网瘾网络游戏成瘾外文翻译文献

文献信息：

文献标题：Risk factors associated with online game addiction: A hierarchical model（与网络游戏成瘾相关的风险因素：层次模型）国外作者：Hyun, Gi Jung, et al

文献出处：《Computers in human behavior》,2015,48:706-713.

字数统计：英文2884单词，16605字符；中文5177汉字

外文文献：

Risk factors associated with online game addiction:

A hierarchical model

Abstract Online gaming addiction has been increasingly recognized as a mental disorder. However, the predictive factors that lead to online gaming addiction are not well established. The aim of this study was to identify factors that may infiuence the development of online gaming addiction. A total of 263 patients with problematic online gaming addiction (255 males (97%) and 8 females (3%), age: mean = 20.4 ±5.8 years)and153 healthy comparison subjects (118 males (77%) and 35 females (23%), age: 21.2 ±5.5 years, range) were recruited for participation in the current study. Hierarchical logistic regression analyses among each set of variables were conducted. Individual factors (sex and age), cognitive factors (IQ and perseverative errors), psychopathological conditions (ADHD, depression, anxiety, and impulsivity), and social interaction factors (family environment, social anxiety, and self-esteem) were evaluated in a stepwise fashion. All four factors were associated with online gaming addiction, with psychopathological conditions being the strongest risk factors for the addiction. Individual factors, psychological factors, and social interactions were associated with the development of pure online gaming addiction. As before, psychological factors (attention, mood, anxiety and impulsivity) were the strongest risk factors for online gaming addiction in patients with pure online gaming addiction.

Psychopathologies, including ADHD and depression, were the strongest factors associated with the development of online gaming addiction inindividuals.

Key words: Online gaming addiction; Social interaction; Psychopathology Individual factors; Cognitive functions

1.Introduction

Recently, it has been suggested that internet addiction is a mental health problem (Lam, Peng, Mai, & Jing, 2009). Internet addiction may impair daily life, academic performance, family relationships, and emotional development, particularly among adolescents. Since 1996, w hen the concept of ‘‘internet addiction’’ was introduced as a new clinical disorder, internet addiction has been studied in terms of the physical and psychological consequences, particularly in China, Korea. However, the causes or aggravating factors that lead to internet addiction remain unknown, although many studies have attempted to explore its causes in terms of demographic factors, psychopathological conditions, psychosocial and family environment, and cognitive functions (Aboujaoude, Koran, Gamel, Large, & Serpe, 2006; Ha et al., 2006; Park et al., 2011; Tsai et al., 2009; Wang & Wang, 2013).

In general, individual factors related to online gaming addiction such as sex and age have been considered in previous studies. Most studies report that the male sex conveys a 2–3 times higher risk for internet addiction than the female sex (Lee, Han, Kim, & Renshaw, 2013; Sasmaz et al., 2014). In addition, older age has been reported to be a risk factor for internet addiction among adolescents (Ahmadi & Saghafi, 2013).

In terms of causal cognitive factors for online gaming addiction, IQ and cognitive fiexibility have been considered. A study by Park et al. (2011) noted that adolescents with internet addiction have lower comprehensive sub-item scores on the Wechsler Adult Intelligence Scale (WAIS-R) in comparison to healthy control subjects. A study by Zhou, Yuan, and Yao (2012) indicatedthat patients with online gaming addiction demonstrate low mental fiexibility and response inhibition in modified versions of go/nogo tests. In our previous study, patients with online gaming

addiction showed increased numbers of perseverative responses and perseverative errors on the Wisconsin Card Sorting Test (Han, Lyoo, & Renshaw, 2012).

Psychopathological conditions that are often co-morbid with onlinegamingaddiction,attentiondeficitandhyperactivity disorder (ADHD), major depressive disorder (MDD), anxiety, and impulsivity have been considered. Several previous studies of internet addiction have noted that some symptoms in patients with problematic internet use are the same as those observed in other psychopathologies (Aboujaoude et al., 2006). For example, ADHD and MDD are the most frequent comorbid disorders in a South Korean sample population, and the clinical characteristics of internet addiction are similar to clinical symptoms in patients with ADHD or MDD (Ha et al., 2006; Park, Lee, Kim, Jeong, & Han, 2013; Yoo et al., 2004). In a survey of 987 Indian adolescents (Goel, Subramanyam, & Kamath, 2013), adolescents with higher internet addiction score also show higher scores on anxiety and depression scales. The association between violent games and aggressive behavior is controversial. Past study suggested the correlation between violent video game and aggressive behavior in healthy subjects (Anderson & Dill, 2000). However, a recent time series analysis and meta-analysis have shown that violent films are negatively correlated with aggressive behaviors (Markey, French, & Markey, in press), and there is no evidence that violent video games increase aggression in children (Ferguson, in press).

In terms of social interaction factors that have been related to online gaming addiction, family environment, social anxiety, and self-esteem have been considered. Based on an online survey of 1642 people (19–60 years of age), Wang and Wang (2013) reported that excessive internet use is motivated by cyberspace social encounters in individuals with poor offiine social support, including support from family members. However, based on a survey of 2348 college students, Yen et al. (2012) suggested that the internet is a good alternative outlet for individuals with social anxiety. In contrast, Lee and Stapinski (2012) reported that online communication exacerbates face-to-face avoidance in individuals with higher levels of perceived social anxiety.

Self-esteem is thought to mediate social interactions and preference for online social interactions (Caplan, 2005; Fioravanti, Dettore, & Casale, 2012; Stinson et al., 2008). Bozoglan, Demirer, and Sahin (2013) found that low self-esteem was associated with internet addiction in a sample of Turkish university students 18–24 years of age. A study by Lemenager et al. (2013) reported selfconfidence deficits in individuals characterized as major users of multiplayer online role-playing games.

Each of these studies, however, focused on only one or two independent factors, without considering the hierarchical importance among variable risk factors. As observed in other addiction disorders as well as in child and adolescent disorders (Lee et al., 2013), identifying the hierarchical importance of risk factors for online gaming addiction may infiuence policy and treatment. Based on patients with online gaming addiction consecutively referred to the Online Game Clinic Center at OO University Hospital, we aimed to assess the hierarchical importance among individual factors, cognitive functions, psychopathological conditions, and levels of psychosocial and family support in patients with online gaming addiction.

2.Method

The current study screened 308 patients with problematic online gaming habits (296 males (96%) and 12 females (4%), age: mean = 21.0 ±5.9 years, range: 12–45 years) who visited the Online Game Clinic Center at OO University Hospital from June 2011 to March 2013. Through advertisements posted at OO University and OO University Medical Center, 153 healthy comparison subjects (118 males (77%) and 35 females (23%), age: 21.2 ± 5.5 years, range: 13–40 years) were recruited to voluntarily participate in the study. Both the patients and the control subjects were screened with the Structured Clinical Interview for DSM-IV (Ha et al., 2006; Kusumakar, MacMaster, Gates, Sparkes, & Khan, 2001), and patients were secondarily diagnosed with comorbidities by two child and adolescent psychiatrists. All subjects were asked to complete questionnaires regarding the severity of internet addiction, individual factors, family environment, social interactions, and comorbidities.

The criteria for online game addiction in the current study were similar to those employed in other previous studies (Han, Hwang, & Renshaw, 2010; Ko et al., 2009). Factors associated with addiction included: (1) online game playing through the internet, more than 4 h per day or 30 h per week, (2) Internet Addiction Scale score >50 (Young, 1996), (3) irritable, anxious, and aggressive behaviors when compelled to stop online game playing, (4) impaired behaviors or distress, economic problems, or maladaptive patterns of regular life due to excessive online game play, and (5) irregular life patterns due to disrupted diurnal rhythms (e.g., sleeping during the day and gaming at night, irregular meals, and failure to maintain personal hygiene), school truancy, or loss of job.

Exclusion criteria included: (1) a history or current episode of other Axis I psychiatric diseases, with the exception of MDD and ADHD, (2) IQ < 80, (3) substance abuse history, with the exception of alcohol or tobacco abuse, and (4) neurological or medical disorder. Ultimately, 263 patients with online gaming addiction (255 males (97%) and 8 females (3%), age: mean = 20.4 ±5.8 years, range: 12–45 years), consisting of 109 with only online gaming addiction (referred to herein as pure online gaming addiction), 92 with online gaming addiction and ADHD, and 62 with online gaming addiction and MDD, were included in the study. The patients excluded were 14 patients with multiple diagnoses of online gaming addiction plus ADHD and major depression, 11 patients with schizophrenia, 5 patients with obsessive–compulsive disorder, 4 patients with alcohol dependence, 7 patients with autism spectrum disorders, and 4 patients with mental retardation. The research protocol for the current study was approved by the OO University Hospital Institutional Review Board. Written informed consent was provided by patients older than 18 years. In adolescents younger than 18 years, written informed consent was provided by parents and adolescents.

3.Discussion

Of the four risk categories including individual factors, cognitive functions, psychopathologies, and social interactions, psychopathologies were the strongest risk

factors for online gaming addiction in all patients. Specifically, sex, self-esteem, attention, depressive moods, family environment, and perseverative errors were significantly associated with online gaming addiction. In patients with pure online gaming addiction, associations were found with individual factors, psychological factors, and social interactions. Of the four categories, psychopathologies were the strongest risk factors for online gaming addiction in patients with pure online gaming addiction. Specifically, sex, age, self-esteem, attention, depressive moods, and family environment were significantly associated with online gaming addiction in these patients.

3.1.Individual factors

Of the two individual factors, sex was a risk factor for online gaming addiction in all patients. Both sex and age were also associated with online gaming addiction in patients with pure online gaming addiction.

Sex differences may be due to different online use between males and females. Female adolescents typically use online networking for enhancing communication and sharing information via instant messaging, chatting, and visiting personal websites. However, male adolescents mainly use online networking for playing online games (Gross, 2004). In addition, temperamental characteristics, including higher levels of novelty seeking, higher comorbidity of ADHD, and higher levels of impulsivity in males are responsible for higher rates of online gaming addiction relative to females (Bozkurt, Coskun, Ayaydin, Adak, & Zoroglu, 2013; Dalbudak et al., 2013; Ko et al., 2010; Wu et al., 2013). A study by Park et al. (2013) reports that increased age is a risk factor for online gaming addiction in adolescent patients.

3.2.Cognitive factors

Although there are controversies in terms of the relation between cognitive function and internet addiction, the results of the current study suggest that perseverative errors but not IQ are associated with online gaming addiction. Disrupted perseverative responses have been reported in patients with internet addiction (Han, Kim, Lee, & Renshaw, 2012; Han, Lyoo et al., 2012; Zhou et al., 2012). Relative to healthy comparison subjects, patients with online gaming addiction tend to show

increased numbers of perseverative errors (Kusumakar et al., 2001). Poor executive functioning, including low mental fiexibility and response inhibition in patients with online gaming addiction, are thought to be associated with the severity of the addiction (Lemenager et al., 2013). A study by Park et al. (2011) reported that lower comprehensive sub-item scores of WAIS-R were associated with internet addiction. The results of the current study, however, did not show any correlation between IQ and online gaming addiction. The difference between the findings of Park et al. and the current research may be due to the number of subjects, the consideration of comorbidities, or the interactions between other risk factors.

3.3.Psychopathological factors

Psychopathologies, especially ADHD and MDD, were the strongest risk factors for online gaming addiction in the current study. In a systematic review of 20 articles about comorbid psychopathologies and pathological internet use, Carli et al. (2013) reported that 100% of articles claim that pathological internet use is associated with symptoms of ADHD, and 75% of the articles assert that pathological internet use is associated with MDD. According to a survey of 2793 college students, adult ADHD is also associated with internet addiction (Yen, Yen, Chen, Tang, & Ko, 2009). The strong association between pathological internet use and ADHD may be related to the characteristics of ADHD, insofar as both sets of patients tend to be easily bored and thrive on instant gratification (Campbell & von Stauffenberg, 2009; Carli et al., 2013). Moreover, Fergurson and Ceranoglu suggest that pathologic gaming behavior may be a symptom of underlying ADHD rather than a unique disease (Fergurson and Ceranoglu, 2014). In our group of patients with pure online gaming addiction (i.e., patients without comorbid ADHD or MDD), K-ARS scores and K-BDI scores were also associated with online gaming addiction. In German game players, emotionally sensitive users play online games more for the purpose of interaction with friends, compared to less sensitive users (Kowert et al., 2014). These results suggest that online gaming addiction may be aggravated by sub-clinical ADHD and MDD. Although there were significant differences between the online gaming addiction group and the healthy comparison group in the present study, the characteristic of

impulsiveness, as assessed via the BIS/BAS scale, was not associated with online gaming addiction. Many studies suggest that a high level of impulsiveness is associated with internet addiction (Bozkurt et al., 2013; Wu et al., 2013). One group has suggested that internet addiction should be classified as an impulse control disorder (Dell’Osso et al., 2008). The divergent results of the current research from previous research may be due to interaction effects among the K-ARS and K-BDI scores.

3.4.Social interaction factors

The results of the current research show that the factor of family environment within the set of social interaction factors is an important risk factor for an online gaming addiction. Tsai et al. (2009) demonstrated that offiine social support and interaction with family and colleagues in a study of 1360 university freshmen negatively correlated with internet addiction. In a study of family factors contributing to internet addiction, Yen, Yen, Chen, Chen, and Ko (2007) reported that a higher level of confiict between parents and children together with a lower level of family cohesion is a risk factor for internet addiction. A study by Han, Kim et al. (2012), Han, Lyoo et al. (2012) reported that a three-week period of intensive family intervention improved the severity of online game addiction and increased the activity of the caudate nucleus in response to the affect scene. The present study also found that family environment factors were associated with online gaming addiction. Although there were no significant results between social anxiety and online gaming addiction in the present study, social anxiety should be considered to be associated with online gaming addiction. Our result may have been due to the control of psychological factors (BAI score) in the previous analysis steps. A study by Rusconi et al. (2012) reported that increased anxiety due to social interaction is associated with prolonged use of the internet and social network services. A robust level of self-esteem is known to be a protective factor against drug and alcohol addiction, as well as internet addiction. Decreased levels of self-esteem have also been reported to be associated with internet addiction and online gaming addiction (Bozoglan et al., 2013; Lemenager et al., 2013). Practical support together with positive role models for

patients with drug or alcohol addiction could lead to successful recovery from such an addiction due to increased self-esteem (Johansen, Brendryen, Darnell, & Wennesland, 2013). Future studies should consider additional factors that can affect online gaming addiction including the type of gaming (offiine, computer, console, apps, etc.), game genre, and other addiction comorbidities.

4.Limitations

There are several limitations to the current results. First, although we determined the statistical design in light of previous studies, the arbitrary classification of independent factors and the arbitrary inclusion criteria of ‘online gaming addiction’may reduce the reliability for factors predicting online gaming addiction. In particular, the time spent gaming as an inclusion criterion for online gaming addiction is controversial (Charlton & Danforth, 2007; Skoric, Lay Ching Teo, & Lijie Neo, 2009). Second, the current study did not consider the use of other online facilities such as smart phone services, internet chatting, or smart television. Third, there was a very low percentage of females in the current study; thus, the generalizability of the results to online gaming addiction in females is limited. Third, because game preference and prevalence of ADHD are associated with males, the effect sizes of risk factors other than sex in the regression analysis might have been small. Readers should consider these limitations when interpreting the results.

5.Conclusion

The current study assessed the hierarchical importance of four sets of risk factors for online gaming addiction. These factor sets included individual factors, cognitive functions, psychopathologies, and social interactions in patients with online gaming addiction. Psychopathologies including ADHD and depression were the strongest risk factors for the addiction. Control and treatment of ADHD and major depressive disorder may be important for the prevention of online game addiction. Additionally, family environment and self-esteem within the set of social interaction factors were important risk factors for online game addiction.

中文译文：

与网络游戏成瘾相关的风险因素：层次模型摘要网络游戏成瘾已被越来越多地认定为一种精神障碍。然而，导致网络游戏成瘾的预测因素尚未确定。本研究的目的是找出可能会影响网络游戏成瘾发展的因素。共有263名有问题的网络游戏成瘾患者（男性255人（97％），女性8人（3%），年龄：平均= 20.4±5.8岁）和153名健康对照者（男性118人（77％）和女性35人（23％），年龄：21.2±5.5岁，范围）参与此次研究。对每组变量进行多层次逻辑回归分析。在个体因素（性别和年龄）、认知因素（智商和持续性错误）、精神病理因素（注意缺陷多动障碍、抑郁、焦虑和冲动）和社会互动因素（家庭环境、社交焦虑和自尊）方面都进行了逐步的评估。所有这四个因素都与网络游戏成瘾有关，精神病理因素是成瘾的最大风险因素。个人因素、心理因素和社会互动与纯网络游戏成瘾的发展有关。如前所述，心理因素（注意力、情绪、焦虑和冲动）是网络游戏成瘾患者网络游戏成瘾的最大风险因素。包括注意缺陷多动障碍和抑郁症在内的精神病理因素是与个人网络游戏成瘾发展有关的最强因素。

关键词：网络游戏成瘾；社会互动；精神病理学的个体因素；认知功能

1.引言

最近，有人认为网络成瘾是一种心理健康问题（林、彭、麦和静，2009）。网络成瘾可能会影响日常生活、学业成绩、家庭关系和情绪发展，特别是对青少年。自1996年以来，当“网络成瘾”的概念被引入作为一种新的临床疾病时，网络成瘾就从生理和心理的角度进行了研究，特别是在中国、韩国。然而，尽管许多研究试图在人口因素、精神病理因素、心理社会和家庭环境以及认知功能方面探索其原因（阿班，古兰，加梅尔，拉奇和塞尔普，2006；河等人，2006；帕克等人，2011；蔡等人，2009；王和汪，2013），但导致网络成瘾的原因或加重因素仍然是未知的。

在以往的研究中，一般都考虑到与网络游戏成瘾有关的个人因素，如性别和年龄。大多数研究报告说，与女性相比，男性的网络成瘾风险高出2-3倍（李，韩，金和伦肖，2013；萨斯曼等人，2014）。此外，据报道，年龄增大是青少年

网络成瘾的一个危险因素（艾哈迈迪和萨加菲，2013）。

在网络游戏成瘾的因果认知因素方面，已经考虑了智商和认知能力。帕克等人（2011）的一项研究指出，与健康对照者相比，网络成瘾青少年在韦克斯勒成人智力量表(WAIS-R)中的综合分项得分较低。周、元和姚（2012）的一项研究表明，网络游戏成瘾的患者在go/nogo测试的修改版本中表现出低的心理智力和反应抑制。在我们以前的研究中，网络游戏成瘾的患者在威斯康星卡片分类测验（韩，刘和伦肖，2012）中表现出持续性反应和持续性错误的数量在增加。

在精神病理学因素中，已经考虑了通常与网络游戏成瘾有关如注意缺陷多动障碍(ADHD)、重度抑郁症(MDD)、焦虑和冲动的并发症。以前对网络成瘾的几项研究已经指出，有问题的互联网使用的患者的一些症状与其他精神病理学观察到的症状相同（阿布贾德等人，2006）。例如，ADHD和MDD是韩国样本人群中最常见的并发症，而网络成瘾的临床特征与ADHD和MDD患者的临床症状相似（河等人，2006；帕克，李，金，郑和韩，2013；柳等人，2004）。在987名印度青少年的调查中（戈埃尔，沙布拉曼亚姆和卡马斯，2013），网络成瘾评分较高的青少年在焦虑和抑郁量表上的得分也较高。暴力游戏与侵略行为之间的关联是有争议的。过去的研究表明，暴力视频游戏与健康受试者的侵略行为之间存在相关性（安德森和迪尔，2000）。然而，最近的时间序列分析和荟萃分析显示，暴力电影与侵略行为呈负相关（马基，法国人和马基，新闻界），没有证据表明暴力视频游戏增加了儿童的侵略行为（弗格森，新闻界）。

在与网络游戏成瘾有关的社会互动因素方面，考虑了家庭环境、社交焦虑和自尊。王和汪（2013）根据对1642人（19-60岁）进行的在线调查发现，过度使用互联网是出于，在现实社会中得到较少支持的人，包括家庭成员的支持，可以在网络社会中得到其他人的支持。然而，根据对2348名大学生的调查，延等人（2012）认为，对于有社交焦虑的个人来说，互联网是一个很好的选择。相比之下，李和Stapinski（2012）报道说，网上交流使社交焦虑程度较高的个体避免了面对面的障碍。

自尊被认为可以调解社会互动和网络社会互动偏好（卡普兰，2005；菲奥拉万蒂，德托雷和卡塞莱，2012；斯廷森等人，2008）。Bozoglan，德米雷尔和沙欣（2013）发现，在18至24岁的土耳其大学生样本中，低自尊与网络成瘾有关。

Lemenager等人的研究（2013）报道了以多人在线角色扮演游戏为主要用户的个人缺乏自信。

然而，这些研究仅关注于一个或两个独立因素，而不考虑变量风险因素之间的分层重要性。正如其他成瘾疾病以及儿童和青少年疾病所观察到的，确定网络游戏成瘾风险因素的分层重要性可能会影响政策和治疗（李等人，2013）。基于OO大学医院的网络游戏门诊中心网络游戏成瘾患者的情况，我们旨在评估网络游戏成瘾患者的个体因素、认知功能、精神病理状态以及社会心理和家庭支持水平之间的分层重要性。

2.方法

目前的研究筛查了308名有问题的网络游戏患者（男性296人（96％），女性12人（4％），年龄：平均= 21.0±5.9岁，范围：12-45岁），他们在2011年6月至2013年3月期间访问了OO大学医院的网络游戏诊所。通过在OO大学和OO大学医学中心发布的广告，招募了153名健康对照者（男性118人（77%），女性35人（23%），年龄：平均= 21.0±5.5岁，范围：13-40岁）自愿参加这项研究。患者和对照者均使用DSM-IV结构化临床访谈进行了筛查（河等人，2006；Kusumakar，麦克马斯特，盖茨，斯帕克斯和卡恩，2001），患者被两名儿童及青少年精神科医生诊断为继发性合并症。所有受试者都被要求填写关于网瘾严重程度、个人因素、家庭环境、社会互动和合并症的问卷。

在目前的研究中，网络游戏成瘾的标准与其他以前研究中所使用的标准类似（韩，黄和罗德曼，2010；柯等人，2009）。与成瘾相关的因素包括：（1）通过互联网进行网络游戏，每天超过4小时或每周超过30小时；（2）网络成瘾量表评分> 50（杨，1996）；（3）被迫停止网络游戏时的急躁、焦虑和攻击性行为；（4）由于沉溺网络游戏导致的行为障碍或苦恼、经济问题或正常生活的不适应行为；（5）由于昼夜节律紊乱导致的不规则生活行为（例如，白天睡觉，晚上游戏，不定期餐饮，不能保持个人卫生），逃学或失业。

排除标准包括：（1）除了MDD和ADHD之外的其他I型精神疾病的病史或现状；（2）IQ<80；（3）除酗酒或烟草滥用外，药物滥用史；（4）神经系统或医学疾病。最终，263名网络游戏成瘾患者（男性255人（97％），女性8人（3％），