant.

IV. DISCUSSION

1. Summary of physiological parameters in the morn- ing and in the afternoon

Blood pressure is assessed by the blood pumped from heart arteries. Hypertension is a long-term disease in the human body and symptoms include headache, dizziness, tinnitus, and dizziness. Matthews et al. showed that if long-term increases in diastolic blood pressure are maintained, probability of suffering from high blood pressure increases [28]. Therefore, it is recommended that users take breaks to avoid high blood pressure when surfing the Internet for an extended time. Sheldon G. Sheps [29]

showed that blood pressure follows a daily pattern.

(8)

Blood pressure is normally lower at night when sleeping and when first waking. After rising from sleep, blood pressure starts increasing. Blood pressure continues to increase during the day, usually peaking in the middle of the afternoon. In the late afternoon and evening, blood pressure begins decreasing again [29]. Fig. 3 shows the average systolic blood pressure in the morning and in the afternoon. Systolic blood pressure was non-significant in the morning and in the afternoon “before surfing the Internet,” “after surfing the Internet,” “resting for 7.5 min,” and “resting for 15 min.” In the morning, the means of “before surfing the Internet,” “after surfing the Inter- net,” “resting for 7.5 min,” and “resting for 15 min” were non-significant. In the afternoon, the means of “before surfing the Internet,” “after surfing the Internet,” “resting for 7.5 min,” and “resting for 15 min” were non-significant.

The mean levels of systolic and diastolic pressures in the morning and in the afternoon were significantly higher in the hypertensive group than in the control group; however, they were non-significant in both groups. In the control group, the variance of systolic pressure in the morning was significantly higher than in the afternoon; however, the variance of diastolic pressure was non-significant [30].

Fig. 4 shows the average diastolic blood pressure in the morning and in the afternoon. Diastolic blood pressure was non-significant in the morning and in the afternoon “before surfing the Internet,” “after surfing the Internet,” “resting for 7.5 min,” and “resting for 15 min.”

In the morning and in the afternoon, the means of “before surfing the Internet,” “after surfing the Internet,” “resting for 7.5 min,” and “resting for 15 min” were non-significant.

Diastolic pressure defines the early stages of hypertension, and is common in young and middle-aged people [29].

The U.S. authority in the hypertension research magazine

“Hypertension: Journal of the American Heart Associa- tion” published a study in October 2006 on blood pressure and its relationship with long working hours. The study showed that long working hours per week may increase the risk of high blood pressure [40]. However, Fig. 4 of the current study shows diastolic blood pressure did not increase significantly. Mean levels of systolic and diastolic pressures in the morning and in the afternoon were significantly higher in the hypertensive group than in the control group; however, these levels were non-significant.

In the control group, the variance of systolic pressure in the morning was significantly greater than in the afternoon;

however, the variance of the diastolic pressure was non-significant [30].

Fig. 5 shows the average heart rate in the morning and in the afternoon. Heart rate was significant lower in the morning “after surfing the Internet” (p = .022) than in the afternoon. Heart rate was non-significant in the morning and in the afternoon “before surfing the Internet,” “resting for 7.5 min,” and “resting for 15 min.” An increase in the heart rate by 10 beats per min (bpm) is associated with an increase in the risk of cardiac death by at least 20%, which is the same with an increase in systolic blood pressure by 10 mmHg [31]. Fig. 5 shows that HR has a faster heartbeat of 10 bpm in the afternoon than in the morning.

Extended periods of rapid heartbeat might cause sudden death.

The heart rate variability fluctuates the autonomic nervous activity when the balance between the sympathetic and parasympathetic nervous system and autonomic nervous activity changes because of relevant factors such as blood pressure, breathing, physical or psychological pressure, and drug therapy. Heart rate variability may reflect the health of a person. Positive HRV (resilience) strengthens the heart and helps transform stress into positive energy. Low HRV (rigidity) is an indicator of early mortality [32]. Fig. 6 shows the average heart rate variability in the morning and in the afternoon. Heart rate variability was non-significant in the morning and in the afternoon “before surfing the Internet,” “after surfing the Internet,” and “resting for 7.5 min.” After “Resting for 15 min,” heart rate variability in the morning tended to be higher (p = .084) than in the afternoon. Heart rate variability gradually increased in the morning “before surfing the Internet,” “after surfing the Internet,” “resting for 7.5 min,” and “resting for 15 min,” but gradually decreased in the afternoon. Similarly, HRV increased in the morning and decreased in the afternoon [33]. Therefore, participants should feel tired in the afternoon.

The autonomic nervous system is unable to control the human body, but is responsible for the heart rate, res- piration, blood pressure, body temperature, and other physiological functions and coordinations in the mainte- nance of the nervous system, which can be divided into sympathetic and parasympathetic. The sympathetic system promotes responses in our body when we feel stress or danger. For example, in a positive response state, our heart rate accelerates, blood pressure increases, faster breathing occurs, and body temperature increases. The parasympathetic system is inhibitory and is responsible for relaxing and resting the body to save energy and to sleep.

The human autonomic nervous system usually requires

(9)

two antagonistic regulations; otherwise, it results in

“autonomic nervous system disorders.” If the sympathetic (LF) is strong, anxiety, tension, heart rate acceleration, blood pressure increase occur; if the parasympathetic (HF) is strong, the body tires, becomes lethargic, and physical strength declines [34]. Fig. 7 shows the average HF in the morning and in the afternoon. HF was non-significant in the morning and in the afternoon “before surfing the Internet,” “after surfing the Internet,” and

“resting for 7.5 min.” HF was significantly lower in the morning after “resting for 15 min” (p = .003) than in the afternoon. HF gradually decreased in the morning “before surfing the Internet,” “after surfing the Internet,”

“resting for 7.5 min,” and “resting for 15 min,” but gradually increased in the afternoon. In the morning, a gradual decrease in HF showed that LF was greater than HF, and energy increased in the morning. In the afternoon, a gradual increase in HF showed the decline in the participant’s energy level [34].

Previous studies show that when the body has pressure or stress, sympathetic activity indices of LF% increases [35-37]. Fig. 8 shows the average LF in the morning and in the afternoon. HF was non-significant in the morning and in the afternoon “before surfing the Internet,” “after surfing the Internet,” and “resting for 7.5 min.” LF was significantly higher in the morning after

“resting for 15 min” (p = .003) than in the afternoon. LF gradually increases in the morning “before surfing the Internet,” “after surfing the Internet,” “resting for 7.5 min,” and “resting for 15 min,” but gradually decreases in the afternoon. After surfing the Internet in the afternoon, people often have less energy. Therefore, the LF in the afternoon was weaker than in morning. The HF and LF are opposite factors. In the morning, when LF (sympathetic) gradually increased, the participant had more energy. In the afternoon, after “resting for 7.5 min,” the LF was still increasing, which may have been because the participants were still thinking after using the Internet.

After “resting for 15 min,” the LF decreased significantly, which shows that participants’ energy levels were decreasing [34].

Sympathetic and parasympathetic balance indices (LF/HF) cause the body to maintain a constant state (homeostasis). However, a decrease in body experience starts the sympathetic and parasympathetic nerves to keep the body at a steady state (homeostasis) [38]. Fig. 9 shows the average LF and HF in the morning and in the afternoon. LF and HF were non-significant in the morn-

ing and in the afternoon “before surfing the Internet,” “after surfing the Internet,” and “resting for 7.5 min.” LF and HF were significantly higher in the morning after “resting for 15 min” (p = .003) than in the afternoon. HF gradually decreased in the morning “before surfing the Internet,”

“resting for 15 min,” but gradually increased in the afternoon. In the morning, the ratio of “resting for 15 min” is higher than in the afternoon because morning activities are mainly sympathetic. In addition, respiratory and cardiac functions are active and the body has a high body temperature and blood pressure to prepare for daytime activities. In the evening, with the parasympathetic function, the heart beat and respiratory rate decreases, body temperature and blood pressure also decrease slightly to prepare for rest or sleep (Ying-Hui, 2010).

2. Summary of physiological parameters when surfing the Internet

Table 1 shows the results of the physiological parameter before and after surfing the Internet. The results show that diastolic blood pressure (DIA) of the p-value is .029, and other physiological parameters of the p-values are from .198 to .830. In Table 1, only DIA shows significant differences (p < .05). Blood pressure is caused by the blood from heart arteries. Systolic blood pressure is pressure, whereas diastolic blood pressure is known as increasing diastolic pressure. Hypertension is a long- term disorder in the human body and symptoms include headache, dizziness, tinnitus, and dizziness. Matthews et al. showed that if long-term increases in the diastolic blood pressure state are maintained, the probability of suffering from high blood pressure increases [28]; therefore, breaks are recommended to avoid high blood pressure when using the Internet for an extended time.

Table 2 shows the results of physiological parameters after surfing the Internet and resting for 7.5 min in the afternoon. In Table 2, HF%, LF%, and LF and HF are significantly different. The sympathetic activity indices (LF %) and the parasympathetic activity indices (HF%) of the p-values are .043, whereas the sympathetic and parasympathetic balance index (LF/HF) of the p-value is .042.

LF and HF cause the body to maintain a constant state (homeostasis). Table 3 shows the results of physiological parameters after surfing the Internet and resting for 15 min in the afternoon. Table 3 shows that HR has significant differences with a p-value of .026, and other parameters are non-significant. Physiological parameters decrease

(10)

when resting for 15 min; however, the p-values are non-significant. Excitement in the autonomic nervous system may change the heart rate and rhythm [39]. Surf- ing the Internet is a type of stimulation and may cause an increase in heartbeat.

V. CONCLUSION

With the increasing population of Internet users, more people are surfing the Internet, playing online games, or finding information through the Internet. However, surfing the Internet for an extended time produces many negative physical and psychological effects. This study examines the level of Internet addiction and physiological effects when surfing the Internet. Heart rate was significantly lower in the morning “after surfing the Internet” (p

= .022) than in the afternoon. HF was significantly lower in the morning after “resting for 15 min” (p = .003) than in the afternoon. LF was significantly higher in the morning after “resting for 15 min” (p = .003) than in the afternoon.

LF and HF were significantly higher in the morning after

“resting for 15 min” (p = .003) than in the afternoon.

When blood pressure increased, the number of heartbeats increased. Sheldon G. Sheps [29] showed that blood pressure continues to rise during the day, usually peaking in the middle of the afternoon. In the late afternoon and evening, blood pressure begins decreasing again. There- fore, the heart rate in the afternoon is higher than in the morning. Similarly, when HRV increases in the morning and decreases in the afternoon [36], participants have less energy in the afternoon. In the morning, the LF is greater than HF; therefore, energy levels are higher than in the afternoon. Thus, in the morning, we should perform important activities, and in the afternoon, we can surf the Internet or perform less important activities.

REFERENCES

1. Laila, M. and Shehla, A., “Emotional Intelligence De- termining Work Attitudes and Outcomes of University Teachers: Evidence from Pakistan,” Interdisciplinary Journal of Contemporary Research in Business, Vol. 2, No. 10, pp. 240-259 (2011).

2. O’Neill, M., “The Lure and Addiction of Life on Line,”

The New York Times (1995). Retrieved June, 2010, from http://www.nytimes.com/1995/03/08/garden/the- lure-and-addiction-of-life-on-line.html

3. Rheingold, H., The Virtual Community: Homesteading

on the Electronic Frontier, Addison-Wesley, Reading, MA, pp.325 (1993).

4. Sandberg, J., “Talk is Cheap,” Wall Street Journal, pp.

120 (1997).

5. Morahan-Martin, J. and Schumacher, P., “Incidence and Correlates of Pathological Internet Use Among College Students,” Computers in Human Behavior, Vol.

16, No. 1, pp. 13-29 (2000).

6. Khan, M. K., “Emotional and Behavioral Effects of Video Games and Internet Overuse,” American Medical Association, Council on Science and Public Health (2007). Retrieved June, 2010, from http://www.amaassn.org/ama1/pub/upload/mm/443/csa ph12a07-fulltext.pdf

7. Griffiths, M. D., Davies, M. N. O., and Chappell, D.,

“Breaking the Stereotype: The Case of Online Gam- ing,” Cyberpsychology and Behavior, Vol. 6, No. 1, pp.81-91 (2003).

8. Shapira, N. A., Goldsmith, T. D., Keck Jr., P. E., Kho- sla, U. M., and McElroy, S. L., “Psychiatric Features of Individuals with Problematic Internet Use,” Journal of Affective Disorders, Vol. 57, No. 1-3, pp. 267-272 (2000).

9. Chak, K. and Leung, L., “Shyness and Locus of Con- trol as Predictors of Internet Addiction and Internet Use,” Cyberpsychology and Behavior, Vol. 7, No. 5, pp.

559-570 (2004).

10. Engelberg, E. and Sjöberg, L., “Internet Use, Social Skills, and Adjustment,” Cyberpsychology and Behav- ior, Vol. 7, No. 1, pp. 41-47 (2004).

11. Yuen, C. N. and Lavin, M. J., “Internet Dependence in the Collegiate Population: The Role of Shyness,” Cy- berpsychology and Behavior, Vol. 7, No. 4, pp. 379- 383 (2004).

12. Taylor, W. C., “Transforming Work Breaks to Pro- mote Health,” American Journal of Preventive Medi- cine, Vol. 29, No. 5, pp. 461-465 (2005).

13. Yeuan, T., “The blog of TY Health Information,”

American Medical Association (2010). Retrieved June, 2010, from http://www.ama-assn.org/

14. Hansson, E., Mattisson, K., Björk, J., Östergren, P. O., and Jakobsson, K., “Relationship Between Commuting and Health Outcomes in a Cross-Sectional Population Survey in Southern Sweden,” BMC Public Health, Vol.

11, No. 1, pp 834 (2011).

15. Meister, W., “Do Not be a Computer Vegetative (in Traditional Chinese),” Faith Hope Love (2004). Retrieved June, 2010, from http://www.fhl.net/main/comtech/

(11)

16. El Ahrache, K. and Imbeau, D., “Comparison of Rest Allowance Models for Static Muscular Work,” Inter- national Journal of Industrial Ergonomics, Vol. 39, No.

1, pp. 73-80 (2009).

17. Freeman, J. V., Dewey, F. E., Hadley, D. M., Myers, J., and Froelicher, V. F., “Autonomic Nervous System In- teraction With the Cardiovascular System During Ex- ercise,” Progress in Cardiovascular Diseases, Vol. 48, No. 5, pp. 342-362 (2006).

18. Eguchi, K., “Disrupted Diurnal BP Variation and Car- diovascular Disease in Diabetes: Relation to Auto- nomic Nervous System,” Autonomic Neuroscience, Vol.

135, No. 1-2, pp. 34-35 (2007).

19. Chanudet, X., Bonnevie, L., and Bauduceau, B.,

“Coronary Heart Disease and Cardiovascular Auto- nomic Neuropathy in the Elderly Diabetic,” Diabetes and Metabolism, Vol. 33, No. 1, pp. S19-S31 (2007).

20. Aksöyek, S., Aytemir, K., Özer, N., Özcebe, O., and Oto, A., “Assessment of Autonomic Nervous System Function in Patients with Behcet's Disease by Spectral Analysis of Heart Rate Variability,” Journal of the Autonomic Nervous System, Vol. 77, No. 2-3, pp. 190- 194 (1999).

21. Lewis, M. J., Short, A. L., and Lewis, K. E., “Auto- nomic Nervous System Control of the Cardiovascular and Respiratory Systems in Asthma,” Respiratory Medicine, Vol. 100, No. 10, pp. 1688-1705 (2006).

22. Esler, M. and Kaye, D., “Increased Sympathetic Nerv- ous System Activity and Its Therapeutic Reduction in Arterial Hypertension, Portal Hypertension and Heart Failure,” Journal of the Autonomic Nervous System, Vol. 72, No. 2-3, pp. 210-219 (1998).

23. Wu, J. S., Lu, F. H., Yang, Y. C., Lin, T. S., Chen, J. J., Wu, C. H., Huang, Y. H., and Chang, C. J., “Epidemi- ological Study on the Effect of Pre-Hypertension and Family History of Hypertension on Cardiac Autonomic Function,” Journal of the American College of Cardi- ology, Vol. 51, No. 19, pp. 1896-1901 (2008).

24. Barron, H. V. and Lesh, M. D., “Autonomic Nervous System and Sudden Cardiac Death,” Journal of the American College of Cardiology, Vol. 27, No. 5, pp.

1053-1060 (1996).

25. Appels, C. W. Y. and Bolk, J .H., “Sudden Death After Emotional Stress. A Case History and Literature Re- view,” European Journal of Internal Medicine, Vol. 20, No. 4, pp. 359-361 (2009).

26. Malik, M. and Camm, A. J., “Heart Rate Variability and Clinical Cardiology,” British Heart Journal, Vol.

71, No. 1, pp. 3-6 (1994).

27. Malik, M., Bigger, J. T., Camm, A. J., Kleiger, R. E., Malliani, A., and Schwartz, P. J., “Heart Rate Variabil- ity. Standards of Measurement, Physiological Interpre- tation, and Clinical Use,” European Heart Journal, Vol.

17, No. 3, pp. 354-381 (1996).

28. Benetos, A., Thomas, F., Safar, M. E., Bean, K. E., and Guize, L., “Should Diastolic and Systolic Blood Pres- sure be Considered for Cardiovascular Risk Evaluation:

A Study in Middle-Aged Men and Women,” Journal of the American College of Cardiology, Vol. 37, No. 1, pp.

163-168 (2001).

29. Sheps, S. G., “High Blood Pressure (Hypertension),”

Mayo Foundation for Medical Education and Re- search (2010). Retrieved June, 2010, from http://www.

mayoclinic. com/health/blood-pressure/AN00391 30. Williams, B. I. and Ledingham, J. G., “Significance of

Intraocular Pressure Measurement in Systemic Hyper- tension,” British Journal of Ophthalmology, Vol. 68, No. 6, pp. 383-388 (1984).

31. Perret-Guillaume, C., Joly, L., and Benetos, A., “Heart Rate as a Risk Factor for Cardiovascular Disease,”

Progress in Cardiovascular Diseases, Vol. 52, No. 1, pp. 6-10 (2009).

32. Healthy Heart Mediation, Heart Rate Variability Training (2010). Retrieved June, 2010, from http://www.healthy-heart-meditation.com/heart-rate-va riability.html

33. Liang, W. C., Yuan, J., Sun, D. C., and Lin, M. H.,

“Variation in Physiological Parameters Before and Af- ter An Indoor Simulated Driving Task in The After- noon,” Biomedical Engineering and Technology Con- ference, Taichung, Taiwan (2006).

34. Gabella, G., Autonomic Nervous System, John Wiley &

Sons, NY (2001).

35. Shusterman, V. and Barnea, O., “Sympathetic Nervous System Activity in Stress and Biofeedback Relaxa- tion,” IEEE Engineering in Medicine and Biology Magazine, Vol. 24, No. 2, pp. 52-57 (2005).

36. Liang, W. C., Yuan, J., Sun, D. C., and Lin, M. H.,

“Changes in Physiological Parameters Induced by In- door Simulated Driving: Effect of Lower Body Exer- cise at Mid-Term Break,” Sensors, Vol. 9, No. 9, pp.

6913-6933 (2009).

37. Kreibig, S. D., “Autonomic Nervous System Activity in Emotion: A Review,” Biological Psychology, Vol. 84, No. 3, pp. 394-421 (2010).

38. McEwen, B. S. and Wingfield, J. C., “What Is In A

(12)

Name? Integrating Homeostasis, Allostasis and Stress,” Hormones and Behavior, Vol. 57, No. 2, pp.

105-111 (2010).

39. Taipei Veterans General Hospital, Understanding of Cariac Arrhythmias (in Traditional Chinese) (2010).

Retrieved June, 2010, from http://www.vghtpe.gov.tw/

40. Liu, J. T., “Work Long Hours May Lead to Hyperten- sion (in Traditional Chinese),” People (2008). Re-

trieved June, 2010, from http://health.people.com.cn/

BIG5/119042/131724/131839/7660918.html

Manuscript Received: Mar. 05, 2012 First Revision Received: Mar. 22, 2012 Second Revision Received: May. 03, 2012 and Accepted: May. 04, 2012