Prevalence of Internet Addiction and its Association with Self Perceived Life Quality in Shenzhen Adolescents

Background: Internet Addiction is a newly emerging issue for adolescents globally. Previous literature suggested a tangled relationship between Internet Addiction, depressive mood and lower subjective and objective ratings in Quality of Life. However, the independent relationship between Internet Addiction and Quality of Life was less discussed. Method: A cross-sectional study was conducted in Shenzhen adolescents to address the independent associations between Internet Addiction and 4 domains of Quality of Life (physical, psychological, social relation and environmental domains), in order to further guide the prevention and intervention for IA. Associations were calculated using linear regression models before and after the adjustment for the existence of depressive mood and confounding demographic factors. Result: The prevalence of Internet Addiction in Shenzhen was 23.2%. Engaging with Internet Addiction resulted in significant decrease in Quality of Life score in physical domain (B = -0.87, p<.001) and psychological domain (B = -0.40, p = 0.011) as well as insignificant decrease in social relation domain (B = -0.36, p = 0.063) and increase in environmental domain (B = 0.02, p = 0.906). Conclusion: Although Quality of Life is considered to be related to many factors, Internet Addiction on its own had a significant impact on lower subjective life satisfaction overall, especially in psychical and psychological wellbeing.


Introduction
Within last two decades, our life has changed rapidly as the so-called era of Internet arrives. Due to the escalation of availability to Internet, the proportion of people who use Internet in the world raised dramatically from less than 1% in 1995 to over 46% in 2016 [1] and over 70% of youth were online in 2017 [2]. In addition to loads of benefits that Internet brings, expanding in the accessibility to Internet may also increase the susceptibility to Internet Addition (IA) [3]. IA is defined as incapability to inhibit internet use, which eventually results in deficits of one's daily life and psychological well-being [4]. It is characterized by symptoms including: (1) need for extending online time (tolerance), (2) withdrawal symptoms, (3) inadequate control on Internet use, (4) continuation of Internet use disregarding to problem awareness, (5) excessive online time, (6) negative consequences [5,6]. A bunch of terms were used in literature to describe this condition, such as Pathological Internet Use [7], Internet Abuse [8], Internet Use Disorder, Problematic Internet Use [9] and etc. In the third section of the Fifth Edition of Diagnostic and Statistical Manual of Mental Disorders (DSM-5) [10], which proposed conditions for future study, Internet Gaming Disorder is included as a potential diagnosis for further discussion.
Given that Internet Addiction is the most commonly used term [11,12], it was then adopted in this current paper for consistency.
Since 2000, large scale studies on the prevalence of IA in youth population found an overt variation across studies, ranging from 0.8-26.7% [13]. In 2014, Cheng and Li [14] pointed out in their systematic review that global occurrence of IA was 6.0% across 31 nations. Although huge discrepancies in settings, population, year of investigation and assessment tools raised concerns, there was a remarkable high rate of IA in youth. The impact of IA on adolescents' life and functioning, therefore, is valuable to discuss.
IA is associated not only with people's subjective views on their quality of life (QoL) (e.g. physical health and psychological wellbeing), but also objective assessment on QoL (e.g. quality of environmental conditions) [14]. Previous literature has illustrated that IA might cause deterioration of social and emotional competence of a person, which eventually leads to a poorer satisfaction of life [15,16]. In 1998, Kraut and colleagues [17] proposed a 'Displacement Theory', stating that people's social development would be undermined by decreased time spent in real life socialization, as excessive time spent online. Social relationships are crucial to keep positive emotions and well-being.
Heavy Internet users, who donate excessive time to online relationship or extramarital affairs, are more likely to experience family problems and social difficulties in real world [4,18]. IA is thus supposed to affect people's real-life social quality and well-being. However, contradictions in research can be seen. Some research suggested positive effect of internet use on foster closer interpersonal relationship [19,20].
Moreover, IA has also been reported to be significantly related to physical problems and mental health issues [21,22]. People who are suspicious to have an Internet gaming disorder often reported a lower level of physical activity [23]. But for the consensus in most research, some large scale studies reported that a marginally higher level of physical health was revealed in people with IA [23] .
Psychological well-being of a person is another domain that is highly correlated with IA. A high comorbidity rate of IA and other mental disorders was reported in previous research, including Attention Deficit Hyperactivity Disorder (ADHD), anxiety and depressive disorder [23,24]. Lots of studies aimed to illustrate the relationship between IA and depressive mood in different populations. In Chinese Han and Tibetan adolescents, those met the criteria for IA claimed to be more likely to meet the criteria for depressive mood and to have a lower rating in subjective psychological QoL [25]. Also, studies attempted to understand the causal relationship between depressive mood and IA. Yu and Shek [6] did a follow-up study and suggested a causal relationship between IA at baseline and later onset depressive mood, resulting in a poorer life satisfaction in terms of mental wellbeing. On the other hand, IA is proposed to be dependent on individual's mood. In the Mood Enhancement Hypothesis, people experiencing negative emotion may engage to internet use, in order to alleviate stress and boost their mood [26]. This is consistent with Person-Affect-Cognition-Executive (I-PACE) model. I-PACE posit that the development and maintenance of IA is predisposed by biopsychological constitutions including genes and personalities, which is mediated by affective and cognitive responses and confounded by coping style and executive functions, resulting in negative consequence in daily life and deteriorating psychological health of people eventually [27]. Thus, a tangled relationship among IA, depressive mood and self-perceived QoL was suggested and deserved further research.
Furthermore, more attention has been drawn to health outcome. As a significant component in measuring people's health outcome, QoL and its relation to IA is a hotly investigated field in the last decade. Depressive mood is highly prevalent in adolescent, which is also a significant factor concerning one's life satisfaction. Depressive mood or depression, as aforementioned, may trigger IA at first and eventually exacerbate as a consequence of IA in I-PACE model. Depression is recognized as the one of the factors that leads to increase in Disability-adjusted life year (DAILY) and decrease in Healthy life expectancy (HLE) worldwide [28]. Previous studies explored the relationship between IA 5 and QoL, indicating risk factors (including depressive mood) of IA [25,29]. However, the pure relationship between IA and different domains of QoL, independent of other significant factors, including depressive mood, is less discussed. In order to promoting the invention on IA, it is of importance to illustrate whether IA on its own has a significant relation to QoL independent from depressive mood. This would eventually help in guiding population-based intervention in maintaining a healthy life satisfaction.
Additionally, understanding the pattern of IA in different population is argued to be significant to develop preventive and treatment plans [6]. Also, different sociocultural factors can have impact on the pattern of IA in an area [25,30]. Shenzhen is recognized as the earliest Special Economic Zone in China set up in 1980s. Given its openness to worldwide and its pioneer position in IT industry in China, people in Shenzhen have great exposure to Internet. Also, Shenzhen is one of the major cities in the China Great Bay Area (GBA). However, fewer epidemiology studies investigating IA prevalence and association to psychosocial factors were conducted in Shenzhen in comparison to Hong Kong, another major city in GBA. Thus, it is necessary to explore the pattern of IA and its related factors and influences in Shenzhen.
The current study aimed to firstly reveal the prevalence of IA and depressive mood in Shenzhen adolescents. It will then identify the relationship between IA and QoL, both overall and separate domains of QoL, and discuss whether there is a relationship between them, independent from depressive mood and other significant demographic factors.
2. Method: 2.1. Subject: The sample was collected in Shenzhen Middle schools in June and October 2017. Eleven middle schools from two districts of Shenzhen were involved. In total, eight classes of each School Year (Grade 1 to Grade 8) were randomly selected to join in the current cross-sectional study. All students in the selected classes were invited to complete a self-designed questionnaire anonymously. These questionnaires, together with consent forms, were distributed to the participants by psychological teachers at these schools and were collected after completion. Anyone who refused to sign the consent was excluded. In total, 2200 questionnaires were sent out, 2148 eligible questionnaires were collected. The representativeness of the sample was shown through the commonness of demographic information of these students to the current middle school students in Shenzhen City.

Measures: Sociodemographic Information
A self-designed questionnaire was sent out to collect basic sociodemographic information of subjects.
Information including age, gender, current grade, academic performance and self-perceived academic pressure, number of children in their family, family social economic status, parental information (occupations, educational level, marital status), relationship with family, schoolmates and teachers, self-perceived health and body weights, exercise level and sleep quality were collected.

Internet Addiction
Internet Addiction Test (IAT), a 20-item self-rated scale, was adopted to examine the presence and severity of IA. IAT was developed by Young [4,31] and was validated in many languages, including Chinese [32,33]. Each item was rated on a scale of 1 to 5. A total score of over 49 was recognized as moderate to severe dependence, which was defined as "having IA" [31]. This scale was widely utilized previously in China as well and satisfactory psychometric properties was reported (Cronbach's Alpha equals 0.713) [6,25].

Depression and Quality of Life
Centre for Epidemiologic studies of Depression Symptom Scale (CES-D), a widely used self-report scale to measure depressive symptomatology in general population, was employed to examine the depressive mood of subjects. This scale was validated in many counties with different age groups [34]. It contains 20 questions. Each question was rated from 0 ("rarely") to 3 ("always"), resulting in a total score of 0 to 60. The higher the score, the greater depressive mood was indicated. Usually, a score of greater than 15 was reported as "above cut-off". This was the approximate 80th percentile in the original Community Mental Health Assessment (CMHA) study [34]. The current study used "≥28" as a cut-off line, which selected about 5% of CMHA sample [34], in order to gain a rate that generate a sample closer to clinical sample.
A short version of World Health Organization Quality of Life (WHOQOL-BREF) was used to measure 7 their life quality [35,36]. It consists of 26 items, to assess four domains including physical health, psychological health, social relationships and environment. A Chinese version was validated and utilized in previous studies [25,37].

Statistical Analysis:
The data was analyzed using IBM SPSS, version 2.1. Firstly, descriptive analysis was carried out to explore the prevalence of depressive emotion, internet addiction and other demographic information in the current sample. The demographic features were compared between the complete sample and the whole sample, to understand whether missing data play a significant role in the current sample.
Subsequently, the comparison between students with and without IA was analyzed through independent Sample T tests, chi square tests and Mann Whitney U tests in regards to differences in depressive emotion, QoL evaluation and related demographic factors. Thereafter, the correlations between continuous basic demographic and clinical characteristics (internet addiction, depressive emotion and QoL) were then examined by Pearson's Correlation for normally distributed variables and Spearman Correlation for non-distributed variables. After that, Analysis of Covariance (ANCOVA) was employed to compare the QoL in four different domains between students with and without IA. In order to exam the association between QoL and IA, univariate regression was performed to understand the association between IA and WHOQoL scores in four different domains. Finally, multivariate regression was performed to explore the independent relationship between IA and QoL, where factors including depressive emotion and other related demographic factors were controlled.

Results:
In total, 2,148 students were consented and completed the questionnaire, of which 1,900 students provided eligible data on Internet Addiction Test. Within this sample, 1,725 students completed the scale for QoL (WHOQoL). The sample was further reduced to 1,482 whose data were available for all possible confounding variables.  Table 1, with comparison to the whole sample. The age of the complete case sample ranged from 9 to 18 years, with an average of 15.29, significantly different 8 from the average age of the whole sample. Other characteristics of the complete case sample and the whole sample were not significantly different. The comparison of characteristics between subjects with and without IA was conducted by Mann Whitney U tests for factors including age, CES-D score and QoL scores, as these data were skewed.
Variables indicating frequencies were compared using Chi Square Tests. The results suggested significant differences in the grade, perceived health status, perceived weight, perceived study pressure, SES, and relationships with family, classmates and teacher between students with and without IA (See Table 2  Before analyzing the independent relationship between IA and QoL scores, associations between key continuous variables were illustrated in Table 3. There was an evident negative association between IAT score and student's self-rated QoL scores in all domains. The results in the simple linear regression analyses revealed that whether the student has met the cutline for IA explained the 11 variances in QoL scores of physical health, psychological health, social relationship and environmental domain by 9.5%, 5.6%, 3.5% and 2.3% respectively. After adjusted for depressive mood, the multivariate regression analyses demonstrated that IA was still significantly negatively associated with QoL score in every domain. However, when all variables were controlled, only physical health (B= -0.87, p < .001), psychological health (B= -0.40, p = .011) and overall QoL (B= -0.45, p = .006) were negatively associated with IA. The multivariate regression models indicating the association between IA and QoL scores accounted for 31.8% of variance in physical health and 40.1% in psychological health. The regression models were demonstrated in Table 4. Table 3 Correlations  Note: a. The 'depression mood' adjusted in the regression models refers to 'with or without depression mood (i.e. CES-D ≥ 28)' rather than CES-D scores; b. Confounders adjusted in the models include: age, gender, grade, selfperceived health status, self-perceived weight, self-perceived academic performance, self-perceived pressure of study, relationship with classmates, relationship with teacher, parental marriage status, family relationship, SES and with/without depressive mood (i.e. CES-D ≥ 28).

Discussion:
Many studies have illustrated the prevalence of IA in Chinese adolescents and risk factors prone to IA as well as its impact on psychological wellbeing. However, fewer studies have underpinned the pure relations between QoL and IA. The current study attempted to reveal the prevalence of IA in Shenzhen and the unique associations between IA and four different domains of QoL, eliminating effects of confounding factors. The prevalence of IA was 23.2% in the current sample, which is around the same 14 rate as in Hong Kong (17-26.8%) [38], but higher than global prevalence (6%) [14]. The concurrence of IA and depressive mood was common. 24.13% of the students with IA reported depressive mood, whereas only 7.82% of those without IA did. Moreover, students with IA tended to have lower QoL scores in all of the four domains. Even after controlling important risk factors, especially the depressive mood, IA was still significantly in relation to lower subjective QoL in terms of physical health, psychological health and overall perception.
Given that different assessment tools and cut-off values were used in different studies, discrepancies in prevalence of IA lay in previous literature. When compared with studies using same scale and cutoff line, the prevalence of IA in Shenzhen was higher than other parts of Mainland China including Guanzhou (10.8%) [39], Anhui (12.0%) and Qinghai (15.0%) [25]. This was inconsistent with previous studies suggesting that economic developed areas had lower IA prevalence than developing areas. On the other hand, stress-eliciting environment is suggested to increase the rate of IA [40], since people tend to engage in Internet for psychological relief. Although as an economic developed area in China, Shenzhen have more access to other outdoor activities for stress relief, greater academic pressure was also seen in Shenzhen middle schools and high schools, which contributed to the high rate in IA [41]. In the current sample, 34.5% of the students reported great pressure on academic study, higher than those in Qinghai and Anhui [25]. With high academic pressure, students tend to spend more time indoor, limiting their access to recreational activities and Internet was convenient to approach. Also, the access to Internet increased rapidly in the recent two decades. Devices like mobile phones and tablets, as well as increased accessibility to Wi-Fi provided easy access to Internet. Coupled with the fact that Shenzhen is famous for its IT industry, the more convenient access to electronic devices might trigger the increase in IA [4].
In the line with previous findings, IA is significantly associated with depressive mood. Current study suggested that students with IA tended to have a higher mean CES-D score and were more likely to meet the criteria for depressive mood. Although different scales were used to classify depressive mood, a systematic review of 20 studies published before 2014 showed that three quarters of studies supports a significant association between IA and depression [42]. A recent study by Lu and colleagues in 2018 also used CES-D to assess the depressive level in Chinese adolescents [25]. They suggested that students with depressive mood were more likely to engage with IA. Our results reconfirmed that the risk of engaging with IA is more significant if the student is currently in a depressive mood.
The mood enhancement hypothesis put forwarded by Bryant and Zillmann argued that people tend to engage with leisure actives (e.g. internet surfing, watching TV) than other activities when they are with negative emotions [26]. Also, Internet use could somehow compensate for the lack of social engagement [25]. On the other hand, social displacement hypothesis stated that indulging in online social communications results in decrease in real life social interaction, which leads to maladjustment problems [17]. When online socialization displaces offline social communication, it is likely to lead to the emergence of negative emotions, especially in adolescents [43]. Although the current study did not specify the causal relationship between depression and IA, it is consistent with previous literatures and indicated a positive relationship between depressive mood and IA. Depressive status leads to social withdrawal and decreases people's interest to activities that they used to enjoy, eventually increasing risk of overindulging in Internet use. In turn, IA increases the risk of developing a depressive mood, leading to a viscous cycle of constantly having depressive mood and IA.
The results in univariate regression models clearly indicated negative associations between IA and QoL in all aspects. Such associations remained significant even after controlling for the existence of depressive mood, a factor that holds a close relationship with lower physical and psychological satisfaction and declines in social relation [44]. Our results suggested that IA, independent from depressive mood, may also increase the risk of lower QoL in terms of physical and psychological wellbeing, as well as satisfaction in social relation and environment in some degree. Such negative associations identified between IA and QoL, thereby, support the I-PACE that excessive Internet use eventually affect daily life physically and psychologically [27].
Excessive time spent on Internet reduced the time for outdoor physical activities and face to face socialization, resulting in a lower QoL rating for physical health. Reduction in outdoor activities and physical unwellness brought by long screen time led to lower satisfaction on physical health [45]. In terms of psychological health, depressive mood is believed to significantly account for lower selfrated score in psychological health. However, our result suggested that IA itself also account for low

Conclusion:
Despite of the limitations, the current cross-sectional study investigated the prevalence of Internet

Consent for Publication: Not Applicable.
Availability of data and material: The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.
Competing Interests: Not Applicable.