ORIGINAL RESEARCH
Associations Between Problematic Internet Use and Adolescents’ Physical and Psychological Symptoms: Possible Role of Sleep Quality Jing An, MM, Ying Sun, MD, Yuhui Wan, MD, Jing Chen, MM, Xi Wang, MM, and Fangbiao Tao, MD, PhD
Objective: To evaluate the associations between problematic Internet use (PIU) and physical and psychological symptoms among Chinese adolescents, and to investigate the possible role of sleep quality in this association. Methods: A cross-sectional school-based study was conducted in 4 cities in China. The Multidimensional Sub-health Questionnaire of Adolescents, the Pittsburgh Sleep Quality Index, and demographic variables were used to measure adolescents’ physical and psychological symptoms and sleep quality, respectively, in 13,723 students (aged 12–20 years). Problematic Internet use was assessed by the 20-item Young Internet Addiction Test. Logistic regressions were used to evaluate the effects of sleep quality and PIU on physical and psychological symptoms, and to identify the mediating effect of sleep quality in adolescents. Results: Prevalence rates of PIU, physical symptoms, psychological symptoms, and poor sleep quality were 11.7%, 24.9%, 19.8%, and 26.7%, respectively. Poor sleep quality was found to be an independent risk factor for both physical and psychological symptoms. The effects of PIU on the 2 health outcomes were partially mediated by sleep quality. Conclusions: Problematic Internet use is becoming a significant public health issue among Chinese adolescents that requires urgent attention. Excessive Internet use may not only have direct adverse health consequences but also have indirect negative effects through sleep deprivation. Key Words: adolescent, health status, internet, sleep deprivation (J Addict Med 2014;8: 282–287)
T
he Internet has become an integral part of contemporary life, bringing huge benefits in terms of expanding access to knowledge, information, flexible work, social interaction, and
From the China Anhui Provincial Key Laboratory of Population Health and Aristogenics, and Department of Maternal, Child and Adolescent Health, School of Public Health, Anhui Medical University, Hefei, China. Received for publication August 26, 2013; accepted April 1, 2014. The authors declare no conflicts of interest. Send correspondence and reprint requests to Fangbiao Tao, MD, PhD, Department of Maternal, Child and Adolescent Health, School of Public Health, Anhui Medical University, 81th Meishan Rd, 230032 Hefei, China. E-mail: [email protected]. C 2014 American Society of Addiction Medicines Copyright ISSN: 1932-0620/14/0804-0282 DOI: 10.1097/ADM.0000000000000026
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entertainment (Bener & Bhugra, 2013). However, as Internet access has expanded into homes, schools, Internet cafes, and businesses, there has been a rapidly growing public awareness of the potential adverse social and health effects arising from excessive, maladaptive, or addictive Internet usage (Block, 2008; Cooney & Morris, 2009); that is, problematic Internet use (PIU)—Internet addiction, Internet dependence, and pathological Internet use. Problematic Internet use is generally defined as “an individual’s inability to control their Internet use, which in turn leads to feelings of distress and functional impairment of daily activity” (Shapira et al., 2003). It has been characterized as an impulse-control disorder (Kim et al., 2010). Internet use has gradually become a commonplace activity, particularly among adolescents. Adolescence represents a rapid and unevenly paced period of physical and mental development (Wang et al., 2011). The prevalence estimates of PIU among adolescents and young adults have been observed worldwide: results showed that PIU was about 1.2% to 8.2% in Europe (Siomos et al., 2008; B´elanger et al., 2011; Villella et al., 2011; Poli & Agrimi, 2012); 1.2% to 5.0% in Middle Eastern countries (Ghassemzadeh et al., 2008; Ak et al., 2013; S¸as¸maz et al., 2013); 2.2% to 17.2% in Asia including China (Choi et al., 2009; Cheung & Wong, 2011; Wang et al., 2011; Xu et al., 2012); and 0% to 26.3% in the United States (Moreno et al., 2011). Recently, ample studies have shown that PIU was associated with various health issues. For example, heavy Internet users were at increased risk for physical health problems (Choi et al., 2009; Bener et al., 2011; Canan et al., 2013). The tendency of PIU among adolescents may thus result in loneliness, depression, low self-esteem, and anxiety (Ybarra et al., 2005; Kim et al., 2006; Derbyshire et al., 2013; Park et al., 2013). Rising PIU rates might also intensify such concerns, primarily because of adverse effects on sleep quality. Increased use of the Internet may simply displace sleep, thus reducing sleeping time (Nuutinen et al., 2013). A relationship between Facebook dependence and poor sleep quality was reported in more than half of the students with sleeping problems (Wolniczak et al., 2013). Poor sleep quality has considerable adverse consequences on individuals (Van den Bulck, 2004), including negative psychosocial outcomes (depressive symptoms and lower self-esteem) and declines in physical health (decreased leptin levels, increased ghrelin levels, and increased hunger and J Addict Med r Volume 8, Number 4, July/August 2014
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appetite) of adolescents (Fredriksen et al., 2004; Spiegel et al., 2004; Park et al., 2013). Given the potential relationship between PIU and sleep quality among adolescents, it is likely that the adolescents’ physical and psychological symptoms of PIU were at least partly attributed to the negative impact on sleep quality. The co-occurrence of PIU and poor sleep quality may lead to adolescents’ physical and psychological symptoms. Few studies, however, were carried out to examine this issue. The present study may thus fill this research gap.
METHODS Study Design and Participants The national school-based physical and psychological health monitoring study was conducted in 4 cities in China (Shenyang, Guangzhou, Xinxiang, and Chongqing) to determine epidemiology and trends in physical and psychological symptoms among Chinese secondary school students. A selfadministered questionnaire was completed within 40 minutes in the classroom in the presence of the teachers. A total of 13,955 students were recruited to participate in the study; about 98.3% (13,817) provided useful data without missing responses for the Pittsburgh Sleep Quality Index (PSQI), 20-item Young Internet Addiction Test (YIAT), and the Multidimensional Sub-health Questionnaire of Adolescents (MSQA). Exclusion criteria included being older than 20 years. Finally, 13,723 adolescents (6592 males and 7131 females) aged 10 to 20 years (mean age: 15.26 ± 1.67 years) were analyzed. Written informed consent was obtained from the students and their parents. Ethical approval was obtained from the Biomedicine Ethical Committee of Anhui Medical University.
Measures Structure of the Questionnaires Multidimensional Sub-health Questionnaire of Adolescents. Adolescents’ physical and psychological symptoms were measured using the MSQA, which is a multidimensional, self-report symptom inventory developed by Tao et al. (2008) in China. The MSQA contains 71 items: physical symptoms (32 items) and psychological symptoms (39 items). Physical symptoms include 3 symptom dimensions: lack of physical energy (11 items), physiological dysfunction (11 items), and weakened immunity (10 items). Psychological symptoms are also divided into 3 symptoms dimensions: emotional symptoms (17 items), social adaptation problems (13 items), and behavioral symptoms (9 items). Each respondent was asked to rate each item on a Likert response scale as follows: 1 = from none or last less than 1 week; 2 = last 1 week or more; 3 = last 2 weeks or more; 4 = last 1 month or more; 5 = last 2 months or more; 6 = last 3 months or more. Because of the skewed nature of the distributions and the large proportion of adolescents reporting the duration of each symptom with none or last less than 1 week on these items, each continuous symptom variable was transformed into a dichotomous variable. In the data analysis, no symptom or the symptom duration of less than 1 month was assigned to 0, and the symptom duration of 1 month or more was assigned C
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to 1. Next, total item scores were calculated. Total scores of physical symptoms 3 or more and total scores of psychological symptoms 8 or more were classified as positive physical symptoms and positive psychological symptoms, respectively. Also, physical symptoms and psychological symptoms were transformed into a dichotomous variable. The validity and reliability of the MSQA has been confirmed (Xing et al., 2008) and Cronbach α coefficient and split-half reliability coefficient of our data were 0.966 and 0.832, respectively. Pittsburgh Sleep Quality Index. Nineteen self-rated questions were used to assess a wide variety of factors relating to sleep quality. PSQI scores range from 0 to 21, with the higher scores indicating worse sleep quality, and a score of more than 7 indicating poor sleep quality problems in China (Buysse et al., 1989; Liu et al., 1996). Then the total scores were subsequently categorized into 3 groups (0–5, 6–7, and >7). 20-Item Young Internet Addiction Test. Problematic Internet use was assessed through the application of the YIAT, which comprised 20 calibrated items. PIU scores range from 20 to 100. The following cutoff points were applied to the total YIAT score: (1) normal Internet use, scores 20 to 49; (2) PIU, scores over 50 (Khazaal et al., 2008). In the mediation effect of sleep quality, the YIAT scores were transformed into a dichotomous variable. Also, in the analysis of the association between PIU and physical symptoms/psychological symptoms, the total scores were subsequently categorized into 4 groups (20–39, 40–59, 60–79, and 80–100). In this study, the split-half reliability and Cronbach α coefficient were 0.864 and 0.923, respectively.
Data Analysis The data were analyzed using SPSS version 13.0. The χ 2 test was used to assess the proportions between the independent and dependent variables. Controlling for gender, paternal and maternal education level, residence (urban or rural areas), and self-perceived economic status, logistic regression analyses were performed to examine the effects of PIU and poor sleep quality on physical and psychological symptoms in adolescents. According to the theoretical framework of Baron–Kenny (Baron & Kenny, 1986), logistic regression analyses were performed to examine the mediating effects of sleep quality in the association of PIU on physical and psychological symptoms in adolescents. Statistical significance was set at P < 0.05.
RESULTS Demographic Characteristics of Participants Approximately two thirds (68.3%) reported middle-level family economic status and 45% reported living in rural areas.
Physical and Psychological Symptoms The prevalence rates of PIU, physical symptoms, psychological symptoms, and poor sleep quality were 11.7% ± 0.54%, 24.9% ± 0.72%, 19.8% ± 0.67%, and 26.7% ± 0.74%, respectively. Table 1 summarizes the estimated prevalence rates of adolescents’ physical and psychological symptoms by demographic variables. Results indicated that girls and rural
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students had more physical and psychological problems than boys and urban students: physical symptoms: 26.0% (female) versus 23.7% (male), 26.6% (rural) versus 23.5% (urban); psychological symptoms: 20.9% (female) versus 18.5% (male), 22.7% (rural) versus 17.3% (urban), P < 0.05. As reported parental educational levels increased, so did physical and psychological symptoms. However, symptoms decreased with reported increases in family economic status (P < 0.05 from Cochran–Armitage trend test).
Relationship Between PIU, Adolescents’ Physical Symptoms/Psychological Symptoms, and Poor Sleep Quality Table 2 shows the coefficient estimates of the PIU and sleep quality variables from model 1 and model 2. The scores of the YIAT and sleep quality were independently and additively associated with physical symptoms/psychological symptoms. Results indicated that, whether in model 1 or model 2, physical symptoms did not show a monotonous pattern: although an increase in YIAT scores level from the reference
category (YIAT scores: 20–39) was associated with consistently higher physical symptoms, it increased the likelihoods of reporting physical symptoms up to the fourth level (YIAT scores: 80–100) but showed reducing trends in the third level (YIAT scores: 60–79). In contrast, psychological symptoms showed a monotonous pattern: adolescents who reported high level of YIAT scores generally had a higher likelihood of reporting psychological symptoms related to low level of YIAT scores (YIAT scores: 20–39). In model 2, after adding sleep quality, it reduced all the likelihoods of the 2 health outcomes from model 1 because of YIAT scores. The increase in sleep quality scores from baseline was associated with consistently higher scores in the 2 health outcomes.
Predicting the Probabilities of the Highest Category for Each Symptom by Sleep Quality Scores and YIAT Scores Figure 1 illustrates the predicted probability of the highest category for physical symptoms and psychological symptoms respectively by sleep quality scores and YIAT scores.
TABLE 1. Distribution of Adolescents’ Physical Symptoms and Psychological Symptoms Variables Gender Male Female Paternal education level Primary schooling/illiterate Secondary schooling University or more Unknown Maternal education level Primary schooling/illiterate Secondary schooling University or more Unknown Residence Rural Urban Self-perceived economic status Lower Middle Upper
Total n (%)
Physical Symptoms n (%)
6592 (48.0) 7131 (52.0)
1560 (23.7) 1855 (26.0)
1839 (13.4) 8886 (64.8) 2688 (19.6) 310 (2.3)
475 (25.8) 2236 (25.2) 626 (23.3) 78 (25.2)
2278 (16.6) 8901 (64.9) 2298 (16.7) 246 (1.8)
567 (24.9) 2246 (25.2) 527 (22.9) 75 (30.5)
6181 (45.0) 7542 (55.0)
1645 (26.6) 1770 (23.5)
2028 (14.8) 9371 (68.3) 2324 (16.9)
656 (32.3) 2239 (23.9) 520 (22.4)
Psychological Symptoms n (%)
χ2 10.104*
χ2 12.282*
1222 (18.5) 1492 (20.9) 4.922
29.531* 418 (22.7) 1779 (20.0) 446 (16.6) 71 (22.9)
9.393†
39.350* 501 (22.0) 1778 (20.0) 366 (15.9) 69 (28.0)
17.978*
62.533* 1406 (22.7) 1308 (17.3)
73.179*
139.995* 592 (29.2) 1746 (18.6) 376 (16.2)
*P < 0.001; †P < 0.05.
TABLE 2. Associations Between PIU and Physical Symptoms/Psychological Symptoms* Physical Symptoms Variables PIU 20–39 40–59 60–79 80–100 Sleep quality 0–5 6–7 >7
Psychological Symptoms
Adjusted OR1 (95% CI)
Adjusted OR2 (95% CI)
Adjusted OR1 (95% CI)
Adjusted OR2 (95% CI)
1 1.540 (1.379–1.720)† 1.511 (1.224–1.867)† 1.637 (1.091–2.454)‡
1 1.399 (1.250–1.565)† 1.308 (1.054–1.622)† 1.526 (1.013–2.298)‡
1 2.887 (2.569–3.243)† 7.403 (5.981–9.163)† 7.717 (5.125–11.620)†
1 2.698 (2.399–3.035)† 6.621 (5.340–8.209)† 7.322 (4.856–11.041)†
— — —
1 1.557 (1.375–1.762)† 2.983 (2.679–3.322)†
— — —
1 1.527 (1.326–1.758)† 2.380 (2.110–2.684)†
*Adjusted OR1 (model 1): controlling for gender, paternal and maternal education level, residence, self-perceived economic status; adjusted OR2 (model 2): adding sleep quality. †P < 0.001; ‡P < 0.05. CI, confidence interval; OR, odds ratio; PIU, problematic Internet use.
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FIGURE 1. Predicted probability of reporting the highest category for each symptom by sleep scores.
Stepwise logistic regression analyses (Paths a, b, c, and c’) were all significant, and Path c’ was not zero. The results indicated that the effect of PIU on physical and psychological symptoms was partially mediated by sleep quality (Table 3).
DISCUSSION FIGURE 2. Mediational model.
With the increase in the YIAT score level, physical and psychological symptoms were observed, especially among those with poor sleep quality. In the same YIAT score level, more physical and psychological symptoms were found with more sleep quality scores (Fig. 1).
Mediation Effect of Sleep Quality in the Association Between PIU and Physical/Psychological Symptoms According to the theoretical framework of Baron– Kenny, researchers introduced a path diagram as a model for depicting a causal chain. The basic causal chain involved in mediation is shown in Figure 2. This model assumes a 3-variable system so that some causal paths were fed into the outcome variable (physical symptoms or psychological symptoms): the direct impact of the independent variable (PIU) (Path c) when sleep quality controlled the indirect impact of PIU (Path c’) and the impact of the mediator (sleep quality) (Path b). There is also a path from the independent variable (PIU) to the mediator (sleep quality) (Path a). Logistic regression analyses were performed to discover the effects of these paths; P < 0.05 was regarded as significant. When Paths a, b, and c were all significant and Path c’ was not significant, with the strongest demonstration of sleep quality occurring when Path c’ is zero, we have strong evidence for sleep quality as a single, dominant mediator. If the residual Path c’ is not zero, this indicates the operation of multiple mediating factors. C
The present study is the first to examine the effects of PIU and poor sleep quality on physical and psychological symptoms among adolescents in China. Our findings suggest that correlations between PIU and physical/psychological symptoms were partially mediated by sleep quality. Our study revealed that the prevalence rate was 11.7%, which was much higher than rates in many previous adolescent studies both in China and abroad (Ghassemzadeh et al., 2008; Poli & Agrimi, 2012; Xu et al., 2012; Derbyshire et al., 2013). Studies of prevalence rates yielded varying estimates because of diversity in culture, PIU criteria, research designs, and study population. A study from Derbyshire et al. (2013) found that 56.8% of moderate–severe Internet users were female; in this study, however, more female students (56.9%) were included. Ghassemzadeh et al.’ study (2008) used a different version of the 20-item YIAT, which did not contain possible Internet addicts (scoring between 40 and 70); thus, a relatively low prevalence was obtained. Poli and Agrimi noted that Internet use was low in students who resided in towns or villages (Poli & Agrimi, 2012). In the present study, the rate of poor sleep quality was 26.7% in secondary school students in China, which was lower than other studies (32.9%–60%) (Lund et al., 2010; Lai & Say, 2013; V´elez et al., 2013). There could be many reasons for this. First, different social and cultural contexts, as well as differing definitions and criteria for measuring poor sleep quality, made it difficult to effectively compare findings. Second, a PSQI of more than 7 was used as the cutoff criteria for this study to establish good diagnostic validity. Finally, prevalence rates had varied widely depending on the sample. Our results showed that adolescents with higher YIAT scores had more severe physical and psychological symptoms.
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TABLE 3. Mediation Effect of Sleep Quality in the Association of PIU and Physical and Psychological Symptoms* Physical Symptoms Paths a b c c’
Psychological Symptoms
β
SE
P
OR (95% CI)
β
SE
P
OR (95% CI)
0.894 0.974 0.418 0.329
0.055 0.050 0.069 0.070
0.000 0.000 0.000 0.000
2.444 (2.194–2.723) 2.650 (2.402–2.923) 1.519 (1.328–1.738) 1.389 (1.212–1.592)
0.894 0.818 1.466 1.396
0.055 0.054 0.069 0.069
0.000 0.000 0.000 0.000
2.444 (2.194–2.723) 2.265 (2.039–2.516) 4.333 (3.787–4.957) 4.040 (3.528–4.626)
*Paths a, b, and c controlling for gender, paternal and maternal education level, residence, and self-perceived economic status. Path c’ adding sleep quality. Path a: the impact of PIU to sleep quality; Path b: the impact of the sleep quality to physical symptoms or psychological symptoms; Path c: the direct impact of PIU to the 2 health outcomes; Path c’: when sleep quality controlled the indirect impact of PIU to the 2 health outcomes. CI, confidence interval; OR, odds ratio; PIU, problematic Internet use; SE, standard error.
Increases in YIAT score levels from baseline were associated with consistently higher physical symptoms and increased likelihoods of physical symptoms up to the fourth level (YIAT scores: 80–100), but showed reducing tendency in the third level (YIAT scores: 60–79). In contrast, psychological symptoms showed a monotonous pattern: adolescents who reported high YIAT scores (vs the reference of YIAT scores: 20–39) generally had a higher likelihood of psychological symptoms. Increases in the sleep quality score level from baseline were associated with consistently higher scores in the 2 health outcomes. This also corresponded to analogous investigations of PIU (Odacı & Celik, 2013; Park et al., 2013). A large-scale offline survey of PIU in US high school students showed that PIU was associated with some risk factors including substance use and aggressive behaviors, as well as with depression (Liu et al., 2011). Our data indicated that poor sleep quality may have deleterious effects on physical and psychological health. Moreover, this study highlighted PIU as an independent risk factor for these health outcomes. Problematic Internet use is detrimental to health and is a public health concern, but in technologically advanced societies, poor sleep quality has also emerged as a relevant public health problem (Cheung & Wong, 2011). Problematic Internet use and poor sleep quality each had direct and independent consequences for physical and psychological symptoms in adolescents (Fredriksen et al., 2004; Young, 2008). Moreover, we found that not only did PIU directly influence physical and psychological symptoms, but also had an indirect effect on sleep quality. Research both in China and abroad showed that people who experienced poor sleep quality had more severe physical and psychological symptoms than those who experienced good sleep quality (Meijer et al., 2010; Williams et al., 2013). A possible explanation might be that PIU displaced physical activity (Marshall et al., 2004), which was known to promote good quality of sleep (Block, 2008; Reid et al., 2010). Electronic media use may also increase physiological and mental arousal, which makes it difficult to fall asleep (Higuchi et al., 2005). In addition, PIU may actually affect the sleep architecture; for example, decreasing slow-wave sleep, REM-sleep, and sleep efficiency (Higuchi et al., 2005; Dworak et al., 2007), or the bright light of a computer screen may suppress melatonin secretion, which in turn may delay the onset of sleep (Higuchi et al., 2003). Also, sleep deprivation impacted neural circuitry underlying regulation of emotions, impulsivity, and reward-seeking behavior (van der
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Helm et al., 2010). Thus, adolescents with PIU may develop sleep problems, thereby triggering physical and psychological symptoms. These findings could provide clues for the prevention and intervention of physical and psychological symptoms for adolescents, indicating the necessity to assess the sleep quality status of PIU adolescents. Our study had several strengths. First, it was a largescale national school-based physical and psychological health monitoring study. Second, it examined the influence of PIU on physical and psychological symptoms, while accounting for the simultaneous exposure to poor sleep quality in adolescents. However, some study limitations should be noted. First, it was a cross-sectional design, which did not account for causality of relationships. Second, all information was self-reported and thus was subject to the measurement error. Third, the MSQA was developed on the basis of Chinese adolescent populations without international evidence support. Despite these limitations, this study suggested that PIU and poor sleep quality may be a valuable indicator for clinicians and public health workers to assess adolescents’ physical and psychological symptoms.
CONCLUSIONS Problematic Internet use is becoming a significant public health issue in Chinese adolescents that requires urgent attention. Excessive Internet use may not only have direct adverse health consequences but also have indirect negative effects through sleep deprivation.
ACKNOWLEDGMENTS The authors are extremely grateful to all of the students who agreed to participate in the study, and they thank the entire team members of the national school-based physical and psychological health monitoring study: Mai JC, Zhang HZ, Tan GQ, and Zhang Z for their dedication to the project. This study is supported by the National Natural Science Foundation of China (81172690 and 81102146). REFERENCES Ak S, Koruklu N, Yılmaz Y. A study on Turkish adolescent’s Internet use: possible predictors of Internet addiction. Cyberpsychol Behav Soc Netw 2013;16:205–209. Baron RM, Kenny DA. The moderator-mediator variable distinction in social psychological research: conceptual, strategic, and statistical considerations. J Pers Soc Psychol 1986;51:1173–1182. C
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Associations Between Problematic Internet Use and Adolescents’ Physical and Psychological Symptoms: Possible Role of Sleep Quality Jing An, MM, Ying Sun, MD, Yuhui Wan, MD, Jing Chen, MM, Xi Wang, MM, and Fangbiao Tao, MD, PhD
Objective: To evaluate the associations between problematic Internet use (PIU) and physical and psychological symptoms among Chinese adolescents, and to investigate the possible role of sleep quality in this association. Methods: A cross-sectional school-based study was conducted in 4 cities in China. The Multidimensional Sub-health Questionnaire of Adolescents, the Pittsburgh Sleep Quality Index, and demographic variables were used to measure adolescents’ physical and psychological symptoms and sleep quality, respectively, in 13,723 students (aged 12–20 years). Problematic Internet use was assessed by the 20-item Young Internet Addiction Test. Logistic regressions were used to evaluate the effects of sleep quality and PIU on physical and psychological symptoms, and to identify the mediating effect of sleep quality in adolescents. Results: Prevalence rates of PIU, physical symptoms, psychological symptoms, and poor sleep quality were 11.7%, 24.9%, 19.8%, and 26.7%, respectively. Poor sleep quality was found to be an independent risk factor for both physical and psychological symptoms. The effects of PIU on the 2 health outcomes were partially mediated by sleep quality. Conclusions: Problematic Internet use is becoming a significant public health issue among Chinese adolescents that requires urgent attention. Excessive Internet use may not only have direct adverse health consequences but also have indirect negative effects through sleep deprivation. Key Words: adolescent, health status, internet, sleep deprivation (J Addict Med 2014;8: 282–287)
T
he Internet has become an integral part of contemporary life, bringing huge benefits in terms of expanding access to knowledge, information, flexible work, social interaction, and
From the China Anhui Provincial Key Laboratory of Population Health and Aristogenics, and Department of Maternal, Child and Adolescent Health, School of Public Health, Anhui Medical University, Hefei, China. Received for publication August 26, 2013; accepted April 1, 2014. The authors declare no conflicts of interest. Send correspondence and reprint requests to Fangbiao Tao, MD, PhD, Department of Maternal, Child and Adolescent Health, School of Public Health, Anhui Medical University, 81th Meishan Rd, 230032 Hefei, China. E-mail: [email protected]. C 2014 American Society of Addiction Medicines Copyright ISSN: 1932-0620/14/0804-0282 DOI: 10.1097/ADM.0000000000000026
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entertainment (Bener & Bhugra, 2013). However, as Internet access has expanded into homes, schools, Internet cafes, and businesses, there has been a rapidly growing public awareness of the potential adverse social and health effects arising from excessive, maladaptive, or addictive Internet usage (Block, 2008; Cooney & Morris, 2009); that is, problematic Internet use (PIU)—Internet addiction, Internet dependence, and pathological Internet use. Problematic Internet use is generally defined as “an individual’s inability to control their Internet use, which in turn leads to feelings of distress and functional impairment of daily activity” (Shapira et al., 2003). It has been characterized as an impulse-control disorder (Kim et al., 2010). Internet use has gradually become a commonplace activity, particularly among adolescents. Adolescence represents a rapid and unevenly paced period of physical and mental development (Wang et al., 2011). The prevalence estimates of PIU among adolescents and young adults have been observed worldwide: results showed that PIU was about 1.2% to 8.2% in Europe (Siomos et al., 2008; B´elanger et al., 2011; Villella et al., 2011; Poli & Agrimi, 2012); 1.2% to 5.0% in Middle Eastern countries (Ghassemzadeh et al., 2008; Ak et al., 2013; S¸as¸maz et al., 2013); 2.2% to 17.2% in Asia including China (Choi et al., 2009; Cheung & Wong, 2011; Wang et al., 2011; Xu et al., 2012); and 0% to 26.3% in the United States (Moreno et al., 2011). Recently, ample studies have shown that PIU was associated with various health issues. For example, heavy Internet users were at increased risk for physical health problems (Choi et al., 2009; Bener et al., 2011; Canan et al., 2013). The tendency of PIU among adolescents may thus result in loneliness, depression, low self-esteem, and anxiety (Ybarra et al., 2005; Kim et al., 2006; Derbyshire et al., 2013; Park et al., 2013). Rising PIU rates might also intensify such concerns, primarily because of adverse effects on sleep quality. Increased use of the Internet may simply displace sleep, thus reducing sleeping time (Nuutinen et al., 2013). A relationship between Facebook dependence and poor sleep quality was reported in more than half of the students with sleeping problems (Wolniczak et al., 2013). Poor sleep quality has considerable adverse consequences on individuals (Van den Bulck, 2004), including negative psychosocial outcomes (depressive symptoms and lower self-esteem) and declines in physical health (decreased leptin levels, increased ghrelin levels, and increased hunger and J Addict Med r Volume 8, Number 4, July/August 2014
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appetite) of adolescents (Fredriksen et al., 2004; Spiegel et al., 2004; Park et al., 2013). Given the potential relationship between PIU and sleep quality among adolescents, it is likely that the adolescents’ physical and psychological symptoms of PIU were at least partly attributed to the negative impact on sleep quality. The co-occurrence of PIU and poor sleep quality may lead to adolescents’ physical and psychological symptoms. Few studies, however, were carried out to examine this issue. The present study may thus fill this research gap.
METHODS Study Design and Participants The national school-based physical and psychological health monitoring study was conducted in 4 cities in China (Shenyang, Guangzhou, Xinxiang, and Chongqing) to determine epidemiology and trends in physical and psychological symptoms among Chinese secondary school students. A selfadministered questionnaire was completed within 40 minutes in the classroom in the presence of the teachers. A total of 13,955 students were recruited to participate in the study; about 98.3% (13,817) provided useful data without missing responses for the Pittsburgh Sleep Quality Index (PSQI), 20-item Young Internet Addiction Test (YIAT), and the Multidimensional Sub-health Questionnaire of Adolescents (MSQA). Exclusion criteria included being older than 20 years. Finally, 13,723 adolescents (6592 males and 7131 females) aged 10 to 20 years (mean age: 15.26 ± 1.67 years) were analyzed. Written informed consent was obtained from the students and their parents. Ethical approval was obtained from the Biomedicine Ethical Committee of Anhui Medical University.
Measures Structure of the Questionnaires Multidimensional Sub-health Questionnaire of Adolescents. Adolescents’ physical and psychological symptoms were measured using the MSQA, which is a multidimensional, self-report symptom inventory developed by Tao et al. (2008) in China. The MSQA contains 71 items: physical symptoms (32 items) and psychological symptoms (39 items). Physical symptoms include 3 symptom dimensions: lack of physical energy (11 items), physiological dysfunction (11 items), and weakened immunity (10 items). Psychological symptoms are also divided into 3 symptoms dimensions: emotional symptoms (17 items), social adaptation problems (13 items), and behavioral symptoms (9 items). Each respondent was asked to rate each item on a Likert response scale as follows: 1 = from none or last less than 1 week; 2 = last 1 week or more; 3 = last 2 weeks or more; 4 = last 1 month or more; 5 = last 2 months or more; 6 = last 3 months or more. Because of the skewed nature of the distributions and the large proportion of adolescents reporting the duration of each symptom with none or last less than 1 week on these items, each continuous symptom variable was transformed into a dichotomous variable. In the data analysis, no symptom or the symptom duration of less than 1 month was assigned to 0, and the symptom duration of 1 month or more was assigned C
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to 1. Next, total item scores were calculated. Total scores of physical symptoms 3 or more and total scores of psychological symptoms 8 or more were classified as positive physical symptoms and positive psychological symptoms, respectively. Also, physical symptoms and psychological symptoms were transformed into a dichotomous variable. The validity and reliability of the MSQA has been confirmed (Xing et al., 2008) and Cronbach α coefficient and split-half reliability coefficient of our data were 0.966 and 0.832, respectively. Pittsburgh Sleep Quality Index. Nineteen self-rated questions were used to assess a wide variety of factors relating to sleep quality. PSQI scores range from 0 to 21, with the higher scores indicating worse sleep quality, and a score of more than 7 indicating poor sleep quality problems in China (Buysse et al., 1989; Liu et al., 1996). Then the total scores were subsequently categorized into 3 groups (0–5, 6–7, and >7). 20-Item Young Internet Addiction Test. Problematic Internet use was assessed through the application of the YIAT, which comprised 20 calibrated items. PIU scores range from 20 to 100. The following cutoff points were applied to the total YIAT score: (1) normal Internet use, scores 20 to 49; (2) PIU, scores over 50 (Khazaal et al., 2008). In the mediation effect of sleep quality, the YIAT scores were transformed into a dichotomous variable. Also, in the analysis of the association between PIU and physical symptoms/psychological symptoms, the total scores were subsequently categorized into 4 groups (20–39, 40–59, 60–79, and 80–100). In this study, the split-half reliability and Cronbach α coefficient were 0.864 and 0.923, respectively.
Data Analysis The data were analyzed using SPSS version 13.0. The χ 2 test was used to assess the proportions between the independent and dependent variables. Controlling for gender, paternal and maternal education level, residence (urban or rural areas), and self-perceived economic status, logistic regression analyses were performed to examine the effects of PIU and poor sleep quality on physical and psychological symptoms in adolescents. According to the theoretical framework of Baron–Kenny (Baron & Kenny, 1986), logistic regression analyses were performed to examine the mediating effects of sleep quality in the association of PIU on physical and psychological symptoms in adolescents. Statistical significance was set at P < 0.05.
RESULTS Demographic Characteristics of Participants Approximately two thirds (68.3%) reported middle-level family economic status and 45% reported living in rural areas.
Physical and Psychological Symptoms The prevalence rates of PIU, physical symptoms, psychological symptoms, and poor sleep quality were 11.7% ± 0.54%, 24.9% ± 0.72%, 19.8% ± 0.67%, and 26.7% ± 0.74%, respectively. Table 1 summarizes the estimated prevalence rates of adolescents’ physical and psychological symptoms by demographic variables. Results indicated that girls and rural
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students had more physical and psychological problems than boys and urban students: physical symptoms: 26.0% (female) versus 23.7% (male), 26.6% (rural) versus 23.5% (urban); psychological symptoms: 20.9% (female) versus 18.5% (male), 22.7% (rural) versus 17.3% (urban), P < 0.05. As reported parental educational levels increased, so did physical and psychological symptoms. However, symptoms decreased with reported increases in family economic status (P < 0.05 from Cochran–Armitage trend test).
Relationship Between PIU, Adolescents’ Physical Symptoms/Psychological Symptoms, and Poor Sleep Quality Table 2 shows the coefficient estimates of the PIU and sleep quality variables from model 1 and model 2. The scores of the YIAT and sleep quality were independently and additively associated with physical symptoms/psychological symptoms. Results indicated that, whether in model 1 or model 2, physical symptoms did not show a monotonous pattern: although an increase in YIAT scores level from the reference
category (YIAT scores: 20–39) was associated with consistently higher physical symptoms, it increased the likelihoods of reporting physical symptoms up to the fourth level (YIAT scores: 80–100) but showed reducing trends in the third level (YIAT scores: 60–79). In contrast, psychological symptoms showed a monotonous pattern: adolescents who reported high level of YIAT scores generally had a higher likelihood of reporting psychological symptoms related to low level of YIAT scores (YIAT scores: 20–39). In model 2, after adding sleep quality, it reduced all the likelihoods of the 2 health outcomes from model 1 because of YIAT scores. The increase in sleep quality scores from baseline was associated with consistently higher scores in the 2 health outcomes.
Predicting the Probabilities of the Highest Category for Each Symptom by Sleep Quality Scores and YIAT Scores Figure 1 illustrates the predicted probability of the highest category for physical symptoms and psychological symptoms respectively by sleep quality scores and YIAT scores.
TABLE 1. Distribution of Adolescents’ Physical Symptoms and Psychological Symptoms Variables Gender Male Female Paternal education level Primary schooling/illiterate Secondary schooling University or more Unknown Maternal education level Primary schooling/illiterate Secondary schooling University or more Unknown Residence Rural Urban Self-perceived economic status Lower Middle Upper
Total n (%)
Physical Symptoms n (%)
6592 (48.0) 7131 (52.0)
1560 (23.7) 1855 (26.0)
1839 (13.4) 8886 (64.8) 2688 (19.6) 310 (2.3)
475 (25.8) 2236 (25.2) 626 (23.3) 78 (25.2)
2278 (16.6) 8901 (64.9) 2298 (16.7) 246 (1.8)
567 (24.9) 2246 (25.2) 527 (22.9) 75 (30.5)
6181 (45.0) 7542 (55.0)
1645 (26.6) 1770 (23.5)
2028 (14.8) 9371 (68.3) 2324 (16.9)
656 (32.3) 2239 (23.9) 520 (22.4)
Psychological Symptoms n (%)
χ2 10.104*
χ2 12.282*
1222 (18.5) 1492 (20.9) 4.922
29.531* 418 (22.7) 1779 (20.0) 446 (16.6) 71 (22.9)
9.393†
39.350* 501 (22.0) 1778 (20.0) 366 (15.9) 69 (28.0)
17.978*
62.533* 1406 (22.7) 1308 (17.3)
73.179*
139.995* 592 (29.2) 1746 (18.6) 376 (16.2)
*P < 0.001; †P < 0.05.
TABLE 2. Associations Between PIU and Physical Symptoms/Psychological Symptoms* Physical Symptoms Variables PIU 20–39 40–59 60–79 80–100 Sleep quality 0–5 6–7 >7
Psychological Symptoms
Adjusted OR1 (95% CI)
Adjusted OR2 (95% CI)
Adjusted OR1 (95% CI)
Adjusted OR2 (95% CI)
1 1.540 (1.379–1.720)† 1.511 (1.224–1.867)† 1.637 (1.091–2.454)‡
1 1.399 (1.250–1.565)† 1.308 (1.054–1.622)† 1.526 (1.013–2.298)‡
1 2.887 (2.569–3.243)† 7.403 (5.981–9.163)† 7.717 (5.125–11.620)†
1 2.698 (2.399–3.035)† 6.621 (5.340–8.209)† 7.322 (4.856–11.041)†
— — —
1 1.557 (1.375–1.762)† 2.983 (2.679–3.322)†
— — —
1 1.527 (1.326–1.758)† 2.380 (2.110–2.684)†
*Adjusted OR1 (model 1): controlling for gender, paternal and maternal education level, residence, self-perceived economic status; adjusted OR2 (model 2): adding sleep quality. †P < 0.001; ‡P < 0.05. CI, confidence interval; OR, odds ratio; PIU, problematic Internet use.
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FIGURE 1. Predicted probability of reporting the highest category for each symptom by sleep scores.
Stepwise logistic regression analyses (Paths a, b, c, and c’) were all significant, and Path c’ was not zero. The results indicated that the effect of PIU on physical and psychological symptoms was partially mediated by sleep quality (Table 3).
DISCUSSION FIGURE 2. Mediational model.
With the increase in the YIAT score level, physical and psychological symptoms were observed, especially among those with poor sleep quality. In the same YIAT score level, more physical and psychological symptoms were found with more sleep quality scores (Fig. 1).
Mediation Effect of Sleep Quality in the Association Between PIU and Physical/Psychological Symptoms According to the theoretical framework of Baron– Kenny, researchers introduced a path diagram as a model for depicting a causal chain. The basic causal chain involved in mediation is shown in Figure 2. This model assumes a 3-variable system so that some causal paths were fed into the outcome variable (physical symptoms or psychological symptoms): the direct impact of the independent variable (PIU) (Path c) when sleep quality controlled the indirect impact of PIU (Path c’) and the impact of the mediator (sleep quality) (Path b). There is also a path from the independent variable (PIU) to the mediator (sleep quality) (Path a). Logistic regression analyses were performed to discover the effects of these paths; P < 0.05 was regarded as significant. When Paths a, b, and c were all significant and Path c’ was not significant, with the strongest demonstration of sleep quality occurring when Path c’ is zero, we have strong evidence for sleep quality as a single, dominant mediator. If the residual Path c’ is not zero, this indicates the operation of multiple mediating factors. C
The present study is the first to examine the effects of PIU and poor sleep quality on physical and psychological symptoms among adolescents in China. Our findings suggest that correlations between PIU and physical/psychological symptoms were partially mediated by sleep quality. Our study revealed that the prevalence rate was 11.7%, which was much higher than rates in many previous adolescent studies both in China and abroad (Ghassemzadeh et al., 2008; Poli & Agrimi, 2012; Xu et al., 2012; Derbyshire et al., 2013). Studies of prevalence rates yielded varying estimates because of diversity in culture, PIU criteria, research designs, and study population. A study from Derbyshire et al. (2013) found that 56.8% of moderate–severe Internet users were female; in this study, however, more female students (56.9%) were included. Ghassemzadeh et al.’ study (2008) used a different version of the 20-item YIAT, which did not contain possible Internet addicts (scoring between 40 and 70); thus, a relatively low prevalence was obtained. Poli and Agrimi noted that Internet use was low in students who resided in towns or villages (Poli & Agrimi, 2012). In the present study, the rate of poor sleep quality was 26.7% in secondary school students in China, which was lower than other studies (32.9%–60%) (Lund et al., 2010; Lai & Say, 2013; V´elez et al., 2013). There could be many reasons for this. First, different social and cultural contexts, as well as differing definitions and criteria for measuring poor sleep quality, made it difficult to effectively compare findings. Second, a PSQI of more than 7 was used as the cutoff criteria for this study to establish good diagnostic validity. Finally, prevalence rates had varied widely depending on the sample. Our results showed that adolescents with higher YIAT scores had more severe physical and psychological symptoms.
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TABLE 3. Mediation Effect of Sleep Quality in the Association of PIU and Physical and Psychological Symptoms* Physical Symptoms Paths a b c c’
Psychological Symptoms
β
SE
P
OR (95% CI)
β
SE
P
OR (95% CI)
0.894 0.974 0.418 0.329
0.055 0.050 0.069 0.070
0.000 0.000 0.000 0.000
2.444 (2.194–2.723) 2.650 (2.402–2.923) 1.519 (1.328–1.738) 1.389 (1.212–1.592)
0.894 0.818 1.466 1.396
0.055 0.054 0.069 0.069
0.000 0.000 0.000 0.000
2.444 (2.194–2.723) 2.265 (2.039–2.516) 4.333 (3.787–4.957) 4.040 (3.528–4.626)
*Paths a, b, and c controlling for gender, paternal and maternal education level, residence, and self-perceived economic status. Path c’ adding sleep quality. Path a: the impact of PIU to sleep quality; Path b: the impact of the sleep quality to physical symptoms or psychological symptoms; Path c: the direct impact of PIU to the 2 health outcomes; Path c’: when sleep quality controlled the indirect impact of PIU to the 2 health outcomes. CI, confidence interval; OR, odds ratio; PIU, problematic Internet use; SE, standard error.
Increases in YIAT score levels from baseline were associated with consistently higher physical symptoms and increased likelihoods of physical symptoms up to the fourth level (YIAT scores: 80–100), but showed reducing tendency in the third level (YIAT scores: 60–79). In contrast, psychological symptoms showed a monotonous pattern: adolescents who reported high YIAT scores (vs the reference of YIAT scores: 20–39) generally had a higher likelihood of psychological symptoms. Increases in the sleep quality score level from baseline were associated with consistently higher scores in the 2 health outcomes. This also corresponded to analogous investigations of PIU (Odacı & Celik, 2013; Park et al., 2013). A large-scale offline survey of PIU in US high school students showed that PIU was associated with some risk factors including substance use and aggressive behaviors, as well as with depression (Liu et al., 2011). Our data indicated that poor sleep quality may have deleterious effects on physical and psychological health. Moreover, this study highlighted PIU as an independent risk factor for these health outcomes. Problematic Internet use is detrimental to health and is a public health concern, but in technologically advanced societies, poor sleep quality has also emerged as a relevant public health problem (Cheung & Wong, 2011). Problematic Internet use and poor sleep quality each had direct and independent consequences for physical and psychological symptoms in adolescents (Fredriksen et al., 2004; Young, 2008). Moreover, we found that not only did PIU directly influence physical and psychological symptoms, but also had an indirect effect on sleep quality. Research both in China and abroad showed that people who experienced poor sleep quality had more severe physical and psychological symptoms than those who experienced good sleep quality (Meijer et al., 2010; Williams et al., 2013). A possible explanation might be that PIU displaced physical activity (Marshall et al., 2004), which was known to promote good quality of sleep (Block, 2008; Reid et al., 2010). Electronic media use may also increase physiological and mental arousal, which makes it difficult to fall asleep (Higuchi et al., 2005). In addition, PIU may actually affect the sleep architecture; for example, decreasing slow-wave sleep, REM-sleep, and sleep efficiency (Higuchi et al., 2005; Dworak et al., 2007), or the bright light of a computer screen may suppress melatonin secretion, which in turn may delay the onset of sleep (Higuchi et al., 2003). Also, sleep deprivation impacted neural circuitry underlying regulation of emotions, impulsivity, and reward-seeking behavior (van der
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Helm et al., 2010). Thus, adolescents with PIU may develop sleep problems, thereby triggering physical and psychological symptoms. These findings could provide clues for the prevention and intervention of physical and psychological symptoms for adolescents, indicating the necessity to assess the sleep quality status of PIU adolescents. Our study had several strengths. First, it was a largescale national school-based physical and psychological health monitoring study. Second, it examined the influence of PIU on physical and psychological symptoms, while accounting for the simultaneous exposure to poor sleep quality in adolescents. However, some study limitations should be noted. First, it was a cross-sectional design, which did not account for causality of relationships. Second, all information was self-reported and thus was subject to the measurement error. Third, the MSQA was developed on the basis of Chinese adolescent populations without international evidence support. Despite these limitations, this study suggested that PIU and poor sleep quality may be a valuable indicator for clinicians and public health workers to assess adolescents’ physical and psychological symptoms.
CONCLUSIONS Problematic Internet use is becoming a significant public health issue in Chinese adolescents that requires urgent attention. Excessive Internet use may not only have direct adverse health consequences but also have indirect negative effects through sleep deprivation.
ACKNOWLEDGMENTS The authors are extremely grateful to all of the students who agreed to participate in the study, and they thank the entire team members of the national school-based physical and psychological health monitoring study: Mai JC, Zhang HZ, Tan GQ, and Zhang Z for their dedication to the project. This study is supported by the National Natural Science Foundation of China (81172690 and 81102146). REFERENCES Ak S, Koruklu N, Yılmaz Y. A study on Turkish adolescent’s Internet use: possible predictors of Internet addiction. Cyberpsychol Behav Soc Netw 2013;16:205–209. Baron RM, Kenny DA. The moderator-mediator variable distinction in social psychological research: conceptual, strategic, and statistical considerations. J Pers Soc Psychol 1986;51:1173–1182. C
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