隨著網路通訊技術的進步，社群媒體和網路遊戲的推陳出新，不同族群使用網際網路的時間也較過去大幅增加，過度依賴網際網路或是沉迷於網路遊戲和社群媒體的現象也越來越多，網路成癮事件更是時有所聞，2018年6月，世界衛生組織(WHO)將網路成癮列為精神疾病。
本研究的性質為統合分析，針對Covid-19流行期間不同地區的獨立研究結果統整之後的統計分析方式，針對收集到的44篇探究Covid-19流行期間網路成癮的文章進行研究，整理出可能的影響因素：評估量表(scale)種類、參與者每日使用網際網路達5小時(Rate_Online5Hr)的比例、參與者罹患憂鬱症狀(depression)比例、參與者罹患焦慮症狀(anxiety)比例、平均年齡(age)、參與者男性(male)比例、有接受高等教育(education)的比例、參與者為學生(student)的比例、高收入(high income)的比例。
研究結果顯示，在參與者每日使用網際網路達5小時的比例、參與者罹患憂鬱症狀比例、參與者罹患焦慮症狀比例、平均年齡、參與者男性比例、有接受高等教育的比例、參與者為學生的比例和高收入的比例中，僅每日使用網際網路達5小時的比例對網路成癮嚴重程度有影響。

With the advancement of internet communication technology and the continuous emergence of social media and online games, the amount of time different groups spend using the internet has significantly increased compared to the past. The phenomena of excessive reliance on the internet or addiction to online games and social media are also becoming more prevalent, with incidents of internet addiction frequently reported. In June 2018, the World Health Organization (WHO) classified internet addiction as a mental disorder.
This study is a meta-analysis that involves a statistical analysis of the combined results from independent studies conducted in different regions during the Covid-19 pandemic. The research examines 44 articles that explore internet addiction during the Covid-19 outbreak, identifying potential influencing factors.：scale、Rate_Online5Hr、depression、anxiety、age、male、education、student、high income.
The results of the study indicate that among the proportion of participants using the internet for five hours daily, the proportion of participants with depressive symptoms, the proportion of participants with anxiety symptoms, the average age, the proportion of male participants, the proportion with higher education, the proportion of participants who are students, and the proportion with high income, only the proportion of participants using the internet for five hours daily significantly affects the severity of internet addiction.

目錄
第一章 緒論	1
第一節 研究背景	1
第二節 網路成癮	2
第二章 研究方法	3
第一節 論文選取	3
第二節 統計方法	6
第三章 研究結果	8
第一節 網路成癮統合分析	8
第二節 網路成癮統合迴歸分析	13
第四章 結論	25
參考文獻	26
附錄一	27
附錄二	29

表目錄

表3.1.1網路成癮效益值之固定效益模式的統合分析·······················8
表3.1.2網路成癮效益值之隨機效應模式的統合分析·······················10
表3.2.1.1網路成癮的評估量表種類對網路成癮效益值的影響分析···········13
表3.2.2.1每日使用網際網路達5小時之百分比對網路成癮效益值的影響分析·16
表3.2.3.1 罹患憂鬱症比例(depression)對網路成癮效益值的影響···········17
表3.2.4.1罹患焦慮症比例(anxiety)對網路成癮效益值的影響··············18
表3.2.5.1 平均年齡(Age)對網路成癮效益值的影響························19
表3.2.6.1 男性比例(male)對網路成癮效益值的影響·······················20
表3.2.7.1 有接受高等教育比例(education)對網路成癮效益值的影響········22
表3.2.8.1 參與者為學生比例(student)對網路成癮效益值的影響············23
表3.2.9.1 參與者家庭高收入的比例(high income)對網路成癮效益值的影響··24

圖目錄

圖3.1.2.1森林圖······················································12
圖3.2.1.1評估量表對網路成癮的效益值之統合回歸預測圖··················15
圖3.2.2.1參與者每日使用網際網路達5小時之百分比對網路成癮的效益值之統合迴歸預測圖·················································17
圖3.2.3.1 罹患憂鬱症比例對網路成癮效益值之統合迴歸預測圖·············18
圖3.2.4.1 罹患焦慮症比例對網路成癮效益值之統合迴歸預測圖·············19
圖3.2.5.1 平均年齡對網路成癮效益值之統合迴歸預測圖···················20
圖3.2.6.1 男性比例對網路成癮效益值之統合迴歸預測圖···················21
圖3.2.7.1 有接受高等教育比例對網路成癮效益值之統合迴歸預測圖·········22
圖3.2.8.1 參與者為學生比例對網路成癮效益值之統合迴歸預測圖···········23
圖3.2.9.1 參與者家庭高收入的比例對網路成癮效益值之統合迴歸預測圖·····24

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